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Quantifying Potential Benefits of Automated Data Acquisition Technologies Integration in Construction Data Management Ba...

Permanent Link: http://ufdc.ufl.edu/UFE0042597/00001

Material Information

Title: Quantifying Potential Benefits of Automated Data Acquisition Technologies Integration in Construction Data Management Based on Bayesian Belief Networks
Physical Description: 1 online resource (91 p.)
Language: english
Creator: OCK,JINTAEK
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2011

Subjects

Subjects / Keywords: BBN -- DAT -- RFID -- USN
Building Construction -- Dissertations, Academic -- UF
Genre: Building Construction thesis, M.S.B.C.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: It is widely recognized that construction activities may be one of the most complex industrial activities. Due to the complexity of construction activities, vast amounts of data are generated from every construction projects. The collecting and analyzing of generated construction data are significant for contractors to control and maintain schedule, duration, labors, materials, and costs of construction projects. Therefore, adequate data management is required for successful project delivery. Recently, as the size of construction projects are getting bigger and more complicated, the improvement of data management methods is necessary in construction activities. In the current construction industry, use of IT (Information Technology) is being emphasized to support these needs. Specifically, the conjunction of various automated DATs (Data Acquisition Technologies) such as Bar Code, GPS (Global Positioning System), PDA (Personal Digital Assistant), RFID (Radio Frequency Identification), and USN (Ubiquitous Sensor Network) are being developed to facilitate the data collection in construction sites. In the real construction practices, manual data collecting system often cause problems of delaying completion or overrun cost of projects. In general, inaccuracy and inefficiency of data management leads to these problems. On the other hand, integration of automated DATs in construction data management enable to reduce inaccuracy by increasing efficiency in data control in construction sites, and it ultimately reflects productivity improvement on construction management. Automated DATs can now be employed in various construction activities. For example, GPS and wireless technique can be used in construction site management, or road construction; and RFID system has great capacity to control and monitor construction materials. Many studies highlight the requirement of advanced DATs? utilization in construction industries enabling to control time and cost for projects in an effort to meet the expected results. Even though previous studies indicate that automated DATs have great benefits for construction data management and it leads the project performance, the actual impact of integration of automated DATs on construction productivity has not been fully investigated. This study attempted to identify and to quantify the potential of integration of DATs in construction site using Bayesian belief networks.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by JINTAEK OCK.
Thesis: Thesis (M.S.B.C.)--University of Florida, 2011.
Local: Adviser: Issa, R. Raymond.
Local: Co-adviser: Flood, Ian.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2011
System ID: UFE0042597:00001

Permanent Link: http://ufdc.ufl.edu/UFE0042597/00001

Material Information

Title: Quantifying Potential Benefits of Automated Data Acquisition Technologies Integration in Construction Data Management Based on Bayesian Belief Networks
Physical Description: 1 online resource (91 p.)
Language: english
Creator: OCK,JINTAEK
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2011

Subjects

Subjects / Keywords: BBN -- DAT -- RFID -- USN
Building Construction -- Dissertations, Academic -- UF
Genre: Building Construction thesis, M.S.B.C.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: It is widely recognized that construction activities may be one of the most complex industrial activities. Due to the complexity of construction activities, vast amounts of data are generated from every construction projects. The collecting and analyzing of generated construction data are significant for contractors to control and maintain schedule, duration, labors, materials, and costs of construction projects. Therefore, adequate data management is required for successful project delivery. Recently, as the size of construction projects are getting bigger and more complicated, the improvement of data management methods is necessary in construction activities. In the current construction industry, use of IT (Information Technology) is being emphasized to support these needs. Specifically, the conjunction of various automated DATs (Data Acquisition Technologies) such as Bar Code, GPS (Global Positioning System), PDA (Personal Digital Assistant), RFID (Radio Frequency Identification), and USN (Ubiquitous Sensor Network) are being developed to facilitate the data collection in construction sites. In the real construction practices, manual data collecting system often cause problems of delaying completion or overrun cost of projects. In general, inaccuracy and inefficiency of data management leads to these problems. On the other hand, integration of automated DATs in construction data management enable to reduce inaccuracy by increasing efficiency in data control in construction sites, and it ultimately reflects productivity improvement on construction management. Automated DATs can now be employed in various construction activities. For example, GPS and wireless technique can be used in construction site management, or road construction; and RFID system has great capacity to control and monitor construction materials. Many studies highlight the requirement of advanced DATs? utilization in construction industries enabling to control time and cost for projects in an effort to meet the expected results. Even though previous studies indicate that automated DATs have great benefits for construction data management and it leads the project performance, the actual impact of integration of automated DATs on construction productivity has not been fully investigated. This study attempted to identify and to quantify the potential of integration of DATs in construction site using Bayesian belief networks.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by JINTAEK OCK.
Thesis: Thesis (M.S.B.C.)--University of Florida, 2011.
Local: Adviser: Issa, R. Raymond.
Local: Co-adviser: Flood, Ian.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2011
System ID: UFE0042597:00001


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1 QUANTIFYING THE POTENTIAL BENEFITS OF AUTOMATED DATA ACQUISITION TECHNOLOGIES INTEGRATION IN CONSTRUCTION DATA MANAGEMENT BASED ON BAYESIAN BELIEF NETWORK ANALYSIS By JINTAEK OCK A THESIS PRESENTED TO THE GRADUATE SCHOOL O F THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN BUILDING CONSTRUCTION UNIVERSITY OF FLORIDA 201 1

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2 201 1 Jintaek Ock

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3 To my parents and wife for uncondition al support and encourageme nt

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4 ACKNOWLEDGMENTS I would like to express my sincere gratitude to all people who encouraged me to complete this study. The completion of this study would not have been accomplished without their assistance and advice. First and foremost, I would l ike to gratefully and sincerely thank my advisor and chair of committee Dr. R. Raymond Issa, for his guidance throughout my graduate study with encouragement I also would like to express my gratitude to the co chair of committee, Dr. Ian Flood who insp ired me to have interests about this research. I am also grateful for the guidance of Dr. E. Douglas Lucas, supportive and foundational member of my graduate committee who has encouraged my fascination to focus on the research.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 7 LIST OF FIGURES ................................ ................................ ................................ .......... 8 ABSTRACT ................................ ................................ ................................ ................... 10 CHAPTER 1 I NTRODUCTION ................................ ................................ ................................ ..... 12 Objective of the Study ................................ ................................ ............................. 14 Scope of the Study ................................ ................................ ................................ 14 Overview of the Chapters ................................ ................................ ....................... 15 2 LITERATURE RE VIEW ................................ ................................ ........................... 17 Introduction ................................ ................................ ................................ ............. 17 Overview of Automated DATs ................................ ................................ ................. 17 Bar Code a nd RFID (Radio Frequency Identification) ................................ ...... 19 GPS (Global Positioning System) ................................ ................................ ..... 26 PDA (Personal Digital Assistant) ................................ ................................ ...... 28 USN (Ubiquitous Sensor Network) and Other DATs ................................ ........ 30 Integration of DATs ................................ ................................ .......................... 35 Over view of Bayesian Belief Networks ................................ ................................ ... 36 Characteristics of Bayesian Belief Networks ................................ .................... 37 Use of Bayesian Belief Networks in the Constr uction Industry ......................... 39 3 METHODOLOGY ................................ ................................ ................................ .... 42 Overview ................................ ................................ ................................ ................. 42 Identificatio n of the Negative Factors Affecting Construction Data Management ... 43 Identification of the Current DATs Implemented in Construction Industry ............... 45 Determination of the Relationships between Negative Factors ............................... 45 Development of BBN Model ................................ ................................ .................... 46 Overview ................................ ................................ ................................ .......... 46 Evaluation of the Negative Factors ................................ ................................ ... 47 Determination of the Relationships among the Factors ................................ .... 49 Assigning the Conditional Probabilities to Factors ................................ ............ 52 Parameter Learning Process ................................ ................................ ............ 56 4 RESULTS AND ANALYSI S ................................ ................................ ..................... 59

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6 Overview ................................ ................................ ................................ .......... 59 Evaluations of the Survey ................................ ................................ ................. 59 Re Organization of Dataset for Bayesian Belief Network (BBN) Analysis ........ 64 BBN Analysis for Large Companies Specialized in Resident Building ............. 65 BBN Analysis for Small Companies Specialized in Resident Building .............. 70 5 CONCLUSIONS ................................ ................................ ................................ ...... 75 6 DISCUSSIONS AND RECOMMENDATIONS ................................ ......................... 80 APPENDIX: THE SAMPLE OF SURVEY FORM ................................ .......................... 81 LIST OF REFERENCES ................................ ................................ ............................... 86 BIOGRA PHICAL SKETCH ................................ ................................ ............................ 91

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7 LIST OF TABLES Table page 2 1 Differences between active and passive RFID tags ................................ ........... 21 2 2 Tag frequencies used by different countries ................................ ....................... 22 2 3 The characteristics of USN techniques ................................ ............................... 31 2 4 Main loca lization techniques for USN and RTLS ................................ ................ 34 3 1 List of predefined negative factors related to construction data management .... 48 3 2 Fifteen cause and effect relationships among the factors in large size company ................................ ................................ ................................ ............. 50 3 3 Fifteen cause and effect relationships among the factors in small size company ................................ ................................ ................................ ............. 50 3 4 The averaged occurrence probabilities of negative factors that affect to construction performance in large companies (%) ................................ .............. 53 3 5 The averaged occ urrence probabilities of negative factors that affect to construction performance in small companies (%) ................................ ............. 54 4 1 The summary of re organizing dataset for BBN analysis ................................ .... 65 4 2 The averaged occurrence probabilities of negative factors in large companies specialized in resident building with individual RFID DATs (%) .......................... 66 4 3 The averaged occurrence probabilities of negative factors in small companies specialized in resident building with individual PDA DATs (%) ......... 70

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8 LIST OF FIGURES Figure page 2 1 Distribution of automated DATs depending on management types in construction industry ................................ ................................ ........................... 18 2 2 Conceptual implementation of barcode with PDA integration for mate rial management on construction site ................................ ................................ ....... 20 2 3 Automated Project Performance Control (APPC) by active RFID tag ................. 23 2 4 GPS data acquiring techonology (DAT) system (Hass 2000) ............................. 27 2 5 Data processing by PDA in construction job site (Kim et al. 2007) ..................... 29 2 6 Illustration of the intelligent lift toolkit concept for elevator and intelligent lift (Adapted from Cho et al. 2010) ................................ ................................ .......... 32 2 7 The concept of wireless localization techniques for USN and RT LS .................. 33 2 8 A simple belief network of construction activity ................................ ................... 38 2 9 Automated process for integration of BBNs and computer sim ulations (Adapted from McCabe et al. 1998) ................................ ................................ .... 40 2 10 The conceptual BBN based model for predicting the probability of construction delay (Luu et al. 2009) ................................ ................................ .... 41 3 1 Dynamic survey form used for this study. ................................ ........................... 44 3 2 The development of a BBN model procedure for this study. .............................. 47 3 3 The development of predefined negative factors that affect to construction performance in GeNIe Ver.2.0 ................................ ................................ ............ 48 3 4 The development of relationships among factors for larg e size company .......... 51 3 5 The development of relationships among factors for small size company .......... 52 3 6 The initial BBN developme nt with probability of occurrence of each factors for large companies ................................ ................................ ................................ 55 3 7 The initial BBN development with probability of occurrence of each factors for small companies ................................ ................................ ................................ 56 3 8 The BBN model after parameter learning process for large companies ............. 57 3 9 The BBN model after parameter learning process for small comp anies ............. 58

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9 4 1 ................................ ................... 60 4 2 ................................ .......................... 60 4 3 ........................... 61 4 4 s ............. 62 4 5 The types of implementaed DATs in large construction companies ................... 62 4 6 The types of implementaed DATs in small const ruction companies ................... 63 4 7 specialized in resident building with individual RFID systems implementation ... 67 4 8 RFID systems implementation ................................ ................................ ............ 68 4 9 RFID systems implementation ................................ ................................ ............ 69 4 10 specialized in resident building with individual PDA systems implementation .... 72 4 11 The chang PDA systems implementation ................................ ................................ ............. 73 4 12 The change of PDA systems implementation ................................ ................................ ............. 74 5 1 Different pro babilities of construction performances depending on the applied types of DATs in large companies specializing in hig hrise residential building construction ................................ ................................ ................................ ........ 78 5 2 Different probabilities o f construction performances depending on the applied types of DATs in small companies specializing in high rise residential building construction ................................ ................................ ................................ ........ 78

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10 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science in Building Construction QUANTIFYING THE POTE NTIAL BENEFITS OF AUTOMATED DATA ACQUI SITION TECHNOLOGIES INTEGRATION IN CONSTRUCTION DATA MANAGEMENT BAS ED ON BAYESIAN BELIE F NETWORK ANALYSIS By Jintaek Ock M ay 2011 Chair: R. Raymond Issa Cochair: Ian Flood Major: Building Construction It is widely recognized that construction activities may be one of the most complex industrial activities. Due to the complexity of construction activities, vast amounts of data are generated from every construction project. The collecti on and analy sis of generated construction data are significant for contractors to be able to track the schedule, duration, labor, m aterials, and costs of construction project s. Therefore, adequate data management is required for successful project delivery. Recently, as the size of construction projects is getting bigger and more complicated, the improvement of data manag ement meth ods is n ecessary in construction activities. In construction industry, use of IT (Information Technology) is being emphasized to support these needs. Specifically, systems using various automated DATs (Data Acquisition Technologies) such as Bar C ode, GPS (Global Positioning System), PDA (Personal Digital Assistant), RFID (Radio Frequency Identification), and USN (Ubiquitous Sensor Network) are being developed to facilitate data collection o n construction sites.

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11 In typical construction practices, manual data collecting system often cause problems of delaying completion or overrun project cost s. In general, inaccuracy and inefficiency of data management leads to these problems. The integration of automated DATs in cons truction data management ena ble s the reduc tion of inaccurac ies by increasing the efficiency in data acqui si tion o n construction sites, and it ultimately re sults in productivity improvement. Automated DATs can now be employed in various construction activities. For example, GPS and wireless technique can be used in construction site management or road construction ; and RFID systems ha ve great capacity to control and monitor construction mate rials. Many studies highlight the requirement of advanced DATs utilization in construction industries enabling us to control time and cost for projects in an effort to meet the expected results. Even though previous studies indicate that automated DAT s have great benefits for construction data management and enhance project performance, the ac tual impact of integration of automated DATs on construction productivity has not been fully investigated. This study identif ied and quantified the potential of integration of DATs o n construction site using Bayesian belief networks.

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12 CHAPTER 1 INTROD UCTION Typical construction processes require adequate project management to control their execution As the construction industr y grows and projects get more complicated, advanced project management practice s need to be adapted and developed For succes sful construction delivery, productivity enhancement, risk management, cost analysis, schedule management, and data management are regarded as crucial project management practices. Of these management practices, data management is, particularly, experienc ing a quite advancement with innovative development of construction information management system s E ffective construction data management plays a significant role in record ing and track ing all construction activities. Furthermore, collected data can be used to assess the status of the project and to take corrective actions if needed In spite of the significance of data management in construction projects, manual or partially automated data collecting procedures are used in construction sites. Manual d ata management procedures are labor intensive, inaccurate, and error prone. The weaknesses in manual data management practices cause a decrease in construction productivity, cost overrun s and schedule delay s On the other hand, the automated data acquis ition technologies (DATs) for construction data management provide benefits for productivity improvement, time saving, and efficiency for data collection o n construction sites. In practic e several of automated DATs such as Bar Code, Global Positioning S ystem (GPS), Personal Digital Assistant (PDA), Radio Frequency Identification (RFID) 3D scanning and USN (Ubiquitous Sensor Network) are used on construction sites enabling contractors to track construction data. However, several limitations and

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13 disadv antages for each DAT type prevent the develop ment of automated data management. Each DAT has its own characteristic and it can be utilized in limited construction activities. In addition, the outputs from current DATs might not be interopera ble between d ifferent information technology (IT) platforms. Another limitation of DATs is the installation costs and uncertainties of DATs applications. One of the disadvantages of DATs is that specialized expertise may be required to operate certain DAT application s and excess data may cause additional processes to be needed Another problem in utilizing automated DATs is that there is no evidence of reliable DAT s ystems in practice. Therefore, the characteristics of different automated DATs are needed to be eval uated and analyzed in order to enhance their capabilities and to overcome limitations enabling the integration of DATs to be used in construction data management In addition, even though th e significance of each automated DAT is emphasized by many resear ches and studies, the very limited studies have been just focused on comprehensive explor ations of automated DAT utilization in the construction industry. In other words, previous studies about automated DATs are mainly focused on a specific DAT for use b y just a certain work group such as materials management, quality management, and workers schedule monitoring. Therefore, comprehensive analysis and perspective studies with comparison of DATs are required to advance the development of automated DATs in the construction industry In this study, the characteristics of each automated DAT are investigated with detailed literature reviews. A l so, the advantages and disadvantages of each DAT are described in detail Then the need for integration of automated DATs in construction

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14 sites will be analyzed, and the potential of integration of DATs for construction data management will be assessed using Bayesian b elief n etwork (BBN) analysis. Objective of the Study Since information system s w ere developed and in troduced to the construction industry, numerous efforts to apply state of the art information technology to construction field have been made. As a result, various kinds of automated DATs are developed for construction project s The use of automated DATs o n construction sites provide s efficient management practices for control of material, schedule, cost, and productivity. However, individual automated DA Ts currently used o n construction sites have limitations and disadvantages. Furtherm ore, small contr actors have a tendency to hesitate about utilizing DATs in their project because of installation cost and uncertainties about DATs performance. Based on these problems of automated DATs, t his study aims to identify current d trend s in construction management through detailed literature reviews and surveys. Subsequently, the potential benefits of the integration of DATs are demonstrate d in this study. In order to clarify the positive factors for DATs integration o n real co nstruction sites, the potential benefits of DATs will be evaluated and quantified using Bayesian belief network (BBN) analysis. In addition, prospective approaches for DATs integrations and direction of future study will be proposed in this study. Scope of the Study One of t he main purposes of this study, current utilization trends are introduced with litera ture reviews and case studies. In tative DATs,

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15 which are Barcode, GPS, PDA, RFID, 3D scanning, and USN (Ubiquitous Sensor Network), is proposed in this study. Since the main objective of this study is to quantify the potential benefits of the integration of DATs for construction data man agement some mechanical fundamentals and technical explanations are excluded from this study. A survey for this study was distributed to Korean contractors because it is considered that Korean firms have a great tendency to use IT in construction projec ts in comparison with companies in other countries. T he data generated from the survey and literature reviews will be used as variables through t he Bayesian belief network analysis for this study. Overview of the Chapters The present study will address the importance of integration of DATs in construction data management practices, and attempt to quantify the potential benefits of DATs integration using Bayesian belief network analysis. Chapter 2 presents a literature review about automated DATs in co nstruction industry. The reviews include the overview of each automated DAT s characteristics and its performances. Also, the characteristics of Bayesian belief network analysis are presented in this section. Chapter 3 describes the methodology of this study. The current data of automated DATs use were collected by survey. In the survey, supplementary questionnaire s were given to respondents in order to verify the current DATs e ffects on construction activities. T he procedure and theory of Bayesian belief network analysis is also discussed in this section.

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16 Chapter 4 summarizes the results of this study, and outlines the data analysis from the Bayesian belief network. Furthermore, the comparison of potential benefits of automated DAT integration in between real construction sites and this study is provided. Chapter 5 provides the conclusions and recommendations based on the findings from this study. The discussion of the possible ramification for DATs integration in real construction practices is highlighted in this section.

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17 CHAPTER 2 LITERATURE REVIEW Introduction This chapter discusses current and relevant automated DATs information and the characteristics of Bayesian belief network analysis. First, an overview of automated DATs and cur rent use of each DAT s introduction is included in this section. Second, some examples of the integration of automated DATs and their potential benefits are highlighted. Lastly, the characteristics and methodology for Bayesian belief network analysis are summarized. Overview of Automated DATs A utomated DATs have rapidly evolved with the development of IT. As a result, the areas of automated the construction industry, and these can be applied to effective data mana gement o n construction projects. During the past few decades, t he effort to manually acquire and manage construction data has been a major barrier to improving the effectiveness of construction management (Nasir et al. 2010; Seo et al. 2010). However, im plementation of recent automated DATs in the construction industry can greatly improve the overall construction management processes by enabling automatic data collection. There are several representative DATs currently used o n construction project sites including Bar Code s Global Positioning System s (GPS), Personal Digital Assistant s (PDA), Radio Frequency Identification (RFID) USN (Ubiquitous Sensor Network) and 3D scanning (Seo and Jung 2008). Seo et al. (2010) classified the distribution of repre sentative DATs use based on the type of project management used o n construction project sites (see Figure 2 1 )

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18 This distribution was generated by the analysis of the frequency of DATs application from previous studies. As shown in Figure 2.1, RFID and GPS are mostly used for material and schedule management, respectively. Figure 2 1 D istribution of automated DATs depending on management types in construction industry When automated DATs are implemented o n construction sites, labor free and accura te construction data can be easily collected. Therefore, controlling or tracking the project status is more efficient, and it leads to productivity improvement. However, each DAT has its limitation of suitability to track construction operations. Thus, the limitations of each DAT application and the integrati on of DATs to alleviate individual DAT s limitations should be studied and analyzed (El Omari and Moselhi 2009).

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19 Bar Code and RFID (Radio Frequency Identification) Bar code applications were intr oduced to the construction industry in 1987 (Chen et al. 2002). During the past few years, bar code systems were mainly deployed in construction material handling and management practices because of the error free and faster data entry characteristics of bar code system data (McCullouch and Lueprasert 1994). Even though the use of bar code system has been widely accepted in construction industry, several limitations and disadvantages of bar code use have emerged. The primary limitation is that the bar co de label is easy to be damaged in the typically harsh construction environment (Bernold 1990). Durable barcode label s ha ve been developed to protect them from the construction environment but a manual scanning operation is still required to record inform ation. T his manual scanning procedure is regarded as one of the limitations of barcode use. Other disadvantages of barcode use are its short reading range and limited data storage capacity (Jang and Skibniewski 2007). In order to overcome the limita tions and disadvantages of barcode use, other individual DATs such as PDA, GPS, or wireless technology, have been integrated with barcode system. Especially, barcode system integrated with PDA s have been widely accepted for construction material managemen t practices by enabling automatic data recording and transfer process ing in relation to construction schedule (Cox et al. 2002 and Tserng et al. 2005). Figure 2 2 illustrates the conceptual implementation of barcode with PDA integration for automated mate rial data management. Recently, b ar code system has been replaced by RFID DAT s with the advent of RFID system A t ypical RFID system is comprise d of tags or transponders, and readers equipped with antenna and scanner, and it provides wireless communicati on between

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20 tags and readers. These fundamental characteristics of RFID technology are capable of eliminat ing the limitations of barcode system. Unlike barcode technology, RFID system s ha ve the ability to identify and track materials and equipment without manual recording operation. Furthermore, R FID system s ha ve large storage capacity, wide reading range, durability, and high level of security (Lee et al. 2006). In general, 64 to 32,768 bytes of data can be saved o nto an RFID tag (Jaselskis and El misal ami 2003). Figure 2 2 Conceptual implementation of barcode with PDA integration for material management o n construction site There are two types of RFID tag s: active and passive T he tag s use different frequencies depending on which countries they ar e used in Table 2 1 summarizes the different characteristics between active and passive RFID tags and the different tag frequencies used by different countries.

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21 With the rapid development of active RFID tags technology, the ir suitability for use in the construction industry is greatly advanced. As a result, the area of RFID DATs application has been expanded to various construction fields such as risk management, waste management, material control process, labor management, and tower crane development. Table 2 1. Differences between active and passive RFID tags Issues Active RFID tags Passive RFID tags Power sources Internal Energy transferred from the reader Power availability Continuous When it is in the field of the reader Communication range Up to 300 feet Up to 40 feet Data storage Large Small Signal strength from tag to reader High Low Signal strength from reader to tag Low High Weight 120 130 g 6 54 g Capabilities Read/Write Read only Operational life 5 10 years Unlimited Memory 2 MB Up to 16 kb Cost ($) 15 50 0. 10 4 Adapted from http://www.inlogic.com/rfid/passive_vs_active.aspx For example, active RFID system tag s which are built with a wide range of scanning capacit y and internal battery, has been mostly used for material control management o n construction job sites (Goodrum et al. 2005). The active RFID tag is, generally, capable of cover ing more than 1,000 feet of reading range (Lee et al. 2007). Since active R FID systems ha ve automatic data reading and recording capacity, material control systems using RFID DAT are more advantageous to productivity

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22 enhancement, rather than the use of integration of barcode with PDA for material management in construction job si tes (Navon and Berkovich 2005). Table 2 2 Tag frequencies used by different countries Frequencies Tags application areas Costs ($) 125 134 KHz USA, Canada, Japan, Europe 3 10 13.56 MHz USA, Canada, Japan, Europe 0.5 5.00 433.05 434.79 MHz In most of USA and Europe and under consideration in Japan 865 868 MHz Europe 866 869 and 923 969 MHz South Korea 0.75 and up 902 928 MHz USA 0.75 and up 952 954 MHz Japan (for passive tag after 2005) 0.75 and up 2400 2500 and 5.725 5.875 GHz USA, Canada, Japan, Europe Adapted from Qiu (2007) Another RFID DAT application was developed for safety management in the construction industry through the evaluation of past accident history. Chae and Yoshida (2009) developed the prototype to estimate the workin g area for the prevention of collision accidents. In this prototype, based on the record of past accidents, a defined function is applied to the RFID reader system and active RFID tags work as a alarm system if the estimation of working area is out of def ined function (Chae and Yoshida 2009). This type of RFID DATs, referred to Automated Project Performance Control (APPC) requires the evaluation of past information to establish desired performance. On job sites, actual performance data are collected by RFID system, and the data are compared to the desired performance. When deviation is detected, the construction management should be analyzed (Navon 2007). Figure 2 3 illustrates a repetitive and cyclic process of Automated Project Performance Control sy stem by RFID DATs.

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23 Historical database s are used in compar ing and analyz ing the actual collected performance. Figure 2 3 Automated Project Performance Control (APPC) by active RFID tag Because of the reading and recording capability of active RFID tags, the RFID system can be applied to labor management process. All workers on job site are given a labor ID card with built in active RFID tag and RFID readers around construction site track and record labor movement. A ccurate data for workers spent time and location are automatically acquired (El Omari and Moselhi 2009). As reviewed above, there is no doubt that RFID DATs are beneficial for effective construction data management. Although RFID DATs are widely accepted in many construction fields a nd the ir use has been shown to increase the effectiveness of data management for construction productivity, the use of individual RFID system s has its own limitations similar to other individual automated DATs (Jaselskis and El Misalami 2003).

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24 Installati on costs, lack of standardization, and relatively limited reading range are considered as the main limitations of individual RFID use. Especially, in order to overcome the limited reading range of current RFID tags, integration of RFID with other location DATs has been proposed by many studies (Lee et al. 2007 and Chin et al. 2008). The currently most used integration DAT is the RFID integrated with a GPS system enabling it to acquire more accurate location data for material management. Essentially, wi th the integration of RFID and GPS DAT in the material management process, construction materials tagged with RFID tag s can be automatically identified and the exact location, even the distance from jobsite, can be simultaneously tracked (Song et al. 2006) This ability presents the potential for effective material management, and it can lead to productivity enhancement. T h is integration of RFID with GPS also can be applied to the construction vehicle tracking process in order to facilitate arranging fo r the arrival of materials just in time and right quantity of materials to be delivered to construction jobsites (Lu et al. 2006). Ano ther prototype of RFID with GPS DAT application is the intelligent tower crane operation framework a s proposed by Han et al. (2004). Throughout their proposed tower crane framework accurate information about materials location is directly transferred to the crane operator and workers on the job site, reducing idle time for material delivery. The study identified that th e tower crane operation equipped with RFID and GPS increased construction productivity by 20% (Han et al. 2004). In addition to the integrati on of RFID with GPS, other automated DATs are capable of integrati on with RFID system s and these provide more eff icient data

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25 collection process. For example, integrations of RFID with wireless DATs have the potential to be implemented in various construction activities. The integration of DATs based on the wireless internet environment enables construction manager to control and monitor construction operation by real time. A prototype for wireless automated DATs integration is proposed by Lee et al. (2006) using RFID+ USN ( Ubiquitous Sensor Network) technology. With this proposed framework, d ata from construction site are easily collected by RFID + USN system, and construction More advanced i ntegration of wireless technology with RFID systems stimulate to develop ment of real time safety moni toring system (Navon and Kolton 2007). Another integration of RFID with other DAT may be the RFID+4D CAD model. Since 4D CAD is providing a schedule managing function in addition to 3D CAD model, the integration of RFID with 4D CAD is ideal for material management a nd schedule control Chin et al. (2008) developed the RFID + 4D CAD integrated model for structural steel work on high rise building s to identify productivity improvement. The developed model was applied to an actual steel work job site to identify product ivity improvement. It was found that the RFID+4D CAD DATs offers more efficiency in process time in structural steel work (Chin et al. 2008). As reviewed above, the integration of RFID with other DATs provide s more efficiency for construction management, especially for material management, rather than the use of individual RFID system s

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26 GPS (Global Positioning System) GPS technolog y is regarded as one of the most popular DATs in the construction industry. Automated GPS DAT is particularly useful for moni toring and controlling earthmoving operations in road construction Navon and Shpatnitsky (2005) pointed out that manual data collection methods in road construction involve lengthy and labor intensive elements. They demonstrated how automated GPS technol og y can improve monitoring and control ling data collection, making timely and high data integration in road construction site. The project algorithm model was developed to convert collected data to productivity, progress, and materials consumption. They concluded that automated data collection system s with GPS yielded more accurate outputs than manual system. Using a GPS system in road construction increases production by 15 30%, increases profitability, dramatically reduces staking costs, and improves q uality control (Navon and Shpatnitsky 2005). In addition to the use of GPS systems in road construction, they can be used in geographical data collection system s Lee and Han (2003) compared the performances and productivities between traditional system a nd GPS employed geographical data collecting system. A survey was conducted in this study, and they concluded that significant productivity enhancements were detected through all site work activities when a GPS system was used on the jobsite Furthermore the study indicated that the site work equipments built in GPS technology performed more accurate ly in cut and fill operations, and data recording process was more efficient than traditional recording system s (Lee and Han 2003). The potential benefits of GPS DATs for material handling were described in a study by Caldas et al. (2006). This study was focused on the pipe locating process in

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27 an industrial construction project. In order to verify the feasibility of material data acquisition, field trial s w ere conducted. Caldas et al. (2006) concluded that the GPS deployment in industrial projects significantly saved time on data collecti o n, and cause d improvement of performance. Typical GPS DAT consists of three subsystems, which are ground subsystem s positioning subsystem s and onboard subsystem s (Lee and Han 2003). Figure 2 3 shows a typical GPS DAT system. A ground subsystem, in general, provides geometric data about the construction site with guidelines for construction equipment. Positioning sub system s transfer the required position data to the system and an onboard subsystem displays current position and required equipment functions Figure 2 4 GPS data acquiring techonology (DAT) system (Hass 2000) In order to maximize the efficiency of GPS utilization in construction projects, the adequate management of data collected by GPS is significant for successful construction management. In general, automated DATs including GPS generate enormous data volume (Seo et al. 2010). Therefore, data r eduction processes for manageable data volume are required to increase efficiency of data management.

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28 Hildreth et al. (2005) proposed a procedure for data reduction to facilitate automated data management. In order to improve the data manag ement performa nce with data reduction process, they conducted field test using GPS. The collected data were analyzed with various statistical functions to identify critical data. Performing several field tests with statistical analysis, the number of data records was reduced by more than 95% (Hildreth et al. 2005). The current GPS system, essentially, is ideal for positioning measurement. However, the suitability of GPS system is limited to road construction or site survey. Furthermore, the installation cost is re latively higher than other automated DATs installation cost. These drawbacks can be alleviated by the integrating GPS with other DATs as previously described Particularly, GPS system s are capable of being utilized with RFID system s for applica tion to m aterial management process thus enhancing accurate positioning measurement. PDA (Personal Digital Assistant) Since the mobile computing system w as first introduced to public during the early 1990s many efforts have been made to utilize PDA techn ologies for automated construction data management processes. The main advantage of PDA DAT s is that the PDA enable s construction workers to communicate each other. Also, the data generated from the job site can be stored in a small size storage device, and the n transferred to the main office or to the supervisor without any document ation process es Due to the eas e of data management with PDA s PDA data recording systems are rapidly being developed for the purpose of automated data management in the constructio n industry

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29 Recently the advent of high speed wireless network system s has advance d the development of PDA technology In the wireless network environment, data collected by PDA can be transferred to the main server in the office. Frequently, automati c synchronizing software and web integrated server system for PDA are used for easiness of data transmittance (Kim et al. 2007). The process of data management by PDA is illustrated in Figure 2 5. Figure 2 5 Data processing by PDA in construction job site (Kim et al. 2007) Although PDA system s ha ve lots of benefits, there a re several issues to be solved for further PDA utilization in construction data management Cox et al. (2002) highlighted three problems related to usability and personal usage of P DA. One issue to consider was the cost. T he cost for purchase and maintenance of PDA for site workers is not competitive in comparison with a manual system. Other problem of PDA o n construction site s is a shortage of workers with technical skill to oper ate the PDA. The last issue to consider is that there are limited web based storage services to handle the collected data (Cox et al. 2002). However, most of construction professionals expect that these problems would be solved when PDA system is popular ized in construction industry. As a result, supplemental web monitoring services will be developed to manage data generated from PDAs (Seo et al 2010).

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30 The PDA system also can be applied to labor management. Oh et al. (2004) proposed the labor managemen t system using PDA for estimating accurate labor costs. record system s w ere not accurate and cause d overtime to be record ed In order to improve labor management o n the construction job site they utilized PDAs to track workers to investigate management performances. With utilization of PDA s by workers, total labor costs were easily estimated, and each devices. Furthermore the autom ated labor data collecti o n system by PDA enable d operators to manage the construction schedule (Oh et al. 2004). USN (Ubiquitous Sensor Network) and Other DATs With the recent rapid development and advancement of wireless technology advanced wireless t echnique s are being combined with RFID for effective construction data management performance. As d iscussed previously integrated RFID system s with wireless technology have alleviate d the limitation of individual RFID use Since USN is recently, consid ered as one of the most advan ced wireless techniques fo r construction data management, the integration of RFID with USN is primary concern ed with increas ing project data processing performance o n large construction industry. USN is defined as wireless dat a transmission technology using wireless data communication system s with detecting sensor s such as RFID (Lee et al. 2009). Zig bee, CDMA, Wi Fi, and Bluetooth are considered as main USN techniques. Table 2 3 summarizes the characteristics of each USN tec hnique. Lee et al. (2009) not ed that USN technology ha s the capability to apply to automatic data management as a new intelligent system For example, the

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31 development of the integration of RFID+USN DAT is mainly focused on the capability of automatic ca pturing and management of construction data without any input by humans Table 2 3 The characteristics of USN techniques USN Technique Characteristic Advantages Disadvantages Zig bee IEEE 802.15.4 standard Combined with sensor Ideal for large area sensor network Low cost Low power operation Easy to use Short range of data transmission Sensitive to other signals CDMA (Code Division Multiple Access) Enable to multiplex Special coding scheme to each transmitter Accurate location measu rement Long range of data transmission Error free recording High cost of implementation Required for network infrastructure Wi Fi IEEE 802.11 standard High speed internet network Wide range of wireless network Stable network environment Not able to use during moving Bluetooth Open wireless technology standard 1:1 or 1:n data transmission Low cost Limited network extension High power operation Short range of data transmission UWB (Ultra Wide Band) Wide range of frequency l evel Low power operated High bandwidth communication Accurate locating and tracking Short range of data transmission Sensitive to other signals An example of a new intelligent construction data management system using RFID+USN wa s proposed by Cho et al. (2010). They pointed out that automatic

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32 construction material locating measurement is significant in reduc ing waiting time for material delivery in high rise building construction. The intelligent construction lift car toolkit as shown in Figu re 2 6 was implemented with USN technology The gathered information from RFID sensors also can be used for monitoring overall construction status (Cho et al. 2010). Figure 2 6 Illustration of the intelligent lift toolkit concept for elevator and in telligent lift ( Adapted from Cho et al. 2010) The RFID+USN DAT approach is most accepted in the material and labor management area because it allows accurate location measurement o n construction sites. Recent advancement s of USN technology have led to the use of the Real Time Locating System (RTLS) which is an advancement of the data collecting system (Lee et al. 2010). Through the deployment of RTLS o n construction sites, operators are able to collect accurate real time construction data for material an d safety management Both USN and RTLS technologies are required to adopt localization techniques in order to identify objects within wireless networks. Figure 2 7 illustrates the concept of

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33 the wireless localization technique. T he main localization pr inciples and advantages of USN and RTLS are listed in Table 2 4. Figure 2 7 The concept of wireless localization techniques for USN and RTLS Among these principles the Angle of Arrival ( AOA) system is the most sensitive to environmental conditions c ausing inaccurate data collection. The Received Signal Strength Indication system (RSSI ) frequently gathers erroneous data from sensors, so it is not adequate for appl ication to construction fields. The Time Difference of Arrival ( TODA) principle require s the time synchronizing process even though it offers accurate data collection system. As compared with all these, the Time of Arrival (TOA ) technique

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34 provides the most precise data collection with a wide range of reading capabilit ies (Lee et al. 2010). Table 2 4 Main localization techniques for USN and RTLS Localization Techniques Process Advantage AOA (Angle of Arrival) The d irectional antennas arrays are used to measure the direction of the transmitted signal The location of the RFID tag can be dete rmined at the intersection of the two angled directional lines. Location can be measured by only two readers TOA (Time of Arrival) T he signal traveling time between a RFID tag and a specific reader is measured for calculating the distance Once the transm ission radii are measured, t he location of the RFID tag can be determined using geometrical triangulation methods (intersection of three distance circles as shown in Figure 2 7b ). Most accurate positioning information can be measured TDOA (Time Difference of Arrival) Similar to TOA process. T he time difference of arrival of the signal transmitted from readers is measured to determine the RFID tag location. (Figure 2 7c) Fast time synchronization is available RSSI (Received Signal Strength Indication) The distance between a reader and a RFID tag can be estimated by calculating the attenuation of the emitted signal strength being received. Possible for measuring 3D locating information with adequate software

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35 Integration of DATs As presented above, individu al automated DATs such as RFID, GPS, and PDA have their own limitations and disadvantages even though each DAT promotes construction performance and data management. Furthermore, individual DAT s are just beneficial for a specific construction area. For e xample, RFID and PDA technologies were mainly used in material tracking and labor management respectively In earth work sites, the GPS system is dominant for construction data management (Giretti et al. 2009). F or the purpose of overcoming these limita tions to independent use in certain area s integrated DATs are crucial for effective construction data management process. Especially, the RFID system has the capability for combining with other DATs to enhance construction data handling by automatic transmission (Choi 2004). For instance, the representative RFID integration techniques are the RFID system combined with GPS, PDA, 4D modeling, and USN. Other example s integration w ere proposed in the study by Omari and Moselhi (2009) They proposed virtual DATs integration model is RFID combined with a 3D scanning system. This integration of DAT is capable of capturing text and 3D graphical image data to report construction progress. Based on the literature reviews, this study has determined that the RFID technologies and integration of RFID with other DATs are widely expected to improve data management o n construction projects. Moreover, it was also determined that b arcode systems are mostly replaced with the RFID technologies du e to the disadvantages of barcode system s PDA technology is also widely applied to

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36 construction jobsites to collect data. Another significant identification from the literature review is that advanced sensor network data collecting systems, such as the RFID system with USN or RTLS, have a great potential for advanced development In closing the approaches to new integration of DATs will lead future researches to concentrate on the improvement of construction data management. Overview of Bayesia n Belief Networks As reviewed above, several integrations of DATs are required to be developed and to be accepted by the construction industry in order for effective construction data management to be possible One of the primary benefits of the integrati on of DATs is that it has capability to overcome the limitation of independent or single DAT use o n construction site. For example, RFID system use enables automatic recording of construction data, and it causes improve ment in construction productivity an d reduc tions in labor cost and time. However, there is no valid procedure to transfer and manage the data from an RFID system (Cho et al. 2010). In order to eliminate the disadvantage of single RFID use, other DATs, such as GPS, PDA, USN and 4D CAD are r equired to be integrated with the RFID system. Although it is evident that integration of DATs improves construction productivity and effective data management, some contractors have not deploy ed automated DATs o n their construction project because of the installation cost of DATs and uncertainties about DATs performance (Seo et al. 2010). The main purpose of this study is to quantify the potential benefits of the integration of automated DATs, and to establish the development of method s for each DAT s e valuation. In this study, the Bayesian belief network, also referred to as

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37 Bayesian network or belief network is employed to quantify the potential benefits of automated DATs integration. Characteristics of Bayesian Belief Networks The Bayesian belief network (BBN) is a form of artificial intelligence that incorporates uncertainty through probability theory and conditional dependence, and it was first developed at Stanford University in the 1970s (McCabe et al. 1998). In other words, a Bayesian belief network describes probabilistic relationships among a set of variables by determining cause and effect relationship through graphical model s (Luu et al. 2009). Since a Bayesian belief network constructs a cause and effect diagram, it is considered a s powerful tool for knowledge representation and reasoning under uncertainty conditions (Cheng et al. 2002). The main advantages of Bayesian belief network are summarized as follows; Bayesian b elief networks (BBNs) provide great flexibility in their capac ity for accepting input and providing output BBNs have the ability to allow the value of a variable to be entered as a known input or to evaluate the likelihood of a variable as an output of the system BBNs can readily calculate the probability of event s before and after the introduction of evidence and update its diagnosis or prediction B elief networks may be developed using expert opinion instead of requiring historical data B elief networks also allows variables to be added or removed without significa ntly affecting the remainder of the network because modifications to the network may be isolated Fundamentally, a Bayesian belief network consists of nodes, representing variables of the domain, and arcs, which represent the relationships between the node s

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38 (McCabe et al. 1998). Figure 2 8 presents a simple belief network related to construction activity Child nodes are conditionally dependent upon their parent nodes. Figure 2 8 A simple belief network of construction activity BBNs are based on Baye s theorem, which follows from the basic conditional probability relationship. Bayes rule can be simply expressed as follows; where P(A) is the probability of A, and P(A | B) is the probability of A given B has occurred Specificall y, a well designed BBN development requires following steps (Poole et al. 1998) : Define the relevant variables. Define the relationship between the variables. Define the states of the variables. This step requires defining the detail level of the system. D efine the conditional probabilities of the relationships. More specifically, BBN development consists of qualitative and quantitative parts (van der Gagg 1996). The qualitative part of BBN, which is called structural learning, is

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39 the graphical representat ion of independence (Lee et al. 2009). The main purpose of this part is to identify significant causal factors applicable to the BBN analysis. The quantitative part of the BBN, the so called parameter learning, is the process of finding dependence relat ions as joint conditional probability distributions among variables using cause and effect relationships from the qualitative part to develop the BBN model (Lee et al. 2009). In the construction industry, the BBN can be a suitable methodology for project risk management (Lee et al. 2009) and construction performance diagnostics (McCabe et al. 1998). U se of Bayesian Belief Networks in the Construction Industry D u e to the easiness of cause and effect calculation and identification in the BBN model, BBNs ca n be used as a diagnostic tool in order to obtain the probabilities of variable factors in construction performance. McCabe et al. (1998) described the automated approach for the improvement of construction operations integrating BBNs and computer simu lation. The computer simulation represents the construction operation whereas the BBNs provide expert analysis of the performance of the operation (McCabe et al. 1998). Figure 2 9 shows the prototype of the automated process. In this application, the BB N provided diagnostic analysis of the simulated construction performance with the generation of recommendations for remedial actions that can improve the performance.

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40 Figure 2 9 A utomated process for integration of BBNs and computer simulations ( Adapte d from McCabe et al. 1998) Luu et al. (2009) applied the BBNs to quantify the probability of construction projects delays in Vietnam. In order to develop the BBNs, qualitative and quantitative analysis were performed. For the requirement of qualitative a nalysis, factors were identified through a survey questionnaire. F or the quantitative part of the BBNs, the cause and effect relationships among the factors were obtained through the use of expert interview s (Luu et al. 2009). Then, they developed the B BN using the Microsoft Belief Networks software, MSBNx ( http://research.microsoft.com/en us/um/redmond/groups/adapt/msbnx/ ). The conceptual BBN model is shown in Figure 2 10. Through the BBN analysis, they concluded that construction delay s are extremely

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41 sensitive to actors related to shortage of materials defective construction work and slow site handover (Luu et al. 2009). Figure 2 10 The conceptual BBN based mo del for predicting the probability of construction delay (Luu et al. 2009)

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42 CHAPTER 3 METHODOLOGY Overview Th e main objective of this study is to look at current automated DATs development and to quantify the potential benefits of the integration of automa ted DATs for effective data management in the construction industry. In order to quantif y the benefits of DATs, Bayesian b elief n etwork analysis was used in this study In order to identify the benefits of DATs, the following steps were followed: An eval uation of previous studies and literature reviews about automated DATs w as conducted to collect information about the development history of DATs and to identify current trends of DATs use o n construction projects. The survey questionnaires were developed to obtain the data about current DAT uses and to identify the DAT use preferences in construction companies. The dynamic survey form was created by Adobe LiveCycle Designer ES2 enabling web based tracking with real time update, and the form was sent to upp er level management personnel in each construction company Construction companies in South Korea have a great tendency of using state of the art technologies o n their construction projects (Seo et al. 2010). In order to obtain most current data about DAT s, the respondents were randomly selected based on the list of 1000 Top Construction Compan ies of 2009 provided by the Construction Association of Korea. The data collected from the survey were analyzed in detail. In order to quantify the potential of auto mated DATs in the construction industry, the Bayesian belief network (BBN) model was developed using the data from the survey and literature reviews. In the BBN model, changing the conditional probabilities of the negative factors was performed based on th e survey results. Th is is also called reduction of negative factors. Through the BBN model, the effects of the DAT integration with construction data management were evaluated by measuring the probability of the successful performance of a given task

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43 I dentification of the Negative Factors Affecting Construction Data Management In Chapter 2 o f this study, published literatures were reviewed to identify negative factors that affect construction data management practices. Of the observed risk factors, the external factors which are not related to data management were not considered for this study. Instead, the internal risk factors related to material, labor, safe, schedule and quality management were evaluated. The survey was also conducted to identify the negative factors that prevent effective data management in the current construction industry. Since the identified factors from the survey would be used as variables in the BBN analysis and the correlations of all factor s are significant to develop t he relationships of each variable in the BBN model, each question of the survey wa s connected and correlated with other questions which might be complicated and might cause respondents inconsistent answers. Therefore, in order to obtain consistent and ac curate survey results from the respondents, an advanced survey method is required for the development of BBN model. Specifically, for obtaining accurate survey answers and for respondents convenience, a dynamic survey form was created using Adobe LiveCycl e Designer version of ES2. With this dynamic survey form, respondents would be asked to answer to different questions according to their previous choices. For example, the negative factors in relation to construction data management were initially defin ed based on the literature reviews and expert advice. The predefined negative factors were provided in Q uestion 5 where the respondents were asked to choose all negative factors that affected their construction performance and to rat e the degree of the ef fects as either high effect,

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44 medium effect or low effect. Q uestion 6 asked the respondents to input the probability of occurrence of their selected factors based on the choice s they made in Q uestion 5, as shown in Figure 3 1. Based on the respondents c hoice, the effect levels of the factors were calculated by multiply ing the degree of the factors by the probability of occurrence. The averaged effect level of the factors would be used as the state level of each node in BBN model. Figure 3 1 D ynamic survey form used for this study. This dynamic process greatly reduced the rate of respondents inconsistent answers In this study, no inconsistent survey answers were identified. Moreover, respondents could easily submit the survey form with just one click, and the collected survey forms

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45 were automatically stored and organized depending on distributor s choice. The full dynamic survey form is provided in Appendix A. For this study, two hundred and twenty five respondents were randomly selected from the list of the 1000 Top Construction Compan ies of 2009 provided by the Construction Association of Korea. S eventy three responses were received, representing a response rate of 32%. Identification of the Current DATs Implement ed in Construction Indust ry In order to identify the current DATs implement ed in construction companies, published literature was reviewed according to the type of DATs and a survey was conducted. In the survey questionnaire, respondents were asked to answer whether DATs were im plemented on their projects or not. If respondents answer ed that they we re using DATs on their projects, the question asking the types of DATs that they implement ed would be automatically generated in the dynamic survey form. Similarly, the question aski ng the reasons for their hesitati on in DATs implementation would appear if the respondents answered that DATs we re not being us ed on their projects. Determination of the Relationships between Negative Factors As presented above, the relationships between factors are significant to develop BBN model. Throughout the survey and literature reviews, the correlations of factors were determined. The acquired relationships between factors would be represented as arc in the BBN analysis model. In the survey que stionnaire, respondents were asked to rate the degree of relationships between factors such as strong relationship, somewhat relationship, and

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46 low relationship. According to the survey results, cause and effect relationships were identified and ranked by averaged degree of relationships. In this study, only the controllable internal factors which are related to the data management were considered. Development of BBN Model Overview Based on the results of survey and literature reviews, the Bayesian beli ef network (BBN) was developed to quantify the potential benefits of integration of DATs in construction industry The qualitative and quantitative analysis phases are required to develop a BBN model (Lee et al. 2009). The BBN development process for thi s study is illustrated in Figure 3 2. For the evaluation of the negative factors effects level, eight of predefined negative factors based on literature reviews were provided to survey respondents. Those are directly related to construction data manageme nt practices. The calculated effect levels of factors were applied to the assigning the conditional probabilities to factors. The relationships of all factors were evaluated by survey results. All dataset for identification of the relationships from s urvey were averaged and applied to a BBN model to describe correlation of all factors. Two BBNs models were constructed for large and small sized construction companies to compare the integration of DATs effects. In this study, GeNIe Ver. 2.0 ( http://genie.sis.pitt.edu/ ) was adopted for BBN analysis.

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47 Figure 3 2 The development of a BBN model procedure for this study. Evaluation of the Negative Factors For the development of a BBNs model, the negative factor s related to construction data management were defined based on literature reviews. The predefined negative factors are summarized in Table 3 1.

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48 Table 3 1. List of predefined negative factors related to construction data management Predefined Negati ve Factors Model Variables Inappropriate material handling on layout F1 Late material handling from layout to specific job site F2 Late material delivery from manufacture F3 Loss productivity due to the waiting time for work order F4 Absence of equipm ents on specific time F5 Problems in quality management F6 Workers injury or accident F7 Defective works or reworks F8 These eight predefined negative factors were used as variables, referred to nodes, for BBN model as shown in Figure 3 3. This proc edure is known as qualitative analysis for BBNs development. Figure 3 3 The development of predefined negative factors that affect to construction performance in GeNIe Ver.2.0

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49 Determination of the Relationships among the Factors Determination of the relationships among the factors identified in the qualitative analysis phase was performed through the survey. The determined relationships represent the cause and effect of the factors. In this study, two different relationships were determined based o n the size of the companies. The large size company is classified that the annual revenue for past year was over $100 million. On the other hand, the company that the annual revenue was below $100 million was classified as small company. Of the seventy three answered respondents, twenty nine companies were classified as large size company, and forty four responses were considered as small size company. In addition, through the survey, respondents were asked to specify the relationships among the eight factors with request for rating the degree of relationship. In order to rank significant relationships among the factors, three degree of relationships phase were provided to survey questionnaire Each phase of degree was assigned with constant number. That is 3 for strong relationship 2 for somewhat relationship and 1 for weak relationship The survey answers were statistically analyzed to validate significant pairs of factors by the size of company. Of the many possible pairs of factors, top of 15 pairs of factors were determined to apply to BBN model. Table 3 2 and Table 3 3 show the cause and effect relationships among the factors in large size company and small size company, respectively. The model variables encoding in Table 3 1 are us ed.

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50 Table 3 2 Fifteen cause and effect relationships among the factors in large size company Rank Relationships Mean Constant Value 1 F3 F4 2.48 2 F2 F3 2.21 3 F1 F2 2.19 4 F2 F 4 2.05 5 F1 F6 1.98 6 F4 F7 1.98 7 F4 F5 1.84 8 F2 F6 1.51 9 F6 F8 1.33 10 F4 F8 1.18 11 F3 F5 1.01 12 F5 F6 0.99 13 F4 F6 0.86 14 F1 F8 0.85 15 F3 F6 0.65 Table 3 3 Fifteen cause and effect relationships among the factors in small size company Rank Relationships Mean Constant V alue 1 F2 F4 2.74 2 F3 F4 2.36 3 F2 F3 2.20 4 F6 F 8 2.14 5 F1 F4 2.02 6 F5 F8 1.87 7 F1 F2 1.74 8 F2 F6 1.59 9 F2 F8 1.40 10 F1 F5 1.19 11 F3 F6 1.12 12 F3 F8 1.00 13 F4 F8 0.81 14 F7 F8 0.62 15 F4 F5 0.38

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51 Bas ed on the 15 determined cause and effect relationships among the factors, the identified factors in Figure 3 3 was modified as shown Figure 3 4 and 3 5 for large and small size company, respectively. Two simple developed BBN models represent that construct ion performance is mainly affected by the problems in quality management (F6) and loss productivity ( F4). In comparison with the relationships among the factors for large and small company, it is found that the factor of problems in quality management (F6 ) in large company is more affected by other factors than small company. Figure 3 4 The development of relationships among factors for large size company

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52 Figure 3 5 The development of relationships among factors for small size company Assigning the Conditional Probabilities to Factors The negative factors related to data management that affect to construction performance was identified, and the relationships for large and small companies were determined based on literature reviews and conducted s urvey. As a final step for BBNs development, the conditional probabilities need to be assigned to each factor. In order to assign probabilities to a BBN model, the survey answers of part 2 were used. In part 2 in the survey, respondents were asked to ch oose the degree of effect and to input the probabilities of occurrence for all factors that are selected by them. The degrees of

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53 effect were divided into three phase with assigning constant numbers such as 3 for high effect (HE) 2 for medium effect (M E) and 1 for low effect (LE) Multiplying assigned constant numbers according to the degree phase to probabilities of occurrence represents the effect level. With the evaluation of the survey answers, the effects level of negative factors for large and small companies were calculated and organized in Table 3 4 and Table 3 5, respectively. The serious negative factor affecting construction performance in large companies is loss productivity due to the waiting time for work order (F4). On the other h and, in small companies, workers injury and accident (F7) is regarded as serious negative factors. Table 3 4 The averaged occurrence probabilities of negative factors that affect to construction performance in large companies (%) Model Variables Factor H igh Effect (HE) Medium Effect (ME) Low Effect (LE) F1 Inappropriate material handling on layout 32.5 41.7 25.8 F2 Late material handling from layout to specific job site 37.2 60.8 2.0 F3 Late material delivery from manufacture 25.9 50.4 23.7 F4 Loss p roductivity due to the waiting time for work order 39.2 44.5 16.3 F5 Absence of equipments on specific time 12.4 22.9 64.7 F6 Problems in quality management 37.2 36.4 26.4 F7 Workers injury or accident 24.9 33.6 41.5 F8 Defective works or reworks 28.6 39.5 31.9

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54 Table 3 5 The averaged occurrence probabilities of negative factors that affect to construction performance in small companies (%) Model Variables Factor High Effect (HE) Medium Effect (ME) Low Effect (LE) F1 Inappropriate material handlin g on layout 29.9 52.6 17.5 F2 Late material handling from layout to specific job site 22.7 53.7 23.6 F3 Late material delivery from manufacture 32.1 49.2 18.7 F4 Loss productivity due to the waiting time for work order 19.5 62.7 17.8 F5 Absence of equ ipments on specific time 22.4 36.1 41.5 F6 Problems in quality management 22.4 57.6 20.0 F7 Workers injury or accident 33.9 39.2 26.9 F8 Defective works or reworks 29.5 41.4 29.1 Figure 3 6 shows the initially developed BBN model which the factors p robability of occurrence for large companies (from Table 3 4) were applied to the model In this BBN model, the probability value of each node is different with the value from Table 3 4. That is because the probability values in Table 3 4 are present the probability of each factor that affects to construction performance. But during the development of BBN model, each probability value from Table 3 4 was recalculated depending on the relationships among the factors based on conditional probability. And, in the construction performance nodes, each states was initially input by the probability of 33% to easily track the probability changing after parameter learning process. Parameter learning process in the GeNIe Ver.2.0 is used to calculate the conditiona l probability of each factor nodes.

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55 Figure 3 6 The initial BBN development with probability of occurrence of each factors for large companies With the same method, the initial BBN model for small companies was developed as shown in Figure 3 7. Eve n though the parameter analysis process for conditional probability of each node was not conducted, it is found that the small companies have more chance of poor construction performance. It can be inferred that the probability of negative factors occurr ence s between large and small companies are different. Moreover, the different relationships among factors were identified through the initial BBN models for large and small companies.

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56 Figure 3 7 The initial BBN development with probability of occurr ence of each factors for small companies Parameter Learning Process In order to fully specify and represent the joint probability distribution in a BBN model, parameter learning process is required. In the GeNIe Ver.2.0 application, the parameter learnin g process function is included. Through this function, the conditional probabilities of each node were automatically generated. Sometimes, parameter learning process can be substituted by expert opinions (Luu et al. 2009).

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57 Applying the parameter learni ng process to initially developed BBN models, the probability values were re generated as shown in Figure 3 8 and Figure 3 9 for large and small companies, respectively. Figure 3 8 The BBN model after parameter learning process for large companies

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58 Figure 3 9 The BBN model after parameter learning process for small companies

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59 CHAPTER 4 RESULTS AND ANALYSIS Overview In this chapter, the evaluations of survey results are described, and the potential benefits of DATs integration are quantified with estimation of the change of construction performance occurrence probability according to the change of certain factors occurrence probability in previously developed BBN models for large and small sizes of construction companies. Evaluations of the S urvey The dynamic survey form was created for acquiring respondents consistent answers. For this study, the developed dynamic survey forms were sent to two hundred and twenty five respondents randomly selected from the lists of 1000 Top Construction Comp any of 2009 provided by Construction Association of Korea. And seventy three responses were received, representing a response rate of 32%. Through the part 1 of survey questionnaire, the respondents working years, the types of construction specialties of respondents were identified as shown in Figure 4 1 and Figure 4 2, respectively. Furthermore, the responded companies were divided into large and small sized companies based on the respondents annual revenue for past years. The large size companies are classified that the companies annual revenue for past year were over $100 million. A n d, the companies which the annual revenue is below than $100 million were organized as small size compan ies in this study. Of the seventy three answered respondents, tw enty nine companies were classified as large size company, and forty four responses were considered as small size company as shown Figure 4 3.

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60 Figure 4 1 The distribution of respondents working years Figure 4 2 The distribution of companies speci alties

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61 Figure 4 3 The companies average annual revenue during past 5 years In the part 3 of survey questionnaire, i n order to evaluate the current DATs use, respondents were asked to choose all DATs that are implemented on their projects. By the eval uation of the survey results, it is observed that twenty eight of twenty nine large companies (97%) are using automated DATs on their projects. On the other hand, thirty two respondents of forty four small companies (73%) answered that automated DATs were implemented for their projects as shown in Figure 4 4 The types of implemented DATs in large and small construction companies are summarized in Figure 4 5 and Figure 4 6, respectively. More specifically, RFID systems and RFID+USN systems are mostly ad opted in large construction companies. On the other hand, PDA systems and RFID systems are predominant in small companies based on the survey results.

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62 Figure 4 4 The implementaion of DATs on the respondents construction projects Figure 4 5 The types of implementaed DATs in large construction companies

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63 Figure 4 6 The types of implementaed DATs in small construction companies In large and small construction companies, individual RFID systems are mostly implemented for their projects. In the second place, RFID integrations with other DATs and PDA systems are widely used in the projects. Based on these yielded results, the fact that RFID system and integration of RFID with other DATs are popular in current construction industry was validated. Furthermore, the noticeable finding that PDA systems are widely accepted in large and small construction companies was identified in this study. Especially, of thirteen respondents who answered that any DATs are not used in the projects, nine respondent s (69%) answered that they are considering PDA integration system for their projects, four respondents (31%) chose RFID integration systems for their projects. With these evaluations, it can be inferred that the integration of PDA systems also have

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64 a grea t potential to be developed in addition to the RFID integration systems. One of the main reasons why PDA integration system is being considered to be implemented in construction project is that widespread smart phone uses. Current smart phone technologie s are capable to replace the PDA functions. Moreover, the explosive increasing smart phone uses may lead to reduce the and it result in the PDA DATs development in construction industry. However, the remarkable difference of the types o f DATs implementation between large and small companies is observed through the evaluation of survey results. That is the highly use rate of barcode system in small construction companies. Twenty two (69%) of thirty two respondents in small companies ans wered that individual barcode systems or integration barcode systems are still using on their projects, whereas three (11%) of twenty eight respondents in large companies answered that barcode systems are adopted on their projects. Due to the high instal lation cost of RFID integration systems, the barcode systems are still prevailed in small construction companies. In other words, barcode systems may be more beneficial for some construction projects of small companies in terms of the costs, rather than u sing RFID systems. Re Organization of Dataset for Bayesian Belief Network (BBN) Analysis In order to identify and quantify the potential benefits of DATs integration in large and small construction companies using BBN analysis, the dataset were re organi zed depending on the construction specialties in large and small companies. Specifically, the companies, which are using only individual DATs on their projects, were selected for quantifying the benefits of DATs integration. Of twenty eight respondents i n large companies, eleven answered that they are using individual DATs, whereas of thirty two

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65 respondents in small companies nineteen answered for individual DATs implementation. The summarized re organizations of results are described in Table 4 1. Tabl e 4 1. The summary of re organizing dataset for BBN analysis Size of companies Specialties Implemented individual DATs Number of respondents Large companies Resident building RFID system 5 Civil structure GPS system 3 Office building RFID syste m 3 Small companies Office building RFID system 7 Resident building PDA system 6 Office building PDA system 4 Civil structure GPS system 2 With these re organizing dataset, the averaged negative factors occurrence probabilities were re developed for BBN analysis. However, due to the insufficient data answers caused by small numbers of respondents, certain negative factors occurrence probabilities were not fully developed. As a result total two (one for large companies and one for small companies specialized in resident building) BBN models were proposed in this study. BBN Analysis for Large Companies S pecialized in Resident Building According to Table 4 1, five respondents indicated that individual RFID systems are implemented on resident building projects in large companies. The averaged negative factors occurrence probabilities for five respondents were identified as shown in Table 4 2. In order to identify the differen ces of occurrence probabilities between five respondents and all respondents in large companies, the estimated values of probabilities from Table 3 4 were also shown in Table 4 2. In the new developed negative factors occurrence probabilities for five respondents, it was identified that F6 model variable, pro blems in quality management,

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66 is mostly occurred in resident building project of large companies implemented individual RFID system. On the other hand, all respondents in large companies answered that F4 model variable, loss productivity due to the waiting time for work order, is mostly occurred on their project. Table 4 2 The averaged occurrence probabilities of negative factors in large companies specialized in resident building with individual RFID DATs ( %) Model Variables Average of five respondents Average of all respondents in large companies (from Table 3 4) High Effect (HE) Medium Effect (ME) Low Effect (LE) High Effect (HE) Medium Effect (ME) Low Effect (LE) F1 22.1 39.8 38.1 32.5 41.7 25.8 F2 26.4 62.6 11.0 37.2 60.8 2.0 F3 35.7 20.6 43.7 25.9 50.4 23.7 F4 38.2 41.5 20.3 39.2 44.5 16.3 F5 23.1 22.5 54.4 12.4 22.9 64.7 F6 49.2 31.9 18.9 37.2 36.4 26.4 F7 23.8 32.4 43.8 24.9 33.6 41.5 F8 30.7 38.1 31.2 28.6 39.5 31.9 These new developed factors probabilities were input to the previous ly developed BBN model for large companies shown in Figure 3 6. After new data input procedure, parameter learning process was conducted to develop conditional probabilities of all factors for new BBN model.

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67 New BBN model for large companies specialized i n resident building with implementation of individual RFID systems is developed as shown Figure 4 7. Figure 4 7 The new developed BBN model for five respondents large companies specialized in resident building with individual RFID systems implementa tion In order to quantify the potential benefits of DATs integration with new developed model, two DATs integrations systems were suggested to this model based on the literature reviews. According to the previous studies, this study assumes that the RFID+ 4D CAD integration is ideal for quality management, and RFID+USN is beneficial for reducing waiting time.

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68 With these assumptions, the benefits of RFID+4D CAD DATs implementation in this case was estimated as normalizing the value of LE (low effect) to 10 0% in factor of problems in quality management (F6 model variable), as shown in Figure 4 8. Figure 4 8 The change of F6 factor s probability in the BBN analysis for five respondents large companies specialized in resident building with individual R FID systems implementation As normalizing the LE value in F6 factor to 100%, the excellent probability of construction performance was increased to 63% from 39%. Based on this BBN model, about 24% increasing performance is expected if five companies adopt RFID+4D CAD DATs on their project. This increased probability of construction performance was

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69 calculated based on the conditional probabilities of all factors with conditionally set up of LE of 100%. In the similar way, the benefits of RFID+USN DATs were estimated as shown Figure 4 9. Figure 4 9 The change of F4 factor s probability in the BBN analysis for five respondents large companies specialized in resident building with individual RFID systems implementation In this BBN model, the probability of excellent construction performance is increased to 59%, and the probability of poor construction performance is reduced to 1%.

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70 In comparison with the BBN model for implemented RFID+4D CAD system, the probability of poor performance was dramatically reduced in this BBN model. T h erefore, it can be concluded that the minimizing the effect of F4 factor with RFID+USN implementation will greatly reduce about 10% of poor construction performance in residential building construction in large size companies. BBN Analysis for Small Companies S pecialized in Resident Building According to Table 4 1, six respondents answered that individual PDA systems are implemented on resident building projects in small companies. In the same way in above, the averaged negat ive factors occurrence probabilities for six respondents were identified as shown in Table 4 3. Table 4 3 The averaged occurrence probabilities of negative factors in small companies specialized in resident building with individual PDA DATs ( %) Model Variables Average of five respondents Average of all respondents in small companies (from Table 3 5) High Effect (HE) Medium Effect (ME) Low Effect (LE) High Effect (HE) Medium Effect (ME) Low Effect (LE) F1 35.1 29.4 35.5 29.9 52.6 17.5 F2 27.6 31.5 40.9 22.7 53.7 23.6 F3 33.9 39.4 26.7 32.1 49.2 18.7 F4 15.2 51.0 33.8 19.5 62.7 17.8 F5 29.9 31.8 38.3 22.4 36.1 41.5 F6 10.3 21.4 68.3 22.4 57.6 20.0 F7 29.4 28.4 42.2 33.9 39.2 26.9 F8 18.6 34.7 46.7 29.5 41.4 29.1

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71 In the new developed negative f actors occurrence probabilities for six respondents in small companies, it was identified that F1 model variable, inappropriate material handling on layout, is mostly occurred in resident building project of large companies implemented individual PDA syst em. Whereas, the occurrence probability of F6 model variable, problems in quality management, was estimated at low level. With these evaluations, it can be inferred that the PDA systems are beneficial for quality management, especially for small compani es. W i th the same way on previous section, these new factors probabilities were applied to the BBN model for small companies shown in Figure 3 7. After the process of parameter learning, the conditional probabilities of all factors for new BBN model wer e developed. New BBN model for small companies specialized in resident building with implementation of individual PDA systems is developed as shown Figure 4 10. In order to quantify the potential benefits of DATs integration with new developed BBN mode l for small companies implemented with individual PDA system, RFID+PDA DATs integration was suggested to this model. Based on the literature reviews in the chapter 2, this study assumes that the RFID+PDA integration is ideal for material and labor managem ent. With the assumptions, the benefits of RFID+PDA DATs implementation in this case was estimated as normalizing the value of LE (low effect) to 100% in the factor of inappropriate material handling on layout (F1 model variable), and in the factor of l ate material handling from layout to specific jot site (F2 model variable), as shown in Figure 4 11.

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72 Figure 4 10 The new developed BBN model for six respondents small companies specialized in resident building with individual PDA systems implementat ion As mentioned above, RFID+PDA integration system is also beneficial for the labor management on construction sites. Recently, RFID+PDA systems are applied to the workers safety management practices. Therefore, another assumption that is the negative effect of workers injury or accident (F7 model variable) can be minimized with the implementation of RFID+PDA integration is proposed for this study. In addition to the normalizing of F1 and F2 factors at low effect level, the F7 factor is also normalized at low effect level to identify the maximum potential benefits of RFID+PDA integration in small companies. The result is shown in Figure 4 12.

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73 Figure 4 11 The change of F1 and F2 factors probability in the BBN analysis for six respondents small com panies specialized in resident building with individual PDA systems implementation By adopting RFID+PDA integration in this BBN model for small companies, the probability of excellent construction performance was increased to 47% from 41%. Moreover, it wa s observed that the probability of poor performance was not relatively improved. However, after the normalizing F7 factors with RFID+PDA integration, the probabilities were dramatically changed. First of all, the probability of excellent performance was 5% more increased. And, the probability of poor performance of 2% was estimated in the BBN model.

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74 Figure 4 12 The change of F1, F2, and F7 factors probability in the BBN analysis for six respondents small companies specialized in resident building with individual PDA systems implementation

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75 CHAPTER 5 CONCLUSIONS The purpose of this study is to identify current automated data acquisition techniques (DATs) and the integration of DATs for construction data management Furthermore, through Bay esian belief networks (BBNs) analysis, the potential benefits of DATs integration are evaluated and quantified. For these objectives of the study, previously published studies according to the types of DATs were reviewed in detail. And, in order to iden tify current trends of DATs implementation in construction industry, survey was conducted. Based on the detailed literature reviews, this study finds that barcode, RFID, PDA, and GPS systems were implemented in construction industry during past decades. However, these individual DATs systems have own limitations and disadvantages to improve construction data management practices. In order to overcome these limitations and disadvantages of individual DATs, the integrations of DATs are widely accepted in construction industry. Moreover, many studies indicated that the integrations of DATs, especially integration RFID with other DATs, have capability to enhance the data management. Specifically, the numerous data generated from construction activities ar e valuable resources to track and monitor construction performances. Also, adequate construction data management is directly related to construction productivity, construction schedule, and cost analysis. Although construction data management is signific ant for overall construction activities, many construction companies still adopt individual DATs for their projects due to the uncertainties and high installation costs of integration of DATs.

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76 Under these conditions, this study attempted to quantify the potential benefits of integration of DATs in real construction industry using BBNs analysis. BBN model is supportive tool for decision making process with graphical outputs of cause and effects among the factors. In this study, one of the representative computer algorithm applications for BBN analysis, GeNIe Ver. 2.0 was used for BBN analysis. For the well development of BBN model, dynamic survey form, which is enabled for respondents to input consistent answers in the suggested questions, was created. Throughout the literature review, eight of the negative factors that affect construction data management were predefined. The predefined negative factors were provided to survey respondents to determine the ir occurrence probabilities on their past proje cts. I n the next step, respondents were asked to specify the relationships among the factors. With the collected dataset, the frequency of the specified relationships of factors was calculated. The top ranked fifteen relationships were selected to deve lop cause and effects relationships for the BBN model. Based on the determined relationships among the factors, two BBN models were initially created for large and small construction companies. Specifically, the companies that have more than $100 millio n of average annual revenue for the p receding 5 years which we re classified as large companies, and companies that have less than $100 million of average annual revenue which were considered as small companies. For the next step, the companies that had implemented individual DATs on their project were identified depending on the ir size. A t otal of seven companies were identified as the companies which are using individual DATs on their projects. Of the

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77 seven companies, one large and one small company w ere selected to quantify the potential benefits of the integration DATs through BBN analysis. In the BBN analysis, the benefits of integration of DATs for construction data management were estimated by calculating the probability of excellent construction performance while changing the factors occurrence probabilities. In the large companies for high rise residential building construction which are implemented with individual RFID systems, two integrations of DATs such as RFID+4D CAD and RFID+USN we re ap plied to the BBN model to estimate the potential benefit of integrated DATs for each The results of the BBN analysis are shown in Figure 5 1. With these results from the BBN model, it can be concluded that the integration of DATs implementation in large companies for high rise building construction can improve the construction data management resulting in a 20 24% increased construction performance. The BBN model was also used to quantify the benefits for small companies constructing a high rise buildi ng with the implementation of an individual PDA system. In this BBN model, RFID+PDA integration system was applied. Based on the results shown in Figure 5 2 f or the BBN analysis, it was determined that the implementation of the RFID+PDA system in small c ompanies improves material and labor data management performance from 6 11%

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78 Figure 5 1 Different probabilities of construction performances depending on the applied types of DATs in large companies specializ ing in hig hrise residential building constr uction Figure 5 2 Different probabilities of construction performances depending on the applied types of DATs in small companies specializing in high rise residential building construction

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79 In comparison with the results from BBN analysis for large comp anies, the range of performance improvement in small companies is relatively small. In other words, the benefits of DATs integration for construction data management in large companies are greater than in small companies. This finding is caused by the de gree of relationships among the factors. As construction projects become larger the factors affecting construction projects will be come more complicated and will be impacted by many more factors. Therefore, improvement of data management in the construc tion industry will have a significant impact on construction project performances.

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80 CHAPTER 6 DISCUSSIONS AND RECO MMENDATIONS This study has presented a procedure to quantify the benefits of the integration of DATs in the construction industry using Bayesian belief networks (BBNs) analysis. The results of this study indicate that the integration of DATs on construction projects will have the capability of improv ing construction perfo rmances. T he approximate range of probability for constructi on performance improvement was estimated to be 20 to 24% for large companies and 6 to 11% for small companies. The results of this study are similar to the findings from previous studies. However, the procedure of this study has limitations. First, the quantified results in this study were generated from BBN analysis based on the survey results. In other words, the determined relationships among all factors were dependent on the respondents view. Therefore, these values are not absolute values. More over, the identified negative factors for this study were predefined based on previous studies. Furthermore, the factors related to costs were not considered in this study. Accordingly future stud ies should be focused on the detailed evaluation of DATs integration in actual construction practices with objective data collection In addition other innovative procedure s can be researched for identifying DATs effects on construction practices.

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81 APPENDIX A THE SAMPLE OF SURVEY FORM

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86 LIST OF REFERENCES Attoh Okine, N. (2002). Probabilistic analysis of factors affecting highway construction costs: a belief network approach. Canada Journal of Civil Engineering 369 374. Bernold, L. (1990). Testing barcode technology in construction environment Journal of Construction Engineering and Management 643 655. Caldas, C.H., Torrent, D.G., and Haas, C.T. (2006). Using global positioning system to improve materials locating processes on industrial projects. Journal of Construction Engineering and Mana gement 741 749. Chae, S. H., and Yoshida, T. (2010). Application of RFID technology to prevention of collision accident with heavy equipment. Automation in Construction 368 374. Chen, Z., Li, H., and Wong, C.T.C. (2002). An application of bar code syst em for reducing construction wastes. Automation in Construction 521 533. Cheng, M.Y., and Chen, J.C. (2002). Integrating barcode and GIS for monitoring construction progress. Automation in Construction 23 33. Chin, S.Y., Yoon, S.W., Choi, C.H., and Cho C.Y. (2008). RFID+4D CAD for progress management of structural steel works in high rise buildings. Journal of Computing in Civil Engineering 74 89. Cho, C.Y., Kwon, S.W., Shin, T.H., Chin, S.Y., and Kim, Y.S. (2010). A development of next generation in telligent construction liftcar toolkit for vertical material movement management. Automation in Construction 14 27. Cox, S., Perdomo, J., and Thabet, W. (2002). Construction field data inspection using pocket PC technology. CIB w78 conference 2002 (pp. 1 8). International Council for Research and Innovation in Building and Construction. El Omari, S., and Moselhi, O. (2009). Data acquisition from construction sites for tracking purposes. Engineering, Construction and Architectural Management 490 503. El O mari, S., and Moselhi, O. (2009). Integrating Automated Data Acquisition Technologies for Progress Reporting of construction projects. 26th International Symposium on Automation and Robotics in Construction (ISARC 2009) (pp. 86 94). Ergen, E., Akinci, B., and Sacks, R. (2007). Tracking and locating components in a precast storage yard utilizing radio frequency identification technology and GPS. Automation in Construction 354 367.

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87 Giretti, A., Carbonari, A., Naticchia, B., and Grassi, M. (2009). Design an d first development of an automated real time safety management system for construction sites. Journal of Civil Engineering and Management 325 336. Goodrum, P.M., McLaren, M.A., and Durfee, A. (2006). The application of active radio frequency identificat ion technology for tool tracking on construction job sites. Automation in Construction 292 302. Grau, D., Caldas, C.H., Haas, C.T., Goodrum, P.M., and Gong, J. (2009). Assessing the impact of materials tracking technologies on construction craft producti vity. Automation in Construction 903 911. Han Y.W., Cho, H.H., Lee, Y.S., Kang, T.K., and Kim, J.S. (2004). A basic study on the development of intelligent tower crane using IT. Korea Journal of Construction Engineering and Management 49 52. Hildreth, J., Vorster, M., and Martinez, J. (2005). Reduction of Short Interval GPS Data for Construction operation analysis. Journal of Construction Engineering and Management 920 927. Jang, M.S., Yoon, S.W., Chin, S.Y., and Kim, Y.S. (2004). Implementing radio f requency identification in the curtainwall process management. Journal of the Architectural Institute of Korea 487 490. Jang, W.S., and Skibniewski, M.J. (2008). A wireless network system for automated tracking of construction materials on project sites. Journal of Civil Engineering and Management 11 19. Jang, W.S., and Skibniewski, M.J. (2007). Wireless sensor technologies for automated tracking and monitoring of construction materials utilizing ZigBee networks. ASCE Construction Research Congress: The Global Construction Community. Jaselskis, E.J., and El Misalami, T. (2003). Implementing radio frequency identification in the construction process. Journal of Construction Engineering and Management 680 688. Kim, Y.S., Oh, S.W., Cho, Y.K., and Seo J.W (2008). A PDA and wireless web integrated system for quality inspection and defect management of apartment housing projects. Automation in Construction 163 179. Ko, C. (2010). RFID 3D location sensing algorithms. Automation in Construction 588 595.

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88 L ee, E.C., Park, Y.T., and Jong, G.S. (2009). Large engineering project risk management using a Bayesian belief network. Expert Systems with Applications 5880 5887. Lee, J.H., Park, K.H., Yun, S.H., and Paek, J.H. (2006). A study on the reduction plan of construction disaster using RFID/USN technology. Korea Journal of Construction Engineering and Management 605 608. Lee, K.P., Lee, H.S., Park, M.S., Kim, H.S., and Baek Y.J. (2010). Development of Real Time Locating System for construction safety managem ent Korea Journal of Construction Engineering and Management 106 115. Lee, N.S., Song, J.H., Yoon, S.W., Chin, S.Y., Kwon, S.W., and Kim, Y.S. (2006). An application for tracking the location of material using RFID and wireless network technology. Kore a Journal of Construction Engineering and Management 523 528. Lee, S.Y., and Han, S.W. (2003). Geospatial construction data collection using GPS. KSCE Journal of Civil Engineering 363 370. Lee, W.J., Shin, T.H., Chin, S.Y., Yoon, J.S., Kwon, S.W., and Kim, Y.S. (2009). A process reference model and information model development for RFID/USN based construction supply chain management. Architectural Institute of Korea 147 158. Lu, M., Chen, W., Shen, X., Lam, H.C., and Liu, J. (2007). Positioning and tr acking construction vehicles in highly dense urban areas and building construction sites. Automation in Construction 647 656. Luu, V.T., Kim, S.Y., Tuan, N.V., and Ogunlana, S.O. (2009). Quantifying schedule risk in construction projects using Bayesian b elief networks. International Journal of Project Management 39 50. McCabe, B. (1998). Belief networks in construction simulation. Proceedings of the 1998 Winter Simulation Conference (pp. 1279 1286). McCabe, B., AbouRizk, S.M., and Goebel, R. (1998). Be lief networks for construction performance diagnostics. Journal of Computing in Civil Engineering 93 100. McCullouch, B.G., and Lueprasert, K. (1994). 2D bar code applications in construction. Journal of Construction Engineering and Management 739 752. Moselhi, O., and Hassanein, A. (2003). Data acquisition and analysis for highway construction using geographic information systems. Canada Journal of Civil Engineering 533 542.

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89 Nasir, D., McCabe, B., and Hartono, L. (2003). Evaluating risk in constructi on schedule model (ERIC S): Construction schedule risk model. Journal of Construction Engineering and Management 518 527. Nasir, H., Haas, C.T., Young, D.A., Razavi, S.N., Caldas, C., and Goodrum, P. (2010). An implementation model for automated construc tion materials tracking and locating. Canada Journal of Civil Engineering 588 599. Navon, R. (2005). Automated project performance control of construction projects. Automation in Construction 467 476. Navon, R. (2007). Research in automated measurement of project performance indicators. Automation in Construction 176 188. Navon, R., and Berkovich, O. (2006). An automated model for materials management and control. Construction Management and Economics 635 646. Navon, R., and Kolton, O. (2007). Algor ithms for automated monitoring and control of fall hazards. Journal of Computing in Civil Engineering 21 28. Navon, R., and Shpantnitsky, Y. (2005). A model for automated monitoring of road construction. Construction Management and Economics 941 951. O h, S.W., Kim, Y.S., Lee, J.B., and Kim, H.S. (2004). Collection and utilization of the construction labor information using PDA and Barcode. Korea Journal of Construction Engineering and Management 65 75. 08). Improving reliability of automated construction activity tracking by using two independent on site data collection systems. Intelligent Computing in Engineering ICE08 (pp. 479 486). Seo. K.S., Park, J.S., and Jung, Y.S. (2010). Effective areas for DAT utilization in construction projects. Korea Journal of Construction Engineering and Management 15 24. Song, J.C., Haas, C.T., and Caldas, C.H. (2006). Tracking the location of materials on construction job sites. Journal of Construction Engineering a nd Management 911 918. Song, J.C., Hass, C.T., and Caldas, C.H. (2007). A proximity based method for locating RFID tagged objects. Advanced Engineering Informatics 367 376. Tserng, H.P., and Dzeng, R.J. (2005). Mobile construction supply chain manageme nt using PDA and Bar codes. Computer Aided Civil and Infrastructure Engineering 242 264.

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90 Van Tol, A.A., and AbouRizk, S.M. (2006). Simulation modeling decision support through belief networks. Simulation Modelling Practice and Theory 614 640. Wang, L. (2008). Enhancing construction quality inspection and management using RFID technology. Automation in Construction 467 479.

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91 BIOGRAPHICAL SKETCH Jintaek Ock was born in Seoul, Korea. He earned his B.E. degree in civil engineering fro m Yonsei University in 2002. After graduation from university, he worked in the environmental engineering field in the Seoul Metropolitan Government for 1 and half year. In 2004, he started his graduate studies majoring in civil engineering at the Univer sity of South Carolina, Columbia, and he earned master s degree in 2007. During his graduate studies he worked as research assistant for sustainable development research. In 2008, he was admitted to the M.E. Rinker, Sr. School of Building Construction of the University of Florida, Gainesville, Florida. In May 2011 he will receive the M.S. in B uilding C onstruction, and he will continue to pursue his Ph.D with specialization in building construction at the University of Florida.