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E-raamat: BIM and Big Data for Construction Cost Management [Taylor & Francis e-raamat]

, , (The University of Hong Kong)
  • Formaat: 156 pages, 16 Tables, black and white; 24 Line drawings, black and white; 14 Halftones, black and white
  • Ilmumisaeg: 26-Oct-2018
  • Kirjastus: Routledge
  • ISBN-13: 9781351172325
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  • Taylor & Francis e-raamat
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BIM and Big Data for Construction Cost ManagementSuurem pilt
  • Formaat: 156 pages, 16 Tables, black and white; 24 Line drawings, black and white; 14 Halftones, black and white
  • Ilmumisaeg: 26-Oct-2018
  • Kirjastus: Routledge
  • ISBN-13: 9781351172325
Teised raamatud teemal:
Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9781351172325

This book is designed to help practitioners and students in a wide range of construction project management professions understand what building information modelling (BIM) and big data could mean for them and how they should prepare to work successfully on BIM-compliant projects and maintain their competencies in this essential and expanding area.

In this book, the state-of-the-art information technologies that support high-profile BIM implementation are introduced, and case studies show how BIM has integrated core quantity surveying and cost management responsibilities and how big data can enable informed decision-making for cost control and cost planning. The authors' combined professional and academic experience demonstrates, with practical examples, the importance of using BIM and particularly the fusion of BIM and big data, to sharpen competitiveness in global and domestic markets.

This book is a highly valuable guide for people in a wide range of construction project management and quantity surveying roles. In addition, implications for project management, facilities management, contract administration, and dispute resolution are also explored through the case studies, making this book essential reading for built environment and engineering professionals.

List of figures
x
List of tables
xii
First foreword xiii
Second foreword xv
Preface xvii
Acknowledgements xix
Abbreviations xx
1 Introduction
1(12)
1.1 Definitions
1(1)
1.2 Current practice of construction cost management
2(7)
1.2.1 Preliminary cost estimate
3(2)
1.2.2 Design-stage cost plan
5(1)
1.2.3 Tendering
6(1)
1.2.4 Cost control
7(1)
1.2.5 Variations and final accounts
8(1)
1.3 The evolving roles of QS
9(1)
1.4 Problems of existing QS practices
10(2)
1.4.1 Fragmented process
10(1)
1.4.2 Estimating `the inestimable'
10(1)
1.4.3 Tedious work
11(1)
1.4.4 Time pressure
11(1)
1.4.5 Image problem
12(1)
1.5 Summary
12(1)
2 BIM theories and technologies
13(21)
2.1 What is BIM?
13(2)
2.2 Overview of commercial BIM software
15(5)
2.2.1 Revit and Navisworks
15(1)
2.2.2 Bentley Systems
16(1)
2.2.3 ArchiCAD
16(1)
2.2.4 Dassault Systemes and CATIA
17(1)
2.2.5 Tekla
18(1)
2.2.6 Glodon
18(1)
2.2.7 RIB iTWO
19(1)
2.3 Expandable BIM took and platforms
20(2)
2.3.1 On sustainability
20(1)
2.3.2 On mechanical, electrical, and plumbing (MEP)
21(1)
2.3.3 On construction
21(1)
2.3.4 On facilities management (FM)
21(1)
2.4 Information in BIM
22(3)
2.4.1 Semantic information in BIM
22(1)
2.4.2 Standards relating to BIM semantics
23(1)
2.4.3 The importance of BIM semantics
23(2)
2.5 Level of Development (LoD)
25(3)
2.5.1 Level of detail
25(1)
2.5.2 Level of development
26(2)
2.6 BIM standards
28(3)
2.7 BIM libraries
31(1)
2.8 Summary
32(2)
3 BIM implementation strategies, prospects, and challenges
34(19)
3.1 From 2D drawings to 3D models to nD BIM
34(2)
3.2 Why is BIM in vogue?
36(6)
3.2.1 Enhancing productivity through virtual design and construction (VDC)
36(1)
3.2.2 Detecting design errors and clashes
37(2)
3.2.3 Improving interoperability
39(1)
3.2.4 Reducing fragmentation and discontinuity
40(2)
3.3 BIM costs and benefits analysis
42(3)
3.3.1 Pertinent benefits
42(1)
3.3.2 Cost drivers of BIM implementation
43(2)
3.3.3 The importance to have a BIM business case
45(1)
3.4 BIM execution plan
45(3)
3.4.1 Specifying BIM objectives at each project stage
46(1)
3.4.2 Mapping out BIM procedure
47(1)
3.4.3 Defining information flows
47(1)
3.4.4 Selecting BIM software, hardware, and human infrastructure
47(1)
3.5 Issues concerning BIM implementation
48(4)
3.5.1 Contractual framework for incorporating BIM
48(1)
3.5.2 Intellectual property (IP) rights
49(1)
3.5.3 Model management
50(1)
3.5.4 Risk management and liability
50(1)
3.5.5 Organisational issues
51(1)
3.6 Summary
52(1)
4 Adopting BIM for cost management
53(22)
4.1 Prospects of BIM for cost management
53(1)
4.2 Developing a QS-BIM execution plan
54(15)
4.2.1 Preliminary cost estimate process in the context of BIM
56(3)
4.2.2 Design-stage cost plan in the context of BIM
59(3)
4.2.3 Tendering
62(3)
4.2.4 Cost control
65(2)
4.2.5 Variations and final accounts
67(2)
4.3 Critical success factors of BIM adoption for QS
69(5)
4.3.1 Demanding a QS-BIM
69(2)
4.3.2 Information availability
71(2)
4.3.3 Compatible with current QS practices
73(1)
4.3.4 Compatible with existing BIM-based QS solutions
73(1)
4.4 Summary
74(1)
5 Case studies
75(20)
5.1 Case No.1 BIM-based QTO
75(6)
5.1.1 Project overview
75(1)
5.1.2 Overview of the adopted BIM software
76(1)
5.1.3 BIM-based QTO
77(3)
5.1.4 Findings and lesson learnt
80(1)
5.2 Case No.2 BIM-based tender document preparation
81(7)
5.2.1 Project overview
81(1)
5.2.2 Overview of the adopted BIM software
82(1)
5.2.3 BIM-based tender document preparation
83(4)
5.2.4 Findings and lesson learnt
87(1)
5.3 Case No.3 BIM-based remeasurement
88(6)
5.3.1 Project overview
88(1)
5.3.2 Overview of the adopted BIM software
89(1)
5.3.3 BIM-based remeasurement of rebar
89(5)
5.3.4 Findings and lesson learnt
94(1)
5.4 Summary
94(1)
6 Big data for construction cost management
95(14)
6.1 What is big data?
95(2)
6.2 Why is big data in vogue?
97(3)
6.2.1 Data, information, and knowledge
97(1)
6.2.2 Unbinding the `bounded rationality'
98(2)
6.3 Cases of big data for construction cost management
100(4)
6.3.1 Big data to help prepare tendering and cost estimate
101(1)
6.3.2 Big data to help prepare bidding
102(1)
6.3.3 Big data to help analyse bidders' behaviour
103(1)
6.4 BIM and big data
104(1)
6.5 Prospects and challenges of big data for construction cost management
105(3)
6.5.1 Big data technologies
105(1)
6.5.2 Life cycle costing (LCC) enabled by BIM and big data
106(1)
6.5.3 Breaking down the silos
107(1)
6.5.4 Big data ethics
108(1)
6.6 Summary
108(1)
7 Current challenges and future outlooks
109(7)
7.1 Lack of standards
109(1)
7.2 Technical challenges
110(2)
7.3 Economic challenges
112(1)
7.4 Organisational challenges
113(1)
7.5 Legal and contractual challenges
114(1)
7.6 Cultural challenges
115(1)
7.7 Summary
115(1)
8 Good practices for adopting BIM for cost management
116(8)
8.1 Encouraging research and development (R&D)
116(1)
8.2 Continuous training and education
117(1)
8.3 Makinga strong BIM business case
118(1)
8.4 Sharing costs and benefits
119(1)
8.5 Embracing innovative procurement models
120(1)
8.6 BIM localisation
121(1)
8.7 Think big, act small
122(1)
8.8 Summary
123(1)
9 The future of BIM and big data in quantity surveying
124(7)
9.1 Cloud BIM
124(1)
9.2 Ubiquitous BIM service
125(1)
9.3 BIM plus Big data
126(1)
9.4 Computational BIM
127(1)
9.5 New quantity surveyors
128(1)
9.6 New QS businesses
129(1)
9.7 Summary
130(1)
10 Conclusion
131(10)
10.1 A sea change in the longstanding QS profession
131(1)
10.2 BIM technologies demystified
131(1)
10.3 BIM as a disruptor
132(1)
10.4 BIM for QS
133(1)
10.5 Big data for QS
134(1)
10.6 Challenges of BIM and big data for QS
135(2)
10.7 Recommendable good practices
137(2)
10.8 A bright future of BIM and big data for QS
139(2)
References 141(14)
Index 155
Weisheng Lu is Associate Dean and Associate Professor, Department of Real Estate and Construction, Faculty of Architecture, Hong Kong University.

Chi Cheung Lai is a PRC Register Cost Engineer and Director at Northcroft Hong Kong Ltd.

Tung Tse PRC Register Cost Engineer at Northcroft Hong Kong Ltd.
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