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Prelims

Prince Boateng (Koforidua Technical University, Koforidua, Ghana)
Zhen Chen (University of Strathclyde, Glasgow, UK)
Stephen O. Ogunlana (Heriot-Watt University, Edinburgh, UK)

Megaproject Risk Analysis and Simulation

ISBN: 978-1-78635-831-8, eISBN: 978-1-78635-830-1

Publication date: 26 April 2017

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Boateng, P., Chen, Z. and Ogunlana, S.O. (2017), "Prelims", Megaproject Risk Analysis and Simulation, Emerald Publishing Limited, Leeds, pp. i-xxxvi. https://doi.org/10.1108/978-1-78635-830-120171009

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Emerald Publishing Limited

Copyright © 2017 Emerald Publishing Limited

Half Title

MEGAPROJECT RISK ANALYSIS AND SIMULATION: A DYNAMIC SYSTEMS APPROACH

Title Page

MEGAPROJECT RISK ANALYSIS AND SIMULATION: A DYNAMIC SYSTEMS APPROACH

BY

PRINCE BOATENG

Koforidua Technical University, Koforidua, Ghana

ZHEN CHEN

University of Strathclyde, Glasgow, UK

STEPHEN O. OGUNLANA

Heriot-Watt University, Edinburgh, UK

United Kingdom – North America – Japan – India – Malaysia – China

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Emerald Publishing Limited

Howard House, Wagon Lane, Bingley BD16 1WA, UK

First edition 2017

Copyright © 2017 Emerald Publishing Limited

The right of Prince Boateng, Zhen Chen, and Stephen O. Ogunlana to be identified as the Authors of this Work has been asserted in accordance with the Copyright, Designs and Patents Act 1988.

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British Library Cataloguing in Publication Data

A catalogue record for this book is available from the British Library

Library of Congress Classification (LCC) – HE1-9990

BIC - KJMP

BISAC - BUS101000

Dewey Decimal Classification (DDC) – 388

ISBN: 978-1-78635-831-8 (Print)

ISBN: 978-1-78635-830-1 (Online)

ISBN: 978-1-78714-301-2 (Epub)

Dedication

To our families

Abbreviations

ANP Analytical Network Process
AHP Analytic Hierarchical Process
BBS Bilfinger Berger Civil (UK) and Siemens plc
CEC City of Edinburgh Council
CDR Cost of Dispute Resolution
CDUD Cost of Delay in Utility Diversion
CI Consistency Index
CLA Cost of Legal Action
CLD Causal Loop Diagram
COD Cost of Delays
COR Cost of Rework
CR Consistency Ratio
DEG De-Escalation to Grievances
Disp. Disputes
DOAF Delay of All Forms
DOC Delay in Obtaining Consent
EC Economic Certainties
EcRM Economic Risks Model
ETN Edinburgh Tram Network (Project)
EG Escalation to Grievances
EnC Environmental Certainties
EnR Environmental Risks
EnRE Environmental Regulation Enforcement
EnRM Environmental Risks Model
EnU Environmental Uncertainties
EP Energy Price
EPC Engineering, Procurement and Construction
EPCO Escalation to Project Cost Overrun
EPTO Escalation to Project Time Overrun
ER Economic Risks
ERM Environmental Resource Management
ErG Error Generation
EU Economic Uncertainties
FE Foreign Exchange
GCP Ground Conditions Problem at a Given Site
GFP Government Funding Policy
IPV Ideal Priority Value
LA Legal Actions
LD Liquidated Damages
LIR Local Inflation Rate
LRC Legislative & Regulation Changes
IRPI Ideal Synthesized Risk Priority Indexes
IRPV Ideal Risk Priority Index
MCDM Multi-Criterion Decision Making
MLDMBI Multi-Level Decision-Making Bodies Involvement
MP Material Price
MPDS Modification to Project Design & Specification
MPH Material Price Hike
MUDFA Multi-Utilities Framework Agreement
NPV Normal Priority Value
O&M Operations and Maintenance
PA Social Acceptability
PC Political Certainties
PDP Political Debates on the Project
PH Political Harmony
PI Political Indecision
PIP Political Interferences in the Project
PMPS Pressure to Modify Project Scope
PoRM Political Risks Model
PQD Project Quality Deficiency
PR Political Risks
Proj.C Project Complexity
PS Political Support
Proj.S Project Scope
PT Project Termination
PU Political Uncertainties
RMSI Respondent’s Mean Scores of Importance
RPCO Risks of Project Cost Overrun
RPI Risk Prioritization Index
RPIG Global Risks Priority Index
RPIL Local Risk Priority Index
RPTO Risks of Project Time Overrun
SC Social Certainties
SD System dynamics
SFM Stock and Flow Model
SG Social Grievances
SI Social Issues
SoRM Social Risks Model
SPV Special Purpose Vehicle
SR Social Risks
SU Social Uncertainties
TC Technical Certainties
TDUU Time to Divert Underground Utilities
TeRM Technical Risks Model
TIE Transport Initiatives Edinburgh Ltd.
TPAS Threat to Personal & Asset Security
TPV Total Priority Value
TR Technical Risks
TRO Traffic Regulation Order
TRPI Total Risk Priority Index
TU Technical Uncertainties
WCP Worksite Coordination Problems
WQS Weighted Quantitative Score
WI Wage Inflation

List of Figures

Chapter 2
Figure 2.1 Stakeholder relationship map for the ETN project. 25
Chapter 3
Figure 3.1 The SDANP framework for megaproject risk assessment. 47
Figure 3.2 ANP network model for risk prioritization. 50
Figure 3.3 Calculation process for the CR method. 52
Figure 3.4 The three components of system dynamics models. 56
Figure 3.5 A simple stock and flow model. 57
Figure 3.6 Social risk entry points during mega construction projects. 61
Chapter 4
Figure 4.1 ANP model structure for STEEP risks prioritization. 89
Figure 4.2 ANP sub-models for STEEP risks prioritization. 90
Chapter 5
Figure 5.1 Causal loop diagram for STEEP risks on the ETN project. 132
Figure 5.2 Causes tree diagram for technical uncertainties entity. 134
Figure 5.3 Uses tree diagram for technical uncertainties entity. 134
Figure 5.4 Causality of technical uncertainties. 134
Figure 5.5 Causal loop diagram for social risks system. 142
Figure 5.6 Causes tree diagrams for social risks model. 144
Figure 5.7 Uses tree diagrams for the social risks model. 146
Figure 5.8 Causal loop diagram for technical risks system. 147
Figure 5.9 Causes tree diagrams for the technical risks model. 149
Figure 5.10 Uses tree diagrams for the technical risks model. 150
Figure 5.11 Causal loop diagram for economic risks system. 152
Figure 5.12 Causes tree diagrams for the economic risks model. 153
Figure 5.13 Uses tree diagrams for the economic risks model. 154
Figure 5.14 Causal loop diagram for environmental risks system. 155
Figure 5.15 Causes tree diagrams for the environmental risks model. 158
Figure 5.16 Uses tree diagrams for the environmental risks model. 159
Figure 5.17 Causal loop diagram for political risks system. 160
Figure 5.18 Causes tree diagrams for the political risks model. 163
Figure 5.19 Uses tree diagrams for the political risks model. 164
Figure 5.20 A typical stock and flow model (SFM). 165
Figure 5.21 Integrated stock and flow diagram for the social risks system. 167
Figure 5.22 Integrated stock and flow diagram for the technical risks system. 167
Figure 5.23 Integrated stock and flow diagram for the economic risk system. 168
Figure 5.24 Integrated stock and flow diagram for the environmental risks system. 169
Figure 5.25 Integrated stock and flow diagram for the political risks model. 170
Figure 5.26 A typical SD equation representation. 184
Figure 5.27 Evaluation tests for the STEEP risks models. 185
Figure 5.28 Dynamic risk-free simulation patterns for social risks system model. 196
Figure 5.29 Dynamic scenario graphs for the social risks system model. 199
Figure 5.30 Simulation behaviour patterns for stocks in the technical risk system model 203
Figure 5.31 Baserun and actual scenario simulation patterns for economic risks. 206
Figure 5.32 Dynamic patterns for stock entities in the environmental risks model. 209
Figure 5.33 Dynamic simulation patterns for stock entities in the political risks model. 212
Chapter 6
Figure 6.1 Proposed framework for dynamic risks assessment in megaproject. 225
Appendices
Figure A1 Model validation process. 251
Figure A2 Behaviour reproduction test for the level of STEEP risks impacts on the system (all variables at baseline levels). 259
Figure A3 Behaviour mode sensitivity graphs for social risks and social grievances. 274
Figure A4 Behaviour mode sensitivity graphs for technical risks. 275
Figure A5 Behaviour mode sensitivity graphs for economic risks. 275
Figure A6 Behaviour mode sensitivity graphs for environmental risks. 276
Figure A7 Behaviour mode sensitivity graphs for political risks. 276
Figure A8 Dynamic confidence bounds sensitivity graph for social grievances. 277
Figure A9 Dynamic confidence bounds sensitivity graph for technical risks. 277
Figure A10 Dynamic confidence bounds sensitivity graph for economic risks. 278
Figure A11 Dynamic confidence bounds sensitivity graph for environmental risks. 278
Figure A12 Dynamic confidence bounds sensitivity graph for political risks. 279
Figure A13 Disaggregation of the dynamic simulation models for transportation megaprojects. 283

List of Tables

Chapter 2
Table 2.1 Basic information of the ETN project. 18
Table 2.2 The internal stakeholders of the ETN project. 19
Table 2.3 The external stakeholders of the ETN project. 21
Table 2.4 Stakeholder’s attitude and influence on ETN Project. 23
Table 2.5 Project organization of the ETN project. 23
Table 2.6 Project environment of the ETN Project. 24
Table 2.7 Original ETN project board governance structure. 26
Table 2.8 Bridges built to accommodate Edinburgh Tram. 27
Table 2.9 Disputes and changes in the ETN project. 29
Table 2.10 Project delivery against key milestones. 30
Table 2.11 Organizations and groups consulted during the EIA for ETN Line One. 34
Table 2.12 Specific risks impacting on the project environment. 38
Table 2.13 Specific technical risks impacting on the social and natural environments. 40
Chapter 3
Table 3.1 Relative importance and data transformation in pairwise comparison. 51
Table 3.2 The average random index. 54
Table 3.3 Typical stakeholders involved in transport projects. 60
Table 3.4 A summary of review on social risks cluster in megaprojects. 62
Table 3.5 A summary of review on technical risks in megaprojects. 65
Table 3.6 A summary of review on economic risks in megaprojects. 69
Table 3.7 A summary of review on environmental risks in megaprojects. 71
Table 3.8 Sources of environmental risks in mega construction projects. 71
Table 3.9 A summary of review on political risks in megaprojects. 75
Chapter 4
Table 4.1 Summary of interviewees’ profile and demography. 79
Table 4.2 Summary of survey conducted. 82
Table 4.3 Summary of descriptive results and analysis for the questionnaire survey. 83
Table 4.4 Respondent’s mean scores of importance. 86
Table 4.5 Matrix for project objectives with respect to decision goal. 94
Table 4.6 Comparison matrices for PR with respect to cost, time and quality. 96
Table 4.7 Pairwise comparison matrix for social risk variables. 98
Table 4.8 Pairwise comparison matrix for technical risk variables. 100
Table 4.9 Pairwise comparison matrix for economic risk variables. 103
Table 4.10 Pairwise comparison matrix for environmental risk variables. 106
Table 4.11 Pairwise comparison matrix for political risk variables. 107
Table 4.12 Unweighted super matrix for potential risks. 111
Table 4.13 Weighted supermatrix for potential risks. 112
Table 4.14 Final mode ANP decision-making priorities for potential risks cluster. 113
Table 4.15 Final mode ANP decision-making priorities for social risk sub-cluster. 114
Table 4.16 Final mode ANP decision-making priorities for technical risk sub-cluster. 115
Table 4.17 Final mode ANP decision-making priorities for economic risks sub-cluster. 116
Table 4.18 Final mode ANP decision-making priorities for Environmental Risk sub-cluster. 117
Table 4.19 Final mode ANP decision-making priorities for political risk variables. 118
Table 4.20 Deriving priorities for risks ratings. 120
Table 4.21 Verbal ratings for potential risks. 120
Table 4.22 Verbal ratings for social risk variables. 121
Table 4.23 Verbal ratings for technical risk variables. 121
Table 4.24 Verbal ratings for economic risk variables. 122
Table 4.25 Verbal ratings for environmental risk variables. 123
Table 4.26 Verbal ratings for political risk variables. 123
Table 4.27 Values of CI, RI, CR and inconsistency for all the pairwise comparison matrices. 124
Table 4.28 Summary of final ANP decision-making priority results for all risks. 125
Chapter 5
Table 5.1 Technical uncertainties influence. 135
Table 5.2 System boundary for social risks system. 136
Table 5.3 System boundary for technical risks system. 137
Table 5.4 System boundary for economic risks system. 138
Table 5.5 System boundary for environmental risks system. 139
Table 5.6 System boundary for political risks system. 140
Table 5.7 Stock variables for STEEP models. 166
Table 5.8 Mathematical equation for the social risks system variables. 171
Table 5.9 Mathematical equation for the technical risks system variables. 173
Table 5.10 Mathematical equation for the economic risks system variables. 176
Table 5.11 Mathematical equation for the environmental risks system variables. 179
Table 5.12 Mathematical equation for the political risks system variables. 181
Table 5.13 ANP inputs to the STEEP risk system modelling. 195
Table 5.14 Summary of the simulation results for the social risks system model. 202
Table 5.15 Summary of dynamic simulation results for technical risks system model. 205
Table 5.16 Dynamic simulation results for the economic risks system model. 208
Table 5.17 Summary of the dynamic simulation results for environmental risks system. 211
Table 5.18 Dynamic simulation results for the political risks system model. 215
Table 5.19 One-way analysis of variance: The extent to which steep risks impact on project objectives. 217
Table 5.20 Data validity on the ETN project. 220
Chapter 6
Table 6.1 SDANP procedure for risks reduction in megaprojects. 228
Table 6.2 Practical guide for using SDANP methodology in megaprojects. 230
Appendices
Table A1 Tests for building confidence in the integrated SDANP models. 253
Table A2 Parameters in the STEEP models. 256
Table A3 Parameter distributions of stock and exogenous system entities for STEEP risks models. 261
Table A4 Numerical sensitivity test for the social risks parameters. 263
Table A5 Numerical sensitivity test for the technical risks parameters. 265
Table A6 Numerical sensitivity test for the economic risks parameters. 267
Table A7 Numerical sensitivity test for the environmental risks parameters. 269
Table A8 Numerical sensitivity test for the political risks parameters. 271
Table A9 The significance of the dynamics simulation models for transportation megaprojects in addressing policy problems. 285
Table C1 Respondent’s mean scores of importance for project objectives (Po ). 291
Table C2 Respondent’s mean scores of importance for potential risks (PR1): Social risks. 297
Table C3 Respondent’s mean scores of importance for potential risks (PR2): Technical risks. 303
Table C4 Respondent’s mean scores of importance for potential risks (PR3): Economic risks. 309
Table C5 Respondent’s mean scores of importance for potential risks (PR4): Environmental risks. 315
Table C6 Respondent’s mean scores of importance for potential risks (PR5): Political risks. 321

List of Exhibits

Chapter 2
Exhibit 2.1 Utility diversions for Edinburgh Trams Network construction 27
Exhibit 2.2 Road interruption due to tram construction in the Edinburgh city centre 32
Exhibit 2.3 Rework and adverse environmental impacts such as waste 35
Exhibit 2.4 The impact of bad weather conditions on productivity and construction delay 36
Exhibit 2.5 Delayed construction process caused by bad weather conditions 36
Exhibit 2.6 Poor well-being conditions on construction site: A worker was having his lunch in a cold rainy day 37

List of Equations

Chapter 3
Equation 3.1 Weighted quantitative score method 49
Equation 3.2 Pairwise comparison matrix computation 52
Equation 3.3 Vector normalization 53
Equation 3.4 Initial eigenvalue computation 53
Equation 3.5 Maximum eigenvalue computation 53
Equation 3.6 Computation of value of consistency index 54
Equation 3.7 Consistency ratio computation 54
Equation 3.8 Final risk prioritization index 55
Equation 3.9 Mathematical definition of the integral for stock computation 58
Equation 3.10 Basic stock computation 58
Chapter 4
Equation 4.1 Respondent’s mean scores of importance 85
Equation 4.2 Priority matrix computation for the project objectives 93

Acknowledgements

This book summarizes a dedicated research funded and conducted under the megaproject management research theme at Heriot-Watt University and in the Scott Sutherland School of Architecture and Built Environment at Robert Gordon University in the United Kingdom. The research was also conducted through collaborative research amongst researchers from 24 European countries inside the COST Action TU1003 MEGAPROJECT (2011–2015), which was funded by the European Cooperation in Science and Technology (COST) and focuses on the effective design and delivery of megaprojects in the European Union. The COST Action on MEGAPROJECT was chaired by Professor Naomi Brookes at the University of Leeds in the United Kingdom.

The authors would like to thank all participants for making time and efforts to support the research through interview and questionnaire survey for data collection from the Edinburgh Tram Network (ETN) project. The authors would also like to thank colleagues at the COST Action TU1003 for their advice and comments on the research into the ETN project. Without their supports, this research cannot be completed.

The research theme on megaproject management at Heriot-Watt University was set up in 2012 and has been strongly supported by the following world renowned experts:

  • Geoff Baskir, Chair, Aircraft/Airport Compatibility Committee, Transportation Research Board, National Academy of Sciences, USA

  • Naomi Brookes, Professor of Complex Project Management, University of Leeds, and CEO, Projektlernen, UK

  • Volker Buscher, Director, Global Digital Business, Arup, UK

  • John Connaughton, Professor of Sustainable Construction, Head of Construction Management and Engineering, University of Reading, UK

  • Henry Ergas, Professor of Infrastructure Economics, University of Wollongong, Australia

  • Stuart Ladds, Head of Property Strategy & Logistics, College of Policing Limited, UK

  • Heng Li, Chair Professor in Construction Informatics, The Hong Kong Polytechnic University, Hong Kong, China

  • Edward W. Merrow, Founder and President, Independent Project Analysis, Inc., USA

  • Stanley G. Mitchell, CEO, Key Facilities Management International, Scotland. Chair, ISO TC 267 Facilities Management Committee

  • David Mosey, Professor of Law and Director, Centre of Construction Law and Dispute Resolution, King’s College London, UK

  • John Pike, Chairman, Bellrock Property Services, UK

  • Rodney Turner, Professor of Project Management, SKEMA Business School, France.

The authors would like to thank the entire publishing team at Emerald. Special thanks to colleagues at Emerald Publishing Limited, including Amy Barson, Senior Content Editor; Carole Caines, Books Production Controller; Nicki Dennis, Publisher; Charlotte Hales, Editorial Assistant; Liron Gilenberg, Cover Designer; Philippa Grand, Executive Publisher; Jen McCall, Publisher; and Kousalya Krishnamoorthy, Project Manager at MPS Limited.

About the Authors

Prince Boateng, PhD, MASCE, AFHEA, is Lecturer in Building Technology & Quantity Surveying in Koforidua Technical University, Ghana. He is a former Lecturer in Construction and Project Management at Robert Gordon University, Aberdeen, the United Kingdom. He is proficient in working with and analysing complex risk data. He uses analytical and system dynamics modelling tools to prioritize and simulate project risks overtime during risks assessment in megaprojects at the construction phase. He has used this expertise in developing innovative risk assessment tool known as SDANP methodology to model and predict project cost and time overruns in many megaprojects in Europe and Africa. Prince’s areas of expertise include risks analysis and modelling with system dynamics and the analytical network process for multi-criteria decision making for the effective megaproject delivery within the European Union and beyond.

Zhen Chen is Lecturer in Construction Management in the Department of Architecture at the University of Strathclyde. He is a former Lecturer in Facilities Management and the founder and leader of Megaproject Management research theme at Heriot-Watt University. He serves at technical committees (Facility Management; Project, Programme and Portfolio Management; and Service Life Planning) at British Standards Institution (BSI), and technical committees (Airport Planning and Operations; and Infrastructure Resilience) at the American Society of Civil Engineers (ASCE). He is a member of the management committee of COST Action TU1003 (The Effective Design and Delivery of Megaprojects in the European Union). He also serves at editorial boards for several international journals at ICE (Engineering Sustainability; Infrastructure Asset Management; Management, Procurement and Law; and Waste and Resource Management) and Elsevier (International Journal of Project Management). He is the Specialty Chief Editor on Construction Management for Frontiers in Built Environment published by EPFL in Switzerland. He is the Associate Editor for Innovative Infrastructure Solutions at Springer and Frontiers in Built Environment at EPFL. He has engaged in more than 30 research projects, worth over £5 million and has authored over 160 publications in construction engineering and management.

Stephen O. Ogunlana, BSc, PhD, is currently the Chair of Construction Project Management at the School of the Built Environment, Heriot-Watt University. Professor Ogunlana has an international reputation for research in the application of system dynamics simulation to construction projects and organizations. He is the author of over 250 scholarly publications in top-tier journals and refereed conferences. He is also the editor of the book Profitable Partnering for Construction Procurement published by Taylor and Francis and Training for Construction Industry Development published by the CIB/AIT and co-editor of Joint Ventures in Construction (Thomas Telford) and Public-Private Partnership in Infrastructure Development — Case Studies from Asia and Europe (Bauhaus Universitat Weimar). His research work has been funded by the Canadian International Development Agency, European Union, Thai National Housing Authority, UNOCAL, Japanese Government, British Council etc. His works on leadership were awarded Emerald Literati Award for two consecutive years (2009 and 2010) for the most outstanding paper in the journal Engineering Construction and Architectural Management. Professor Ogunlana is the joint coordinator of CIB W107 Commission on Construction in Developing Economies and a member of the Editorial Board for over 10 internationally refereed academic journals including Engineering Construction and Architectural Management, the International Journal of Financial Management of Property and Construction, International Journal of Energy Sector Management, International Journal of Construction Management, Journal of Engineering Development and Technology, Surveying and the Built Environment, Civil Engineering Dimensions and Akruti Journal of Infrastructure. He has acted as external examiner for several top universities in the world.

Preface

This book provides technical details on a dynamic systems approach to megaproject risk analysis and simulation, and it is based on the authors’ long-term research into megaproject management, multi-criteria decision making, and system dynamics. For the first time, the authors have attempted to find a technical solution to tackle overruns on cost and time in megaprojects, and this is based on a comprehensive set of risks associated with social, technical, economic, environmental and political (STEEP) issues in megaproject environment and a dynamic systems approach called SDANP. The approach is an integrated use of tools including analytic network process (ANP) and system dynamics (SD) for risks prioritization and simulation.

The new SDANP model is described in this book with a case study on the Edinburgh Tram Network (ETN) project, which was a live case project during the time of the authors’ research into a dynamic systems approach to megaproject risk analysis and simulation. Through this experimental research, the SDANP model has provided interesting results on cost and time overruns with accuracy rates above 80%, respectively, for the ETN project over the time period between 2007 and 2013. The authors expect that this dynamic systems approach to megaproject risk analysis and simulation can be widely tested for the benefits of stakeholders in dealing with cost and time overruns in megaproject development.

Prince Boateng

Zhen Chen

Stephen O. Ogunlana

Foreword

As our journey into the uncertainties of the twenty-first century continues, of one thing we can be sure: megaprojects are viewed as increasingly important in creating solutions to societal problems. Megaprojects will provide the new power plants that will give us with green energy, they will deliver transport systems that work for all without increasing carbon emissions, they will provide us with the integrated hospitals and healthcare that we need and they will even delight us with cultural and sporting events! We remain optimistic that the huge complexities of megaprojects in people, capital and technology can be tamed and we can look forward to feeling the benefits of their successful implementations.

However, at their heart, megaprojects pose a conundrum. Time after time (and despite their apparent benefits) we do not seem to be able to deliver them on time, to budget and actually producing the output functionality that we need. We only have vague ideas why some succeed and, where they fail, we discover worryingly psychological failings in their planning and design. Given their importance in facing twenty-first century challenges, we desperately need to undertake more research to help us deliver megaprojects more effectively and to insure that the results of that research are available to the widest possible population of stakeholders.

It is precisely this gap that Boateng, Chen and Ogunlana have aimed at with the work that they report upon in this book. They take one of the most clearly identified complexities in delivering megaproject, namely risk, and explore new ways of conceptualizing it and dealing with it. They employ a wide range of novel systems dynamics and frameworks to develop an understanding of risk in megaprojects. They provide interesting applications of techniques used elsewhere in simulation to megaprojects. They illustrate their work with an insightful case of the Edinburgh Tram Project, a megaproject which embodies both the huge benefits that megaprojects can bring and the significant issues that inhibit their delivery. Boateng, Chen and Ogunlana are to be congratulated for the zeal with which they have pursued their research objectives and their fervour to share the results of their endeavours with others.

This book provides a valuable addition to the work currently being undertaken by academics and practitioners alike in understanding megaproject design and delivery. It is through such committed work that we really will be able to tame megaprojects and insure that they can reliably deliver the outcomes that society so desperately needs.

Professor Naomi Brookes, PhD DIC

Visiting Professor in Complex Project Management,

University of Leeds

Chair – MEGAPROJECT COST Action

C.E.O. – Projektlernen

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