Table Of ContentData-driven BIM for Energy
Efficient Building Design
This research book aims to conceptualise the scale and spectrum of Building
Information Modelling (BIM) and artificial intelligence (AI) approaches in energy
efficient building design and to develop its functional solutions with a focus on
four crucial aspects of building envelop, building layout, occupant behaviour and
heating, ventilation and air-conditioning (HVAC) systems. Drawn from theoretical
development on the sustainability, informatics and optimisation paradigms in built
environment, the energy efficient building design will be marked through the
power of data and BIM-intelligent agents during the design phase. It will be further
developed via smart derivatives to reach harmony in the systematic integration
of energy efficient building design solutions, a gap that is missed in the extant
literature and that this book aims to fill. This approach will inform a vision for the
future and provide a framework to shape and respond to our built environment
and how it transforms the way we design and build. By considering the balance
of BIM, AI and energy efficient outcomes, the future development of buildings
will be regenerated in a direction that is sustainable in the long run. This book
is essential reading for those in the AEC industry as well as computer scientists.
Dr Saeed Banihashemi is Associate Professor and Postgraduate Program Director
of Building and Construction Information Management in the School of Design
and Built Environment, Faculty of Arts and Design, University of Canberra (UC),
Australia.
Dr Hamed Golizadeh is Assistant Professor of Building and Construction
Management at the University of Canberra, Australia.
Professor Farzad Pour Rahimian is Professor of Digital Engineering and
Manufacturing at Teesside University, UK.
Spon Research
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Data-driven BIM for Energy Efficient Building Design
Saeed Banihashemi, Hamed Golizadeh and Farzad Pour Rahimian
For more information about this series, please visit: www.routledge.com
Data-driven BIM for Energy
Efficient Building Design
Saeed Banihashemi, Hamed Golizadeh
and Farzad Pour Rahimian
First published 2023
by Routledge
4 Park Square, Milton Park, Abingdon, Oxon OX14 4RN
and by Routledge
605 Third Avenue, New York, NY 10158
Routledge is an imprint of the Taylor & Francis Group, an informa business
© 2023 Saeed Banihashemi, Hamed Golizadeh and Farzad Pour Rahimian
The right of Saeed Banihashemi, Hamed Golizadeh and Farzad Pour
Rahimian to be identified as authors of this work has been asserted in
accordance with sections 77 and 78 of the Copyright, Designs and Patents
Act 1988.
All rights reserved. No part of this book may be reprinted or reproduced or
utilised in any form or by any electronic, mechanical, or other means, now
known or hereafter invented, including photocopying and recording, or in any
information storage or retrieval system, without permission in writing from
the publishers.
Trademark notice: Product or corporate names may be trademarks or
registered trademarks, and are used only for identification and explanation
without intent to infringe.
British Library Cataloguing-in-Publication Data
A catalogue record for this book is available from the British Library
ISBN: 978-1-032-07348-4 (hbk)
ISBN: 978-1-032-07554-9 (pbk)
ISBN: 978-1-003-20765-8 (ebk)
DOI: 10.1201/9781003207658
Typeset in Times New Roman
by Apex CoVantage, LLC
Saeed dedicates this book to Shiva, his lovely wife,
and his parents, for all their devotion and care.
Contents
List of Figures xi
List of Tables xiii
List of Abbreviations xiv
Preface xv
1 Classics of Data-Driven BIM for Energy Efficient Design 1
1.1 Background 1
1.2 BIM and Energy Efficient Design Problematisation 2
1.3 Objectives and Topical Investigation 6
1.4 Problem to Solution Discourse 6
1.5 Significance of the Study 8
1.6 The Outline 9
2 Sustainability, Information and Optimisation: Antecedents
of the Data and BIM-Enabled EED 13
2.1 Paradigms of Sustainability, Information and Optimisation
Theories 13
2.1.1 Sustainability 13
2.1.2 Information Theory 14
2.1.3 Optimisation Theory 16
2.1.4 Trilateral Interaction 18
2.2 Sustainable Construction Drivers 20
2.3 BIM and Sustainable Construction 23
2.4 Artificial Intelligence (AI) 27
2.4.1 AI Application in Sustainable Construction 28
2.4.2 AI Application in BIM 28
2.5 Calculative, Simulative, Predictive and Optimisation Methods
for Energy Efficient Buildings 29
2.5.1 Calculative Methods 30
2.5.2 Simulative Methods 30
viii Contents
2.5.3 Predictive Methods 31
2.5.4 Optimisation Methods 33
2.6 Summary 35
3 BIM and Energy Efficient Design 46
3.1 Background 46
3.2 The Current State of the Art of BIM-EED 47
3.2.1 BIM-Compatible EED 49
3.2.2 BIM-Integrated EED 49
3.2.3 BIM-Inherited EED 50
3.2.4 BIM-EED Adoption 50
3.2.5 Simulation Software 51
3.2.6 Interoperability 52
3.2.7 Level of Details 54
3.3 Themes and Gaps 56
3.3.1 Themes 56
3.3.2 Outcomes 56
3.3.3 Gaps 57
3.3.3.1 Confusion 57
3.3.3.2 Neglect 58
3.3.3.3 Application 59
3.4 The Future of BIM-EED 59
3.5 Implications 62
3.6 Summary 62
4 Building Energy Parameters 69
4.1 Building Energy Parameters 69
4.1.1 Physical Properties and Building Envelop 69
4.1.2 Building Layout 70
4.1.3 Occupant Behaviour 71
4.1.4 HVAC and Appliances 71
4.2 Delphi 72
4.2.1 Round 1 73
4.2.2 Round 2 76
4.2.3 Round 3 80
4.3 Summary 82
5 AI Algorithms Development 86
5.1 Dataset Generation 86
5.2 Data Size Reduction 92
5.2.1 An Overview 92
Contents ix
5.2.2 Metaheuristic-Parametric Approach in Data Size
Reduction 92
5.3 Data Interpretation Approach 97
5.4 AI Development 98
5.4.1 Introduction 98
5.4.2 Artificial Neural Network 99
5.4.2.1 A NN Model Configuration and Performance
Analysis 100
5.4.2.2 Final ANN Model 103
5.4.3 Decision Tree 104
5.4.3.1 An Overview 104
5.4.3.2 DT Model Configuration and Performance
Analysis 106
5.4.4 Hybrid Objective Function Development 114
5.5 Summary 120
6 BIM-Inherited EED Framework Development and Verification 124
6.1 Optimisation Procedure 124
6.2 Integration Framework 125
6.2.1 Database Development 127
6.2.2 Database Exchange 129
6.2.3 Database Optimisation 130
6.2.4 Database Switchback 132
6.2.5 Database Updated 132
6.3 Testing and Validation 134
6.3.1 Case Study 134
6.3.2 Energy Simulation 136
6.3.3 Baseline Case Simulation Results 137
6.3.4 Case Optimisation Procedure 139
6.3.5 Case Optimisation Results 139
6.3.6 Optimisation Reliability Tests 143
6.4 Sensitivity Analysis 147
6.5 Summary 150
7 Conclusion 152
7.1 Review of Background, Problem, Aim and Method 152
7.2 Review of Research Processes and Findings 154
7.2.1 Objective 1: Examining the Potential and Challenges
of BIM to Optimise Energy Efficiency in Residential
Buildings 154
7.2.2 Objective 2: Identifying Variables That Play Key Roles
in Energy Consumption of Residential Buildings 156
