Data-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. 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Le 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