Data-driven BIM for Energy Efficient Building Design

✍ Scribed by Saeed Banihashemi, Hamed Golizadeh, Farzad Pour Rahimian

• Publisher: Routledge
• Year: 2022
• Tongue: English
• Leaves: 187
• Series: Spon Research
• Category: Library

✦ Synopsis

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 develop its functional solutions with a focus towards four crucial aspects of building envelop, building layout, occupant behaviour and heating, ventilation and air-conditioning (HVAC) systems. Drawn from the 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 a harmony in the systematic integration of energy efficient building design solutions; a gap which is missed in the extant literature and this book aims to fill that. This approach will inform a vision for future, 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 are sustainable in the long run. This book is essential reading for those in the AEC industry as well as computer scientists.

✦ Table of Contents

Cover
Half Title
Series
Title
Copyright
Dedication
Contents
List of Figures
List of Tables
List of Abbreviations
Preface
1 Classics of Data-Driven BIM for Energy Efficient Design
1.1 Background
1.2 BIM and Energy Efficient Design Problematisation
1.3 Objectives and Topical Investigation
1.4 Problem to Solution Discourse
1.5 Significance of the Study
1.6 The Outline
2 Sustainability, Information and Optimisation: Antecedents of the Data and BIM-Enabled EED
2.1 Paradigms of Sustainability, Information and Optimisation Theories
2.1.1 Sustainability
2.1.2 Information Theory
2.1.3 Optimisation Theory
2.1.4 Trilateral Interaction
2.2 Sustainable Construction Drivers
2.3 BIM and Sustainable Construction
2.4 Artificial Intelligence (AI)
2.4.1 AI Application in Sustainable Construction
2.4.2 AI Application in BIM
2.5 Calculative, Simulative, Predictive and Optimisation Methods for Energy Efficient Buildings
2.5.1 Calculative Methods
2.5.2 Simulative Methods
2.5.3 Predictive Methods
2.5.4 Optimisation Methods
2.6 Summary
3 BIM and Energy Efficient Design
3.1 Background
3.2 The Current State of the Art of BIM-EED
3.2.1 BIM-Compatible EED
3.2.2 BIM-Integrated EED
3.2.3 BIM-Inherited EED
3.2.4 BIM-EED Adoption
3.2.5 Simulation Software
3.2.6 Interoperability
3.2.7 Level of Details
3.3 Themes and Gaps
3.3.1 Themes
3.3.2 Outcomes
3.3.3 Gaps
3.3.3.1 Confusion
3.3.3.2 Neglect
3.3.3.3 Application
3.4 The Future of BIM-EED
3.5 Implications
3.6 Summary
4 Building Energy Parameters
4.1 Building Energy Parameters
4.1.1 Physical Properties and Building Envelop
4.1.2 Building Layout
4.1.3 Occupant Behaviour
4.1.4 HVAC and Appliances
4.2 Delphi
4.2.1 Round 1
4.2.2 Round 2
4.2.3 Round 3
4.3 Summary
5 AI Algorithms Development
5.1 Dataset Generation
5.2 Data Size Reduction
5.2.1 An Overview
5.2.2 Metaheuristic-Parametric Approach in Data Size Reduction
5.3 Data Interpretation Approach
5.4 AI Development
5.4.1 Introduction
5.4.2 Artificial Neural Network
5.4.2.1 ANN Model Configuration and Performance Analysis
5.4.2.2 Final ANN Model
5.4.3 Decision Tree
5.4.3.1 An Overview
5.4.3.2 DT Model Configuration and Performance Analysis
5.4.4 Hybrid Objective Function Development
5.5 Summary
6 BIM-Inherited EED Framework Development and Verification
6.1 Optimisation Procedure
6.2 Integration Framework
6.2.1 Database Development
6.2.2 Database Exchange
6.2.3 Database Optimisation
6.2.4 Database Switchback
6.2.5 Database Updated
6.3 Testing and Validation
6.3.1 Case Study
6.3.2 Energy Simulation
6.3.3 Baseline Case Simulation Results
6.3.4 Case Optimisation Procedure
6.3.5 Case Optimisation Results
6.3.6 Optimisation Reliability Tests
6.4 Sensitivity Analysis
6.5 Summary
7 Conclusion
7.1 Review of Background, Problem, Aim and Method
7.2 Review of Research Processes and Findings
7.2.1 Objective 1: Examining the Potential and Challenges of BIM to Optimise Energy Efficiency in Residential Buildings
7.2.2 Objective 2: Identifying Variables That Play Key Roles in Energy Consumption of Residential Buildings
7.2.3 Objective 3: Investigating the AI-Based Algorithms in Energy Optimisation
7.2.4 Objective 4: Developing a Framework of AI Application in BIM in Terms of Energy Optimisation Purposes and Processes
7.2.5 Objective 5: Assessing and Validating the Functionality of the Framework Using Case Studies
7.3 Contribution to Knowledge
7.3.1 Originality
7.3.2 Implications for Practice
7.4 Limitations
7.5 Recommendations for Future Studies
Index