Saeed Banihashemi, Hamed Golizadeh, Farzad Pour Rahimian

Data-driven BIM for Energy Efficient Building Design (eBook, ePUB)

• Format: ePub

Produktbeschreibung

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 a 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 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.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 186
• Erscheinungstermin: 16. Dezember 2022
• Englisch
• ISBN-13: 9781000801965
• Artikelnr.: 66258900

Autorenporträt

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.

Inhaltsangabe

1. Classics of the Data-driven BIM for Energy Efficient Design 2.
Sustainability, Information and Optimisation: Antecedents of the Data and
BIM-Enabled EED 3. BIM and Energy Efficient Design 4. Building Energy
Parameters 5. AI Algorithms Development 6. BIM-inherited EED Framework
Development and Verification 7. Conclusion