Provides in-depth tangible results from actual work undertaken in these innovative fields, in prolonged collaboration with the industry partners Includes real projects and case studies developed by the authors

Provides in-depth tangible results from actual work undertaken in these innovative fields, in prolonged collaboration with the industry partners Includes real projects and case studies developed by the authors

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.

The offsite and modular market is continuing to grow. This book builds on the success of a number of initiatives, including formative findings from literature, research and development and practice-based evidence (success stories). It presents new thinking and direction from leading experts in the fields of: design, process, construction, engineering, manufacturing, logistics, robotics, delivery platforms, business and transformational strategies, change management, legislation, organisational learning, software design, innovation and biomimetics. This book is particularly novel and timely, as it brings together a number of cogent subjects under one collective 'umbrella'. Each of these chapters contain original findings, all of which culminate in three 'Key Learning Points' which provide new insight into the cross-cutting themes, interrelationships and symbiotic forces that exist between each of these chapters. This approach also provides readers with new contextualised understanding of the wider issues affecting the offsite market, from the need to embrace societal challenges, through to the development of rich value-laden solutions required for creating sector resilience. Content includes a balance between case studies and practice-based work, through to technical topics, theoretical propositions, pioneering research and future offsite opportunities ready for exploitation. This work includes: stakeholder integration, skills acquisition, new business models and processes, circularity and sustainable business strategies, robotics and automation, innovation and change, lean production methodologies and new construction methods, Design for Manufacturing and Assembly, scaled portfolio platforms and customisability, new legal regulatory standards and conformance issues and offsite feasibility scenario development/integration.

The offsite and modular market is continuing to grow. This book builds on the success of a number of initiatives, including formative findings from literature, research and development and practice-based evidence (success stories). It presents new thinking and direction from leading experts in the fields of: design, process, construction, engineering, manufacturing, logistics, robotics, delivery platforms, business and transformational strategies, change management, legislation, organisational learning, software design, innovation and biomimetics. This book is particularly novel and timely, as it brings together a number of cogent subjects under one collective 'umbrella'. Each of these chapters contain original findings, all of which culminate in three 'Key Learning Points' which provide new insight into the cross-cutting themes, interrelationships and symbiotic forces that exist between each of these chapters. This approach also provides readers with new contextualised understanding of the wider issues affecting the offsite market, from the need to embrace societal challenges, through to the development of rich value-laden solutions required for creating sector resilience. Content includes a balance between case studies and practice-based work, through to technical topics, theoretical propositions, pioneering research and future offsite opportunities ready for exploitation. This work includes: stakeholder integration, skills acquisition, new business models and processes, circularity and sustainable business strategies, robotics and automation, innovation and change, lean production methodologies and new construction methods, Design for Manufacturing and Assembly, scaled portfolio platforms and customisability, new legal regulatory standards and conformance issues and offsite feasibility scenario development/integration.

This book significantly contributes the digital transformation of construction. The book explores the capabilities of deep learning to provide smart solutions for the construction industry, particularly in areas of managing equipment, design optimization, energy optimization and detect cracks for buildings and highways. It provides conceptual solutions but also practical techniques. A new deep learning CNN-based highway cracks detection is demonstrated, and its usefulness is tested. The resulting deep learning CNN model will enable users to scan long distance of highway and detect types of cracks accurately in a very short time compared to traditional approaches. The book explores the integration of IoT and blockchain to provide practical solutions to tackle existing challenges like the endemic fragmentation in supply chain, the need for monitoring construction projects remotely and tracking equipment on the site. The Blockchain of Things (BCoT) concept has been introduced to exploit the advantages of IoT and blockchain, and different applications were developed based on this integration in leading industries such as shared economy and health care. Workable potential use cases to exploit successful utilization of BCoT for the construction industry are explored in the book’s chapters. This book will appeal to researchers in providing a comprehensive review of related literature on blockchain, the IoT and construction identify gaps and offer a springboard for future research. Construction practitioners, research and development institutes and policy makers will also benefit from its usefulness as a reference book and collection of case studies on the application of these new approaches in construction.

This book outlines the data-driven modelling of building energy performance to support retrofit decision-making. It explains how to determine the appropriate machine learning (ML) model, explores the selection and expansion of a reasonable dataset and discusses the extraction of relevant features and maximisation of model accuracy. This book develops a framework for the quick selection of a ML model based on the data and application. It also proposes a method for optimising ML models for forecasting buildings energy loads by employing multi-objective optimisation with evolutionary algorithms. The book then develops an energy performance prediction model for non-domestic buildings using ML techniques, as well as utilising a case study to lay out the process of model development. Finally, the book outlines a framework to choose suitable artificial intelligence methods for modelling building energy performances. This book is of use to both academics and practising energy engineers, as it provides theoretical and practical advice relating to data-driven modelling for energy retrofitting of non-domestic buildings.

This book outlines the data-driven modelling of building energy performance to support retrofit decision-making. It explains how to determine the appropriate machine learning (ML) model, explores the selection and expansion of a reasonable dataset and discusses the extraction of relevant features and maximisation of model accuracy. This book develops a framework for the quick selection of a ML model based on the data and application. It also proposes a method for optimising ML models for forecasting buildings energy loads by employing multi-objective optimisation with evolutionary algorithms. The book then develops an energy performance prediction model for non-domestic buildings using ML techniques, as well as utilising a case study to lay out the process of model development. Finally, the book outlines a framework to choose suitable artificial intelligence methods for modelling building energy performances. This book is of use to both academics and practising energy engineers, as it provides theoretical and practical advice relating to data-driven modelling for energy retrofitting of non-domestic buildings.