This book provides knowledge into Cognitive Digital Twins for smart lifecycle management of built environment and infrastructure focusing on challenges and opportunities. It focuses on the challenges and opportunities of data-driven cognitive systems by integrating the heterogeneous data from multiple resources that can easily be used in a machine learning model and adjust the algorithms. It comprises Digital Twins incorporating cognitive features that will enable sensing complex and unpredicted behavior and reason about dynamic strategies for process optimization to support decision-making in lifecycle management of the built environment and infrastructure. The book introduces the Knowledge Graph (KG)-centric framework for Cognitive Digital Twins involving process modeling and simulation, ontology-based Knowledge Graph, analytics for process optimizations, and interfaces for data operability. It offers contributions of Cognitive Digital Twins for the integration of IoT, Big data, AI, smart sensors, machine learning and communication technologies, all connected to a novel paradigm of self-learning hybrid models with proactive cognitive capabilities. The book presents the topologies of models described for autonomous real time interpretation and decision-making support of complex system development based on Cognitive Digital Twins with applications in critical domains such as maintenance of complex engineering assets in built environment and infrastructure. It offers the essential material to enlighten pertinent research communities of the state-of-the-art research and the latest development in the area of Cognitive Digital Twins, as well as a valuable reference for planners, designers, developers, and ICT experts who are working towards the development and implementation of autonomous Cognitive IoT based on big data analytics and context–aware computing.
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Ibrahim Yitmen received his PhD in Architecture from Istanbul Technical University, Turkey. Since February 2018 he has been an Associate Professor in Management of Construction Production at Jönköping University, Sweden. His research focus is mainly on Innovation in Construction involving socio-technical issues regarding digital transformation in AEC industry, and his recent special interest is on Digital Twin-based Smart Built Environment, Augmented Reality/Mixed Reality for Cognitive Buildings, Integration of Digital Twins and Deep Learning for Smart Planning and Construction, Blockchain Technology in Construction Supply Chains, Cyber Physical Systems for Construction 4.0, Integration of Unmanned Aerial Vehicles and BIM for Construction Safety Planning and Monitoring. Dr. Yitmen have published more than 100 research papers in refereed international journals and in peer reviewed conference proceedings. He has recently been the editor of the book titled "BIM-enabled Cognitive Computing for Smart Built Environment: Potential, Requirements, and Implementation" published by CRC Press. He was one of the guest editors of the MDPI Journal Applied Sciences for the Special Issue "Cognitive Buildings". Dr. Yitmen is currently leading research projects titled "Integration of Blockchain and Digital Twins for Smart Asset Lifeycle Management" financed by Smart Built Environment (Sweden), titled "Adapting Cognitive Digital Twins for the Production of Sustainable Modular Houses" financed by Vinnova (Sweden), and titled "Collaborative Digital Platform for Dynamic Workflows in Construction Production and Information Exchange Planning financed by Jönköpings Läns Byggmästareförening". Dr. Yitmen serves on the scientific committees of the international conferences held by the International Council for Research and Innovation in Building and Construction (CIB), American Society of Civil Engineers (ASCE), and European Council on Computing in Construction (EC3). He is an active Member of American Society for Engineering Management, USA since 2010.
This book explicitly brings together Cognitive Digital Twins for smart lifecycle management of built environment and infrastructure and focuses on the challenges and opportunities of data-driven cognitive systems by integrating the heterogeneous data from multiple resources.
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Paperback. Zustand: New. This book provides knowledge into Cognitive Digital Twins for smart lifecycle management of built environment and infrastructure focusing on challenges and opportunities. It focuses on the challenges and opportunities of data-driven cognitive systems by integrating the heterogeneous data from multiple resources that can easily be used in a machine learning model and adjust the algorithms. It comprises Digital Twins incorporating cognitive features that will enable sensing complex and unpredicted behavior and reason about dynamic strategies for process optimization to support decision-making in lifecycle management of the built environment and infrastructure. The book introduces the Knowledge Graph (KG)-centric framework for Cognitive Digital Twins involving process modeling and simulation, ontology-based Knowledge Graph, analytics for process optimizations, and interfaces for data operability. It offers contributions of Cognitive Digital Twins for the integration of IoT, Big data, AI, smart sensors, machine learning and communication technologies, all connected to a novel paradigm of self-learning hybrid models with proactive cognitive capabilities. The book presents the topologies of models described for autonomous real time interpretation and decision-making support of complex system development based on Cognitive Digital Twins with applications in critical domains such as maintenance of complex engineering assets in built environment and infrastructure. It offers the essential material to enlighten pertinent research communities of the state-of-the-art research and the latest development in the area of Cognitive Digital Twins, as well as a valuable reference for planners, designers, developers, and ICT experts who are working towards the development and implementation of autonomous Cognitive IoT based on big data analytics and context-aware computing. Bestandsnummer des Verkäufers LU-9781032136288
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