A smart city is a city that uses technology to provide services and solve city problems. The main goals of a smart city are to improve policy efficiency, reduce waste and inconvenience, improve social and economic quality, and maximize social inclusion. Due to the breadth of technologies that have been implemented under the smart city label, it is difficult to distill a precise definition of a smart city. As the world’s population continues to urbanize – by 2050, 66% of the world’s population is expected to be urban – there is a global trend toward the creation of smart cities. This tendency not only causes many physical, social, behavioural, economic, and infrastructure issues, but it also creates many opportunities. Increased understandings of how to design, adapt, and operate smart cities intelligently and effectively are required to solve these obstacles in implementing smart cities. This endeavour “Facets of a Smart City: Computational and Experimental Techniques for Sustainable Urban Development”, seeks to collect a coherent whole of studies aimed at the best computational and experimental techniques developed for building smart cities.
This book aims to complement methodical articles that require advanced knowledge of the subject matters on smart cities and application from their readers and aims to bridge the knowledge gap by providing background information via case studies that recent graduates and new practitioners usually lack.
This book is divided into six major domains, which include (i) information modelling, (ii) internet of things, (iii) intelligent transportation systems, (iv) water supply, (v) waste management and (vi) sustainable environment. The editors hope this book will offer a ‘quickstart background’ on computational and experimental techniques for sustainable urban development for smart cities via case studies for recent graduates, early-career practitioners or experts who want to dabble into a new sub-field of computation and its diverse applications. This book also covers computational techniques, including artificial neural networks, stochastic models, particle swarm optimization, machine learning, adaptive neuro-fuzzy Inference System, etc. Goals of the case studies presented in this book using these computational techniques to offer readers examples of supervised, unsupervised and reinforcement learning strategies in the context of smart city applications.
About the Editor
Dr. Pijush Samui is working as an associate professor in the civil engineering department at NIT Patna, India. His research focuses on the application of Artificial Intelligence, reliability analysis, site characterization, and earthquake engineering. Dr. Pijush is the recipient of the prestigious CIMO fellowship from Finland, Shamsher Prakash Research Award from IIT Roorkee and IGS Sardar Resham Singh Memorial Award. He was elected Fellow of International Congress of Disaster Management. He has been selected as an adjunct professor at Ton Duc Thang University (Ho Chi Minh City, Vietnam). He has been Visiting Professor at Far East Federal University (Russia). He has published more than 100 international journal papers. He holds the title of docent at Tampere University.
Dr. Anasua GuhaRay is presently an Associate Professor in Department of Civil Engineering, BITS-Pilani Hyderabad Campus and a cross-appointed faculty in the Department of Civil and Environmental Engineering, Hiroshima University in Japan. Her major areas of research include reliability analysis, utilisation of waste materials for ground improvement, alkali activated binders and smart cities. She has published a number of papers in peer reviewed journals and conferences and is the principal investigator of a number of sponsored projects including DST, Indo-Austria and Indo-Japan bilateral projects, Asian Smart Cities. She is the recipient of “Outstanding Reviewer Recognition” from International Journal of Geomechanics, ASCE in the year 2015, 2017 and 2018.
Dr. Elham Mahmoudi is an associate researcher at the Ruhr-Universität Bochum, Germany. Her research interests include numerical simulation of geotechnical problems considering inherent uncertainty and heterogeneities, sensitivity analysis, optimisation concepts, machine learning, and probabilistic analysis. Dr. Mahmoudi joined the collaborative research centre 837, dealing mainly with system and parameter identification methods in mechanised tunnelling. Dr Mahmoudi has published her interdisciplinary research findings in the framework of book chapters, journal papers and conference papers. She is currently a research group leader on the topic of machine learning and artificial intelligence in the construction industry at the institute of computing in Engineering, Bochum, Germany.
