Zhidong Li

This book presents, for the first time, data analytics for smart infrastructures. The authors draw on over a decade’s experience working with industry and demonstrating the capabilities of data analytics for infrastructure and asset management. The volume gives data-driven solutions to cover critical capabilities for infrastructure and asset management across three domains: 1) situation awareness 2) predictive analytics and 3) decision support. The reader will gain from various data analytic techniques including anomaly detection, performance evaluation, failure prediction, trend analysis, asset prioritization, smart sensing and real-time/online systems. These data analytic techniques are vital to solving problems in infrastructure and asset management. The reader will benefit from case studies drawn from critical infrastructures such as water management, structural health monitoring and rail networks. This groundbreaking work will be essential reading for those studying and practicing analytics in the context of smart infrastructure.

This book presents, for the first time, data analytics for smart infrastructures. The authors draw on over a decade’s experience working with industry and demonstrating the capabilities of data analytics for infrastructure and asset management. The volume gives data-driven solutions to cover critical capabilities for infrastructure and asset management across three domains: 1) situation awareness 2) predictive analytics and 3) decision support. The reader will gain from various data analytic techniques including anomaly detection, performance evaluation, failure prediction, trend analysis, asset prioritization, smart sensing and real-time/online systems. These data analytic techniques are vital to solving problems in infrastructure and asset management. The reader will benefit from case studies drawn from critical infrastructures such as water management, structural health monitoring and rail networks. This groundbreaking work will be essential reading for those studying and practicing analytics in the context of smart infrastructure.

This book provides a holistic perspective of various data sources, methodologies, and technologies that can be harnessed to measure, monitor, and enhance sustainability efforts across diverse sectors. Sustainability is a social goal and a broad paradigm on environmental, societal, and economic considerations for people to co-exist on Earth over a long time. The recent rapid advances in artificial intelligence (AI) and data analytics research are claimed to enable machines to exhibit human-like cognition and have been used to tackle different aspects of sustainability related issues. Cognizant of the holistic link of historical achievements to future developments, data analytics is, therefore, claimed to be the driving force to achieve sustainable development. From water management and transportation to energy systems and agriculture, each chapter delves into the unique opportunities and challenges associated with leveraging data and analytics for sustainable development.