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Coles Applications of AI and IoT Geotechnical Engineering for Sustainable Infrastructure at Bayshore Shopping Centre in Ottawa, ON
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Artificial intelligence (AI) and the Internet of Things (IoT) are transforming the way geotechnical engineers monitor, analyze, and manage soil-structure systems. Applications of AI and IoT in Geotechnical Engineering for Sustainable Infrastructure explores how these emerging digital technologies can enhance decision-making, improve infrastructure resilience, and support sustainable development in civil engineering projects. It examines the integration of AI and IoT in geotechnical engineering, including slope stability assessments, data-driven techniques for soil classification and characterization, foundation and liquefaction analysis, optimizing earthwork practices and operations, tunnels, and earth-retaining structures using real-time sensor networks and data-driven models. This book also discusses machine learning techniques for predicting soil behavior, real-time monitoring of pile foundations, automated site investigation methods, and digital tools for risk assessment and infrastructure health monitoring/assessment. This book is intended for researchers, graduate students, and practicing engineers in geotechnical and civil engineering. It will also benefit professionals working in smart infrastructure, disaster risk reduction, and infrastructure asset management who are interested in integrating digital technologies into sustainable engineering practice.
Artificial intelligence (AI) and the Internet of Things (IoT) are transforming the way geotechnical engineers monitor, analyze, and manage soil-structure systems. Applications of AI and IoT in Geotechnical Engineering for Sustainable Infrastructure explores how these emerging digital technologies can enhance decision-making, improve infrastructure resilience, and support sustainable development in civil engineering projects. It examines the integration of AI and IoT in geotechnical engineering, including slope stability assessments, data-driven techniques for soil classification and characterization, foundation and liquefaction analysis, optimizing earthwork practices and operations, tunnels, and earth-retaining structures using real-time sensor networks and data-driven models. This book also discusses machine learning techniques for predicting soil behavior, real-time monitoring of pile foundations, automated site investigation methods, and digital tools for risk assessment and infrastructure health monitoring/assessment. This book is intended for researchers, graduate students, and practicing engineers in geotechnical and civil engineering. It will also benefit professionals working in smart infrastructure, disaster risk reduction, and infrastructure asset management who are interested in integrating digital technologies into sustainable engineering practice.
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