Panagiotis G. Asteris – författare

The design of structures and infrastructure facilities has typically assumed a future climate that is much the same as today’s. However, a changing climate and the resulting more extreme weather events mean those climate bands are becoming outdated, leaving infrastructure operating outside or at the limit of its tolerance levels.With its evaluation of a host of assets in the built environment, Climate Change Effects on Infrastructure: Advanced Tools for Monitoring, Mitigation, and Lifecycle Optimization instructs readers how to assess and predict structural vulnerabilities resulting from climate change as well as ensuing hazard patterns using mathematical tools and data-driven models. The carefully compiled contributions also comprehensively survey the latest technological developments and the most useful approaches to counteract and/or prevent damage and connected socio-economic risks.The volume proves to be an even more invaluable resource thanks to its real-world application examples that spotlight the urgency of further investigations into the implementation of cost-effective and sustainable adaptive measures that can enhance or extend the reliable performance, durability, and efficiency of structures and infrastructure systems throughout their entire lifecycle.Reviews advanced computational models, simulations, and data analytics techniques for determining the effects of climate change on infrastructure.Describes advanced methods to enhance infrastructure resilience and offset or prevent the adverse impacts of climate change.Features case studies and applied examples for different structure types and infrastructure systems from a variety of regions and assorted scenarios.

Machine Learning Applications in Structural Engineering is a practical guide to machine learning in structural engineering. With first-hand examples of machine learning applications, this book is a vital reference for both entry-level readers and advanced professionals. For experts, the book offers insights into emerging applications that are shaping the future of the discipline, making it a compelling choice for engineers looking to leverage machine learning for smarter, more resilient structural solutions. This accessible style makes complex concepts manageable, and the book offers clear explanations while showcasing the potential of machine learning as a versatile tool for advancing structural engineering practices.It is aimed at engineers, researchers, and students with an interest in integrating new, machine learning technologies into daily practice. Readers will find a balance of foundational theory with hands-on, data-driven solutions tailored to meet real-world demands.Provides a domain-specific resource that combines machine learning with structural engineeringDescribes advanced machine learning techniques for a wide range of structural engineering applicationsDemonstrates how data-driven approaches are reshaping decision-making, enhancing resilience, and providing valuable predictive insights that are of particular importance in structural health monitoring and disaster resilienceIncludes theoretical approaches that are illustrated with extensive use of practical case studies and real-world examples