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Full Description
Resilient Structures: Machine Learning Applications in Structural Engineering is a practical guide to machine learning in structural engineering. It is aimed at engineers, researchers and students with an interest in integrating new, machine learning technologies into daily practice; the book provides a balance of foundational theory with hands-on, data-driven solutions tailored to meet real-world demands. 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 actively shaping the future of the discipline, making it a compelling choice for engineers looking to leverage machine learning for smarter, more resilient structural solutions. Enables experienced professionals to explore new applications and approaches. An accessible style makes complex concepts manageable; the book offers clear explanations while showcasing the potential of machine learning as a versatile tool for advancing structural engineering practices.
Contents
1. Concrete Technology and Machine Learning Applications
2. Earthquake Engineering Models with Machine Learning
3. Wind Engineering
4. Steel Structure
5. Structural Health Monitoring and Predictive Maintenance
6. Data Integration and Model Optimization in Structural Engineering
7. Case Studies in Machine Learning for Structural Engineering
