Machine Learning Applications in Thin-Walled Structure Engineering: Innovations and Future Directions covers plate and shell structures, cold-formed steel sections, reinforced plastics components, and aluminum frameworks?across a wide range of applications. By highlighting the transformative synergy between artificial intelligence and structural engineering, the book presents innovative methods to streamline design evaluations, detect anomalies, and forecast structural performance under diverse conditions of load, stress, and environmental influence. Sections cover the integration of ML with digital twin technology for real-time monitoring in support of proactive assessment, intervention efforts to extend service life, and advanced algorithms for material selection and behavior prediction.Other topics explored include hybrid models that combine traditional analytical methods with ML to increase simulation precision and emerging trends such as adaptive systems for more resilient, efficient, and sustainable structural solutions. With its interdisciplinary approach and practical examples, this resource proves to be essential to establish a solid understanding of the challenges posed by lightweight systems and how ML techniques can enhance their design, analysis, and maintenance that is critical for engineers striving to improve both current strategies and future advancements in thin-walled structures’ long-term safety and reliability.

Machine Learning Applications in Thin-Walled Structure Engineering: Innovations and Future Directions covers plate and shell structures, cold-formed steel sections, reinforced plastics components, and aluminum frameworks?across a wide range of applications. By highlighting the transformative synergy between artificial intelligence and structural engineering, the book presents innovative methods to streamline design evaluations, detect anomalies, and forecast structural performance under diverse conditions of load, stress, and environmental influence. Sections cover the integration of ML with digital twin technology for real-time monitoring in support of proactive assessment, intervention efforts to extend service life, and advanced algorithms for material selection and behavior prediction.Other topics explored include hybrid models that combine traditional analytical methods with ML to increase simulation precision and emerging trends such as adaptive systems for more resilient, efficient, and sustainable structural solutions. With its interdisciplinary approach and practical examples, this resource proves to be essential to establish a solid understanding of the challenges posed by lightweight systems and how ML techniques can enhance their design, analysis, and maintenance that is critical for engineers striving to improve both current strategies and future advancements in thin-walled structures’ long-term safety and reliability.