Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Machine Learning Applications in Thin-Walled Structural Engineering: Innovations and Future Directions

Machine Learning Applications in Thin-Walled Structural Engineering: Innovations and Future Directions

Kumar, A. Praveen
Ma, Quanjin
Afdhal

Paperback

Series: Woodhead Publishing Civil and Structural Engineering

Technology & EngineeringGeneral Construction & Renovation

PREORDER - Expected ship date February 1, 2027

ISBN10: 044344157X
ISBN13: 9780443441578
Publisher: Woodhead Publishing
Published: Feb 1 2027
Pages: 500
Language: English

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.

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