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Elsevier

Machine Learning Applications in Thin-Walled Structural Engineering

Innovations and Future Directions

  • 1st Edition - November 1, 2026
  • Latest edition
  • Editors: A. Praveen Kumar, Quanjin Ma, Dr. Afdhal
  • Language: English

Machine Learning Applications in Thin-Walled Structure Engineering brings into sharp focus in-demand knowledge applicable to plate and shell structures, cold–formed steel sections,… Read more

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Description

Machine Learning Applications in Thin-Walled Structure Engineering brings into sharp focus in-demand knowledge applicable to plate and shell structures, cold–formed steel sections, reinforced plastics components, and aluminum frameworks across a wide range of field applications. By highlighting the transformative synergy between artificial intelligence and structural engineering, it presents innovative methods to streamline design evaluations, detect anomalies early, and forecast structural performance under diverse conditions of load, stress, and environmental influence.

The book covers––among other key recent developments––the integration of ML with digital twin technology for real-time monitoring in support of proactive assessment and intervention efforts to extend service life; the use of advanced algorithms for material selection and behavior prediction; 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—critical for engineers striving to improve both current strategies and future advancements in thin-walled structures’ long-term safety and reliability.

Key features

  • Integrates advanced machine learning techniques with structural engineering principles to explore specific applications, such as predictive maintenance and optimization of thin-walled structures, showcasing how data-driven approaches can revolutionize design practices
  • Bridges the gap between theory and practice by presenting detailed case studies, demonstrating how real-world applications of machine learning inform strategic decision-making and result in effective project outcomes
  • Provides forward-looking insights, equipping readers with the know-how to anticipate and adapt to future innovations, ensuring they remain at the forefront of this evolving field

Readership

Academics, researchers, and postgraduate students in civil and structural engineering; mechanical engineering; aerospace and automotive engineering; data science focusing on machine learning applications in optimization techniques and advanced computational methods in structural design and analysis; Engineering practitioners, R&D professionals, and industry consultants in civil, structural, aerospace, automotive engineering and manufacturing industries working on thin-walled structures, structural optimization, crashworthiness, and materials design; software developers creating engineering tools and machine learning models for structural applications; and policy makers or sustainability experts involved in green engineering and environmentally conscious design practices for thin-walled structures

Table of contents

1. An Introduction to Thin-walled Structures and the Transformative Role of Machine Learning in Structural Engineering

2. Advanced Machine Learning Techniques for Structural Optimization of Thin-walled Components: Strategies for Enhanced Performance

3. Machine Learning Algorithms for Predicting Failure Modes in Thin-walled Structures: Techniques and Applications

4. Innovative Algorithms for Efficient Design Space Exploration and Case Studies in Thin-walled Structures

5. Advancements in Machine Learning for Material Design and Structural Optimization for Crashworthiness

6. Artificial Intelligence in the Design Process of Thin-walled Structures: Automating Design Choices through Machine Learning Models

7. Exploring Future Trends in Machine Learning for Thin-walled Structures

8. Comparative Study of Supervised and Unsupervised Learning Methods for Thin-walled Structure Applications: Benefits and Limitations

9. Hybrid Modeling Approaches: Combining Machine Learning with Traditional Analysis Methods for Thin-walled Structures

10. Case Studies of Machine Learning Applications in the Analysis and Design of Thin-walled Structures

11. Artificial Intelligence for Lightweight Structures for Crashworthiness Applications: Overview, Case studies, and Future Potentials

12. Integrating Sustainability into Design and Data Management of Thin-walled Structures through Machine Learning Approaches

13. Using Deep Learning for Image Recognition in Structural Inspections of Thin-walled Components: Innovations in Visual Analysis

14. Data Preparation and Preprocessing for Machine Learning in Structural Engineering

Product details

  • Edition: 1
  • Latest edition
  • Published: November 1, 2026
  • Language: English

About the editors

AK

A. Praveen Kumar

Dr A. Praveen Kumar is an Assistant Professor at the Department of Mechanical Engineering, Easwari Engineering College, India. He completed his Ph.D. degree in the area of crashworthiness of thin-walled structures. His major areas of research interest are 3D printing of composite parts, metal forming simulation, additive manufacturing, composite materials and structures. He is ranked among the top 2% of researchers globally in the fields of "Materials" and "Mechanical Engineering & Transports" Released by Stanford University and Elsevier in 2024. Dr Kumar has published 97 research papers and is currently a Editorial Board Member in reputed journals like Discover Materials (Springer) and International Journal of Protective Structures (Sage).

Affiliations and expertise
Assistant Professor, Department of Mechanical Engineering, Easwari Engineering College, Chennai, India

QM

Quanjin Ma

Dr Quanjin Ma is a Postdoctoral Researcher at the Institute of Advanced Materials and Technology, Guangdong University of Technology, China. His research includes: 3D-printed lightweight structures, composite sandwich structures, energy-absorbing characteristics, filament-wound composite structures, impact failure behaviour and mechanism and 3D-printedcelectromagnetic absorbing structures. He is ranked among the top 2% of researchers globally in the fields of "Materials" and "Mechanical Engineering & Transports" Released by Stanford University and Elsevier in 2024.
Affiliations and expertise
Postdoctoral Researcher Institute of Advanced Materials and Technology, Guangdong University of Technology, Guangzhou, China

DA

Dr. Afdhal

Dr. Afdhal is an Assistant Professor within the Solid Mechanics and Lightweight Structures research group at the Faculty of Mechanical and Aerospace Engineering, Institut Teknologi Nasional Bandung, Indonesia. He earned his PhD with a dissertation focused on the development of a constitutive material model that integrates the effects of anisotropy and viscoplasticity. Subsequently, during his postdoctoral fellowship at the Department of Mechanics and Materials, Czech Technical University in Prague, he conducted research on the dynamic behavior of auxetic structures. Leveraging this expertise, he designed auxetic structures fabricated through additive manufacturing, augmented by machine learning techniques. His current research interests encompass material modeling and simulation, viscoplasticity, auxetic structure design, additive manufacturing, and the application of machine learning to the discovery and design of advanced materials and structures. Dr. Akbar has been extensively engaged in both national and international research initiatives.
Affiliations and expertise
Assistant Professor, Solid Mechanics and Lightweight Structures Research Group, Faculty of Mechanical and Aerospace Engineering, Institut Teknologi Bandung, Bandung, West Java, Indonesia