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Elsevier

Artificial Intelligence and Machine Learning in Structural Engineering and Materials

  • 1st Edition - June 1, 2027
  • Latest edition
  • Editors: Jamal A. Abdalla, M. Z. Naser
  • Language: English

Artificial Intelligence and Machine Learning in Structural Engineering and Materials explores the profound potential of AI and ML to revolutionize the way structural systems a… Read more

Description

Artificial Intelligence and Machine Learning in Structural Engineering and Materials explores the profound potential of AI and ML to revolutionize the way structural systems and materials are designed, analyzed, and maintained, unlocking new possibilities for precision, adaptability, and optimization.

The book’s detailed chapters cover a wide range of topics, offering both fundamental knowledge and cutting-edge insights into the integration of these sophisticated technologies. Key areas include structural health monitoring, response and behavior of structural systems under various loads, strengthening, retrofitting, and design enhancement, as well as evaluation of the mechanical properties, durability, sustainability, and resilience of the materials employed. Additionally, the book examines AI and ML applications in reliability, risk, and lifecycle analysis, along with the prediction of structural interactions with environmental factors like wind, fluid, soil, fire, and extreme natural hazards.

This work goes beyond a technically sound and thorough research reference resource, blending data-driven models with traditional engineering methods to refine existing practices, while providing clear, actionable techniques for advancing engineering solutions through the strategic uptake of innovations. Readers are presented with a structured overview of how these tools can fundamentally transform the field, emphasizing their capacity to drive efficiency, lower costs, increase safety, and promote long-term environmental viability.

Key features

  • Introduces the most popular ML methodologies, including but not limited to, commonly used supervised learning algorithms in regression, classification, and deep learning
  • Delivers a state-of-the-art and state-of-practice review, as well as the current research developments in AI and ML for structural engineering and materials
  • Provides a standardized framework, guidelines, and a design reference for effectively integrating AI and ML into structural engineering and materials applications

Readership

Academics, researchers, and postgraduate students in civil, structural, and construction engineering; materials science; computer science, particularly those focusing on AI and ML applications in optimization techniques and advanced computational methods for structural design, analysis, and the prediction of the behavior and performance of structural materials under various loading conditions. Engineering practitioners, R&D professionals, and industry consultants in civil, structural, and construction engineering, as well as architecture and infrastructure management, who seek to improve design processes, upgrade structural performance, and enhance materials through advanced computational methods and innovative technologies; data scientists and software developers creating engineering tools and machine learning models for structural applications; and policymakers involved in setting standards and regulations who can benefit from understanding how AI and ML maximize safety and sustainability in structural systems

Table of contents

1. A Review of Artificial Intelligence and Machine Learning Applicable to Structural Engineering and Structural Materials

2. Applications of Machine Learning in Structural Health Monitoring (SHM)

3. Application of Machine Learning in Analysis, Design, and Prediction of Response and Behavior of Structural Systems

4. Application of Machine Learning in Prediction of Performance and Behavior of Structural Components

5. Application of Machine Learning in Prediction of Mechanical Properties, Characteristics, and Behavior of Structural Materials

6. Application of Machine Learning in Optimization of Design of Structural Components and Structural Systems

7. Application of Machine Learning in Strengthening, Retrofitting, and Rehabilitation of Structural Components and Structural Systems

8. Application of Machine Learning in Reliability, Risk, and Lifecycle Analysis and Assessment of Structural Systems 

9. Application of Machine Learning in Predicting Durability, Sustainability, and Resilience of Structural Materials, Components, and Systems

10. Application of Machine Learning in Predicting Structure-Interaction with Wind, Fluid, Soil, Fire, and Extreme Natural Hazard Loadings

Product details

  • Edition: 1
  • Latest edition
  • Published: June 1, 2027
  • Language: English

About the editors

Jamal A. Abdalla

Editor
Jamal A. Abdalla holds a bachelor's degree in Civil Engineering from the University of Khartoum (Sudan), a master's degree from Purdue University, and a PhD from the University of California, Berkeley, followed by postdoctoral research at Stanford University. He is a professor of Civil Engineering at the American University of Sharjah (AUS) in the United Arab Emirates. Before joining AUS, Dr. Abdalla held academic positions at Qatar University and the University of Khartoum. His research spans a wide range of areas, including computing in civil engineering, the strengthening and retrofitting of reinforced concrete structures, the application of Artificial Intelligence (AI) and Machine Learning (ML) in structural engineering, and sustainable construction materials. Dr. Abdalla has served as an Associate Editor for the ASCE Journal of Computing in Civil Engineering and currently sits on the editorial boards of several academic journals. Throughout his career, he has conducted workshops, delivered seminars, organized conferences, and served on numerous technical committees. Additionally, he has been actively involved in reviewing papers for both journals and conferences. Dr. Abdalla is a Fellow of both the American Society of Civil Engineers (ASCE) and the Structural Engineering Institute (SEI), and is a registered professional engineer in the State of Virginia, USA.
Affiliations and expertise
Professor, Department of Civil Engineering, College of Engineering, American University of Sharjah, Sharjah, United Arab Emirates

M. Z. Naser

Editor
M. Z. Naser is a tenure-track associate Professor at the Department of Civil and Environmental Engineering and Earth Sciences and a member of the Artificial Intelligence Research Institute for Science and Engineering (AIRISE) at Clemson University. At the moment, his research group is creating causal & eXplainable machine learning methodologies to discover new knowledge hidden within systems belonging to the domains of structural engineering and materials science to help realize functional, sustainable, and resilient infrastructure. He is currently serving as the chair of the ASCE Advances in Information Technology committee and on a number of international editorial boards, as well as codal building committees (in ASCE, ACI, PCI, and FiB). He is a registered professional engineer in the states of Michigan and South Carolina.
Affiliations and expertise
associate Professor, Department of Civil and Environmental Engineering and Earth Sciences, College of Engineering, Computing and Applied Sciences, Clemson University, Clemson, SC, USA