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Elsevier

Advances in Machine Learning for Engineering Applications and Sustainable Development

  • 1st Edition, Volume 145 - February 1, 2027
  • Latest edition
  • Editors: Nafis Faizi, Afzal Husain Khan, Mohammad Shuaib
  • Language: English

Advances in Machine Learning for Engineering Applications and Sustainable Development, published as Volume 145 of the serial Advances in Computers, provides a comprehensive… Read more

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Description

Advances in Machine Learning for Engineering Applications and Sustainable Development, published as Volume 145 of the serial Advances in Computers, provides a comprehensive and timely exploration of how artificial intelligence (AI) and machine learning (ML) are transforming engineering practice and advancing sustainability goals. The volume brings together contributions from leading researchers to examine the integration of data-driven methods in concrete technology, digital fabrication, structural health monitoring, materials engineering, and industrial process optimisation. It highlights emerging paradigms such as AI-driven 3D printing, predictive analytics for failure detection, lifecycle and environmental impact assessment, and circular economy strategies.
Spanning foundational methods and real-world implementations, the volume emphasises sustainable materials, eco-efficient construction processes, and intelligent system design. It pays particular attention to the use of ML in improving resource efficiency, enabling recycling of industrial by-products, and enhancing the performance of green materials such as biochar-based composites. By combining theoretical insights, mathematical modelling, and application-oriented case studies, this volume serves as a critical reference for advancing resilient, efficient, and environmentally responsible engineering systems.

Key features

  • Integrates machine learning and AI applications across civil, materials, and industrial engineering domains
  • Explores sustainable construction practices, including green materials and eco-efficient concrete production
  • Highlights emerging technologies such as AI-driven digital fabrication and 3D printing
  • Presents predictive analytics methods for structural health monitoring and failure detection
  • Examines lifecycle assessment, circular economy strategies, and resource optimisation through AI
  • Offers a balance of theoretical modelling, methodological advances, and practical engineering applications

Readership

Researchers and academics in machine learning, artificial intelligence, and engineering systems; Civil, structural, and materials engineers focused on sustainable construction and smart infrastructure; Professionals in industrial automation, digital manufacturing, and process optimisation; Environmental engineers and sustainability specialists working on resource efficiency and lifecycle analysis; Advanced postgraduate students in engineering, computer science, and applied data science

Table of contents

1. Machine learning in concrete construction: Applications, challenges, and future aspects

2. Digital Fabrication for Cementitious Materials: AI-Driven 3D Printing Technologies

3. Internet of Things in Sustainable Concrete Production

4. AI-Driven Quality Control and Crack Detection in Concrete Structures with mathematical modelling

5. Artificial Intelligence for Recycling By-Products and Enhancing Performance

6. Machine Learning for Structural Health Monitoring in Engineering Systems

7. Predictive Analytics for Material Performance and Failure Detection

8. AI in the Design and Optimization of Engineering Systems

9. Eco-Efficient Technologies for Sustainable Concrete Production Using Machine Learning and AI

10. AI-Driven Analysis of Engineering Properties of Green Materials and Their Applications

11. Biochar as a Sustainable Supplement in Cement Mortar and Concrete: Machine Learning applications

12. AI-Based Benefit-Cost Analysis of Sustainable Engineering Solutions

13. Circular Economy in Engineering Applications: AI and Machine Learning for Resource Optimization and Waste Reduction

14. Machine Learning for Sustainable Industrial Process Automation

15. AI-Driven Innovations in Sustainable Engineering Projects

16. Artificial Intelligence for Energy-Efficient Systems Design

17. Artificial Intelligence (AI) Tools for Lifecycle and Environmental Impact Assessment in Construction

18. Future Trends in AI and Machine Learning for Sustainable Development

Product details

  • Edition: 1
  • Latest edition
  • Volume: 145
  • Published: February 1, 2027
  • Language: English

About the editors

NF

Nafis Faizi

Dr. Nafis Faizi is an Assistant Professor at JN Medical College for the past 7 years. He is also a trainer for Epidemiological Research Unit and a member of Statistics Without Borders and Global Health Training Network. For the past 9 years, he has been conducting regular workshops and training in biostatistics, data analysis and research writing. His primary qualifications are MBBS and MD in Community Medicine from AMU, Aligarh followed by master’s in public health (MPH) from UK. He is also a faculty for International People’s Health University and teaches epidemiology at People’s Open Access Intitiative, UK.
Affiliations and expertise
Assistant Professor, JN Medical College, Aligarh, India

AK

Afzal Husain Khan

Dr. Afzal Husain Khan works in the Civil and Architectural Engineering Department at Jazan University, Saudi Arabia. He received his PhD from Universiti Sains Malaysia, Malaysia, and B.E. and M. Tech degrees from the Aligarh Muslim University, India. He is an academic editor of PLOS ONE, Advances in Civil Engineering (Wiley), Associate Editor in TEIEE, and Guest Editor in Elsevier/Springer/Frontiers journals. He has been involved in many funded projects under the Deanship of Scientific Research (DSR) in Saudi Arabia. He has 90+ peer-reviewed publications (h-index >30; >3000+ citations), 4 books (edited), 6 patents, and 5 book chapters to his credit.

Affiliations and expertise
Civil and Architectural Engineering Department, Jazan University, Jazan, Saudi Arabia

MS

Mohammad Shuaib

Dr. Mohammed Shuaib works at College of Computer science & Engineering at Jazan University, specializing in blockchain, IoT, cybersecurity, and machine learning applications in engineering. He holds a PhD in Computer Engineering from Universiti Teknologi Malaysia (UTM), Malaysia. With 4 patents, Dr. Shuaib is a prolific researcher with more than 58 publications in ISI-ranked and Scopus-indexed journals, alongside 25 conference papers. His expertise spans machine learning, AI-driven optimizations, and sustainable development, particularly in applying AI techniques such as artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and reinforcement learning (RL) across engineering disciplines.

Dr. Shuaib's editorial experience currently as Associate Editor for Sustainable Computing: Informatics and Systems, and his roles on editorial boards including, position him as an ideal editor for a book addressing the intersection of machine learning, AI, engineering applications, and sustainable development. He is also a member of professional organizations such as IEEE, ACM, and IAENG, reflecting his commitment to advancing research in his fields of interest.

Affiliations and expertise
Jazan University, Jazan, Saudi Arabia