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.
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
