Submission Deadline: 30 June 2026 View: 618 Submit to Special Issue
Prof. Amorn Pimanmas
Email: amorn.pi@ku.ac.th
Affiliation: Department of Civil Engineering, Kasetsart University, 10900, Bangkok, Thailand
Research Interests: behavior and design of reinforced concrete structures, seismic performance assessment and structural retrofitting, nonlinear finite element analysis of structural components, application of machine learning in structural engineering, performance-based design and evaluation of buildings and bridges, development of structural design codes and standards
Dr. Arslan Khan
Email: arskhan@fiu.edu
Affiliation: Department of Civil and Environmental Engineering, Florida International University, Miami 33199, FL, USA
Research Interests: machine learning applications in structural and materials engineering, precast and prestressed concrete systems, ultra-high-performance and self-compacting concrete, structural health monitoring using remote sensing (InSAR), finite element modeling and simulation, sustainable and smart construction materials, repair and strengthening of concrete structures
The integration of machine learning (ML) into structural engineering has emerged as a transformative approach for analyzing, designing, and monitoring reinforced concrete (RC) structures. Traditional methods, while well-established, often face limitations in modeling the complex, nonlinear behavior of RC elements under varying load and environmental conditions. In contrast, data-driven models have demonstrated superior capabilities in predicting structural responses, optimizing material composition, and enhancing damage detection strategies.
This Special Issue aims to bring together high-quality research that explores the intersection of ML techniques and RC structures. We welcome both theoretical developments and practical applications. Topics of interest include, but are not limited to:
• Predictive modeling of mechanical properties of RC materials
• ML-driven structural health monitoring and damage detection in RC components
• Optimization of RC design using AI and evolutionary algorithms
• Applications of deep learning and ensemble models in structural analysis
• Data-driven approaches to assess durability, fatigue, and seismic performance
• Integration of digital twin and smart sensor data with ML models
• Hybrid modeling combining physical theories with ML algorithms
Submissions that present novel methodologies, experimental validations, case studies, or interdisciplinary collaborations are especially encouraged. The goal is to foster knowledge exchange between civil engineering and artificial intelligence communities, accelerating the adoption of intelligent tools in the lifecycle management of RC infrastructure.
This Special Issue will serve as a platform for researchers, practitioners, and policymakers to share insights and innovative solutions that can lead to more resilient, cost-effective, and sustainable concrete infrastructure systems.
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