Submission Deadline: 30 September 2026 View: 164 Submit to Special Issue
Assoc. Prof. Dr Binh Thai Pham
Email: binhpt@utt.edu.vn
Affiliation: Department of Geotechnical Engineering, University of Transport Technology, Hanoi 100000, Vietnam
Research Interests: geosciences, civil engineering, artificial intelligence, GIS and geoinformatics, data-driven technologies, natural hazard assessment, hydrology management
Dr. Indra Prakash
Email: indra52prakash@gmail.com
Affiliation: Dy.DG (R), Geological Survey of India, Gandhingar, 382010, India
Research Interests: geosciences, civil engineering, machine learning, artificial intelligence, GIS and geoinformatics, landslide hazard assessment, slope stability analysis, groundwater management, flood prediction, geotechnical and geological data integration, environmental monitoring, sustainable resource management, data analytics and modeling
Dr. Abolfazl Jaafari
Email: jaafari@rifr-ac.ir
Affiliation: Research Institute of Forests and Rangelan, Agricultural Research, Education and Extension Organization (AREEO), Tehran 1496793612, Iran
Research Interests: geospatial artificial intelligence (GeoAI), vulnerability and resilience assessment, earth observation
Recent advances in artificial intelligence (AI), data analytics, and high-performance computing are fundamentally reshaping how complex civil engineering and Earth science problems are modeled, analyzed, and managed. Traditional physics-based and numerical modeling approaches—while rigorous and well-established—often face limitations when dealing with highly nonlinear behavior, large-scale spatial–temporal systems, data scarcity or uncertainty, and multi-hazard interactions. The rapid growth of sensing technologies, remote sensing platforms, and large engineering datasets has further highlighted the need for intelligent, data-driven methods that can complement and enhance classical computational frameworks.
Advanced AI techniques, including machine learning, deep learning, hybrid AI–physics models, and uncertainty-aware algorithms, have demonstrated strong potential in addressing these challenges. In civil engineering, AI-based models have been successfully applied to structural analysis, damage detection, infrastructure health monitoring, construction management, and lifecycle optimization. When integrated with computational mechanics, numerical simulation, and domain knowledge, these approaches can significantly improve predictive accuracy, computational efficiency, interpretability, and decision support.
The importance of this research area is further amplified by global challenges such as rapid urbanization, climate change, and aging infrastructure. Reliable, intelligent, and scalable modeling tools are urgently needed to support resilient infrastructure design, sustainable development, and risk-informed planning. Advanced AI provides not only powerful new methodologies but also a unifying framework to bridge data, physics, and engineering judgment across multiple scales and disciplines.
The objective of this Special Issue is to bring together state-of-the-art research at the intersection of artificial intelligence, data science, and computational modeling — applied to problems in civil engineering. We aim to showcase novel AI methodologies (e.g., machine learning, deep learning, generative models, hybrid AI-physics) that enhance modeling, simulation, prediction, decision-making, planning, monitoring, and risk management in civil engineering contexts. Contributions that integrate AI with traditional computational modeling (e.g., finite-element, continuum mechanics, geomechanics modeling), or that demonstrate improved realism, scalability, robustness, or interpretability, are especially welcomed.
Suggested themes for this Special Issue include, but are not limited to:
· AI-augmented computational modeling in civil engineering
Machine learning, deep learning, and hybrid AI–physics models for structural mechanics, computational solid mechanics, fluid–structure interaction, soil–structure interaction, and nonlinear civil engineering systems.
· Digital twins and AI-driven simulation of civil infrastructure
Development and updating of digital twins for buildings, bridges, tunnels, pavements, and underground structures using numerical models integrated with sensor data, IoT, and monitoring systems.
· Structural Health Monitoring (SHM) and damage detection
AI-based analysis of vibration, strain, acoustic emission, or imaging data for damage identification, localization, and prognosis of civil structures; data augmentation and learning from limited monitoring data.
· Geotechnical engineering applications of AI
Artificial intelligence for soil and rock characterization, constitutive behavior modeling, slope stability assessment, foundation performance, retaining structures, tunnels, and geotechnical risk evaluation.
· AI-based modeling for hydraulic and water-related civil infrastructure
Data-driven and hybrid models for urban drainage systems, flood modeling in built environments, hydraulic structures, sediment transport affecting infrastructure, and water-related hazard mitigation.
· Remote sensing and geospatial AI for civil infrastructure assessment
Integration of satellite imagery, UAV data, LiDAR, and GIS with AI techniques for monitoring, inspection, deformation analysis, and condition assessment of civil engineering assets.
· Smart infrastructure management and maintenance optimization
AI-enabled predictive maintenance, lifecycle assessment, performance degradation modeling, and asset management for transportation, structural, and underground infrastructure systems.
· Uncertainty quantification, reliability, and risk analysis in civil engineering using AI
Probabilistic AI models for structural reliability, geotechnical uncertainty, safety assessment, resilience evaluation, and decision-making under uncertainty.
· Explainable and trustworthy AI for safety-critical civil engineering applications
Interpretable AI models, physics-informed learning, and transparent decision frameworks to ensure robustness, reliability, and regulatory acceptance in civil engineering practice.
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