Submission Deadline: 31 December 2026 View: 1790 Submit to Special Issue
Prof. Pang-jo Chun
Email: chun@g.ecc.u-tokyo.ac.jp
Affiliation: Department of Civil Engineering, The University of Tokyo, Tokyo, Japan
Research Interests: artificial intelligence and machine learning in civil engineering digital twins for infrastructure maintenance and management computer vision and UAV-based structural health monitoring large language models (LLMs) and generative ai for engineering applications natural hazard mitigation and resilience of civil infrastructure
Assoc. Prof. Xian Tao
Email: taoxian2013@ia.ac.cn
Affiliation: Institute of Automation, Chinese Academy of Sciences, Beijing, China
Research Interests: industrial vision, anomaly detection, deep learning
Civil infrastructure, particularly bridges, faces increasing threats from aging populations and intensifying natural hazards such as earthquakes, typhoons, and floods. Traditional maintenance and disaster mitigation strategies often struggle with the scale and complexity of these challenges. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies, offering unprecedented capabilities in data analysis, predictive modeling, and decision-making support.
This Special Issue aims to showcase cutting-edge research at the intersection of AI and structural engineering. We seek contributions that demonstrate how AI can enhance the resilience, safety, and longevity of bridge infrastructure against natural disasters. The scope encompasses both theoretical advancements and practical applications of AI in monitoring, assessment, and risk mitigation strategies.
Potential topics include, but are not limited to:
· Fusion of Finite Element Method (FEM) and AI for high-fidelity simulation.
· Advanced image processing and 3D point cloud analysis for automated inspection.
· Large Language Models (LLMs) and generative AI for maintenance records and decision support.
· AI-driven Structural Health Monitoring (SHM) and damage detection.
· Digital Twins for disaster response and lifecycle management.
· Surrogate modeling for real-time hazard simulation.
This collection intends to provide a comprehensive overview of the state-of-the-art in AI-empowered bridge engineering.
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