Mini-symposium Title
Machine Learning Applications in Civil Engineering
Description
Rapid advances in machine learning, artificial intelligence, and data-driven computational methods are driving a significant transformation in civil engineering. As aging, urbanization, climate change, natural hazards, and complex operational demands increase the challenges for civil infrastructure systems, machine learning offers engineers new opportunities to improve the analysis, design, monitoring, maintenance, and management of these systems.
This mini-symposium brings together researchers, engineers, and practitioners to discuss recent developments and emerging applications of machine learning in civil engineering. We welcome contributions on a wide range of topics, including but not limited to structural health monitoring, damage detection and diagnosis, performance prediction, risk and reliability assessment, infrastructure asset management, smart construction, geotechnical engineering, transportation systems, disaster prevention and mitigation, and resilience assessment of civil infrastructure.
Particular emphasis will be placed on integrating machine learning with numerical simulation, sensing technologies, digital twins, optimization methods, uncertainty quantification, and physics-informed modeling. Topics may include supervised and unsupervised learning, deep learning, reinforcement learning, surrogate modeling, data assimilation, computer vision, natural language processing, and hybrid physics-data-driven approaches. Studies addressing interpretability, generalization, robustness, data scarcity, model validation, and practical implementation are also highly encouraged.
By providing a platform for interdisciplinary exchange, this symposium advances the development of reliable, interpretable, and practical machine learning methods for civil engineering applications. We aim to promote innovative solutions that enhance the safety, sustainability, efficiency, and resilience of civil infrastructure systems.
Lead Organizer
Associate Researcher. Wei-Tze (Aries) Chang, National Center for Research on Earthquake Engineering, TAIWAN
Email: wtchang@niar.org.tw
Co-Organizers
Associate Professor Peng-Yu Chen, Department of Civil Engineering, National Central University, TAIWAN.
Email: sam75782008@ncu.edu.tw
Associate Professor Rih-Teng Wu, Department of Civil Engineering, National Taiwan University, TAIWAN.
Email: rihtengwu@ntu.edu.tw