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Machine Learning for Structural Health Monitoring
This special issue belongs to the section “Civil Engineering“.
Special Issue Information
Dear Colleagues,
Recently, machine learning has brought a novel paradigm and a huge revolution in structural health monitoring, which is further enhanced by cutting-edge deep learning and computer vision techniques. With the vigorous development of various neural networks and supervised, unsupervised, semi-supervised, and self-supervised, and reinforcement learning algorithms, machine learning enables the autonomous discovery of embedded knowledge and the intelligent diagnosis of structural health based on monitoring data in a purely data-driven manner or a data-model-driven manner. This Special Issue aims to provide a platform to share current scientific and technical progress about ML for SHM.
Dr. Yang Xu
Dr. Shiyin Wei
Dr. Rongrong Hou
Dr. Yong Huang
Guest Editors
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Keywords
- artificial intelligence for structural health monitoring
- learning-based data science and technology of structural health monitoring
- knowledge guided mechanics modeling, structural dynamics, and system identification
- computer-vision-assisted structural damage recognition, change detection, and disaster evaluation
- machine-learning-enhanced structural condition assessment and reliability analysis
- deep-learning-based Bayesian model solving for complex structural uncertainty analysis
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Published Papers
Appl. Sci. - ISSN 2076-3417