Regional Seismic Risk Assessment of Infrastructure Systems through Machine Learning: Active Learning Approach
Publication: Journal of Structural Engineering
Volume 146, Issue 12
Abstract
Regional seismic risk assessment involves many infrastructure systems, and it is computationally intensive to conduct an individual simulation of each system. This paper suggests an approach using active learning to select informative samples that help build machine learning models with fewer samples for regional damage assessment. The potential of the approach is demonstrated with (1) failure mode prediction of bridge columns, and (2) regional damage assessment of the California two-span bridge inventory with seat abutments. The active learning approach involves the selection of column attributes or bridge models that are more informative to the creation of machine learning-based decision boundaries. The results reveal that an active learning target model based on 100 bridge samples can achieve a level of accuracy of 80%, which is equivalent to a machine learning model based on 480 bridge samples in the case of damage prediction following an earthquake. With the proposed approach, the computational complexity associated with regional risk assessment of bridge systems with specific attributes can be drastically reduced. The proposed approach also will help plan experimental studies that are more informative for damage assessment.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available in the GitHub repository: https://github.com/sujithmangalathu/Active_learning_regional_risk_assessment, including the databases of column failure mode and bridge damage state and Python codes of machine learning models.
Acknowledgments
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2020R1A4A1018826).
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Published In
Journal of Structural Engineering
Volume 146 • Issue 12 • December 2020
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Mar 4, 2020
Accepted: Jun 16, 2020
Published online: Sep 22, 2020
Published in print: Dec 1, 2020
Discussion open until: Feb 22, 2021
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