Human–Machine Collaboration Framework for Bridge Health Monitoring
Publication: Journal of Bridge Engineering
Volume 29, Issue 7
Abstract
In bridge health monitoring (BHM), a prominent goal is to rapidly deliver assessment metrics for these essential and aging urban lifelines when subjected to natural hazard. A vibration-based machine learning (ML) BHM paradigm has been established over the past three decades to allow near-real-time automated health state classification, with a particular focus on the tasks of feature engineering and ML damage identification. This paper presents the human–machine collaboration (H-MC) framework to address challenges of this paradigm, especially in the context of reinforced concrete highway BHM. These challenges include specification of strong motion events, data multidimensionality, and ML model interpretability. The H-MC framework for BHM employs the techniques of multivariate novelty detection and probability of exceedance envelope models with ordinal filter-based feature selection to maximize the use of available data from both recorded and simulated events while maintaining the statistical and physical significance of the results. The framework is demonstrated using a numerical example and two case studies. The findings show the effectiveness of the proposed method for efficient damage assessment to facilitate rapid decision-making.
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Acknowledgments
This research was mainly supported by the Department of Conservation, California Geological Survey, Strong Motion Instrumentation Program agreement 1019-016. Additional financial supports from US NSF’s Graduate Research Fellowship Program, Taisei Chair of Civil Engineering, UC-Berkeley, and Caltrans’ PEER-Bridge Project TO 001 “Bridge Rapid Assessment Center for Extreme Events (BRACE2)” of the agreement number 65A0774 are acknowledged.
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Published In
Journal of Bridge Engineering
Volume 29 • Issue 7 • July 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Aug 3, 2023
Accepted: Jan 16, 2024
Published online: Apr 16, 2024
Published in print: Jul 1, 2024
Discussion open until: Sep 16, 2024
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