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Bridge damage characterisation using machine learning: methods and advances
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DOI：10.1016/j.rineng.2025.107192.png)
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Abstract
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• Identification of critical research gaps in using deflection as a global damage characterisation for bridges • First comprehensive review of bridge deflections accounting for all critical deterioration mechanisms and their interactions. • Conceptual framework that incorporates rigorous alignment of measured bridge detections with model predictions incorporating creep and shrinkage, improving damage characterisation accuracy • - Physics-Based approach to correlate deflection patterns to specific damage states
Keywords:
deflection
bridge
damage characterisation
Machine Learning
actionable damage states
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