Machine learning driven seismic performance limit state identification for performance-based seismic design of bridge piers

•Data-driven machine learning (ML) approach for identifying seismic performance limit states of bridge piers.•Model tuning using grid search algorithm and crossvalidation for optimizing model predictions.•Comparison of various ML approaches for seismic performance limit identification.•Comparison of...

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Bibliographic Details
Published in:	Engineering structures 2022-03, Vol.255, p.113919, Article 113919
Main Authors:	Todorov, Borislav, Muntasir Billah, AHM
Format:	Article
Language:	eng
Subjects:	Bridge failure Bridge pier Bridge piers Buckling Concrete bridges Critical components Damage detection Drift Earthquake damage Earthquake engineering Earthquake loads Earthquake prediction Earthquakes Failure modes Learning algorithms Limit states Machine learning Optimization Performance prediction Performance-based design Piers Reinforced concrete Search algorithms Seismic activity Seismic design Seismic engineering Seismic performance Seismic response Service restoration Spalling Tuning
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Summary:	•Data-driven machine learning (ML) approach for identifying seismic performance limit states of bridge piers.•Model tuning using grid search algorithm and crossvalidation for optimizing model predictions.•Comparison of various ML approaches for seismic performance limit identification.•Comparison of ML models with available empirical equations to identify column seismic performance limits.•Provide an open-source data-driven machine learning model. Performance-based earthquake engineering requires the identification of different damage states in structural components. Bridge piers are one of the most critical components in the bridge system that dictate the overall performance of bridges under seismic loading. The ability to predict different damage states of a bridge pier following an earthquake can be useful in restoring service or prescribing appropriate remediation. Machine learning techniques are becoming attractive in earthquake engineering for predicting failure modes of buildings and bridges. This study implements several machine learning techniques to capture different performance limit states such as spalling, core crushing, and bar buckling of reinforced concrete bridge piers under seismic loading. Using experimental data from the large scale testing of bridge piers, this study compares different machine learning techniques for predicting drift capacity performance limit states of circular and rectangular bridge piers. Model tuning using grid search algorithm and 5-fold crossvalidation is performed to optimize model predictions over a wide tuning parameter search space. The efficiency of different methods is compared using a reserved 20% testing dataset to determine the optimal machine learning regression method for each limit state. This study compares the respective bar buckling and concrete spalling drift limit state models against existing physics-based empirical models, where the machine learning models are found to have significantly improved accuracy and precision.
ISSN:	0141-0296 1873-7323