Sujith Mangalathu

TL;DR: This paper uses extensive experimental databases to suggest random forest machine learning models for failure mode predictions of reinforced concrete columns and shear walls, employs the recently developed SHapley Additive exPlanations approach to rank input variables for identification of failure modes, and explains why the machine learning model predicts a specific failure mode for a given sample or experiment.

TL;DR: The efficiency of various machine learning techniques is evaluated using extensive experimental data from 536 experimental tests, and it has been seen from the comparison that lasso regression has a better efficiency and reasonable accuracy in the classification and prediction.

TL;DR: A multi-parameter fragility methodology using artificial neural network to generate bridge-specific fragility curves without grouping the bridge classes is suggested.

TL;DR: A machine learning model based on the Random Forest method, which has 86% accuracy in identifying the failure mode of shear walls, is proposed and an open-source data-driven classification model that can be used in design offices across the world is provided.

TL;DR: This paper contributes to the critical need ofKnowing the prediction of failure mode of columns is critical in deciding the operational and recovery strategies of a bridge after a seismic event.