Seismic performance assessment of corroded RC columns based on data-driven machine-learning approach

Corrosion of steel reinforcements is a major factor that will adversely affect the seismic performance of the reinforced concrete (RC) columns. This paper investigates the application of machine learning (ML)-based approach for seismic failure mode and maximum bearing capacity prediction for corroded RC columns. A comprehensive database consisting of 180 cyclic tests of corroded RC columns are collected. Six ML algorithms including three single learning methods (k-Nearest neighbors, Decision tree, Artificial neural network) and three ensemble learning methods (Random forest, AdaBoost, CatBoost) are selected to develop the predictive model. The performance of the six models are evaluated and the application of ML-based approaches for life-cycle seismic performance assessment of RC column is demonstrated with a case-study column. The results show that the Random forest and CatBoost models have the best performance for seismic failure mode prediction with an accuracy of 89%. The best model for bearing capacity prediction is the CatBoost model which has a R2 of 0.92, and the CatBoost model is superior to the traditional mechanism-based code models for bearing capacity prediction. The ML-based models can conveniently predict the seismic failure mode and bearing capacity of RC columns in its life-cycle context without complicated numerical simulations or theoretical calculations.

Automated summary

This paper investigates the application of machine learning for predicting the seismic failure mode and maximum bearing capacity of corroded reinforced concrete columns. A comprehensive database of corroded RC columns is collected, and six machine learning algorithms are used to develop predictive models. The results show that the Random forest and CatBoost models have the best performance for predicting the seismic failure mode, while the CatBoost model outperforms traditional code models for bearing capacity prediction.