Machine Learning-Based Mid-Span Displacement Prediction for RC Columns Under Blast Loading with Bayesian Optimization.

This article aims to predict the mid-span displacements of reinforced concrete (RC) columns exposed to blast loading with machine learning models. Machine learning models including the gradient boosting decision tree (GBDT) model and the random forest (RF) model are developed. The model hyperparameters in the machine learning models are globally optimized using Bayesian optimization method. The dataset used to train and test the models is constructed by collecting data from shock-tube-simulated experiments. Results show that the performance of the optimized models is significantly improved. By comparing the performance metrics of two models, it is evident that the GBDT model is superior to the RF model in predicting mid-span displacements. The optimized GBDT model is then used to predict the displacements of columns during field tests, and the experimental results and predictive results are relatively consistent, which verifies the effectiveness of GBDT model with Bayesian optimization in damage assessment for RC columns. [ABSTRACT FROM AUTHOR]