The determination of shear strength and the identification of potential failure modes are the crucial steps in designing and evaluating the structural performance of reinforced concrete (RC) columns. However, the current design codes and guidelines do not clearly provide a detailed procedure for governing failure types of RC columns. This study predicted the shear strength and identified the failure modes of rectangular RC columns using various Machine Learning (ML) models. Six ML models, including Multivariate Adaptive Regression Splines (MARSs), Naïve Bayes (NBs), K-nearest Neighbors (KNNs), Decision Tree (DT), Support Vector Machine (SVM), and Artificial Neural Network (ANN), were developed to calculate the shear strength and to classify the failure modes of rectangular RC columns. A total of 541 experimental data samples were collected from literature and utilized for developing the ML models. The results reveal that the ANN and KNNs models outperformed other ML models in predicting the shear strength of rectangular RC columns with the $R^2$ value larger than 0.99. Additionally, the KNNs model achieved the highest accuracy, mostly 100%, for identifying the failure modes of rectangular RC columns. Based on the superior performance of the ANN and KNNs models, a graphical user interface was also developed to rapidly predict the shear strength and failure modes of rectangular RC columns.

The impact effect is a crucial issue in civil engineering and has received considerable attention for decades. For the first time, this study develops hybrid machine learning models that integrate the novel Extreme Gradient Boosting (XGB) model with Particle Swam Optimization (PSO), Grey Wolf Optimizer (GWO), Moth Flame Optimizer (MFO), Jaya (JA), and Multi-Verse Optimizer (MVO) algorithms for predicting the permanent transverse displacement of circular hollow section (CHS) steel members under impact loads. The hybrid machine learning models are developed using data collected from 357 impact tests of CHS steel members. The efficacy of hybrid machine learning models is evaluated using three performance metrics. The results show that the GWO-XGB model achieves high accuracy and outperforms the other models. The values of R², RMSE, and MAE obtained from the GWO-XGB model for the test set are 0.981, 2.835 mm, and 1.906 mm, respectively. The SHAP-based model explanation shows that the initial impact velocity of the indenter, the impact mass, and the ratio of impact position to the member length are the most sensitive parameters, followed by the yield strength of the steel member and the member length; meanwhile, member diameter and member thickness are the parameters least sensitive to the permanent transverse displacement of CHS steel members. Finally, this study develops a web application tool to help rapidly estimate the permanent transverse displacement of CHS steel members under impact loads.