Implementation of boosting algorithms for prediction of punching shear strength of RC column footings

Abstract

For more accurate and reliable prediction of punching shear strength of reinforced concrete (RC) column footings, machine learning (ML) was utilized. To develop ML-based strength prediction models, an experimental database was built by collecting test results (total 218 specimens without shear reinforcement and subjected to concentric loads) from the literature, and three boosting algorithms including the adaptive boosting (AdaBoost), gradient boosting (GBR), and extreme gradient boosting (XGBoost) were implemented. Input variables were carefully selected based on existing study results, and optimal hyperparameters of the ML-based models were determined based on the grid search with k-fold cross-validation. The ML-based models with the optimal hyperparameters showed highly accurate predictions without any significant and evident bias for all input variables, and outperformed current design codes and existing models. The relative feature importance of input variables showed that the effective footing depth is the most important variable for the prediction performance, and the boxplot analysis, ANOVA test, and t-test results revealed that spring or uniform load supports can be used instead of sand supports to avoid the complexity of the test setup. Finally, based on the study results, a simple design equation was proposed to widen the scope of application and to consider the effects of the shear span-to-depth ratio, steel yield strength, and test setup.