Comparative study on the performance of different machine learning techniques to predict the shear strength of RC deep beams: Model selection and industry implications

This study presents a comprehensive and rigorous process to develop the most appropriate machine learning (ML) model for predicting the shear strength of RC deep beams (RCDBs). The process consists of the crucial stages and state-of-the-art techniques of ML, including the development of ML models, selection of input features using Shapley Additive explanations, optimisation of the training process, assessment of data randomness, comparisons to the conventional practice codes, and development of novel web-based design platform based on the proposed ML model. For this purpose, seven machine learning models, i.e., linear regression, artificial neural networks (ANN), support vector machines, decision trees, ensemble of trees (EoT), extreme gradient boosting (XGBoost), and Gaussian process regression (GPR) were developed to predict the shear strength of RC deep beams based on a database of 518 samples with 15 input features. The four best models (i.e., ANN, EoT, XGBoost, and GPR) were then considered to assess the influence of varying the number of input features on the prediction performance. The results proved that GPR is the most reliable and accurate ML model. In addition, a set of nine optimal input features is proposed for predicting the shear strength of RCDBs. It was observed that randomly dividing the dataset into training and testing sets can significantly impact the predicted results. In some cases, the R2 value dropped to under 0.78, highlighting the importance of carefully considering the methodology for dividing the dataset when conducting machine learning experiments. The shear strength predicted by ML models was then compared with the three most prominent practice codes (i.e., ACI318, EC2, CSA 23.3-04), which indicated ML approach is highly reliable and accurate over conventional methods. In addition, the study used the Monte Carlo method to evaluate the robustness of the machine learning models and developed a user-interface platform to facilitate the practical application of the proposed machine learning model.