Accurate Prediction of Punching Shear Strength of Steel Fiber-Reinforced Concrete Slabs: A Machine Learning Approach with Data Augmentation and Explainability

Abstract

Reinforced concrete slabs are widely used in building structures due to their economic, durable, and aesthetic advantages. The determination of their ultimate strength often hinges on punching shear strength. Presently, methods such as closed hoops, steel bending, and fiber reinforcement are employed to enhance punching shear strength, with fiber reinforcement gaining popularity due to its ease of implementation and efficacy in improving concrete durability. This study introduces a novel approach employing six machine learning algorithms rooted in decision trees and decision tree-based ensemble learning to predict punching shear strength in steel fiber-reinforced concrete slabs. To overcome experimental data limitations, a data augmentation approach based on the Gaussian mixture model is employed. The validation of the data augmentation is conducted through “synthetic training—real testing” and “real training—real testing”. Additionally, the best machine learning model is analyzed for explainability using Shapley Additive exPlanation (SHAP). Results demonstrate that the proposed data augmentation method effectively captures the original data distribution, enhancing the robustness and accuracy of the machine learning model. Moreover, SHAP provides better insights into the features influencing punching shear strength. Thus, the proposed data enhancement model offers a reliable approach for modeling small experimental datasets in structural engineering.

Data-driven prediction on critical mechanical properties of engineered cementitious composites based on machine learning

“…The findings indicate that the XGB model exhibited superior robustness and accuracy 21 . At the same time, the problem of small experimental datasets was solved by using an innovative method of data augmentation, facilitating the achievement of a higher prediction accuracy for the punching shear strength of steel fiber-reinforced concrete slabs 22 . Jayasinghe used eight machine-learning models to develop a framework for predicting the shear capacity of recycled aggregate concrete (RAC) beams, and the results revealed that the XGBoost model had the best prediction effect, with the coefficient of determination R 2 on the test set reaching 0.95 (slender beam) and 0.78 (deep beam) 23 .…”