Data-driven shear strength prediction of steel fiber reinforced concrete beams using machine learning approach

Abstract

Abstract The incorporation of steel fibers in a concrete mix enhances the shear capacity of reinforced concrete beams and a comprehensive understanding of this phenomenon is imperative to have an accurate estimation in engineering designs. Although significant studies have been carried out on shear capacity estimation, mechanics-based models are not yet available due to the complex underlying phenomenon. This paper presents a data-driven approach to the shear strength of SFRC beams and incorporates the largest database compilation of 507 experimental data. Input features considered in this study are the ratio of shear span to effective depth, concrete compressive strength, longitudinal reinforcement ratio, volume fraction, aspect ratio, and type of fiber. Eleven machine learning (ML) models, namely linear regression, ridge regression, lasso regression, decision tree, random forest, support vector machine, k-nearest neighbors, artificial neural network, XGBoost, AdaBoost, and CatBoost, are evaluated to examine their shear strength estimation of SFRC beams. The XGBoost is resulting in the most accurate predictions (85%) with the lowest root mean squared error and low mean absolute error. A study on the importance of the input parameters reveals that shear span to effective depth ratio, longitudinal reinforcement ratio, concrete strength, and volume fraction of fiber are the most influential parameters of shear strength of SFRC.