Machine Learning-Based Models for Shear Strength Prediction of UHPFRC Beams

Xiangyong Ni () and Kangkang Duan
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Xiangyong Ni: Department of Disaster Mitigation for Structures, Tongji University, Shanghai 200092, China
Kangkang Duan: Department of Civil Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada

Mathematics, 2022, vol. 10, issue 16, 1-26

Abstract: Estimating shear strength is a crucial aspect of beam design. The goal of this research is to develop a shear strength calculation technique for ultra-high performance fiber reinforced concrete (UHPFRC) beams. To begin, a shear test database of 200 UHPFRC beam specimens is established. Then, random forest (RF) is used to evaluate the importance of influence factors for the shear strength of UHPFRC beams. Subsequently, three machine learning (ML)-based models, including artificial neural network (ANN), support vector regression (SVR), and eXtreme-gradient boosting (XGBoost), are proposed to compute shear strength. Results demonstrate that the area of longitudinal reinforcement has the greatest influence on the shear capacity of UHPFRC beams, and ten parameters with high importance (e.g., the area of longitudinal reinforcement, the stirrup strength, the cross-section area, the shear span ratio, fiber volume fraction, etc.) are selected as input parameters. The models of ANN, SVR, and XGBoost have close accuracy, and their R 2 are 0.8825, 0.9016, and 0.8839, respectively, which are much larger than those of existing theoretical models. In addition, the average ratios of prediction values of ANN, SVR, and XGBoost models to experimental results are 1.08, 1.02, and 1.10, respectively; the coefficients of variation are 0.28, 0.21, and 0.28, respectively. The SVR model has the best accuracy and reliability. The accuracy and reliability of ML-based models are much better than those of existing models for calculating the shear strength of UHPFRC beams.

Keywords: ultra-high performance fiber reinforced concrete (UHPFRC); beams; machine learning-based models; shear strength (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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