Predicting tensile strength of spliced and non-spliced steel bars using machine learning- and regression-based methods

• Published in: Construction & building materials 2022-03, Vol.325, p.126835, Article 126835
• Main Authors: Dabiri, Hamed, Kheyroddin, Ali, Faramarzi, Asaad
• Format: Article
• Language: English
• Subjects: ANN; Machine learning; Nonlinear regression; Ridge regression; Steel bars; Tensile strength
• ISSN: 0950-0618; 1879-0526
• DOI: 10.1016/j.conbuildmat.2022.126835

•A database containing over 200 experimental tests on tensile strength of non-spliced and spliced steel bars are gathered.•Three models are proposed for predicting tensile strength of rebars using nonlinear regression, ridge regression and ANN.•The results of the prediction models are assessed through Taylor diagram and performance metrics.•Acceptable performance metrics proved the reliability and accuracy of ML-based models for predicting tensile strength.•Equations are suggested for predicting tensile strength of the bars spliced by couplers. Mechanical properties of steel reinforcement bars, which have a critical effect in the overall performance of reinforced concrete (RC) structures, should be reported and assessed before being used in structural elements. Determining bars’ properties could be time-consuming and expensive specifically in the case of incorporating splices. Therefore, this study aims to predict tensile strength of bars using machine learning-based methods including nonlinear regression, ridge regression and artificial neural network. To this end, a comprehensive database including over 200 tests on non-spliced and spliced steel bars by mechanical couplers was collected from the available peer-reviewed international publications. Bar size, splice method, steel grade, temperature and splice characteristics (length and outer diameter of couplers) were the input parameters considered for predicting tensile strength. The efficiency of the models was evaluated through Taylor diagram and common performance metrics namely coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The results demonstrated that the predicted values agreed well with the actual values reported in the experimental studies used for collecting the database. A parametric study was also conducted in order to examine the influence of coupler length, coupler outer diameter and temperature on the tensile strength of spliced bars. Based on the parametric study results, three different equations were suggested for calculating tensile strength of spliced bars using the mentioned parameters. The outcomes of this study can assist practitioners to effectively and accurately estimate tensile strength of spliced and non-spliced steel bars in reinforced concrete structures without the need to carry out expensive and timely physical tests.