Prediction of shear capacity of recycled aggregate concrete beams with machine learning
In order to solve the problems that the current recycled aggregate concrete(RAC)beams lack a unified calculation model of shear capacity,the related test workload is large and it is difficult to draw regular conclusions,a prediction model of shear capacity of RAC beams based on machine learning is established.The shear performance test data of 468 RAC rectangular beams are collected according to the existing literature.By studying the effects of section width,section effective height,recycled coarse aggregate replacement rate,cube compressive strength,axial tensile strength,shear span ratio,longitudinal reinforcement ratio and stirrup characteristic value on the shear capacity of RAC beams,combined with five machine learning algorithms of logical regression(LR),decision tree(DT),AdaBoost(AB),support vector machine(SVM)and artificial neural network(ANN),the prediction model of shear bearing capacity of RAC beams is established,the prediction effect is compared,and the prediction accuracy of different machine learning algorithms is analyzed.The results show that ANN and AdaBoost can accurately predict the shear capacity of RAC beams,the determination coefficient R2 is more than 0.9,and the average absolute error MAE is 18.66 and 15.96 respectively.According to the accuracy statistical index,it is suggested that ANN and AdaBoost should be preferred in the prediction and calculation of RAC beams.Finally,based on the collected test data and nonlinear regression analysis,the proposed formula of shear capacity of RAC beam is proposed.
machine learningdatabaserecycled aggregate concrete beamsprediction of shear capacity
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