Numerical Test and Strength Prediction of Concrete Failure Process Based on RVM Algorithm

Abstract

Recycled aggregate concrete (RAC) based on the machine learning (ML) method predicts the nonlinear uncertainty relationship between various mixing ratios and strength. Uniaxial compressive strength is one of the important indices to evaluate its performance. Machine learning is one of the essential methods for solving this nonlinear uncertainty relationship. To realize the selection of concrete raw materials and the learning and application of other influencing factors and provide guidance for engineering construction and application, this paper establishes a database of concrete uniaxial compressive strength based on Abaqus simulation software. The simulation results are highly consistent with the actual values. Based on the simulation database, with different water-cement ratios, different curing days, and recycled aggregate replacement rates as the input data set, the uniaxial compressive strength of concrete is the output data set. The data set is divided into a training set and a test set. A prediction model of the uniaxial compressive strength of concrete based on a relevance vector machine (RVM) algorithm is established. The results show that the maximum error between the simulated and experimental uniaxial compressive strength values is only 0.2 MPa. The correlation coefficient R between the predicted and simulated values of the concrete uniaxial compressive strength prediction model based on the RVM algorithm is 0.975. The model can effectively predict the compressive strength of RAC to meet the engineering requirements.