Prediction Method of Compressive Strength for Small Core Specimens Based on MOPSO-RBFNN
To solve the problem of difficulty in obtaining standard size core samples using drilling method in areas with dense steel bars or small component sizes in practical engineering,this study proposes a compressive strength prediction method of small core specimens based on radial basis function neural network.Firstly,this paper conducted axial compressive tests on 82 sets of 50mm diameter fine aggregate concrete core specimens and 150mm cube specimens cured under the same conditions and age.Secondly,the strength values obtained from the compressive tests of the two specimens were compared and analyzed,and the MOPSO algorithm was used to optimize the hyperparame-ters of the radial basis neural network.a compressive strength prediction model for core drilling sampling specimens based on the MOPSO-RBFNN model was established,and the prediction results were compared with those of other methods.The results showed that the MAE and R2 of the MOPSO-RBFNN model were 1.311 and 0.987,respectively.This paper also verified the effectiveness of the proposed prediction method by comparing the error evaluation indicators of this method with other methods.The research results provide technical support for predicting the compressive strength of small core specimens,and are of great significance for improving the reliability and accuracy of engi-neering testing.
万方数据
维普
