Development of an IRMO-BPNN Based Single Pile Ultimate Axial Bearing Capacity Prediction Model

Abstract

The ultimate axial bearing capacity (UABC) of a single pile is an important parameter in pile design. BP neural network (BPNN) has a strong nonlinear mapping ability and can effectively predict the UABC of a single pile. However, frequent immersion in unstable search results with local vibration leads BPNN to a less usable solution. The weights and biases of the BPNN model are optimized using the improved radial movement optimization (IRMO) algorithm in this study, and a new method named the IRMO-BP neural network (IRMO-BPNN) is proposed to predict the UABC of a single pile. The IRMO-BPNN model was developed from a database of 196 static load test (SLT) samples, and model hyper-parameter analysis was carried out to determine the optimal number of hidden nodes, population size, and the number of iterations. The prediction accuracy and stability of the IRMO-BPNN model are verified by comparing it with the GA-based ANN model, ANFIS-GMDH-PSO model, and RBFANN model. The results show that the IRMO-BPNN model can accurately predict the UABC of a single pile and improves the situation that the BPNN model is easy to fall into local optimal values and its search results are unstable. The IRMO-BPNN model has significant advantages over other models.