Prediction Model of Ultimate Bearing Capacity of Submarine Corroded Pipelines

Accurate prediction of the ultimate bearing capacity of submarine corroded pipelines is of great significance for assessing the life of submarine pipelines and ensuring the safe operation of submarine oil and gas pipelines. Aiming at the disadvantages of low learning efficiency of BP neural network (BPNN) model, sensitive to initial weights and easy to fall into local optimal state, artificial bee colony (ABC) algorithm was used to optimize the initial weights and thresholds of BPNN, and ABC-BPNN prediction model for ultimate bearing capacity of submarine corroded pipeline was established. Using MATLAB software to build the model and to make predictions, and compare and analyze with the BPNN model, the BPNN model optimized by the genetic algorithm (GA-BPNN) and the BPNN model optimized by the particle swarm optimization algorithm (PSO-BPNN). The results showed that the average relative error of the ABC-BPNN model to predict the ultimate bearing capacity of submarine corrosion pipelines was 1. 975 0%, which was better than the prediction results of the BPNN, GA-BPNN and PSO-BPNN models. The line fitted by the prediction results of the ABC-BPNN model was the closest to the Y=X line, which proved the accuracy and robustness of the ABC-BPNN model as a tool for predicting the ultimate bearing capacity of submarine corrosion pipelines.