With the increase of human activities on the ocean, it is inevitable that weights drop into the sea, which may lead to damages of the pipelines once impacted. The failure of pipelines may cause great financial losses and serious environment pollution. In order to ensure the safety of pipelines during operation period, it is necessary to predict the damage fast and accurately, which can provide reference for engineering practice. Back-propagation neural network(hereinafter BP neural network) is a common mathematic model to predict the pipeline damage. However, it is prone to plunge into local minimum which leads to errors. To solve the problem, this paper proposes a modified BP neural network model to predict the pipeline damage based on genetic algorithm(hereinafter GA-BP neural network). Sample space for the construction of the proposed model is consisted of the data by finite element method(hereinafter FEM). Subsequently, damage predictions are made and the results are compared with that by BP neural network and by FEM. The analysis indicates that, compared with the BP neural network, the GA-BP neural network has higher prediction accuracy and are more close to the FEM results. Besides, the average relative error is around 1.27%. Therefore, the engineering accuracy requirements can be satisfied and the considerable calculation time is saved.
Transaction of Oceanology and Limnology