Sun B.,Gansu Boiler and Pressure Vessel Inspection and Research Center Lanzhou |
Wu J.,Gansu Boiler and Pressure Vessel Inspection and Research Center Lanzhou |
Li L.,CPECC East China Design Branch |
Xinan Shiyou Daxue Xuebao/Journal of Southwest Petroleum University | Year: 2013
The remaining strength of the in-service corroded oil and gas pipeline was predicted based on artificial neural network's ability to approximate complex function. But artificial neural network has drawbacks as follows: the initial distribution of weight and threshold value is a stochastic process and it was the local optimization algorithm, and that the local minimum solution tends to appear in the convergence process. Therefore, the weight and threshold value of BP neural network using L-M algorithm were optimized based on the global search ability and independence of the gradient information of genetic algorithm, and with consideration of the influencing factors of failure pressure of oil and gas pipeline determined by sensitivity analysis, the GA-BP(L-M) network model was built. The network was trained using sample of Modified ASME B31G and predictions were made. The results show that the GA-BP(L-M) network model can better predict failure pressure of oil and gas pipeline, which proves to be a more scientific and accurate model.