Lifespan Prediction of Polyethylene Pipe Based on BP Neural Network Algorithm
GU Yaxin, ZHAO Ziyi
（School of Materials Science and Engineering, Shenyang Jianzhu University, Shenyang 110168, China）
Abstract:The influence factors for the lifespan of polyethylene (PE) pipe in practical application were analyzed, and the controllable factors were selected to conduct a lifespan prediction. A lifespan prediction model was established according to the experimental results, and the changeable variables of model were designed by reference to the experimental variables using MATLAB software. For the experimental design, a certain numerical gradient was selected according to the hydrostatic tests. The experimental design was according to the hydrostatic tests. Select the experimental temperatures, the effective chlorine contents in the experimental water, and the experimental pressures for totally 192 groups of experiments and 384 test samples. The PE pipe was simulated in the practical use of water distribution systems. In the computational modeling process, the BP neural network fatigue equation model with three-factor variables was selected for life prediction. The obtained results indicated that the BP neural network has a scientific significance to calculate the service lifespan of PE pipe, and the goodness of fit, R2, was up to 0.87 in probability according to this network model. Compared to the conventional algorithms of PE pipe, the prediction results have superiority in calculation simplification, specimen expansion, model threshold and importance variability.
Key words:BP neural network; hydrostatic test; failure analysis; lifespan prediction