Algorithm Prediction Model of Maximum Stress of Rubber Elastic Pipe Based on Particle Swarm Optimization Neural Network
The use of particle swarm optimization based neural networks to predict the maximum stress of rubber elastic pipelines has emerged in the context of the widespread application and development of pipelines,in pursuit of greater convenience and practicality.Pipeline cracking incidents occur frequently,and predicting the nature and situation of pipeline cracking can effectively prevent and avoid accidents,playing a certain protective role in property and personal safety.Taking four different materials of rubber elastic pipes as the research object,simulation experiments were conducted to calculate the maximum stress values under conditions such as upper pipe wall pressure of 1 000 N,1 500 N,2 000 N,lower pipe wall pressure of 200 N,500 N,inner pipe temperature of 30℃,40℃,and ambient temperature of 30℃,20℃,etc.A total of 120 sets of data were calculated,and then organized into a dataset of particle swarm optimization neural network algorithm.By adjusting the parameters of this algorithm,an algorithm prediction model with an accuracy of 96%for predicting the maximum stress value of rubber elastic pipes was obtained.
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