基于PSO–GPR含缺陷管道失效应力预测  

FAILURE STRESS PREDICTION OF DEFECTIVE PIPELINE BASED ON PSO–GPR

作　　者：冯超 贾澳银 李陈涛 夏成宇[1] 钱利勤[1,2] FENG Chao;JIA Aoyin;LI Chentao;XIA Chengyu;QIAN Liqin(College of Mechanical Engineering,Yangtze University,Jingzhou 434000,Hubei,China;College of Automotive Studies,Tongji University,Shanghai 200092,China)

机构地区：[1]长江大学机械工程学院,湖北荆州434000 [2]同济大学汽车学院,上海200092

出　　处：《力学与实践》2023年第2期260-266,共7页Mechanics in Engineering

摘　　要：传统对含缺陷管道失效应力的预测方法存在误差偏大的问题。针对该问题,利用MATLAB软件建立基于PSO–GPR(particle swarm optimization–Gaussian process regression)含缺陷管道失效应力预测模型。通过对60组含缺陷管道的试验数据进行测试,发现预测结果与实测结果均在95%置信区间内,表明可以将均值作为预测结果。对预测结果进行分析表明:高斯过程回归的预测结果与实测结果的最小相对误差为0.003%,最大相对误差为1.205%,平均相对误差为0.319%,基于预测结果和实测结果的散点均落在曲线y=x的±1.3%误差带中,验证了高斯过程回归预测模型的准确性,为管道的工程实际应用与维修提供较为精准的判断依据。The traditional prediction method of failure stress of pipeline with defects has the problem of large error.Aiming at this problem,the failure stress prediction model of pipeline with defects based on PSO–GPR(particle swarm optimization–Gaussian process regression)is established by using MATLAB software.By testing the experimental data of 60 groups of pipelines with defects,it is found that both the predicted results and the measured results are within 95%confidence interval,which indicates that the mean value can be used as the predicted results.The analysis of the prediction results shows that the minimum relative error between the prediction results of Gaussian process regression and the measured results is 0.003%,the maximum relative error is 1.205%,and the average relative error is 0.319%.The scattered points based on the prediction results and the measured results all fall in the±1.3%error zone of curve y=x,which verifies the accuracy of the Gaussian process regression prediction model and provides more accurate auxiliary judgment help for practical engineering application and pipeline maintenance.

关 键 词：PSO(particle SWARM optimization)优化 高斯过程回归 预测模型 含缺陷管道 

分 类 号：X937[环境科学与工程—安全科学]