摘要
以季节性冻土区环青海湖段青藏铁路路基冻害为背景,通过现场采集的粉质黏土人工盐渍化后,考虑温度、水分、盐分、压实度4个因素进行室内冻胀试验,测试了不同温度环境下路基土体的冻胀率。根据试验所得参数,建立BP神经网络冻胀预报模型对土体冻胀率进行预测。结果表明:运用BP神经网络的预测结果与试验结果具有良好的一致性,误差为1%~5%。
Based on the frost damage of Qinghai-Tibet railway subgrade ringing Qinghai Lake in the seasonal frozen soil region,the Indoor Frost Heaving Test was conducted after the powder clay collected on site and the artificial salinization,considering such four factors as temperature,moisture,salinity and compaction degree to test the frost heaving rate of subgrade soil under different temperature conditions.According to the parameters of the test,the frost heaving rate is forecasted by establishment of frost heave prediction model with BP Neural network.The results show that the predicted results obtained by using the BP neural network method are consistent with the experimental results,and the error is between 1%and 5%.
作者
王彦虎
王旭
杨楠
王跃武
张延杰
WANG Yanhu;WANG Xu;YANG Nan;WANG Yuewu;ZHANG Yanjie(0x09School of Civil Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China;Xining Railway Section of Qinghai-Tibet Railway Company,Xining 810006,China)
出处
《路基工程》
2018年第2期14-18,共5页
Subgrade Engineering
基金
青藏铁路公司科技研究开发计划课题(QZ2015-G01)
冻土工程国家重点实验室开放基金(SKLFSE201607)
关键词
铁路路基
人工盐渍土
冻胀模型
BP神经网络
冻胀率
railway subgrade
artificial saline soil
frost heave model
BP neural network
frost heaving rate
