Jia-feng Chen, Hai-bin Wei, Bao-ping An, Zhang Peng, Yang-peng Zhang
{"title":"BP神经网络在季节性冻土区路堤沉降预测中的应用","authors":"Jia-feng Chen, Hai-bin Wei, Bao-ping An, Zhang Peng, Yang-peng Zhang","doi":"10.1109/ICDMA.2013.66","DOIUrl":null,"url":null,"abstract":"Considering the function of temperature in Freeze-thaw cycle of seasonal frozen area, temperature is an important factor as BP neural network input layer. The settlement in Season frozen area is predicted based on actual measurements, which will enable the prediction results more in line with the actual engineering of the seasonally frozen ground region, and at the same time improve the prediction accuracy.","PeriodicalId":403312,"journal":{"name":"2013 Fourth International Conference on Digital Manufacturing & Automation","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Application of BP Neural Network Embankment Settlement Prediction in Seasonal Frozen Areas\",\"authors\":\"Jia-feng Chen, Hai-bin Wei, Bao-ping An, Zhang Peng, Yang-peng Zhang\",\"doi\":\"10.1109/ICDMA.2013.66\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Considering the function of temperature in Freeze-thaw cycle of seasonal frozen area, temperature is an important factor as BP neural network input layer. The settlement in Season frozen area is predicted based on actual measurements, which will enable the prediction results more in line with the actual engineering of the seasonally frozen ground region, and at the same time improve the prediction accuracy.\",\"PeriodicalId\":403312,\"journal\":{\"name\":\"2013 Fourth International Conference on Digital Manufacturing & Automation\",\"volume\":\"42 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-06-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 Fourth International Conference on Digital Manufacturing & Automation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDMA.2013.66\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 Fourth International Conference on Digital Manufacturing & Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDMA.2013.66","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Application of BP Neural Network Embankment Settlement Prediction in Seasonal Frozen Areas
Considering the function of temperature in Freeze-thaw cycle of seasonal frozen area, temperature is an important factor as BP neural network input layer. The settlement in Season frozen area is predicted based on actual measurements, which will enable the prediction results more in line with the actual engineering of the seasonally frozen ground region, and at the same time improve the prediction accuracy.
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