Stability Analysis of Rock Slope Based on Improved Principal Component Analysis Model: Taking Fuwushan Slope as an Example
Aiming at the problems of low accuracy, low efficiency, and many parameters required in the current calculation of rock slope stability, a prediction model of rock slope stability is proposed, which combines principal component analysis (PCA) and relevance vector machine (RVM). In this model, PCA is...
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2021-01-01
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Series: | Geofluids |
Online Access: | http://dx.doi.org/10.1155/2021/9015065 |
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doaj-200e3cb91c8f4159bde21c44c1b753282021-09-13T01:23:55ZengHindawi-WileyGeofluids1468-81232021-01-01202110.1155/2021/9015065Stability Analysis of Rock Slope Based on Improved Principal Component Analysis Model: Taking Fuwushan Slope as an ExampleLihua Huang0Liudan Mao1YiRong Zhu2YuLing Wang3School of Management EngineeringZhejiang Zhongyu Engineering Technology Co.Glodon Company LimitedSchool of Management EngineeringAiming at the problems of low accuracy, low efficiency, and many parameters required in the current calculation of rock slope stability, a prediction model of rock slope stability is proposed, which combines principal component analysis (PCA) and relevance vector machine (RVM). In this model, PCA is used to reduce the dimension of several influencing factors, and four independent principal component variables are selected. With the help of RVM mapping the nonlinear relationship between the safety factor of slope stability and the principal component variables, the prediction model of rock slope stability based on PCA-RVM is established. The results show that under the same sample, the maximum relative error of the PCA-RVM model is only 1.26%, the average relative error is 0.95%, and the mean square error is 0.011, which is far lower than that of the RVM model and the GEP model. By comparing the results of traditional calculation method and PCA-RVM model, it can be concluded that the PCA-RVM model has the characteristics of high prediction accuracy, small discreteness, and high reliability, which provides reference value for accurately predicting the stability of rock slope.http://dx.doi.org/10.1155/2021/9015065 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Lihua Huang Liudan Mao YiRong Zhu YuLing Wang |
spellingShingle |
Lihua Huang Liudan Mao YiRong Zhu YuLing Wang Stability Analysis of Rock Slope Based on Improved Principal Component Analysis Model: Taking Fuwushan Slope as an Example Geofluids |
author_facet |
Lihua Huang Liudan Mao YiRong Zhu YuLing Wang |
author_sort |
Lihua Huang |
title |
Stability Analysis of Rock Slope Based on Improved Principal Component Analysis Model: Taking Fuwushan Slope as an Example |
title_short |
Stability Analysis of Rock Slope Based on Improved Principal Component Analysis Model: Taking Fuwushan Slope as an Example |
title_full |
Stability Analysis of Rock Slope Based on Improved Principal Component Analysis Model: Taking Fuwushan Slope as an Example |
title_fullStr |
Stability Analysis of Rock Slope Based on Improved Principal Component Analysis Model: Taking Fuwushan Slope as an Example |
title_full_unstemmed |
Stability Analysis of Rock Slope Based on Improved Principal Component Analysis Model: Taking Fuwushan Slope as an Example |
title_sort |
stability analysis of rock slope based on improved principal component analysis model: taking fuwushan slope as an example |
publisher |
Hindawi-Wiley |
series |
Geofluids |
issn |
1468-8123 |
publishDate |
2021-01-01 |
description |
Aiming at the problems of low accuracy, low efficiency, and many parameters required in the current calculation of rock slope stability, a prediction model of rock slope stability is proposed, which combines principal component analysis (PCA) and relevance vector machine (RVM). In this model, PCA is used to reduce the dimension of several influencing factors, and four independent principal component variables are selected. With the help of RVM mapping the nonlinear relationship between the safety factor of slope stability and the principal component variables, the prediction model of rock slope stability based on PCA-RVM is established. The results show that under the same sample, the maximum relative error of the PCA-RVM model is only 1.26%, the average relative error is 0.95%, and the mean square error is 0.011, which is far lower than that of the RVM model and the GEP model. By comparing the results of traditional calculation method and PCA-RVM model, it can be concluded that the PCA-RVM model has the characteristics of high prediction accuracy, small discreteness, and high reliability, which provides reference value for accurately predicting the stability of rock slope. |
url |
http://dx.doi.org/10.1155/2021/9015065 |
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