相关向量机模型在边坡稳定性预测中的应用
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U417.1

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河北省交通运输厅科学技术项目计划(编号:T-2012131);天津市交委科技计划项目(编号:2021-24)


Application of correlation vector machine model in slope stability prediction
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    摘要:

    近年来,边坡稳定性预测得到了广泛的研究,及时、准确的预测可以有效的预防边坡破坏灾害的发生。本文提出了一种基于相关向量机(RVM)的边坡稳定性预测模型,结合京-新高速公路高堑边坡工程实例,通过对比支持向量机(RVM)模型、RBF神经网络模型和支持向量机(SVM)模型的拟合及预测结果来分析其可行性。结果表明:相较于SVM模型和RBF神经网络模型,RVM模型的三种预测指标值均是最小的。其中,平均绝对误差(MAE)分别降低了86.02%和22.11%,均方根误差(RMSE)分别降低了72.05%和1.09%,相对均方误差(RRMSE)也分别降低了75.89%和21.13%,表明RVM是一种预测边坡稳定性的稳健工具,该方法能较为准确地预测出不同指标下的边坡安全系数。

    Abstract:

    In recent years, slope stability prediction has been widely studied, and timely and accurate prediction can effectively prevent the occurrence of slope failure disasters.The fractal model of prediction for high cutting side slope deformation was built on relevance vector machine. According to the instance of high cutting side slope project in Jing-Xin high way, the feasibility of the fractal model was analyzed by results of fitting and prediction which were drawn from comparing SVM model, RBF neural networks model and RVM combined model.The results show that comparing to SVM model and RBF neural networks model, the average absolute error (MAE) of slope safety factor which was predicted by RVM model is reduced by 86.02% and 22.11% respectively, the root mean square error (RMSE) is reduced by 72.05% and1.09% and the relative mean square error (RRMSE) is reduced by 75.89% and 21.13% respectively. The results show that RVM is a robust tool to predict the slope stability, and the method can accurately predict the slope safety factors under different indexes.

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孙吉书,夏健超,王建平,等. 相关向量机模型在边坡稳定性预测中的应用[J]. 科学技术与工程, 2021, 21(28): 12234-12242.
Sun Jishu, Xia Jianchao, Wang Jianping, et al. Application of correlation vector machine model in slope stability prediction[J]. Science Technology and Engineering,2021,21(28):12234-12242.

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  • 收稿日期:2021-02-26
  • 最后修改日期:2021-07-21
  • 录用日期:2021-05-24
  • 在线发布日期: 2021-09-29
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