徐海峰, 朱晶, 王艳波. 基于SVM法的大坝安全稳定预测模型分析及应用[J]. 黄河水利职业技术学院学报, 2020, 32(4): 7-11. DOI: 10.13681/j.cnki.cn41-1282/tv.2020.04.002
    引用本文: 徐海峰, 朱晶, 王艳波. 基于SVM法的大坝安全稳定预测模型分析及应用[J]. 黄河水利职业技术学院学报, 2020, 32(4): 7-11. DOI: 10.13681/j.cnki.cn41-1282/tv.2020.04.002
    Xv Haifeng, Zhu Jing, Wang Yanbo. Analysis and Application of Dam Safety and Stability Prediction Model Based on SVM Algorithm[J]. Journal of Yellow River Conservancy Technical Institute, 2020, 32(4): 7-11. DOI: 10.13681/j.cnki.cn41-1282/tv.2020.04.002
    Citation: Xv Haifeng, Zhu Jing, Wang Yanbo. Analysis and Application of Dam Safety and Stability Prediction Model Based on SVM Algorithm[J]. Journal of Yellow River Conservancy Technical Institute, 2020, 32(4): 7-11. DOI: 10.13681/j.cnki.cn41-1282/tv.2020.04.002

    基于SVM法的大坝安全稳定预测模型分析及应用

    Analysis and Application of Dam Safety and Stability Prediction Model Based on SVM Algorithm

    • 摘要: 为了便于大坝安全监控,建立了一种具有小样本、非线性的SVM大坝安全系数预测模型。以白羊山水库工程为例,探讨了该模型的应用。 在考虑坝高、坝基土层、坝基沉降等因素的情况下,采用遗传算法对预测模型的松弛系数及惩罚因子进行优化搜索, 并用该模型对大坝进行安全稳定预测。 经检验,预测精度可以满足设计要求。

       

      Abstract: In order to monitor the dam safety conviently, a small sample nonlinear SVM dam safety factor prediction model was established. Taking the Baiyangshan reservoir project as an example, the application of this model was discussed. Considering the factors of dam height, soil layer and settlement of dam foundation, the genetic algorithm is used to search the relaxation coefficient and penalty factor of the prediction model. This model is used to predict the safety and stability of the dam. The inspection shows the accuracy of prediction can meet the design requirements.

       

    /

    返回文章
    返回