强度折减采样结合SVM代理模型的土质边坡可靠度高效分析

卢建; 曾鹏; 冯兵; 王鑫; 严祖龙

doi:10.19509/j.cnki.dzkq.tb20240756

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强度折减采样结合SVM代理模型的土质边坡可靠度高效分析

更新时间：2026-11-05

- 强度折减采样结合SVM代理模型的土质边坡可靠度高效分析
- Bulletin of Geological Science and Technology Vol. 45, Issue 3, Pages: 265-276(2026)
- 作者机构：
  
  1. 成都理工大学环境与土木工程学院
  2. 成都理工大学地质灾害防治与地质环境保护国家重点实验室
- 作者简介：
- 基金信息：
- DOI：10.19509/j.cnki.dzkq.tb20240756
  CLC： TU43
- Published：2026
- 稿件说明：
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卢建, 曾鹏, 冯兵, et al. 强度折减采样结合SVM代理模型的土质边坡可靠度高效分析[J]. Bulletin of Geological Science and Technology, 2026, 45(3): 265-276.
DOI：

卢建, 曾鹏, 冯兵, et al. 强度折减采样结合SVM代理模型的土质边坡可靠度高效分析[J]. Bulletin of Geological Science and Technology, 2026, 45(3): 265-276. DOI： 10.19509/j.cnki.dzkq.tb20240756.

摘要

边坡可靠度分析的高昂计算成本限制了其在工程实践中的广泛应用，亟需开发更加高效、准确的分析方法。本研究提出了一种有限差分强度折减法采样（strength reduction sampling，简称SRS）结合主动学习支持向量机（support vector machine，简称SVM）代理模型的新型土质边坡可靠度分析方法（SRS-SVM）。通过强度折减采样策略，高效生成邻近极限状态面高信息量的训练样本点，提升了SVM代理模型的训练效率。通过4个经典边坡案例对所提方法的高效性和准确性进行了验证。研究结果表明，本方法相比于传统的可靠度方法，如经典响应面（classical response surface method，简称CRSM）、径向基函数（radial basis function，简称RBF）和高斯过程回归结合强度折减采样（strength reduction sampling combined with Gaussian process regression，简称SRS-GPR）等，在计算效率（数值模型计算小于40次）和计算准确性（系统失稳概率相对误差绝对值小于1.5%）均表现出明显的优势，并且在处理功能函数非线性及复杂边坡时展现出较强的适应性。本研究提出的SRS-SVM方法将强度折减采样与主动学习SVM分类模型结合，在计算效率、计算精度以及处理复杂问题等方面表现优异，具有良好的工程应用前景，可为工程边坡风险评价与防治提供高效的技术支撑。

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references

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