摘要
为了实现红层公路边坡稳定性的快速评价,以四川省仁沐新高速公路工程为依托,通过红层边坡稳定性影响因素的分析,形成边坡快速评价的指标体系,基于机器学习理论,建立了以SVM算法为核心的红层公路边坡稳定性快速评价模型,并运用该模型对仁沐新高速公路全线16个边坡进行快速评价,其结果与现场的地质判断是吻合的,验证了评价方法的实用性与准确性,可为红层地区公路的施工与防治提供参考。
In order to realize the rapid evaluation of the slope stability of the red-bed highway,based on the Renmuxin Expressway Project in Sichuan Province,an index system for rapid evaluation of the slope is established through the analysis of the factors affecting the stability of the red-bed slope.Based on machine learning theory,a rapid evaluation model for the slope stability of red-bed highway is established with SVM algorithm as the basis,and the model is used to quickly evaluate the 16 slopes along the Renmxin Expressway.The results are consistent with the geological judgment on site,which verifies the practicability and accuracy of the evaluation method,and provides a reference for the construction and slope protection of highways in the red-bed area.
作者
张硕
郑达
张文
ZHANG Shuo;ZHENG Da;ZHANG Wen(College of Environment and Civil Engineering, Chengdu University of Technology, Chengdu 610059,China;Institute of Exploration Technology, CAGS, Chengdu 611734,China)
出处
《西北水电》
2022年第3期1-7,共7页
Northwest Hydropower
基金
西藏自治区自然资源厅(藏财采[2020]0890-1)
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自主研究课题(SKLGP2020Z008).
关键词
红层
公路边坡
稳定性快速评价
机器学习
SVM算法
red-bed rock
excavation slope
rapid evaluation of slope stability
machine learning
SVM algorithm
