基于多源信息综合的滑坡稳定性发展趋势评价
Evaluation of Landslide Stability Development Trends Based on Integration of Multi-source Information
朱涛 1翟亚锋 1翟会君1
作者信息
- 1. 河南省第四地质勘查院有限公司 郑州市 450001
- 折叠
摘要
为准确掌握滑坡稳定性,该文以现场勘查成果为基础,先通过传递系数法掌握滑坡目前的稳定状态,再结合滑坡变形数据,利用ISSA-KELM模型和重标极差法分别评价滑坡后续变形规律及发展趋势,并以两者结果对比评价滑坡稳定性的发展趋势.实例分析结果为:滑坡的弱变形区稳定性系数范围为 1.11～1.39,强变形区稳定性系数范围为1.03～1.23,处于欠稳定-稳定状态.在变形趋势判断结果中,累计变形序列的Hurst指数范围为0.641～0.821,变形速率序列的Hurst指数范围为0.448～0.506,说明滑坡不同位置处的变形发展特征存在一定差异,但累计变形均呈持续增加趋势,这与变形预测结果一致,因此,综合得出滑坡后续稳定性均会趋于不利方向发展,局部具有较大的潜在失稳风险.
Abstract
In order to accurately grasp the stability of the landslide,based on on-site survey results,the current stability of the landslide is mastered by the transfer coefficient method,and then combined with the landslide deformation data,the ISSA-KELM model and the re-standardized range method are used to evaluate the subsequent deformation law and development trends of the landslide.A comparative assess-ment between these methods is conducted to evaluate the landslide stability development trends.The case analysis results indicate that the stability coefficient of the weak deformation area ranges from 1.11-1.39,while in the strong deformation zone,it ranges from1.03-1.23,indicating a state from marginally stable to stable.Regarding deformation trend assessments,the Hurst index range of the cumulative de-formation sequence is 0.641-0.821,and the Hurst index range of the deformation rate sequence is 0.448-0.506,indicating that there are certain differences in the deformation development characteris-tics at different locations of the landslide,however,the cumulative deformation shows a continuous in-creasing trend,which is consistent with the deformation prediction results.Therefore,it can be conclu-ded that the subsequent landslide stability will tend towards unfavorable development trends,posing large potential instability risks in some parts.
关键词
滑坡/传递系数法/核极限学习机/变形预测/重标极差法Key words
landslide/transfer coefficient method/ISSA-KELM model/deformation prediction/re-standardized range method引用本文复制引用
出版年
2024
NETL
NSTL
万方数据