Scholarlink

<p>The hydraulic conductivity of rock masses is subject to the influence of multiple factors, including the development of fractures, weathering processes, and unloading conditions. Notably, these characteristics also exhibit a significant correlation with the ground stress state in which the rock mass is situated. However, traditional empirical methods often fail to capture the complex nonlinear relationships among these variables. This study presents a framework to evaluate the hydraulic conductivity distribution based on three key indicators: depth, Rock Quality Designation (RQD), and the Weathering and Unloading Index (WUI). Unlike traditional statistical methods, the proposed model employs the Multivariate Adaptive Regression Splines (MARS) method for assessing hydraulic conductivity distribution. This model is trained on comprehensive packer test data and validated using 34 fractured rock sections from borehole P117 within profile II1-II1 of the Jinping Ⅰ Hydropower Station. The results indicate that the model accurately reflects the factors influencing the hydraulic conductivity. Additionally, the physical significance of the model parameters is evident, providing valuable theoretical insights and practical implications for the design of the water curtain system and the development of seepage analysis.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Analysis and evaluation of hydraulic conductivity using various field data: an example from Southwest China

  • 期刊论文

  • 发表日期：

    2026年7月28日

查看全文
  • Kejing Chen,
  • Qinghui Jiang

摘要

The hydraulic conductivity of rock masses is subject to the influence of multiple factors, including the development of fractures, weathering processes, and unloading conditions. Notably, these characteristics also exhibit a significant correlation with the ground stress state in which the rock mass is situated. However, traditional empirical methods often fail to capture the complex nonlinear relationships among these variables. This study presents a framework to evaluate the hydraulic conductivity distribution based on three key indicators: depth, Rock Quality Designation (RQD), and the Weathering and Unloading Index (WUI). Unlike traditional statistical methods, the proposed model employs the Multivariate Adaptive Regression Splines (MARS) method for assessing hydraulic conductivity distribution. This model is trained on comprehensive packer test data and validated using 34 fractured rock sections from borehole P117 within profile II1-II1 of the Jinping Ⅰ Hydropower Station. The results indicate that the model accurately reflects the factors influencing the hydraulic conductivity. Additionally, the physical significance of the model parameters is evident, providing valuable theoretical insights and practical implications for the design of the water curtain system and the development of seepage analysis.