Scholarlink

Rock fractures exhibit roughness that influences the mechanical and hydraulic properties of the rock mass, which are important to various underground projects. This paper aims to develop a method to estimate the Joint Roughness Coefficient (JRC) of rock fractures using terrestrial laser scanners (TLS) and deep neural network for quick and safe investigation. While TLS offers fast acquisition of 3D point cloud, errors are inevitably contained in the scan data. By analyzing scan data of a granite outcrop, the effect of these errors on JRC measurement is investigated. In addition, a deep neural network is trained to estimate the error-corrected JRC of the scan data. The performance of the developed estimator is evaluated, and its implication is guided as a result.

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 of Errors in Terrestrial Laser Scanner to Estimate Joint Roughness Coefficient of Rock Mass using Deep Learning

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Seung-won Lee,
  • Seokwon Jeon

摘要

Rock fractures exhibit roughness that influences the mechanical and hydraulic properties of the rock mass, which are important to various underground projects. This paper aims to develop a method to estimate the Joint Roughness Coefficient (JRC) of rock fractures using terrestrial laser scanners (TLS) and deep neural network for quick and safe investigation. While TLS offers fast acquisition of 3D point cloud, errors are inevitably contained in the scan data. By analyzing scan data of a granite outcrop, the effect of these errors on JRC measurement is investigated. In addition, a deep neural network is trained to estimate the error-corrected JRC of the scan data. The performance of the developed estimator is evaluated, and its implication is guided as a result.