Enhanced Method for Measuring Joint Roughness Using Sub-Millimeter Resolution 3D Laser Scanning
Bibliographic record
Abstract
ABSTRACT: Accurate estimation of joint roughness is critical for characterizing the mechanical and hydraulic properties of rock joints, thereby contributing to informed assessments of the rock mass classification, shear strength, and permeability estimates. The most prevalent way to quantify joint roughness is the Joint Roughness Coefficient (JRC). However, this conventional approach has shown subjectivity and bias, which can significantly affect engineering design strategies. To accurately assess JRC, this study proposes to use 3D laser scanning technology. In this work, a high-resolution scanner was used in the field for the first time on borehole cores. Scanned 3D roughness surfaces were used to obtain statistical roughness parameters, followed by the determination of JRC using empirical equations. This paper examines the use of a sub-millimeter resolution scanner on the example of joint surfaces of core samples and discusses the importance of scanning resolution and noise reduction in the measurement of roughness of discontinuities. It was found that this approach is also applicable to several-meter-long joints in outcrop formations, provided that a scaling relationship for JRC is applied. The results indicated a similarity in JRC values derived from laser scanning and visual profiling tools. 1. INTRODUCTION Joint roughness is a measure of surface irregularities of a rock fracture and is described by surface topography. This rock property is important to quantify the mechanical and hydraulic properties of rock joints. There is a range of contact and non-contact methods to measure it. Contact techniques include linear profiling and local surface orientation methods (Barton & Choubey, 1977; Fecker & Rengers, 1971). Non-contact tools are triangulation (structured light projection, photogrammetry) and distance measurement methods (laser scanning and laser profilometry) (Fardin, Feng, & Stephansson, 2004; Huang, Oelfke, & Speck, 1992; Jessell et al., 1995; Tatone & Grasselli, 2009). The most common roughness parameter used in geotechnical practice is the Joint Roughness Coefficient (JRC) introduced by Barton and Choubey (1977). JRC is estimated based on a comparison of profiles of a joint surface obtained from a profile comb (Fig.1) to the standardized set of 10 roughness profiles of JRC values ranging from 0 to 20 (Fig.2).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".