3D Laser Scanning Sampling Interval for Rock Joint Roughness
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Solution Overview
Problem
Current methods for evaluating the roughness of rock joints using 3D laser scanning are inaccurate due to the lack of a theoretical method for determining an optimal sampling interval.
Innovation Solution
A method and device for determining a reasonable sampling interval using 3D laser scanning, which involves collecting rock joint samples, acquiring point cloud data, preprocessing the data, conducting mechanical tests, calculating joint roughness coefficients (JRCs) under different intervals, fitting relational curves, and reversing calculating the JRC to determine the sampling interval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a small sampling interval is used for 3D laser scanning of rock joints, then the measurement precision of roughness is improved, but the productivity and data processing workload increase significantly
Solution Approach 1:
The patent applies parameter changes by establishing a functional relationship between sampling interval and joint roughness coefficient (JRC). Through theoretical derivation and experimental verification, the patent determines optimal sampling interval parameters (e.g., 0.5mm, 1.0mm, 2.0mm) that balance measurement precision and processing efficiency. The method transforms the roughness evaluation from a qualitative assessment to a quantitative parameter-based determination, where the sampling interval is selected based on the derived formula and verified through scatter plot analysis and fitting equations.
2Ease of operation
If contact measurement methods are used for rock joint roughness, then the ease of operation is improved, but the measurement precision and safety are worsened for high and steep cliffs
Solution Approach 1:
The patent replaces mechanical contact measurement systems with optical 3D laser scanning systems. Instead of using physical instruments like needle outline rulers that require direct contact with the rock joint surface, the invention employs laser-based non-contact scanning technology to acquire point cloud data. This substitution eliminates safety hazards associated with manual measurement on high and steep cliffs while maintaining high measurement precision through optical field interaction with the rock joint surface.
3Ease of operation
If existing sampling interval selection methods are used, then the ease of operation is improved, but the measurement precision of roughness evaluation deteriorates due to lack of theoretical basis
Solution Approach 1:
The patent applies preliminary action by pre-establishing the theoretical relationship between sampling interval and JRC through derivation and experimental verification before actual roughness evaluation. The optimal sampling interval parameters are determined in advance through scatter plot analysis, fitting equations, and verification experiments. This preliminary work creates a ready-to-use theoretical framework and parameter set that can be directly applied in subsequent measurements, eliminating the need for trial-and-error selection while ensuring measurement precision.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach reduces errors in roughness evaluation of rock joints by determining a reasonable sampling interval, facilitating further applications of rock joint roughness data in engineering practices.
Implementation Method 1
3D laser scanning can quickly acquire point cloud data on a surface of a small-sized rock mass
Data Source
AI summary
A method for determining a reasonable sampling interval with three-dimensional (3D) laser scanning includes: collecting rock joints; acquiring point cloud data of the rock joints with 3D laser scanning; performing preprocessing on acquired point cloud data; conducting indoor mechanical tests to determine rock mechanics parameters; calculating, with preprocessed point cloud data of the rock joints, joint roughness coefficients (JRCs) under different intervals obtained from statistical roughness parameters; fitting different relational curves according to a scatter plot between the different intervals and the JRCs to obtain a fitting equation; reversely calculating a JRC of the rock joint sample with the rock mechanics parameters to obtain a reversely calculated JRC; and substituting, according to a relational equation between the different intervals and the JRCs, the reversely calculated JRC into the fitting equation to determine a reasonable sampling interval for roughness evaluation.


