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Machine learning-based optimization of photogrammetric JRC accuracy.

Qinzheng Yang1, Ang Li2, Yipeng Liu3

  • 1School of Highway, Chang'an University, Xi'an, 710064, People's Republic of China.

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Summary
This summary is machine-generated.

This study enhances joint roughness coefficient (JRC) estimation using photogrammetry by developing optimization models. These models leverage ground sample distance (GSD) and point density to significantly improve accuracy.

Keywords:
3D reconstructionJRC optimizationJoint roughness coefficientPhotogrammetry

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Area of Science:

  • Geotechnical Engineering
  • Photogrammetry
  • Rock Mechanics

Background:

  • Accurate estimation of the joint roughness coefficient (JRC) is crucial for rock mechanics and geotechnical engineering.
  • Traditional JRC estimation methods can be subjective and time-consuming.
  • Photogrammetry offers a potential for objective and efficient JRC assessment.

Purpose of the Study:

  • To develop and validate optimization models for improving the accuracy of photogrammetric joint roughness coefficient (JRC) estimation.
  • To investigate the influence of ground sample distance (GSD) and point density on JRC estimation accuracy.
  • To establish a robust photogrammetric methodology for JRC assessment.

Main Methods:

  • Developed an algorithm for automatic spatial equipment positioning using Structure from Motion and Multi-View Stereo (SfM-MVS) principles and the shooting parameter selection algorithm (SPSA).
  • Designed a portable positioning plate with ground control points and checkpoints, coupled with an SPSA-guided moving camera capture strategy.
  • Conducted photogrammetric experiments on rock samples, collecting 48 datasets, and analyzed correlations between GSD, point density, RMSE, and JRC metrics.

Main Results:

  • Established a dataset linking GSD, point density, RMSE, and JRC estimation metrics, revealing key correlations and sensitivities.
  • Utilized seven machine learning algorithms to develop photogrammetric JRC accuracy optimization models.
  • Achieved an average improvement of 85.73% in JRC accuracy using Linear Multidimensional Regression and Gaussian Process Regression models.

Conclusions:

  • The proposed optimization models significantly enhance photogrammetric JRC estimation accuracy.
  • The study demonstrates the effectiveness of integrating SPSA, portable positioning plates, and advanced machine learning for JRC assessment.
  • The developed method provides a more objective and accurate approach for evaluating rock joint roughness in the field.