Uncertainty quantification based on symbolic regression and probabilistic programming and its application
The joint roughness coefficient (JRC) is critical to evaluate the strength and deformation behavior of joint rock mass in rock engineering. Various methods have been developed to estimate JRC value based on the statistical parameter of rock joints. The JRC value is uncertain due to the complex, rand...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
|
| Series: | Machine Learning with Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666827025000155 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850222409808871424 |
|---|---|
| author | Yuyang Zhao Hongbo Zhao |
| author_facet | Yuyang Zhao Hongbo Zhao |
| author_sort | Yuyang Zhao |
| collection | DOAJ |
| description | The joint roughness coefficient (JRC) is critical to evaluate the strength and deformation behavior of joint rock mass in rock engineering. Various methods have been developed to estimate JRC value based on the statistical parameter of rock joints. The JRC value is uncertain due to the complex, random rock joint. Uncertainty is an essential characteristic of rock joints. However, the traditional determinative method cannot deal with uncertainty during the analysis, evaluation, and characterization of the mechanism for the rock joint. This study developed a novel JRC determination framework to estimate the JRC value and evaluate the uncertainty of rock joints based on symbolic regression and probabilistic programming. The symbolic regression was utilized to generate the general empirical equation with the unknown coefficient for the JRC determination of rock joints. The probabilistic programming was used to quantify the uncertainty of the rock joint roughness. The ten standard rock joint profiles illustrated and investigated the developed framework. And then, the developed framework was applied to the collected rock joint profile from the literature. The predicted JRC value was compared with the traditional empirical equations. The results show that the generalization performance of the developed framework is better than the traditional determinative empirical equation. It provides a scientific, reliable, and helpful to estimate the JRC value and characterize the mechanical behavior of joint rock mass. |
| format | Article |
| id | doaj-art-fe614b0930884dcdab3f44b38da81592 |
| institution | OA Journals |
| issn | 2666-8270 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Machine Learning with Applications |
| spelling | doaj-art-fe614b0930884dcdab3f44b38da815922025-08-20T02:06:20ZengElsevierMachine Learning with Applications2666-82702025-06-012010063210.1016/j.mlwa.2025.100632Uncertainty quantification based on symbolic regression and probabilistic programming and its applicationYuyang Zhao0Hongbo Zhao1Prologis Management LLC, Denver 80202, USA; Corresponding author.School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, PR ChinaThe joint roughness coefficient (JRC) is critical to evaluate the strength and deformation behavior of joint rock mass in rock engineering. Various methods have been developed to estimate JRC value based on the statistical parameter of rock joints. The JRC value is uncertain due to the complex, random rock joint. Uncertainty is an essential characteristic of rock joints. However, the traditional determinative method cannot deal with uncertainty during the analysis, evaluation, and characterization of the mechanism for the rock joint. This study developed a novel JRC determination framework to estimate the JRC value and evaluate the uncertainty of rock joints based on symbolic regression and probabilistic programming. The symbolic regression was utilized to generate the general empirical equation with the unknown coefficient for the JRC determination of rock joints. The probabilistic programming was used to quantify the uncertainty of the rock joint roughness. The ten standard rock joint profiles illustrated and investigated the developed framework. And then, the developed framework was applied to the collected rock joint profile from the literature. The predicted JRC value was compared with the traditional empirical equations. The results show that the generalization performance of the developed framework is better than the traditional determinative empirical equation. It provides a scientific, reliable, and helpful to estimate the JRC value and characterize the mechanical behavior of joint rock mass.http://www.sciencedirect.com/science/article/pii/S2666827025000155Rock jointJoint roughness coefficientUncertainty quantificationSymbolic regressionProbabilistic programming |
| spellingShingle | Yuyang Zhao Hongbo Zhao Uncertainty quantification based on symbolic regression and probabilistic programming and its application Machine Learning with Applications Rock joint Joint roughness coefficient Uncertainty quantification Symbolic regression Probabilistic programming |
| title | Uncertainty quantification based on symbolic regression and probabilistic programming and its application |
| title_full | Uncertainty quantification based on symbolic regression and probabilistic programming and its application |
| title_fullStr | Uncertainty quantification based on symbolic regression and probabilistic programming and its application |
| title_full_unstemmed | Uncertainty quantification based on symbolic regression and probabilistic programming and its application |
| title_short | Uncertainty quantification based on symbolic regression and probabilistic programming and its application |
| title_sort | uncertainty quantification based on symbolic regression and probabilistic programming and its application |
| topic | Rock joint Joint roughness coefficient Uncertainty quantification Symbolic regression Probabilistic programming |
| url | http://www.sciencedirect.com/science/article/pii/S2666827025000155 |
| work_keys_str_mv | AT yuyangzhao uncertaintyquantificationbasedonsymbolicregressionandprobabilisticprogramminganditsapplication AT hongbozhao uncertaintyquantificationbasedonsymbolicregressionandprobabilisticprogramminganditsapplication |