Morphological classification method and data-driven estimation of the joint roughness coefficient by consideration of two-order asperity
The roughness of the joint surface plays a significant role in evaluating the shear strength of rock. The waviness (first-order) and unevenness (second-order) of natural joints have different effects on the characterization of joint surface roughness. To accurately quantify the influence of the two-...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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De Gruyter
2023-07-01
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| Series: | Reviews on Advanced Materials Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/rams-2022-0336 |
| _version_ | 1827899351970611200 |
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| author | Hu Yunpeng Feng Wenkai Li Wenbin Yi Xiaoyu Liu Kan Ye Longzhen Zhao Jiachen Lu Xianjing Zhang Ruichao |
| author_facet | Hu Yunpeng Feng Wenkai Li Wenbin Yi Xiaoyu Liu Kan Ye Longzhen Zhao Jiachen Lu Xianjing Zhang Ruichao |
| author_sort | Hu Yunpeng |
| collection | DOAJ |
| description | The roughness of the joint surface plays a significant role in evaluating the shear strength of rock. The waviness (first-order) and unevenness (second-order) of natural joints have different effects on the characterization of joint surface roughness. To accurately quantify the influence of the two-order asperity on the joint roughness coefficient (JRC) prediction of joint surface profile curve, the optimal sampling interval of the asperity was determined through the change of the Rp{R}_{{\rm{p}}} value of the joint surface profile curve. The separation of the two-order asperity of 48 joint surface profile curves was completed at the optimal sampling interval, and morphological parameters of the asperity such as iave{i}_{{\rm{ave}}}, Rmax{R}_{{\rm{\max }}}, and Rp{R}_{{\rm{p}}} were counted from three aspects: asperity angle of the profile curve, asperity degree, and the trace length. Based on the statistical results of the morphological parameters considering the two-order asperity, the new nonlinear prediction models were proposed. The results showed that the curve slope mutation point SI = 2 mm is the optimal separation distance of the two-order asperity of the joint surface profile curve. The refined separation method that considers the waviness and unevenness of morphological parameters can characterize the detailed morphological features of the joint surface in more dimensions. The support vector regression (SVR) and random forest (RF) models that take into account a two-order asperity separated results have higher accuracy than traditional models. The prediction accuracy has improved by 7–8% in SVR model compared with SVR(SO) and RF(SO). The SVR nonlinear model that considering separation of two-orders of joint surface roughness is more suitable for the prediction of JRC. |
| first_indexed | 2024-03-12T23:16:52Z |
| format | Article |
| id | doaj.art-d6ba63e1490d4b6f9a0383f2e53f19b2 |
| institution | Directory Open Access Journal |
| issn | 1605-8127 |
| language | English |
| last_indexed | 2024-03-12T23:16:52Z |
| publishDate | 2023-07-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Reviews on Advanced Materials Science |
| spelling | doaj.art-d6ba63e1490d4b6f9a0383f2e53f19b22023-07-17T05:25:58ZengDe GruyterReviews on Advanced Materials Science1605-81272023-07-01621pp. 24325110.1515/rams-2022-0336Morphological classification method and data-driven estimation of the joint roughness coefficient by consideration of two-order asperityHu Yunpeng0Feng Wenkai1Li Wenbin2Yi Xiaoyu3Liu Kan4Ye Longzhen5Zhao Jiachen6Lu Xianjing7Zhang Ruichao8College of Environment and Civil Engineering, State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu610059, ChinaCollege of Environment and Civil Engineering, State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu610059, ChinaState Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu610059, ChinaState Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu610059, ChinaKey Laboratory of Geohazard Prevention of Hilly Mountains, Ministry of Natural Resources, Fuzhou350002, ChinaKey Laboratory of Geohazard Prevention of Hilly Mountains, Ministry of Natural Resources, Fuzhou350002, ChinaState Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu610059, ChinaHenan Xinhua Wuyue Pumped Storage Power Generation Co., Ltd., Xinyang465450, ChinaCentral China branch of China Power Construction New Energy Group Co., Ltd., Changsha410019, ChinaThe roughness of the joint surface plays a significant role in evaluating the shear strength of rock. The waviness (first-order) and unevenness (second-order) of natural joints have different effects on the characterization of joint surface roughness. To accurately quantify the influence of the two-order asperity on the joint roughness coefficient (JRC) prediction of joint surface profile curve, the optimal sampling interval of the asperity was determined through the change of the Rp{R}_{{\rm{p}}} value of the joint surface profile curve. The separation of the two-order asperity of 48 joint surface profile curves was completed at the optimal sampling interval, and morphological parameters of the asperity such as iave{i}_{{\rm{ave}}}, Rmax{R}_{{\rm{\max }}}, and Rp{R}_{{\rm{p}}} were counted from three aspects: asperity angle of the profile curve, asperity degree, and the trace length. Based on the statistical results of the morphological parameters considering the two-order asperity, the new nonlinear prediction models were proposed. The results showed that the curve slope mutation point SI = 2 mm is the optimal separation distance of the two-order asperity of the joint surface profile curve. The refined separation method that considers the waviness and unevenness of morphological parameters can characterize the detailed morphological features of the joint surface in more dimensions. The support vector regression (SVR) and random forest (RF) models that take into account a two-order asperity separated results have higher accuracy than traditional models. The prediction accuracy has improved by 7–8% in SVR model compared with SVR(SO) and RF(SO). The SVR nonlinear model that considering separation of two-orders of joint surface roughness is more suitable for the prediction of JRC.https://doi.org/10.1515/rams-2022-0336two-order asperity of joint surfacestatistical parameters of morphologydata-driven modelprediction of jrc |
| spellingShingle | Hu Yunpeng Feng Wenkai Li Wenbin Yi Xiaoyu Liu Kan Ye Longzhen Zhao Jiachen Lu Xianjing Zhang Ruichao Morphological classification method and data-driven estimation of the joint roughness coefficient by consideration of two-order asperity Reviews on Advanced Materials Science two-order asperity of joint surface statistical parameters of morphology data-driven model prediction of jrc |
| title | Morphological classification method and data-driven estimation of the joint roughness coefficient by consideration of two-order asperity |
| title_full | Morphological classification method and data-driven estimation of the joint roughness coefficient by consideration of two-order asperity |
| title_fullStr | Morphological classification method and data-driven estimation of the joint roughness coefficient by consideration of two-order asperity |
| title_full_unstemmed | Morphological classification method and data-driven estimation of the joint roughness coefficient by consideration of two-order asperity |
| title_short | Morphological classification method and data-driven estimation of the joint roughness coefficient by consideration of two-order asperity |
| title_sort | morphological classification method and data driven estimation of the joint roughness coefficient by consideration of two order asperity |
| topic | two-order asperity of joint surface statistical parameters of morphology data-driven model prediction of jrc |
| url | https://doi.org/10.1515/rams-2022-0336 |
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