Gaussian Process Model of Surrounding Rock Classification Based on Digital Characterization of Rock Mass Structure and Its Application
As the important data basis of surrounding rock classification, rock mass structural information obtained by traditional image processing and feature extraction algorithms could not be quantitatively analyzed because of the uncertainty and geometric randomness of structural planes. In this paper, ba...
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| Veröffentlicht in: | Mathematical problems in engineering 2020, Vol.2020 (2020), p.1-15 |
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| creator | Li, Weiteng Wang, Gang Sun, Shang-qu He, Peng |
| description | As the important data basis of surrounding rock classification, rock mass structural information obtained by traditional image processing and feature extraction algorithms could not be quantitatively analyzed because of the uncertainty and geometric randomness of structural planes. In this paper, based on straight line detection, intelligent scissors, and morphological edge detection algorithms, the multiple interpretation system of rock mass image including linear bunching extraction, magnetic tracking extraction, and multiparameter characterization was researched and developed, and the actual distribution information and the related probability distribution model of structural planes could be obtained directly. On the basis of this, plenty of corresponding random rating-values meeting the probability distribution models of these evaluation indices were gained by Monte Carlo Simulation. The distribution probability affiliated with different rock mass grade was attained by inductive statistics, and the robust evaluation of surrounding rock classification could be carried out. Taking the robust results as learning samples, the response model of surrounding rock grade based on Gaussian process classification was established, making the evaluation of surrounding rock subclassification more rapid and robust. |
| doi_str_mv | 10.1155/2020/5264072 |
| format | Article |
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In this paper, based on straight line detection, intelligent scissors, and morphological edge detection algorithms, the multiple interpretation system of rock mass image including linear bunching extraction, magnetic tracking extraction, and multiparameter characterization was researched and developed, and the actual distribution information and the related probability distribution model of structural planes could be obtained directly. On the basis of this, plenty of corresponding random rating-values meeting the probability distribution models of these evaluation indices were gained by Monte Carlo Simulation. The distribution probability affiliated with different rock mass grade was attained by inductive statistics, and the robust evaluation of surrounding rock classification could be carried out. Taking the robust results as learning samples, the response model of surrounding rock grade based on Gaussian process classification was established, making the evaluation of surrounding rock subclassification more rapid and robust.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2020/5264072</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Algorithms ; Classification ; Cracks ; Cutting tools ; Edge detection ; Evaluation ; Feature extraction ; Gaussian process ; Geology ; Image classification ; Image processing ; Methods ; Monte Carlo simulation ; Morphology ; Planes ; Probability distribution ; Robustness ; Rock masses ; Statistical methods ; Structural analysis</subject><ispartof>Mathematical problems in engineering, 2020, Vol.2020 (2020), p.1-15</ispartof><rights>Copyright © 2020 Peng He et al.</rights><rights>Copyright © 2020 Peng He et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c360t-ddf5339d5ebcd3356a32e46cd9c0fda1c80645b75fc2a1faec10fbd8d9a4653e3</citedby><cites>FETCH-LOGICAL-c360t-ddf5339d5ebcd3356a32e46cd9c0fda1c80645b75fc2a1faec10fbd8d9a4653e3</cites><orcidid>0000-0002-0476-6113 ; 0000-0001-7187-0618 ; 0000-0002-3099-5913 ; 0000-0002-3833-8846</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,4010,27900,27901,27902</link.rule.ids></links><search><contributor>Constantoudis, Vassilios</contributor><creatorcontrib>Li, Weiteng</creatorcontrib><creatorcontrib>Wang, Gang</creatorcontrib><creatorcontrib>Sun, Shang-qu</creatorcontrib><creatorcontrib>He, Peng</creatorcontrib><title>Gaussian Process Model of Surrounding Rock Classification Based on Digital Characterization of Rock Mass Structure and Its Application</title><title>Mathematical problems in engineering</title><description>As the important data basis of surrounding rock classification, rock mass structural information obtained by traditional image processing and feature extraction algorithms could not be quantitatively analyzed because of the uncertainty and geometric randomness of structural planes. In this paper, based on straight line detection, intelligent scissors, and morphological edge detection algorithms, the multiple interpretation system of rock mass image including linear bunching extraction, magnetic tracking extraction, and multiparameter characterization was researched and developed, and the actual distribution information and the related probability distribution model of structural planes could be obtained directly. On the basis of this, plenty of corresponding random rating-values meeting the probability distribution models of these evaluation indices were gained by Monte Carlo Simulation. The distribution probability affiliated with different rock mass grade was attained by inductive statistics, and the robust evaluation of surrounding rock classification could be carried out. Taking the robust results as learning samples, the response model of surrounding rock grade based on Gaussian process classification was established, making the evaluation of surrounding rock subclassification more rapid and robust.</description><subject>Algorithms</subject><subject>Classification</subject><subject>Cracks</subject><subject>Cutting tools</subject><subject>Edge detection</subject><subject>Evaluation</subject><subject>Feature extraction</subject><subject>Gaussian process</subject><subject>Geology</subject><subject>Image classification</subject><subject>Image processing</subject><subject>Methods</subject><subject>Monte Carlo simulation</subject><subject>Morphology</subject><subject>Planes</subject><subject>Probability distribution</subject><subject>Robustness</subject><subject>Rock masses</subject><subject>Statistical methods</subject><subject>Structural analysis</subject><issn>1024-123X</issn><issn>1563-5147</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>BENPR</sourceid><recordid>eNqF0E1Lw0AQBuAgCtbqzbMseNTY_cgm6bFGrYWKYhW8hel-tFtjtu4miP4Af7dbU_DoaebwzDvwRtExwReEcD6gmOIBp2mCM7oT9QhPWcxJku2GHdMkJpS97EcH3q8wpoSTvBd9j6H13kCNHpwVynt0Z6WqkNVo1jpn21qaeoEerXhFRQWBaiOgMbZGl-CVRGG5MgvTQIWKJTgQjXLmqxMh5PfwLtyhWeNa0bROIaglmjQejdbraht2GO1pqLw62s5-9Hxz_VTcxtP78aQYTWPBUtzEUmrO2FByNReSMZ4CoypJhRwKrCUQkeM04fOMa0GBaFCCYD2XuRxCknKmWD867XLXzr63yjflyrauDi9LmvBQXJLnWVDnnRLOeu-ULtfOvIH7LAkuN02Xm6bLbdOBn3V8aWoJH-Y_fdJpFYzS8KfJkGdZxn4Ar16J-w</recordid><startdate>2020</startdate><enddate>2020</enddate><creator>Li, Weiteng</creator><creator>Wang, Gang</creator><creator>Sun, Shang-qu</creator><creator>He, Peng</creator><general>Hindawi Publishing Corporation</general><general>Hindawi</general><general>Hindawi Limited</general><scope>ADJCN</scope><scope>AHFXO</scope><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KR7</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><orcidid>https://orcid.org/0000-0002-0476-6113</orcidid><orcidid>https://orcid.org/0000-0001-7187-0618</orcidid><orcidid>https://orcid.org/0000-0002-3099-5913</orcidid><orcidid>https://orcid.org/0000-0002-3833-8846</orcidid></search><sort><creationdate>2020</creationdate><title>Gaussian Process Model of Surrounding Rock Classification Based on Digital Characterization of Rock Mass Structure and Its Application</title><author>Li, Weiteng ; 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In this paper, based on straight line detection, intelligent scissors, and morphological edge detection algorithms, the multiple interpretation system of rock mass image including linear bunching extraction, magnetic tracking extraction, and multiparameter characterization was researched and developed, and the actual distribution information and the related probability distribution model of structural planes could be obtained directly. On the basis of this, plenty of corresponding random rating-values meeting the probability distribution models of these evaluation indices were gained by Monte Carlo Simulation. The distribution probability affiliated with different rock mass grade was attained by inductive statistics, and the robust evaluation of surrounding rock classification could be carried out. 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| subjects | Algorithms Classification Cracks Cutting tools Edge detection Evaluation Feature extraction Gaussian process Geology Image classification Image processing Methods Monte Carlo simulation Morphology Planes Probability distribution Robustness Rock masses Statistical methods Structural analysis |
| title | Gaussian Process Model of Surrounding Rock Classification Based on Digital Characterization of Rock Mass Structure and Its Application |
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