Developing rock mass classification method using precise description of joints

Abstract Rock mass classification is essential for assessing the quality of macroscopic rock mass and is the basis for rock mass stability analysis and geotechnical engineering design. The joint observation technology limits traditional rock mass classification methods in that they only collect join...
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Autor*in:	Jin, Changyu [verfasserIn] Liang, Junyu Cui, Jianxin Wang, Qiang

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Rock mass classification Jointed rock mass Borehole imaging Joint scanning Volumetric joint number

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Environmental earth sciences - Berlin : Springer, 2009, 82(2023), 21 vom: 26. Sept.
Übergeordnetes Werk:	volume:82 ; year:2023 ; number:21 ; day:26 ; month:09

Links:	Volltext

DOI / URN:	10.1007/s12665-023-11184-8

Katalog-ID:	SPR053209974

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520			\|a Abstract Rock mass classification is essential for assessing the quality of macroscopic rock mass and is the basis for rock mass stability analysis and geotechnical engineering design. The joint observation technology limits traditional rock mass classification methods in that they only collect joint information from one-dimensional or two-dimensional space and cannot comprehensively obtain the joint occurrence in three-dimensional space. Consequently, empirical formulas are frequently used in studies on joint distribution laws, resulting in less accurate calculations of joint parameters. This study develops a method for classifying rock masses using a precise description of the joints. Initially, it utilizes the borehole camera and the Sirovision joint scanning system to acquire accurate three-dimensional joint occurrence data. The subjective and the objective weights of each evaluation index are derived from the analytic hierarchy process (AHP) and the CRITIC technique according to the cloud model theory. The game theory is then employed to determine the combined weight and evaluate the quality of a rock mass method with the cloud model (GA-CM). The proposed classification method is applied to the slope of an open-pit mine. The results indicate that compared to the traditional methods, the proposed method is objective, accurate, and field-applicable and also reduces the influence of subjective factors on rock mass quality evaluation and enhances the classification reliability.
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allfields	10.1007/s12665-023-11184-8 doi (DE-627)SPR053209974 (SPR)s12665-023-11184-8-e DE-627 ger DE-627 rakwb eng Jin, Changyu verfasserin aut Developing rock mass classification method using precise description of joints 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Rock mass classification is essential for assessing the quality of macroscopic rock mass and is the basis for rock mass stability analysis and geotechnical engineering design. The joint observation technology limits traditional rock mass classification methods in that they only collect joint information from one-dimensional or two-dimensional space and cannot comprehensively obtain the joint occurrence in three-dimensional space. Consequently, empirical formulas are frequently used in studies on joint distribution laws, resulting in less accurate calculations of joint parameters. This study develops a method for classifying rock masses using a precise description of the joints. Initially, it utilizes the borehole camera and the Sirovision joint scanning system to acquire accurate three-dimensional joint occurrence data. The subjective and the objective weights of each evaluation index are derived from the analytic hierarchy process (AHP) and the CRITIC technique according to the cloud model theory. The game theory is then employed to determine the combined weight and evaluate the quality of a rock mass method with the cloud model (GA-CM). The proposed classification method is applied to the slope of an open-pit mine. The results indicate that compared to the traditional methods, the proposed method is objective, accurate, and field-applicable and also reduces the influence of subjective factors on rock mass quality evaluation and enhances the classification reliability. Rock mass classification (dpeaa)DE-He213 Jointed rock mass (dpeaa)DE-He213 Borehole imaging (dpeaa)DE-He213 Joint scanning (dpeaa)DE-He213 Volumetric joint number (dpeaa)DE-He213 Liang, Junyu aut Cui, Jianxin aut Wang, Qiang aut Enthalten in Environmental earth sciences Berlin : Springer, 2009 82(2023), 21 vom: 26. Sept. (DE-627)599673451 (DE-600)2493699-6 1866-6299 nnns volume:82 year:2023 number:21 day:26 month:09 https://dx.doi.org/10.1007/s12665-023-11184-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 82 2023 21 26 09
spelling	10.1007/s12665-023-11184-8 doi (DE-627)SPR053209974 (SPR)s12665-023-11184-8-e DE-627 ger DE-627 rakwb eng Jin, Changyu verfasserin aut Developing rock mass classification method using precise description of joints 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Rock mass classification is essential for assessing the quality of macroscopic rock mass and is the basis for rock mass stability analysis and geotechnical engineering design. The joint observation technology limits traditional rock mass classification methods in that they only collect joint information from one-dimensional or two-dimensional space and cannot comprehensively obtain the joint occurrence in three-dimensional space. Consequently, empirical formulas are frequently used in studies on joint distribution laws, resulting in less accurate calculations of joint parameters. This study develops a method for classifying rock masses using a precise description of the joints. Initially, it utilizes the borehole camera and the Sirovision joint scanning system to acquire accurate three-dimensional joint occurrence data. The subjective and the objective weights of each evaluation index are derived from the analytic hierarchy process (AHP) and the CRITIC technique according to the cloud model theory. The game theory is then employed to determine the combined weight and evaluate the quality of a rock mass method with the cloud model (GA-CM). The proposed classification method is applied to the slope of an open-pit mine. The results indicate that compared to the traditional methods, the proposed method is objective, accurate, and field-applicable and also reduces the influence of subjective factors on rock mass quality evaluation and enhances the classification reliability. Rock mass classification (dpeaa)DE-He213 Jointed rock mass (dpeaa)DE-He213 Borehole imaging (dpeaa)DE-He213 Joint scanning (dpeaa)DE-He213 Volumetric joint number (dpeaa)DE-He213 Liang, Junyu aut Cui, Jianxin aut Wang, Qiang aut Enthalten in Environmental earth sciences Berlin : Springer, 2009 82(2023), 21 vom: 26. Sept. (DE-627)599673451 (DE-600)2493699-6 1866-6299 nnns volume:82 year:2023 number:21 day:26 month:09 https://dx.doi.org/10.1007/s12665-023-11184-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 82 2023 21 26 09
allfields_unstemmed	10.1007/s12665-023-11184-8 doi (DE-627)SPR053209974 (SPR)s12665-023-11184-8-e DE-627 ger DE-627 rakwb eng Jin, Changyu verfasserin aut Developing rock mass classification method using precise description of joints 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Rock mass classification is essential for assessing the quality of macroscopic rock mass and is the basis for rock mass stability analysis and geotechnical engineering design. The joint observation technology limits traditional rock mass classification methods in that they only collect joint information from one-dimensional or two-dimensional space and cannot comprehensively obtain the joint occurrence in three-dimensional space. Consequently, empirical formulas are frequently used in studies on joint distribution laws, resulting in less accurate calculations of joint parameters. This study develops a method for classifying rock masses using a precise description of the joints. Initially, it utilizes the borehole camera and the Sirovision joint scanning system to acquire accurate three-dimensional joint occurrence data. The subjective and the objective weights of each evaluation index are derived from the analytic hierarchy process (AHP) and the CRITIC technique according to the cloud model theory. The game theory is then employed to determine the combined weight and evaluate the quality of a rock mass method with the cloud model (GA-CM). The proposed classification method is applied to the slope of an open-pit mine. The results indicate that compared to the traditional methods, the proposed method is objective, accurate, and field-applicable and also reduces the influence of subjective factors on rock mass quality evaluation and enhances the classification reliability. Rock mass classification (dpeaa)DE-He213 Jointed rock mass (dpeaa)DE-He213 Borehole imaging (dpeaa)DE-He213 Joint scanning (dpeaa)DE-He213 Volumetric joint number (dpeaa)DE-He213 Liang, Junyu aut Cui, Jianxin aut Wang, Qiang aut Enthalten in Environmental earth sciences Berlin : Springer, 2009 82(2023), 21 vom: 26. Sept. (DE-627)599673451 (DE-600)2493699-6 1866-6299 nnns volume:82 year:2023 number:21 day:26 month:09 https://dx.doi.org/10.1007/s12665-023-11184-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 82 2023 21 26 09
allfieldsGer	10.1007/s12665-023-11184-8 doi (DE-627)SPR053209974 (SPR)s12665-023-11184-8-e DE-627 ger DE-627 rakwb eng Jin, Changyu verfasserin aut Developing rock mass classification method using precise description of joints 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Rock mass classification is essential for assessing the quality of macroscopic rock mass and is the basis for rock mass stability analysis and geotechnical engineering design. The joint observation technology limits traditional rock mass classification methods in that they only collect joint information from one-dimensional or two-dimensional space and cannot comprehensively obtain the joint occurrence in three-dimensional space. Consequently, empirical formulas are frequently used in studies on joint distribution laws, resulting in less accurate calculations of joint parameters. This study develops a method for classifying rock masses using a precise description of the joints. Initially, it utilizes the borehole camera and the Sirovision joint scanning system to acquire accurate three-dimensional joint occurrence data. The subjective and the objective weights of each evaluation index are derived from the analytic hierarchy process (AHP) and the CRITIC technique according to the cloud model theory. The game theory is then employed to determine the combined weight and evaluate the quality of a rock mass method with the cloud model (GA-CM). The proposed classification method is applied to the slope of an open-pit mine. The results indicate that compared to the traditional methods, the proposed method is objective, accurate, and field-applicable and also reduces the influence of subjective factors on rock mass quality evaluation and enhances the classification reliability. Rock mass classification (dpeaa)DE-He213 Jointed rock mass (dpeaa)DE-He213 Borehole imaging (dpeaa)DE-He213 Joint scanning (dpeaa)DE-He213 Volumetric joint number (dpeaa)DE-He213 Liang, Junyu aut Cui, Jianxin aut Wang, Qiang aut Enthalten in Environmental earth sciences Berlin : Springer, 2009 82(2023), 21 vom: 26. Sept. (DE-627)599673451 (DE-600)2493699-6 1866-6299 nnns volume:82 year:2023 number:21 day:26 month:09 https://dx.doi.org/10.1007/s12665-023-11184-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 82 2023 21 26 09
allfieldsSound	10.1007/s12665-023-11184-8 doi (DE-627)SPR053209974 (SPR)s12665-023-11184-8-e DE-627 ger DE-627 rakwb eng Jin, Changyu verfasserin aut Developing rock mass classification method using precise description of joints 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Rock mass classification is essential for assessing the quality of macroscopic rock mass and is the basis for rock mass stability analysis and geotechnical engineering design. The joint observation technology limits traditional rock mass classification methods in that they only collect joint information from one-dimensional or two-dimensional space and cannot comprehensively obtain the joint occurrence in three-dimensional space. Consequently, empirical formulas are frequently used in studies on joint distribution laws, resulting in less accurate calculations of joint parameters. This study develops a method for classifying rock masses using a precise description of the joints. Initially, it utilizes the borehole camera and the Sirovision joint scanning system to acquire accurate three-dimensional joint occurrence data. The subjective and the objective weights of each evaluation index are derived from the analytic hierarchy process (AHP) and the CRITIC technique according to the cloud model theory. The game theory is then employed to determine the combined weight and evaluate the quality of a rock mass method with the cloud model (GA-CM). The proposed classification method is applied to the slope of an open-pit mine. The results indicate that compared to the traditional methods, the proposed method is objective, accurate, and field-applicable and also reduces the influence of subjective factors on rock mass quality evaluation and enhances the classification reliability. Rock mass classification (dpeaa)DE-He213 Jointed rock mass (dpeaa)DE-He213 Borehole imaging (dpeaa)DE-He213 Joint scanning (dpeaa)DE-He213 Volumetric joint number (dpeaa)DE-He213 Liang, Junyu aut Cui, Jianxin aut Wang, Qiang aut Enthalten in Environmental earth sciences Berlin : Springer, 2009 82(2023), 21 vom: 26. Sept. (DE-627)599673451 (DE-600)2493699-6 1866-6299 nnns volume:82 year:2023 number:21 day:26 month:09 https://dx.doi.org/10.1007/s12665-023-11184-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 82 2023 21 26 09
language	English
source	Enthalten in Environmental earth sciences 82(2023), 21 vom: 26. Sept. volume:82 year:2023 number:21 day:26 month:09
sourceStr	Enthalten in Environmental earth sciences 82(2023), 21 vom: 26. Sept. volume:82 year:2023 number:21 day:26 month:09
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Rock mass classification Jointed rock mass Borehole imaging Joint scanning Volumetric joint number
isfreeaccess_bool	false
container_title	Environmental earth sciences
authorswithroles_txt_mv	Jin, Changyu @@aut@@ Liang, Junyu @@aut@@ Cui, Jianxin @@aut@@ Wang, Qiang @@aut@@
publishDateDaySort_date	2023-09-26T00:00:00Z
hierarchy_top_id	599673451
id	SPR053209974
language_de	englisch
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author	Jin, Changyu
spellingShingle	Jin, Changyu misc Rock mass classification misc Jointed rock mass misc Borehole imaging misc Joint scanning misc Volumetric joint number Developing rock mass classification method using precise description of joints
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topic_title	Developing rock mass classification method using precise description of joints Rock mass classification (dpeaa)DE-He213 Jointed rock mass (dpeaa)DE-He213 Borehole imaging (dpeaa)DE-He213 Joint scanning (dpeaa)DE-He213 Volumetric joint number (dpeaa)DE-He213
topic	misc Rock mass classification misc Jointed rock mass misc Borehole imaging misc Joint scanning misc Volumetric joint number
topic_unstemmed	misc Rock mass classification misc Jointed rock mass misc Borehole imaging misc Joint scanning misc Volumetric joint number
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title	Developing rock mass classification method using precise description of joints
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title_full	Developing rock mass classification method using precise description of joints
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author_browse	Jin, Changyu Liang, Junyu Cui, Jianxin Wang, Qiang
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doi_str_mv	10.1007/s12665-023-11184-8
title_sort	developing rock mass classification method using precise description of joints
title_auth	Developing rock mass classification method using precise description of joints
abstract	Abstract Rock mass classification is essential for assessing the quality of macroscopic rock mass and is the basis for rock mass stability analysis and geotechnical engineering design. The joint observation technology limits traditional rock mass classification methods in that they only collect joint information from one-dimensional or two-dimensional space and cannot comprehensively obtain the joint occurrence in three-dimensional space. Consequently, empirical formulas are frequently used in studies on joint distribution laws, resulting in less accurate calculations of joint parameters. This study develops a method for classifying rock masses using a precise description of the joints. Initially, it utilizes the borehole camera and the Sirovision joint scanning system to acquire accurate three-dimensional joint occurrence data. The subjective and the objective weights of each evaluation index are derived from the analytic hierarchy process (AHP) and the CRITIC technique according to the cloud model theory. The game theory is then employed to determine the combined weight and evaluate the quality of a rock mass method with the cloud model (GA-CM). The proposed classification method is applied to the slope of an open-pit mine. The results indicate that compared to the traditional methods, the proposed method is objective, accurate, and field-applicable and also reduces the influence of subjective factors on rock mass quality evaluation and enhances the classification reliability. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract Rock mass classification is essential for assessing the quality of macroscopic rock mass and is the basis for rock mass stability analysis and geotechnical engineering design. The joint observation technology limits traditional rock mass classification methods in that they only collect joint information from one-dimensional or two-dimensional space and cannot comprehensively obtain the joint occurrence in three-dimensional space. Consequently, empirical formulas are frequently used in studies on joint distribution laws, resulting in less accurate calculations of joint parameters. This study develops a method for classifying rock masses using a precise description of the joints. Initially, it utilizes the borehole camera and the Sirovision joint scanning system to acquire accurate three-dimensional joint occurrence data. The subjective and the objective weights of each evaluation index are derived from the analytic hierarchy process (AHP) and the CRITIC technique according to the cloud model theory. The game theory is then employed to determine the combined weight and evaluate the quality of a rock mass method with the cloud model (GA-CM). The proposed classification method is applied to the slope of an open-pit mine. The results indicate that compared to the traditional methods, the proposed method is objective, accurate, and field-applicable and also reduces the influence of subjective factors on rock mass quality evaluation and enhances the classification reliability. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract Rock mass classification is essential for assessing the quality of macroscopic rock mass and is the basis for rock mass stability analysis and geotechnical engineering design. The joint observation technology limits traditional rock mass classification methods in that they only collect joint information from one-dimensional or two-dimensional space and cannot comprehensively obtain the joint occurrence in three-dimensional space. Consequently, empirical formulas are frequently used in studies on joint distribution laws, resulting in less accurate calculations of joint parameters. This study develops a method for classifying rock masses using a precise description of the joints. Initially, it utilizes the borehole camera and the Sirovision joint scanning system to acquire accurate three-dimensional joint occurrence data. The subjective and the objective weights of each evaluation index are derived from the analytic hierarchy process (AHP) and the CRITIC technique according to the cloud model theory. The game theory is then employed to determine the combined weight and evaluate the quality of a rock mass method with the cloud model (GA-CM). The proposed classification method is applied to the slope of an open-pit mine. The results indicate that compared to the traditional methods, the proposed method is objective, accurate, and field-applicable and also reduces the influence of subjective factors on rock mass quality evaluation and enhances the classification reliability. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	Developing rock mass classification method using precise description of joints
url	https://dx.doi.org/10.1007/s12665-023-11184-8
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author2	Liang, Junyu Cui, Jianxin Wang, Qiang
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up_date	2024-07-03T17:51:56.443Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR053209974</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231020064714.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">231002s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s12665-023-11184-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR053209974</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12665-023-11184-8-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Jin, Changyu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Developing rock mass classification method using precise description of joints</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Rock mass classification is essential for assessing the quality of macroscopic rock mass and is the basis for rock mass stability analysis and geotechnical engineering design. The joint observation technology limits traditional rock mass classification methods in that they only collect joint information from one-dimensional or two-dimensional space and cannot comprehensively obtain the joint occurrence in three-dimensional space. Consequently, empirical formulas are frequently used in studies on joint distribution laws, resulting in less accurate calculations of joint parameters. This study develops a method for classifying rock masses using a precise description of the joints. Initially, it utilizes the borehole camera and the Sirovision joint scanning system to acquire accurate three-dimensional joint occurrence data. The subjective and the objective weights of each evaluation index are derived from the analytic hierarchy process (AHP) and the CRITIC technique according to the cloud model theory. The game theory is then employed to determine the combined weight and evaluate the quality of a rock mass method with the cloud model (GA-CM). The proposed classification method is applied to the slope of an open-pit mine. The results indicate that compared to the traditional methods, the proposed method is objective, accurate, and field-applicable and also reduces the influence of subjective factors on rock mass quality evaluation and enhances the classification reliability.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Rock mass classification</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Jointed rock mass</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Borehole imaging</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Joint scanning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Volumetric joint number</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liang, Junyu</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cui, Jianxin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Qiang</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Environmental earth sciences</subfield><subfield code="d">Berlin : Springer, 2009</subfield><subfield code="g">82(2023), 21 vom: 26. 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