Determination of Joint Roughness Coefficients Using Roughness Parameters
This study used precisely digitized standard roughness profiles to determine roughness parameters such as statistical and 2D discontinuity roughness, and fractal dimensions. Our methods were based on the relationship between the joint roughness coefficient (JRC) values and roughness parameters calcu...
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Veröffentlicht in: | Rock mechanics and rock engineering 2014-11, Vol.47 (6), p.2061-2073 |
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creator | Jang, Hyun-Sic Kang, Seong-Seung Jang, Bo-An |
description | This study used precisely digitized standard roughness profiles to determine roughness parameters such as statistical and 2D discontinuity roughness, and fractal dimensions. Our methods were based on the relationship between the joint roughness coefficient (JRC) values and roughness parameters calculated using power law equations. Statistical and 2D roughness parameters, and fractal dimensions correlated well with JRC values, and had correlation coefficients of over 0.96. However, all of these relationships have a 4th profile (JRC 6–8) that deviates by more than ±5 % from the JRC values given in the standard roughness profiles. This indicates that this profile is statistically different than the others. We suggest that fractal dimensions should be measured within the entire range of the divider, instead of merely measuring values within a suitable range. Normalized intercept values also correlated with the JRC values, similarly to the fractal dimension values discussed above. The root mean square first derivative values, roughness profile indexes, 2D roughness parameter, and fractal dimension values decreased as the sampling interval increased. However, the structure function values increased very rapidly with increasing sampling intervals. This indicates that the roughness parameters are not independent of the sampling interval, and that the different relationships between the JRC values and these roughness parameters are dependent on the sampling interval. |
doi_str_mv | 10.1007/s00603-013-0535-z |
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Our methods were based on the relationship between the joint roughness coefficient (JRC) values and roughness parameters calculated using power law equations. Statistical and 2D roughness parameters, and fractal dimensions correlated well with JRC values, and had correlation coefficients of over 0.96. However, all of these relationships have a 4th profile (JRC 6–8) that deviates by more than ±5 % from the JRC values given in the standard roughness profiles. This indicates that this profile is statistically different than the others. We suggest that fractal dimensions should be measured within the entire range of the divider, instead of merely measuring values within a suitable range. Normalized intercept values also correlated with the JRC values, similarly to the fractal dimension values discussed above. The root mean square first derivative values, roughness profile indexes, 2D roughness parameter, and fractal dimension values decreased as the sampling interval increased. However, the structure function values increased very rapidly with increasing sampling intervals. This indicates that the roughness parameters are not independent of the sampling interval, and that the different relationships between the JRC values and these roughness parameters are dependent on the sampling interval.</description><identifier>ISSN: 0723-2632</identifier><identifier>EISSN: 1434-453X</identifier><identifier>DOI: 10.1007/s00603-013-0535-z</identifier><identifier>CODEN: RMREDX</identifier><language>eng</language><publisher>Vienna: Springer Vienna</publisher><subject>Applied sciences ; Buildings. Public works ; Civil Engineering ; Computation methods. Tables. 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Our methods were based on the relationship between the joint roughness coefficient (JRC) values and roughness parameters calculated using power law equations. Statistical and 2D roughness parameters, and fractal dimensions correlated well with JRC values, and had correlation coefficients of over 0.96. However, all of these relationships have a 4th profile (JRC 6–8) that deviates by more than ±5 % from the JRC values given in the standard roughness profiles. This indicates that this profile is statistically different than the others. We suggest that fractal dimensions should be measured within the entire range of the divider, instead of merely measuring values within a suitable range. Normalized intercept values also correlated with the JRC values, similarly to the fractal dimension values discussed above. The root mean square first derivative values, roughness profile indexes, 2D roughness parameter, and fractal dimension values decreased as the sampling interval increased. However, the structure function values increased very rapidly with increasing sampling intervals. This indicates that the roughness parameters are not independent of the sampling interval, and that the different relationships between the JRC values and these roughness parameters are dependent on the sampling interval.</description><subject>Applied sciences</subject><subject>Buildings. Public works</subject><subject>Civil Engineering</subject><subject>Computation methods. Tables. Charts</subject><subject>Correlation</subject><subject>Correlation coefficient</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Exact sciences and technology</subject><subject>Fractal analysis</subject><subject>Fractals</subject><subject>Geophysics/Geodesy</subject><subject>Geotechnics</subject><subject>Intervals</subject><subject>Mathematical analysis</subject><subject>Mechanical properties</subject><subject>Original Paper</subject><subject>Parameter identification</subject><subject>Rocks</subject><subject>Roughness</subject><subject>Roughness coefficient</subject><subject>Sampling</subject><subject>Soil mechanics. Rocks mechanics</subject><subject>Structural analysis. Stresses</subject><subject>Surface roughness</subject><subject>Two dimensional</subject><issn>0723-2632</issn><issn>1434-453X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp1kM1KLDEUhIMoOP48gLuGi-CmNclJOt3Ly1x1FEERBXchnU7GyEzizelZ6NObYUREcHE4i_qqKIqQI0ZPGaXqDCltKNSUlZMg6_ctMmECRC0kPG2TCVUcat4A3yV7iC-UFlG1EzL750aXlyGaMaRYJV9dpxDH6j6t5s_RIVbT5LwPNrg4YvWIIc6_iXcmm-U6AQ_IjjcLdIeff588Xpw_TGf1ze3l1fTvTW0ktGPdu86CsG2reuBCsJ575vt-gG7o2kbJRrIOJBV0MG6QAEr2ALYrmmVODh72yckm9zWn_yuHo14GtG6xMNGlFWrWCM6F4h0v6J8f6Eta5VjaFYopxUA2UCi2oWxOiNl5_ZrD0uQ3zaheb6s32-qyrV5vq9-L5_gz2aA1C59NtAG_jLxtO66YKhzfcFikOHf5W4Nfwz8AL6OIuQ</recordid><startdate>20141101</startdate><enddate>20141101</enddate><creator>Jang, Hyun-Sic</creator><creator>Kang, Seong-Seung</creator><creator>Jang, Bo-An</creator><general>Springer Vienna</general><general>Springer</general><general>Springer Nature B.V</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7TN</scope><scope>7UA</scope><scope>7XB</scope><scope>88I</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>H96</scope><scope>HCIFZ</scope><scope>KR7</scope><scope>L.G</scope><scope>L6V</scope><scope>M2P</scope><scope>M7S</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>Q9U</scope></search><sort><creationdate>20141101</creationdate><title>Determination of Joint Roughness Coefficients Using Roughness Parameters</title><author>Jang, Hyun-Sic ; Kang, Seong-Seung ; Jang, Bo-An</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a538t-be9c34c887b32441b2f1fbbd39d98675651935040daed53375b33c9986c1e5df3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Applied sciences</topic><topic>Buildings. Public works</topic><topic>Civil Engineering</topic><topic>Computation methods. Tables. Charts</topic><topic>Correlation</topic><topic>Correlation coefficient</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Exact sciences and technology</topic><topic>Fractal analysis</topic><topic>Fractals</topic><topic>Geophysics/Geodesy</topic><topic>Geotechnics</topic><topic>Intervals</topic><topic>Mathematical analysis</topic><topic>Mechanical properties</topic><topic>Original Paper</topic><topic>Parameter identification</topic><topic>Rocks</topic><topic>Roughness</topic><topic>Roughness coefficient</topic><topic>Sampling</topic><topic>Soil mechanics. Rocks mechanics</topic><topic>Structural analysis. Stresses</topic><topic>Surface roughness</topic><topic>Two dimensional</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jang, Hyun-Sic</creatorcontrib><creatorcontrib>Kang, Seong-Seung</creatorcontrib><creatorcontrib>Jang, Bo-An</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Oceanic Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>SciTech Premium Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Science Journals</collection><collection>Engineering Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>ProQuest Central Basic</collection><jtitle>Rock mechanics and rock engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jang, Hyun-Sic</au><au>Kang, Seong-Seung</au><au>Jang, Bo-An</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Determination of Joint Roughness Coefficients Using Roughness Parameters</atitle><jtitle>Rock mechanics and rock engineering</jtitle><stitle>Rock Mech Rock Eng</stitle><date>2014-11-01</date><risdate>2014</risdate><volume>47</volume><issue>6</issue><spage>2061</spage><epage>2073</epage><pages>2061-2073</pages><issn>0723-2632</issn><eissn>1434-453X</eissn><coden>RMREDX</coden><abstract>This study used precisely digitized standard roughness profiles to determine roughness parameters such as statistical and 2D discontinuity roughness, and fractal dimensions. Our methods were based on the relationship between the joint roughness coefficient (JRC) values and roughness parameters calculated using power law equations. Statistical and 2D roughness parameters, and fractal dimensions correlated well with JRC values, and had correlation coefficients of over 0.96. However, all of these relationships have a 4th profile (JRC 6–8) that deviates by more than ±5 % from the JRC values given in the standard roughness profiles. This indicates that this profile is statistically different than the others. We suggest that fractal dimensions should be measured within the entire range of the divider, instead of merely measuring values within a suitable range. Normalized intercept values also correlated with the JRC values, similarly to the fractal dimension values discussed above. The root mean square first derivative values, roughness profile indexes, 2D roughness parameter, and fractal dimension values decreased as the sampling interval increased. However, the structure function values increased very rapidly with increasing sampling intervals. This indicates that the roughness parameters are not independent of the sampling interval, and that the different relationships between the JRC values and these roughness parameters are dependent on the sampling interval.</abstract><cop>Vienna</cop><pub>Springer Vienna</pub><doi>10.1007/s00603-013-0535-z</doi><tpages>13</tpages></addata></record> |
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subjects | Applied sciences Buildings. Public works Civil Engineering Computation methods. Tables. Charts Correlation Correlation coefficient Earth and Environmental Science Earth Sciences Exact sciences and technology Fractal analysis Fractals Geophysics/Geodesy Geotechnics Intervals Mathematical analysis Mechanical properties Original Paper Parameter identification Rocks Roughness Roughness coefficient Sampling Soil mechanics. Rocks mechanics Structural analysis. Stresses Surface roughness Two dimensional |
title | Determination of Joint Roughness Coefficients Using Roughness Parameters |
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