Identification of the Representative Volume Element at the Maji Dam Site Based on a Multisample Nonparametric Test
A representative volume element (RVE) denotes the size beyond which the fluctuation in the mechanical and geometrical properties of a rock mass significantly decreases, and determining the RVE is very important for evaluating the design, construction and stability of rock engineering projects. In ro...
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Veröffentlicht in: | Rock mechanics and rock engineering 2019-05, Vol.52 (5), p.1287-1301 |
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description | A representative volume element (RVE) denotes the size beyond which the fluctuation in the mechanical and geometrical properties of a rock mass significantly decreases, and determining the RVE is very important for evaluating the design, construction and stability of rock engineering projects. In rock mechanics analysis, it is more effective to use nonparametric test methods to check the statistical similarity of small sample sizes and samples that are not described by a specific family of probability distributions. Two-sample nonparametric tests are statistical tests that consider both spatial and size effects and are commonly utilized in RVE calculations; however, these tests cannot simultaneously consider the statistical similarity between any two sample volumes within a certain size range. Therefore, a new multisample nonparametric statistical method (called the Jonckheere–Terpstra test) is presented in this paper to more accurately determine RVE size. The Jonckheere–Terpstra test is a nonparametric test and can be used to determine whether the distributions of multiple independent samples from multiple populations are significantly different. In this work, the fracture geometries at the Maji dam site are simulated using a 3D fracture network model. A total of 100 variable-scale cubes are randomly distributed in the 3D fracture network model, and the statistical distributions of the P
32
values for each sample size are estimated by the KS goodness-of-fit test. The results reveal that the probability distribution form of each sample dataset is not identical and that it is difficult to determine the distribution form of the population. The Jonckheere–Terpstra test method was demonstrated to be effective at determining the statistical similarity of all sample volumes by calculating the P
32
values and the corresponding cube sizes. An RVE with a size of 14 m was selected as the RVE for the entire rock mass at the Maji dam site along the Nu River. |
doi_str_mv | 10.1007/s00603-018-1664-1 |
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32
values for each sample size are estimated by the KS goodness-of-fit test. The results reveal that the probability distribution form of each sample dataset is not identical and that it is difficult to determine the distribution form of the population. The Jonckheere–Terpstra test method was demonstrated to be effective at determining the statistical similarity of all sample volumes by calculating the P
32
values and the corresponding cube sizes. An RVE with a size of 14 m was selected as the RVE for the entire rock mass at the Maji dam site along the Nu River.</description><identifier>ISSN: 0723-2632</identifier><identifier>EISSN: 1434-453X</identifier><identifier>DOI: 10.1007/s00603-018-1664-1</identifier><language>eng</language><publisher>Vienna: Springer Vienna</publisher><subject>Civil Engineering ; Computer simulation ; Cubes ; Dams ; Damsites ; Design engineering ; Distribution ; Earth and Environmental Science ; Earth Sciences ; Engineering ; Geophysics/Geodesy ; Goodness of fit ; Mathematical models ; Mechanics ; Nonparametric statistics ; Original Paper ; Permeability ; Population (statistical) ; Probability distribution ; Probability theory ; Rivers ; Rock masses ; Rock mechanics ; Rocks ; Samples ; Similarity ; Size effects ; Spatial distribution ; Stability ; Stability analysis ; Statistical analysis ; Statistical distributions ; Statistical methods ; Statistical tests ; Test methods ; Tests ; Three dimensional models</subject><ispartof>Rock mechanics and rock engineering, 2019-05, Vol.52 (5), p.1287-1301</ispartof><rights>Springer-Verlag GmbH Austria, part of Springer Nature 2018</rights><rights>Rock Mechanics and Rock Engineering is a copyright of Springer, (2018). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a339t-88bf8348d0973094ea27f78b2b5736f0be5879e8f038ea102b146ab1da94c31e3</citedby><cites>FETCH-LOGICAL-a339t-88bf8348d0973094ea27f78b2b5736f0be5879e8f038ea102b146ab1da94c31e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00603-018-1664-1$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00603-018-1664-1$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Liu, Ying</creatorcontrib><creatorcontrib>Wang, Qing</creatorcontrib><creatorcontrib>Chen, Jianping</creatorcontrib><creatorcontrib>Song, Shengyuan</creatorcontrib><title>Identification of the Representative Volume Element at the Maji Dam Site Based on a Multisample Nonparametric Test</title><title>Rock mechanics and rock engineering</title><addtitle>Rock Mech Rock Eng</addtitle><description>A representative volume element (RVE) denotes the size beyond which the fluctuation in the mechanical and geometrical properties of a rock mass significantly decreases, and determining the RVE is very important for evaluating the design, construction and stability of rock engineering projects. In rock mechanics analysis, it is more effective to use nonparametric test methods to check the statistical similarity of small sample sizes and samples that are not described by a specific family of probability distributions. Two-sample nonparametric tests are statistical tests that consider both spatial and size effects and are commonly utilized in RVE calculations; however, these tests cannot simultaneously consider the statistical similarity between any two sample volumes within a certain size range. Therefore, a new multisample nonparametric statistical method (called the Jonckheere–Terpstra test) is presented in this paper to more accurately determine RVE size. The Jonckheere–Terpstra test is a nonparametric test and can be used to determine whether the distributions of multiple independent samples from multiple populations are significantly different. In this work, the fracture geometries at the Maji dam site are simulated using a 3D fracture network model. A total of 100 variable-scale cubes are randomly distributed in the 3D fracture network model, and the statistical distributions of the P
32
values for each sample size are estimated by the KS goodness-of-fit test. The results reveal that the probability distribution form of each sample dataset is not identical and that it is difficult to determine the distribution form of the population. The Jonckheere–Terpstra test method was demonstrated to be effective at determining the statistical similarity of all sample volumes by calculating the P
32
values and the corresponding cube sizes. An RVE with a size of 14 m was selected as the RVE for the entire rock mass at the Maji dam site along the Nu River.</description><subject>Civil Engineering</subject><subject>Computer simulation</subject><subject>Cubes</subject><subject>Dams</subject><subject>Damsites</subject><subject>Design engineering</subject><subject>Distribution</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Engineering</subject><subject>Geophysics/Geodesy</subject><subject>Goodness of fit</subject><subject>Mathematical models</subject><subject>Mechanics</subject><subject>Nonparametric statistics</subject><subject>Original Paper</subject><subject>Permeability</subject><subject>Population (statistical)</subject><subject>Probability distribution</subject><subject>Probability theory</subject><subject>Rivers</subject><subject>Rock masses</subject><subject>Rock mechanics</subject><subject>Rocks</subject><subject>Samples</subject><subject>Similarity</subject><subject>Size effects</subject><subject>Spatial distribution</subject><subject>Stability</subject><subject>Stability analysis</subject><subject>Statistical analysis</subject><subject>Statistical distributions</subject><subject>Statistical methods</subject><subject>Statistical tests</subject><subject>Test methods</subject><subject>Tests</subject><subject>Three dimensional models</subject><issn>0723-2632</issn><issn>1434-453X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp1kF1LwzAUhoMoOKc_wLuA19WTpG3SS51TB5uCTvEupO2pdvTLJBX892ZO8MqrA-95P-Ah5JTBOQOQFw4gBREBUxFL0zhie2TCYhFHcSJe98kEJBcRTwU_JEfObQDCU6oJsYsSO19XdWF83Xe0r6h_R_qIg0UXPkH9RPrSN2OLdN5gGzRq_I9pZTY1vTYtfao90ivjsKShwtDV2PjamXZokN733WCsadHbuqBrdP6YHFSmcXjye6fk-Wa-nt1Fy4fbxexyGRkhMh8plVdKxKqETArIYjRcVlLlPE-kSCvIMVEyQ1WBUGgY8JzFqclZabK4EAzFlJztegfbf4xhWG_60XZhUnMmAgpIeRJcbOcqbO-cxUoPtm6N_dIM9Bat3qHVAa3eotUsZPgu44K3e0P71_x_6Bt4jnvQ</recordid><startdate>20190501</startdate><enddate>20190501</enddate><creator>Liu, Ying</creator><creator>Wang, Qing</creator><creator>Chen, Jianping</creator><creator>Song, Shengyuan</creator><general>Springer Vienna</general><general>Springer Nature B.V</general><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>20190501</creationdate><title>Identification of the Representative Volume Element at the Maji Dam Site Based on a Multisample Nonparametric Test</title><author>Liu, Ying ; Wang, Qing ; Chen, Jianping ; Song, Shengyuan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a339t-88bf8348d0973094ea27f78b2b5736f0be5879e8f038ea102b146ab1da94c31e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Civil Engineering</topic><topic>Computer simulation</topic><topic>Cubes</topic><topic>Dams</topic><topic>Damsites</topic><topic>Design engineering</topic><topic>Distribution</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Engineering</topic><topic>Geophysics/Geodesy</topic><topic>Goodness of fit</topic><topic>Mathematical models</topic><topic>Mechanics</topic><topic>Nonparametric statistics</topic><topic>Original Paper</topic><topic>Permeability</topic><topic>Population (statistical)</topic><topic>Probability distribution</topic><topic>Probability theory</topic><topic>Rivers</topic><topic>Rock masses</topic><topic>Rock mechanics</topic><topic>Rocks</topic><topic>Samples</topic><topic>Similarity</topic><topic>Size effects</topic><topic>Spatial distribution</topic><topic>Stability</topic><topic>Stability analysis</topic><topic>Statistical analysis</topic><topic>Statistical distributions</topic><topic>Statistical methods</topic><topic>Statistical tests</topic><topic>Test methods</topic><topic>Tests</topic><topic>Three dimensional models</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Ying</creatorcontrib><creatorcontrib>Wang, Qing</creatorcontrib><creatorcontrib>Chen, Jianping</creatorcontrib><creatorcontrib>Song, Shengyuan</creatorcontrib><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>Science Database</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>Liu, Ying</au><au>Wang, Qing</au><au>Chen, Jianping</au><au>Song, Shengyuan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identification of the Representative Volume Element at the Maji Dam Site Based on a Multisample Nonparametric Test</atitle><jtitle>Rock mechanics and rock engineering</jtitle><stitle>Rock Mech Rock Eng</stitle><date>2019-05-01</date><risdate>2019</risdate><volume>52</volume><issue>5</issue><spage>1287</spage><epage>1301</epage><pages>1287-1301</pages><issn>0723-2632</issn><eissn>1434-453X</eissn><abstract>A representative volume element (RVE) denotes the size beyond which the fluctuation in the mechanical and geometrical properties of a rock mass significantly decreases, and determining the RVE is very important for evaluating the design, construction and stability of rock engineering projects. In rock mechanics analysis, it is more effective to use nonparametric test methods to check the statistical similarity of small sample sizes and samples that are not described by a specific family of probability distributions. Two-sample nonparametric tests are statistical tests that consider both spatial and size effects and are commonly utilized in RVE calculations; however, these tests cannot simultaneously consider the statistical similarity between any two sample volumes within a certain size range. Therefore, a new multisample nonparametric statistical method (called the Jonckheere–Terpstra test) is presented in this paper to more accurately determine RVE size. The Jonckheere–Terpstra test is a nonparametric test and can be used to determine whether the distributions of multiple independent samples from multiple populations are significantly different. In this work, the fracture geometries at the Maji dam site are simulated using a 3D fracture network model. A total of 100 variable-scale cubes are randomly distributed in the 3D fracture network model, and the statistical distributions of the P
32
values for each sample size are estimated by the KS goodness-of-fit test. The results reveal that the probability distribution form of each sample dataset is not identical and that it is difficult to determine the distribution form of the population. The Jonckheere–Terpstra test method was demonstrated to be effective at determining the statistical similarity of all sample volumes by calculating the P
32
values and the corresponding cube sizes. An RVE with a size of 14 m was selected as the RVE for the entire rock mass at the Maji dam site along the Nu River.</abstract><cop>Vienna</cop><pub>Springer Vienna</pub><doi>10.1007/s00603-018-1664-1</doi><tpages>15</tpages></addata></record> |
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subjects | Civil Engineering Computer simulation Cubes Dams Damsites Design engineering Distribution Earth and Environmental Science Earth Sciences Engineering Geophysics/Geodesy Goodness of fit Mathematical models Mechanics Nonparametric statistics Original Paper Permeability Population (statistical) Probability distribution Probability theory Rivers Rock masses Rock mechanics Rocks Samples Similarity Size effects Spatial distribution Stability Stability analysis Statistical analysis Statistical distributions Statistical methods Statistical tests Test methods Tests Three dimensional models |
title | Identification of the Representative Volume Element at the Maji Dam Site Based on a Multisample Nonparametric Test |
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