Development of new models for the estimation of deformation moduli in rock masses based on in situ measurements
Knowledge of the deformation properties of the rock mass is essential for the stress–strain analysis of structures such as dams, tunnels, slopes, and other underground structures and the most important parameter of the deformability of the rock mass is the deformation modulus. This paper describes s...
Gespeichert in:
| Veröffentlicht in: | Bulletin of engineering geology and the environment 2018-08, Vol.77 (3), p.1191-1202 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1202 |
|---|---|
| container_issue | 3 |
| container_start_page | 1191 |
| container_title | Bulletin of engineering geology and the environment |
| container_volume | 77 |
| creator | Radovanović, Slobodan Ranković, Vesna Anđelković, Vladimir Divac, Dejan Milivojević, Nikola |
| description | Knowledge of the deformation properties of the rock mass is essential for the stress–strain analysis of structures such as dams, tunnels, slopes, and other underground structures and the most important parameter of the deformability of the rock mass is the deformation modulus. This paper describes statistical models based on multiple linear regression and artificial neural networks. The models are developed using the test results of the deformation modulus obtained during the construction of the Iron Gate 1 dam on the Danube River and correlate these with measurements of the velocities of longitudinal waves and pressures in the rock mass. The parameters used for defining the models were obtained by in situ testing during dam construction, meaning that scale effects were also taken into account. For the analysis, 47 experimental results from in situ testing of the rock mass were obtained; 38 of these were used for modelling and nine were used for testing of the models. The model based on the artificial neural networks showed better performance in comparison to the model based on multiple linear regression. |
| doi_str_mv | 10.1007/s10064-017-1027-2 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2087842773</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2087842773</sourcerecordid><originalsourceid>FETCH-LOGICAL-c316t-78a5e6137b0321d9700a680703da0ce04a31bdad5258a4c91b3a0022f7ceab043</originalsourceid><addsrcrecordid>eNp1kE1PhDAQhhujievqD_DWxDM6bYHC0ayfySZe9NwUGJQVKHZA47-3hI2evHQ6M887k3kZOxdwKQD0FYU3jSMQOhIgdSQP2ErEKonyROnD37_Mj9kJ0Q5AJJkUK-Zu8BNbN3TYj9zVvMcv3rkKW-K183x8Q440Np0dG9fPQIWhvk8DOLUNb3ruXfnOO0uExAtLWPHQDnVqxol3aGnyOK-gU3ZU25bwbB_X7OXu9nnzEG2f7h8319uoVCIdI53ZBFOhdAFKiirXADbNQIOqLJQIsVWiqGyVyCSzcZmLQlkAKWtdoi0gVmt2scwdvPuYwglm5ybfh5VGQqazWGqtAiUWqvSOyGNtBh9u9d9GgJl9NYuvJvhqZl-NDBq5aCiw_Sv6v8n_i34Augp7mw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2087842773</pqid></control><display><type>article</type><title>Development of new models for the estimation of deformation moduli in rock masses based on in situ measurements</title><source>Springer Nature - Complete Springer Journals</source><creator>Radovanović, Slobodan ; Ranković, Vesna ; Anđelković, Vladimir ; Divac, Dejan ; Milivojević, Nikola</creator><creatorcontrib>Radovanović, Slobodan ; Ranković, Vesna ; Anđelković, Vladimir ; Divac, Dejan ; Milivojević, Nikola</creatorcontrib><description>Knowledge of the deformation properties of the rock mass is essential for the stress–strain analysis of structures such as dams, tunnels, slopes, and other underground structures and the most important parameter of the deformability of the rock mass is the deformation modulus. This paper describes statistical models based on multiple linear regression and artificial neural networks. The models are developed using the test results of the deformation modulus obtained during the construction of the Iron Gate 1 dam on the Danube River and correlate these with measurements of the velocities of longitudinal waves and pressures in the rock mass. The parameters used for defining the models were obtained by in situ testing during dam construction, meaning that scale effects were also taken into account. For the analysis, 47 experimental results from in situ testing of the rock mass were obtained; 38 of these were used for modelling and nine were used for testing of the models. The model based on the artificial neural networks showed better performance in comparison to the model based on multiple linear regression.</description><identifier>ISSN: 1435-9529</identifier><identifier>EISSN: 1435-9537</identifier><identifier>DOI: 10.1007/s10064-017-1027-2</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Artificial neural networks ; Construction ; Correlation analysis ; Dam construction ; Dams ; Deformability ; Deformation ; Earth and Environmental Science ; Earth Sciences ; Formability ; Foundations ; Geoecology/Natural Processes ; Geoengineering ; Geological engineering ; Geotechnical Engineering & Applied Earth Sciences ; Hydraulics ; In situ measurement ; In situ tests ; Iron ; Longitudinal waves ; Mass ; Mathematical models ; Modelling ; Modulus of deformation ; Nature Conservation ; Neural networks ; Original Paper ; Parameters ; Regression analysis ; Rivers ; Rocks ; Slope ; Statistical analysis ; Statistical models ; Strain analysis ; Testing ; Tunnels ; Underground structures</subject><ispartof>Bulletin of engineering geology and the environment, 2018-08, Vol.77 (3), p.1191-1202</ispartof><rights>Springer-Verlag Berlin Heidelberg 2017</rights><rights>Bulletin of Engineering Geology and the Environment is a copyright of Springer, (2017). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-78a5e6137b0321d9700a680703da0ce04a31bdad5258a4c91b3a0022f7ceab043</citedby><cites>FETCH-LOGICAL-c316t-78a5e6137b0321d9700a680703da0ce04a31bdad5258a4c91b3a0022f7ceab043</cites><orcidid>0000-0002-9664-0971</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10064-017-1027-2$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10064-017-1027-2$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Radovanović, Slobodan</creatorcontrib><creatorcontrib>Ranković, Vesna</creatorcontrib><creatorcontrib>Anđelković, Vladimir</creatorcontrib><creatorcontrib>Divac, Dejan</creatorcontrib><creatorcontrib>Milivojević, Nikola</creatorcontrib><title>Development of new models for the estimation of deformation moduli in rock masses based on in situ measurements</title><title>Bulletin of engineering geology and the environment</title><addtitle>Bull Eng Geol Environ</addtitle><description>Knowledge of the deformation properties of the rock mass is essential for the stress–strain analysis of structures such as dams, tunnels, slopes, and other underground structures and the most important parameter of the deformability of the rock mass is the deformation modulus. This paper describes statistical models based on multiple linear regression and artificial neural networks. The models are developed using the test results of the deformation modulus obtained during the construction of the Iron Gate 1 dam on the Danube River and correlate these with measurements of the velocities of longitudinal waves and pressures in the rock mass. The parameters used for defining the models were obtained by in situ testing during dam construction, meaning that scale effects were also taken into account. For the analysis, 47 experimental results from in situ testing of the rock mass were obtained; 38 of these were used for modelling and nine were used for testing of the models. The model based on the artificial neural networks showed better performance in comparison to the model based on multiple linear regression.</description><subject>Artificial neural networks</subject><subject>Construction</subject><subject>Correlation analysis</subject><subject>Dam construction</subject><subject>Dams</subject><subject>Deformability</subject><subject>Deformation</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Formability</subject><subject>Foundations</subject><subject>Geoecology/Natural Processes</subject><subject>Geoengineering</subject><subject>Geological engineering</subject><subject>Geotechnical Engineering & Applied Earth Sciences</subject><subject>Hydraulics</subject><subject>In situ measurement</subject><subject>In situ tests</subject><subject>Iron</subject><subject>Longitudinal waves</subject><subject>Mass</subject><subject>Mathematical models</subject><subject>Modelling</subject><subject>Modulus of deformation</subject><subject>Nature Conservation</subject><subject>Neural networks</subject><subject>Original Paper</subject><subject>Parameters</subject><subject>Regression analysis</subject><subject>Rivers</subject><subject>Rocks</subject><subject>Slope</subject><subject>Statistical analysis</subject><subject>Statistical models</subject><subject>Strain analysis</subject><subject>Testing</subject><subject>Tunnels</subject><subject>Underground structures</subject><issn>1435-9529</issn><issn>1435-9537</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp1kE1PhDAQhhujievqD_DWxDM6bYHC0ayfySZe9NwUGJQVKHZA47-3hI2evHQ6M887k3kZOxdwKQD0FYU3jSMQOhIgdSQP2ErEKonyROnD37_Mj9kJ0Q5AJJkUK-Zu8BNbN3TYj9zVvMcv3rkKW-K183x8Q440Np0dG9fPQIWhvk8DOLUNb3ruXfnOO0uExAtLWPHQDnVqxol3aGnyOK-gU3ZU25bwbB_X7OXu9nnzEG2f7h8319uoVCIdI53ZBFOhdAFKiirXADbNQIOqLJQIsVWiqGyVyCSzcZmLQlkAKWtdoi0gVmt2scwdvPuYwglm5ybfh5VGQqazWGqtAiUWqvSOyGNtBh9u9d9GgJl9NYuvJvhqZl-NDBq5aCiw_Sv6v8n_i34Augp7mw</recordid><startdate>20180801</startdate><enddate>20180801</enddate><creator>Radovanović, Slobodan</creator><creator>Ranković, Vesna</creator><creator>Anđelković, Vladimir</creator><creator>Divac, Dejan</creator><creator>Milivojević, Nikola</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>7TG</scope><scope>7UA</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</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>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>L6V</scope><scope>M7S</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PHGZM</scope><scope>PHGZT</scope><scope>PKEHL</scope><scope>PQEST</scope><scope>PQGLB</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>SOI</scope><orcidid>https://orcid.org/0000-0002-9664-0971</orcidid></search><sort><creationdate>20180801</creationdate><title>Development of new models for the estimation of deformation moduli in rock masses based on in situ measurements</title><author>Radovanović, Slobodan ; Ranković, Vesna ; Anđelković, Vladimir ; Divac, Dejan ; Milivojević, Nikola</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c316t-78a5e6137b0321d9700a680703da0ce04a31bdad5258a4c91b3a0022f7ceab043</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Artificial neural networks</topic><topic>Construction</topic><topic>Correlation analysis</topic><topic>Dam construction</topic><topic>Dams</topic><topic>Deformability</topic><topic>Deformation</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Formability</topic><topic>Foundations</topic><topic>Geoecology/Natural Processes</topic><topic>Geoengineering</topic><topic>Geological engineering</topic><topic>Geotechnical Engineering & Applied Earth Sciences</topic><topic>Hydraulics</topic><topic>In situ measurement</topic><topic>In situ tests</topic><topic>Iron</topic><topic>Longitudinal waves</topic><topic>Mass</topic><topic>Mathematical models</topic><topic>Modelling</topic><topic>Modulus of deformation</topic><topic>Nature Conservation</topic><topic>Neural networks</topic><topic>Original Paper</topic><topic>Parameters</topic><topic>Regression analysis</topic><topic>Rivers</topic><topic>Rocks</topic><topic>Slope</topic><topic>Statistical analysis</topic><topic>Statistical models</topic><topic>Strain analysis</topic><topic>Testing</topic><topic>Tunnels</topic><topic>Underground structures</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Radovanović, Slobodan</creatorcontrib><creatorcontrib>Ranković, Vesna</creatorcontrib><creatorcontrib>Anđelković, Vladimir</creatorcontrib><creatorcontrib>Divac, Dejan</creatorcontrib><creatorcontrib>Milivojević, Nikola</creatorcontrib><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection (ProQuest)</collection><collection>Natural Science Collection (ProQuest)</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>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest Central (New)</collection><collection>ProQuest One Academic (New)</collection><collection>ProQuest One Academic Middle East (New)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Applied & Life Sciences</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Environment Abstracts</collection><jtitle>Bulletin of engineering geology and the environment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Radovanović, Slobodan</au><au>Ranković, Vesna</au><au>Anđelković, Vladimir</au><au>Divac, Dejan</au><au>Milivojević, Nikola</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Development of new models for the estimation of deformation moduli in rock masses based on in situ measurements</atitle><jtitle>Bulletin of engineering geology and the environment</jtitle><stitle>Bull Eng Geol Environ</stitle><date>2018-08-01</date><risdate>2018</risdate><volume>77</volume><issue>3</issue><spage>1191</spage><epage>1202</epage><pages>1191-1202</pages><issn>1435-9529</issn><eissn>1435-9537</eissn><abstract>Knowledge of the deformation properties of the rock mass is essential for the stress–strain analysis of structures such as dams, tunnels, slopes, and other underground structures and the most important parameter of the deformability of the rock mass is the deformation modulus. This paper describes statistical models based on multiple linear regression and artificial neural networks. The models are developed using the test results of the deformation modulus obtained during the construction of the Iron Gate 1 dam on the Danube River and correlate these with measurements of the velocities of longitudinal waves and pressures in the rock mass. The parameters used for defining the models were obtained by in situ testing during dam construction, meaning that scale effects were also taken into account. For the analysis, 47 experimental results from in situ testing of the rock mass were obtained; 38 of these were used for modelling and nine were used for testing of the models. The model based on the artificial neural networks showed better performance in comparison to the model based on multiple linear regression.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s10064-017-1027-2</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-9664-0971</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1435-9529 |
| ispartof | Bulletin of engineering geology and the environment, 2018-08, Vol.77 (3), p.1191-1202 |
| issn | 1435-9529 1435-9537 |
| language | eng |
| recordid | cdi_proquest_journals_2087842773 |
| source | Springer Nature - Complete Springer Journals |
| subjects | Artificial neural networks Construction Correlation analysis Dam construction Dams Deformability Deformation Earth and Environmental Science Earth Sciences Formability Foundations Geoecology/Natural Processes Geoengineering Geological engineering Geotechnical Engineering & Applied Earth Sciences Hydraulics In situ measurement In situ tests Iron Longitudinal waves Mass Mathematical models Modelling Modulus of deformation Nature Conservation Neural networks Original Paper Parameters Regression analysis Rivers Rocks Slope Statistical analysis Statistical models Strain analysis Testing Tunnels Underground structures |
| title | Development of new models for the estimation of deformation moduli in rock masses based on in situ measurements |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-18T20%3A50%3A37IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Development%20of%20new%20models%20for%20the%20estimation%20of%20deformation%20moduli%20in%20rock%20masses%20based%20on%20in%20situ%20measurements&rft.jtitle=Bulletin%20of%20engineering%20geology%20and%20the%20environment&rft.au=Radovanovi%C4%87,%20Slobodan&rft.date=2018-08-01&rft.volume=77&rft.issue=3&rft.spage=1191&rft.epage=1202&rft.pages=1191-1202&rft.issn=1435-9529&rft.eissn=1435-9537&rft_id=info:doi/10.1007/s10064-017-1027-2&rft_dat=%3Cproquest_cross%3E2087842773%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2087842773&rft_id=info:pmid/&rfr_iscdi=true |