Computation of influence functions for automatic mining subsidence prediction

This paper presents a computer tool that automatically predicts mining subsidence using the generalized n - k - g influence function detailed in (González Nicieza et al. Int J Rock Mech Min Sci 42(3):372–387, 2005 ). This function depends on two physical concepts: the first is gravity, which charact...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Computational geosciences 2010, Vol.14 (1), p.83-103
Hauptverfasser: Díaz-Fernández, M. E., Álvarez-Fernández, M. I., Álvarez-Vigil, A. E.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 103
container_issue 1
container_start_page 83
container_title Computational geosciences
container_volume 14
creator Díaz-Fernández, M. E.
Álvarez-Fernández, M. I.
Álvarez-Vigil, A. E.
description This paper presents a computer tool that automatically predicts mining subsidence using the generalized n - k - g influence function detailed in (González Nicieza et al. Int J Rock Mech Min Sci 42(3):372–387, 2005 ). This function depends on two physical concepts: the first is gravity, which characterizes the forces acting on the ground, and the second, the convergence of the roof and floor of the mine workings due to the stress state of the ground. The developed tool also allows other influence functions to be used to predict subsidence, namely the spatial influence function (Ramírez Oyanguren et al. 2000 ) and the normal-type classical (Knothe, Arch Gór Hut 1, 1952 ) and modified (González Nicieza et al. Bull Eng Geol Environ 66(3):319–329, 2007 ) time functions. Moreover, the inputting and periodic updating of data from subsidence monitoring surveys is controlled by one of the tool’s modules using a method that minimizes errors resulting from time discontinuities in landmarks measurements. In addition, when actual landmarks measurements exist, the developed tool allows calibration of the subsidence parameters, minimizing the errors between actual measurements and those obtained by prediction. The tool includes a viewer, developed using OpenGL, which enables the results of the calculations carried out to be viewed, allowing the point of view to be varied. It also includes the option of viewing and saving the results of the calculations carried out over the original topographic plane defined in the AutoCAD DXF data file format. The efficacy of the tool is demonstrated via its application to a real case of mining work carried out in a village in the Principality of Asturias, Spain.
doi_str_mv 10.1007/s10596-009-9134-1
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_743584187</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>743584187</sourcerecordid><originalsourceid>FETCH-LOGICAL-a370t-63f25f43b4ce4dc999481ac9cc6985065ade0a6f4c249e01719e077ba2822173</originalsourceid><addsrcrecordid>eNp10M1KxDAUBeAgCo6jD-CuuHFVzU3SJlnK4B-MuJl9yKTJ0KFNatIufHtTKwiCmySE71wuB6FrwHeAMb9PgCtZlxjLUgJlJZygFVSclsCkPM1vRnCZCT9HFykdcYacwgq9bUI_TKMe2-CL4IrWu26y3tjCTd7Mv6lwIRZ6GkOflSn61rf-UKRpn9rmWw7RNu23vURnTnfJXv3ca7R7etxtXsrt-_Pr5mFbasrxWNbUkcoxumfGssZIKZkAbaQxtRQVrivdWKxrxwxh0mLgkE_O95oIQoDTNbpdxg4xfEw2japvk7Fdp70NU1Kc0UowELO8-SOPYYo-76YIrkQtAERGsCATQ0rROjXEttfxUwFWc7tqaVfl0tTcroKcIUsmZesPNv4O_j_0BV3GfOY</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>205868118</pqid></control><display><type>article</type><title>Computation of influence functions for automatic mining subsidence prediction</title><source>SpringerNature Journals</source><creator>Díaz-Fernández, M. E. ; Álvarez-Fernández, M. I. ; Álvarez-Vigil, A. E.</creator><creatorcontrib>Díaz-Fernández, M. E. ; Álvarez-Fernández, M. I. ; Álvarez-Vigil, A. E.</creatorcontrib><description>This paper presents a computer tool that automatically predicts mining subsidence using the generalized n - k - g influence function detailed in (González Nicieza et al. Int J Rock Mech Min Sci 42(3):372–387, 2005 ). This function depends on two physical concepts: the first is gravity, which characterizes the forces acting on the ground, and the second, the convergence of the roof and floor of the mine workings due to the stress state of the ground. The developed tool also allows other influence functions to be used to predict subsidence, namely the spatial influence function (Ramírez Oyanguren et al. 2000 ) and the normal-type classical (Knothe, Arch Gór Hut 1, 1952 ) and modified (González Nicieza et al. Bull Eng Geol Environ 66(3):319–329, 2007 ) time functions. Moreover, the inputting and periodic updating of data from subsidence monitoring surveys is controlled by one of the tool’s modules using a method that minimizes errors resulting from time discontinuities in landmarks measurements. In addition, when actual landmarks measurements exist, the developed tool allows calibration of the subsidence parameters, minimizing the errors between actual measurements and those obtained by prediction. The tool includes a viewer, developed using OpenGL, which enables the results of the calculations carried out to be viewed, allowing the point of view to be varied. It also includes the option of viewing and saving the results of the calculations carried out over the original topographic plane defined in the AutoCAD DXF data file format. The efficacy of the tool is demonstrated via its application to a real case of mining work carried out in a village in the Principality of Asturias, Spain.</description><identifier>ISSN: 1420-0597</identifier><identifier>EISSN: 1573-1499</identifier><identifier>DOI: 10.1007/s10596-009-9134-1</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Computers ; Convergence ; Data mining ; Earth and Environmental Science ; Earth Sciences ; Geotechnical Engineering &amp; Applied Earth Sciences ; Hydrogeology ; Mathematical Modeling and Industrial Mathematics ; Monitoring systems ; Original Paper ; Soil Science &amp; Conservation ; Subsidence</subject><ispartof>Computational geosciences, 2010, Vol.14 (1), p.83-103</ispartof><rights>Springer Science+Business Media B.V. 2009</rights><rights>Springer Science+Business Media B.V. 2010</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a370t-63f25f43b4ce4dc999481ac9cc6985065ade0a6f4c249e01719e077ba2822173</citedby><cites>FETCH-LOGICAL-a370t-63f25f43b4ce4dc999481ac9cc6985065ade0a6f4c249e01719e077ba2822173</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/s10596-009-9134-1$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10596-009-9134-1$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>315,781,785,27925,27926,41489,42558,51320</link.rule.ids></links><search><creatorcontrib>Díaz-Fernández, M. E.</creatorcontrib><creatorcontrib>Álvarez-Fernández, M. I.</creatorcontrib><creatorcontrib>Álvarez-Vigil, A. E.</creatorcontrib><title>Computation of influence functions for automatic mining subsidence prediction</title><title>Computational geosciences</title><addtitle>Comput Geosci</addtitle><description>This paper presents a computer tool that automatically predicts mining subsidence using the generalized n - k - g influence function detailed in (González Nicieza et al. Int J Rock Mech Min Sci 42(3):372–387, 2005 ). This function depends on two physical concepts: the first is gravity, which characterizes the forces acting on the ground, and the second, the convergence of the roof and floor of the mine workings due to the stress state of the ground. The developed tool also allows other influence functions to be used to predict subsidence, namely the spatial influence function (Ramírez Oyanguren et al. 2000 ) and the normal-type classical (Knothe, Arch Gór Hut 1, 1952 ) and modified (González Nicieza et al. Bull Eng Geol Environ 66(3):319–329, 2007 ) time functions. Moreover, the inputting and periodic updating of data from subsidence monitoring surveys is controlled by one of the tool’s modules using a method that minimizes errors resulting from time discontinuities in landmarks measurements. In addition, when actual landmarks measurements exist, the developed tool allows calibration of the subsidence parameters, minimizing the errors between actual measurements and those obtained by prediction. The tool includes a viewer, developed using OpenGL, which enables the results of the calculations carried out to be viewed, allowing the point of view to be varied. It also includes the option of viewing and saving the results of the calculations carried out over the original topographic plane defined in the AutoCAD DXF data file format. The efficacy of the tool is demonstrated via its application to a real case of mining work carried out in a village in the Principality of Asturias, Spain.</description><subject>Computers</subject><subject>Convergence</subject><subject>Data mining</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Geotechnical Engineering &amp; Applied Earth Sciences</subject><subject>Hydrogeology</subject><subject>Mathematical Modeling and Industrial Mathematics</subject><subject>Monitoring systems</subject><subject>Original Paper</subject><subject>Soil Science &amp; Conservation</subject><subject>Subsidence</subject><issn>1420-0597</issn><issn>1573-1499</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp10M1KxDAUBeAgCo6jD-CuuHFVzU3SJlnK4B-MuJl9yKTJ0KFNatIufHtTKwiCmySE71wuB6FrwHeAMb9PgCtZlxjLUgJlJZygFVSclsCkPM1vRnCZCT9HFykdcYacwgq9bUI_TKMe2-CL4IrWu26y3tjCTd7Mv6lwIRZ6GkOflSn61rf-UKRpn9rmWw7RNu23vURnTnfJXv3ca7R7etxtXsrt-_Pr5mFbasrxWNbUkcoxumfGssZIKZkAbaQxtRQVrivdWKxrxwxh0mLgkE_O95oIQoDTNbpdxg4xfEw2japvk7Fdp70NU1Kc0UowELO8-SOPYYo-76YIrkQtAERGsCATQ0rROjXEttfxUwFWc7tqaVfl0tTcroKcIUsmZesPNv4O_j_0BV3GfOY</recordid><startdate>2010</startdate><enddate>2010</enddate><creator>Díaz-Fernández, M. E.</creator><creator>Álvarez-Fernández, M. I.</creator><creator>Álvarez-Vigil, A. E.</creator><general>Springer Netherlands</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SC</scope><scope>7UA</scope><scope>7XB</scope><scope>88I</scope><scope>8AL</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</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>GNUQQ</scope><scope>H8D</scope><scope>H96</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>L.G</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0N</scope><scope>M2P</scope><scope>P5Z</scope><scope>P62</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope></search><sort><creationdate>2010</creationdate><title>Computation of influence functions for automatic mining subsidence prediction</title><author>Díaz-Fernández, M. E. ; Álvarez-Fernández, M. I. ; Álvarez-Vigil, A. E.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a370t-63f25f43b4ce4dc999481ac9cc6985065ade0a6f4c249e01719e077ba2822173</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Computers</topic><topic>Convergence</topic><topic>Data mining</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Geotechnical Engineering &amp; Applied Earth Sciences</topic><topic>Hydrogeology</topic><topic>Mathematical Modeling and Industrial Mathematics</topic><topic>Monitoring systems</topic><topic>Original Paper</topic><topic>Soil Science &amp; Conservation</topic><topic>Subsidence</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Díaz-Fernández, M. E.</creatorcontrib><creatorcontrib>Álvarez-Fernández, M. I.</creatorcontrib><creatorcontrib>Álvarez-Vigil, A. E.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>Computing 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>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric &amp; 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>ProQuest Central Student</collection><collection>Aerospace Database</collection><collection>Aquatic Science &amp; Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy &amp; Non-Living Resources</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Aquatic Science &amp; Fisheries Abstracts (ASFA) Professional</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Computing Database</collection><collection>Science Database</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>Earth, Atmospheric &amp; 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>ProQuest Central Basic</collection><jtitle>Computational geosciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Díaz-Fernández, M. E.</au><au>Álvarez-Fernández, M. I.</au><au>Álvarez-Vigil, A. E.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Computation of influence functions for automatic mining subsidence prediction</atitle><jtitle>Computational geosciences</jtitle><stitle>Comput Geosci</stitle><date>2010</date><risdate>2010</risdate><volume>14</volume><issue>1</issue><spage>83</spage><epage>103</epage><pages>83-103</pages><issn>1420-0597</issn><eissn>1573-1499</eissn><abstract>This paper presents a computer tool that automatically predicts mining subsidence using the generalized n - k - g influence function detailed in (González Nicieza et al. Int J Rock Mech Min Sci 42(3):372–387, 2005 ). This function depends on two physical concepts: the first is gravity, which characterizes the forces acting on the ground, and the second, the convergence of the roof and floor of the mine workings due to the stress state of the ground. The developed tool also allows other influence functions to be used to predict subsidence, namely the spatial influence function (Ramírez Oyanguren et al. 2000 ) and the normal-type classical (Knothe, Arch Gór Hut 1, 1952 ) and modified (González Nicieza et al. Bull Eng Geol Environ 66(3):319–329, 2007 ) time functions. Moreover, the inputting and periodic updating of data from subsidence monitoring surveys is controlled by one of the tool’s modules using a method that minimizes errors resulting from time discontinuities in landmarks measurements. In addition, when actual landmarks measurements exist, the developed tool allows calibration of the subsidence parameters, minimizing the errors between actual measurements and those obtained by prediction. The tool includes a viewer, developed using OpenGL, which enables the results of the calculations carried out to be viewed, allowing the point of view to be varied. It also includes the option of viewing and saving the results of the calculations carried out over the original topographic plane defined in the AutoCAD DXF data file format. The efficacy of the tool is demonstrated via its application to a real case of mining work carried out in a village in the Principality of Asturias, Spain.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><doi>10.1007/s10596-009-9134-1</doi><tpages>21</tpages></addata></record>
fulltext fulltext
identifier ISSN: 1420-0597
ispartof Computational geosciences, 2010, Vol.14 (1), p.83-103
issn 1420-0597
1573-1499
language eng
recordid cdi_proquest_miscellaneous_743584187
source SpringerNature Journals
subjects Computers
Convergence
Data mining
Earth and Environmental Science
Earth Sciences
Geotechnical Engineering & Applied Earth Sciences
Hydrogeology
Mathematical Modeling and Industrial Mathematics
Monitoring systems
Original Paper
Soil Science & Conservation
Subsidence
title Computation of influence functions for automatic mining subsidence prediction
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-18T13%3A34%3A38IST&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=Computation%20of%20influence%20functions%20for%20automatic%20mining%20subsidence%20prediction&rft.jtitle=Computational%20geosciences&rft.au=D%C3%ADaz-Fern%C3%A1ndez,%20M.%20E.&rft.date=2010&rft.volume=14&rft.issue=1&rft.spage=83&rft.epage=103&rft.pages=83-103&rft.issn=1420-0597&rft.eissn=1573-1499&rft_id=info:doi/10.1007/s10596-009-9134-1&rft_dat=%3Cproquest_cross%3E743584187%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=205868118&rft_id=info:pmid/&rfr_iscdi=true