Intelligent prediction model based on genetic algorithm and support vector machine for evaluation of mining-induced building damage
Characteristics of factors influencing mining-induced building damage are diverse, nonlinear, and multi-linear. For a better description of these factors, an intelligent prediction model for building damage induced by underground mining is developed based on the support vector machine (SVM). Based o...
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Veröffentlicht in: | Tehnički vjesnik 2015-06, Vol.22 (3), p.743-753 |
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description | Characteristics of factors influencing mining-induced building damage are diverse, nonlinear, and multi-linear. For a better description of these factors, an intelligent prediction model for building damage induced by underground mining is developed based on the support vector machine (SVM). Based on a comprehensive consideration of geological, mining, and building factors, 10 factors are carefully selected. In particular, the mining-induced damage grade of the brick-concrete building structure is used as the main input variable in the proposed model. The damage grade and largest crack width of the brick-concrete building structure are selected as output variables in the proposed model. A total of 32 typical cases of mining-induced building damage in China are collected and used as training data. The radial basis function (RBF) is used for SVM classification and the application of the largest-crack-width regression model. To improve the model's generalizability and predictive capacity, the genetic algorithm (GA) is adopted to select effective parameters for the SVM model, and then the corresponding identification of six group samples is performed. The classification and regression results show that the proposed prediction model using GA-SVM can predict the mining-induced damage of a brick-concrete building structure, and the evaluation results show good agreement with monitored data. This suggests the practicality of the proposed model in a wide range of engineering problems.Original Abstract: Znacajke cimbenika koji utjecu na stetu nastalu na zgradama zbog iskapanja zemlje su razlicite, nelinearne i multi linearne. Za bolji opis tih cimbenika razvijen je inteligentni model zasnovan na potpornom vektorskom stroju (SVM) kojim se moze predvidjeti steta na zgradama nastala podzemnim iskapanjem. Na temelju opseznog razmatranja geoloskih, rudarskih i gradevnih faktora, 10 ih je pazljivo odabrano. Posebice je, kao glavna ulazna varijabla u predlozenom modelu, upotrebljen stupanj ostecenja gradevine od opeke i betona, nastao podzemnim iskapanjem. Stupanj ostecenja i najsira pukotina gradevinske konstrukcije od opeke i betona izabrani su kao izlazne varijable u predlozenom modelu. Ukupno su odabrana 32 tipicna slucaja ostecenja zgrada u Kini zbog iskapanja zemlje te upotrebljena kao podaci za uvjezbavanje (training data). Funkcija radijalne baze (radial basis function - RBF) upotrebljena je za SVM klasifikaciju i primjenu modela regresije s najvecom sirinom pukoti |
doi_str_mv | 10.17559/TV-20150213085300 |
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For a better description of these factors, an intelligent prediction model for building damage induced by underground mining is developed based on the support vector machine (SVM). Based on a comprehensive consideration of geological, mining, and building factors, 10 factors are carefully selected. In particular, the mining-induced damage grade of the brick-concrete building structure is used as the main input variable in the proposed model. The damage grade and largest crack width of the brick-concrete building structure are selected as output variables in the proposed model. A total of 32 typical cases of mining-induced building damage in China are collected and used as training data. The radial basis function (RBF) is used for SVM classification and the application of the largest-crack-width regression model. To improve the model's generalizability and predictive capacity, the genetic algorithm (GA) is adopted to select effective parameters for the SVM model, and then the corresponding identification of six group samples is performed. The classification and regression results show that the proposed prediction model using GA-SVM can predict the mining-induced damage of a brick-concrete building structure, and the evaluation results show good agreement with monitored data. This suggests the practicality of the proposed model in a wide range of engineering problems.Original Abstract: Znacajke cimbenika koji utjecu na stetu nastalu na zgradama zbog iskapanja zemlje su razlicite, nelinearne i multi linearne. Za bolji opis tih cimbenika razvijen je inteligentni model zasnovan na potpornom vektorskom stroju (SVM) kojim se moze predvidjeti steta na zgradama nastala podzemnim iskapanjem. Na temelju opseznog razmatranja geoloskih, rudarskih i gradevnih faktora, 10 ih je pazljivo odabrano. Posebice je, kao glavna ulazna varijabla u predlozenom modelu, upotrebljen stupanj ostecenja gradevine od opeke i betona, nastao podzemnim iskapanjem. Stupanj ostecenja i najsira pukotina gradevinske konstrukcije od opeke i betona izabrani su kao izlazne varijable u predlozenom modelu. Ukupno su odabrana 32 tipicna slucaja ostecenja zgrada u Kini zbog iskapanja zemlje te upotrebljena kao podaci za uvjezbavanje (training data). Funkcija radijalne baze (radial basis function - RBF) upotrebljena je za SVM klasifikaciju i primjenu modela regresije s najvecom sirinom pukotine. Kako bi primjena modela bila sto sira i njegova sposobnost predvidanja sto veca, za izbor ucinkovitih parametara za SVM model upotrebljen je genetski algoritam (GA), i tada je izvrsena odgovarajuca identifikacija sest grupa uzoraka. Rezultati klasifikacije i regresije pokazuju da se predlozenim modelom, koji koristi GA-SVM, moze predvidjeti steta na konstrukciji od opeke i betona, nastala iskapanjem zemlje, a rezultati procjene u skladu su s pracenim podacima. To navodi na prakticnost primjene predlozenog modela u rjesavanju razlicitih inzinjerskih problema.</description><identifier>ISSN: 1330-3651</identifier><identifier>EISSN: 1848-6339</identifier><identifier>DOI: 10.17559/TV-20150213085300</identifier><language>eng</language><subject>Buildings ; Construction ; Construction equipment ; Damage ; Genetic algorithms ; Mathematical models ; Regression ; Support vector machines</subject><ispartof>Tehnički vjesnik, 2015-06, Vol.22 (3), p.743-753</ispartof><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,864,27924,27925</link.rule.ids></links><search><creatorcontrib>Liu, Lang</creatorcontrib><creatorcontrib>Lai, Xinping</creatorcontrib><creatorcontrib>Song, Ki-Il</creatorcontrib><creatorcontrib>Lao, Dezheng</creatorcontrib><title>Intelligent prediction model based on genetic algorithm and support vector machine for evaluation of mining-induced building damage</title><title>Tehnički vjesnik</title><description>Characteristics of factors influencing mining-induced building damage are diverse, nonlinear, and multi-linear. For a better description of these factors, an intelligent prediction model for building damage induced by underground mining is developed based on the support vector machine (SVM). Based on a comprehensive consideration of geological, mining, and building factors, 10 factors are carefully selected. In particular, the mining-induced damage grade of the brick-concrete building structure is used as the main input variable in the proposed model. The damage grade and largest crack width of the brick-concrete building structure are selected as output variables in the proposed model. A total of 32 typical cases of mining-induced building damage in China are collected and used as training data. The radial basis function (RBF) is used for SVM classification and the application of the largest-crack-width regression model. To improve the model's generalizability and predictive capacity, the genetic algorithm (GA) is adopted to select effective parameters for the SVM model, and then the corresponding identification of six group samples is performed. The classification and regression results show that the proposed prediction model using GA-SVM can predict the mining-induced damage of a brick-concrete building structure, and the evaluation results show good agreement with monitored data. This suggests the practicality of the proposed model in a wide range of engineering problems.Original Abstract: Znacajke cimbenika koji utjecu na stetu nastalu na zgradama zbog iskapanja zemlje su razlicite, nelinearne i multi linearne. Za bolji opis tih cimbenika razvijen je inteligentni model zasnovan na potpornom vektorskom stroju (SVM) kojim se moze predvidjeti steta na zgradama nastala podzemnim iskapanjem. Na temelju opseznog razmatranja geoloskih, rudarskih i gradevnih faktora, 10 ih je pazljivo odabrano. Posebice je, kao glavna ulazna varijabla u predlozenom modelu, upotrebljen stupanj ostecenja gradevine od opeke i betona, nastao podzemnim iskapanjem. Stupanj ostecenja i najsira pukotina gradevinske konstrukcije od opeke i betona izabrani su kao izlazne varijable u predlozenom modelu. Ukupno su odabrana 32 tipicna slucaja ostecenja zgrada u Kini zbog iskapanja zemlje te upotrebljena kao podaci za uvjezbavanje (training data). Funkcija radijalne baze (radial basis function - RBF) upotrebljena je za SVM klasifikaciju i primjenu modela regresije s najvecom sirinom pukotine. Kako bi primjena modela bila sto sira i njegova sposobnost predvidanja sto veca, za izbor ucinkovitih parametara za SVM model upotrebljen je genetski algoritam (GA), i tada je izvrsena odgovarajuca identifikacija sest grupa uzoraka. Rezultati klasifikacije i regresije pokazuju da se predlozenim modelom, koji koristi GA-SVM, moze predvidjeti steta na konstrukciji od opeke i betona, nastala iskapanjem zemlje, a rezultati procjene u skladu su s pracenim podacima. To navodi na prakticnost primjene predlozenog modela u rjesavanju razlicitih inzinjerskih problema.</description><subject>Buildings</subject><subject>Construction</subject><subject>Construction equipment</subject><subject>Damage</subject><subject>Genetic algorithms</subject><subject>Mathematical models</subject><subject>Regression</subject><subject>Support vector machines</subject><issn>1330-3651</issn><issn>1848-6339</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><recordid>eNqFUctOwzAQjBBIVKU_wMlHLgG_kxxRxaNSJS6l18jxIzVK7GA7lTjz4xjKhROnnd2dHWl2iuIawVtUMdbc7fYlhohBjAisGYHwrFigmtYlJ6Q5z5gQWBLO0GWxivENQoghhbghi-Jz45IeBttrl8AUtLIyWe_A6JUeQCeiViC3ea2TlUAMvQ82HUYgnAJxniYfEjhqmXwAo5AH6zQwGeujGGbxI-UNGK2zri-tU7PMgt1sB5UHQIlR9PqquDBiiHr1W5fF6-PDbv1cbl-eNuv7bSmzrVQ2na5QjSBXmCuCa06RULiqjJHEcESVEh3nlaRcN7RjlCnTIVqb7zvCREeWxc1Jdwr-fdYxtaONMrsXTvs5tlmc8QajBv1PrTjOr64IyVR8osrgYwzatFOwowgfLYLtTz7tbt_-zYd8AYnKhSA</recordid><startdate>20150601</startdate><enddate>20150601</enddate><creator>Liu, Lang</creator><creator>Lai, Xinping</creator><creator>Song, Ki-Il</creator><creator>Lao, Dezheng</creator><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7SR</scope><scope>7TB</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>FR3</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>RC3</scope></search><sort><creationdate>20150601</creationdate><title>Intelligent prediction model based on genetic algorithm and support vector machine for evaluation of mining-induced building damage</title><author>Liu, Lang ; Lai, Xinping ; Song, Ki-Il ; Lao, Dezheng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c308t-9be718106d26d328641ad277ffc3f614ddab667c46e94b545dfb148f9be735ab3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Buildings</topic><topic>Construction</topic><topic>Construction equipment</topic><topic>Damage</topic><topic>Genetic algorithms</topic><topic>Mathematical models</topic><topic>Regression</topic><topic>Support vector machines</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Lang</creatorcontrib><creatorcontrib>Lai, Xinping</creatorcontrib><creatorcontrib>Song, Ki-Il</creatorcontrib><creatorcontrib>Lao, Dezheng</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</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>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><jtitle>Tehnički vjesnik</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Lang</au><au>Lai, Xinping</au><au>Song, Ki-Il</au><au>Lao, Dezheng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Intelligent prediction model based on genetic algorithm and support vector machine for evaluation of mining-induced building damage</atitle><jtitle>Tehnički vjesnik</jtitle><date>2015-06-01</date><risdate>2015</risdate><volume>22</volume><issue>3</issue><spage>743</spage><epage>753</epage><pages>743-753</pages><issn>1330-3651</issn><eissn>1848-6339</eissn><abstract>Characteristics of factors influencing mining-induced building damage are diverse, nonlinear, and multi-linear. For a better description of these factors, an intelligent prediction model for building damage induced by underground mining is developed based on the support vector machine (SVM). Based on a comprehensive consideration of geological, mining, and building factors, 10 factors are carefully selected. In particular, the mining-induced damage grade of the brick-concrete building structure is used as the main input variable in the proposed model. The damage grade and largest crack width of the brick-concrete building structure are selected as output variables in the proposed model. A total of 32 typical cases of mining-induced building damage in China are collected and used as training data. The radial basis function (RBF) is used for SVM classification and the application of the largest-crack-width regression model. To improve the model's generalizability and predictive capacity, the genetic algorithm (GA) is adopted to select effective parameters for the SVM model, and then the corresponding identification of six group samples is performed. The classification and regression results show that the proposed prediction model using GA-SVM can predict the mining-induced damage of a brick-concrete building structure, and the evaluation results show good agreement with monitored data. This suggests the practicality of the proposed model in a wide range of engineering problems.Original Abstract: Znacajke cimbenika koji utjecu na stetu nastalu na zgradama zbog iskapanja zemlje su razlicite, nelinearne i multi linearne. Za bolji opis tih cimbenika razvijen je inteligentni model zasnovan na potpornom vektorskom stroju (SVM) kojim se moze predvidjeti steta na zgradama nastala podzemnim iskapanjem. Na temelju opseznog razmatranja geoloskih, rudarskih i gradevnih faktora, 10 ih je pazljivo odabrano. Posebice je, kao glavna ulazna varijabla u predlozenom modelu, upotrebljen stupanj ostecenja gradevine od opeke i betona, nastao podzemnim iskapanjem. Stupanj ostecenja i najsira pukotina gradevinske konstrukcije od opeke i betona izabrani su kao izlazne varijable u predlozenom modelu. Ukupno su odabrana 32 tipicna slucaja ostecenja zgrada u Kini zbog iskapanja zemlje te upotrebljena kao podaci za uvjezbavanje (training data). Funkcija radijalne baze (radial basis function - RBF) upotrebljena je za SVM klasifikaciju i primjenu modela regresije s najvecom sirinom pukotine. Kako bi primjena modela bila sto sira i njegova sposobnost predvidanja sto veca, za izbor ucinkovitih parametara za SVM model upotrebljen je genetski algoritam (GA), i tada je izvrsena odgovarajuca identifikacija sest grupa uzoraka. Rezultati klasifikacije i regresije pokazuju da se predlozenim modelom, koji koristi GA-SVM, moze predvidjeti steta na konstrukciji od opeke i betona, nastala iskapanjem zemlje, a rezultati procjene u skladu su s pracenim podacima. To navodi na prakticnost primjene predlozenog modela u rjesavanju razlicitih inzinjerskih problema.</abstract><doi>10.17559/TV-20150213085300</doi><tpages>11</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Buildings Construction Construction equipment Damage Genetic algorithms Mathematical models Regression Support vector machines |
title | Intelligent prediction model based on genetic algorithm and support vector machine for evaluation of mining-induced building damage |
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