Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and back-break induced by blasting
Blasting is the process of use of explosives to excavate or remove the rock mass. The main objective of blasting operation is to provide proper rock fragmentation and to avoid undesirable environmental impacts such as ground vibration, flyrock and back-break. Therefore, proper predicting and subsequ...
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| Veröffentlicht in: | Engineering with computers 2016-04, Vol.32 (2), p.255-266 |
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| creator | Saghatforoush, Amir Monjezi, Masoud Shirani Faradonbeh, Roohollah Jahed Armaghani, Danial |
| description | Blasting is the process of use of explosives to excavate or remove the rock mass. The main objective of blasting operation is to provide proper rock fragmentation and to avoid undesirable environmental impacts such as ground vibration, flyrock and back-break. Therefore, proper predicting and subsequently optimizing these impacts may reduce damage on facilities and equipment. In this study, an artificial neural network (ANN) was developed to predict flyrock and back-break resulting from blasting. To do this, 97 blasting works in Delkan iron mine, Iran, were investigated and required blasting parameters were collected. The most influential parameters on flyrock and back-break, i.e. burden, spacing, hole length, stemming, and powder factor were considered as model inputs. Results of absolute error (Ea) and root mean square error (RMSE) (0.0137 and 0.063 for Ea and RMSE, respectively) reveal that ANN as a powerful tool can predict flyrock and back-break with high degree of accuracy. In addition, this paper presents a new metaheuristic approximation approach based on the ant colony optimization (ACO) for solving the problem of flyrock and back-break in Delkan iron mine. Considering changeable parameters of the ACO algorithm, blasting pattern parameters were optimized to minimize results of flyrock and back-break. Eventually, implementing ACO algorithm, reductions of 61 and 58 % were observed in flyrock and back-break results, respectively. |
| doi_str_mv | 10.1007/s00366-015-0415-0 |
| format | Article |
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The main objective of blasting operation is to provide proper rock fragmentation and to avoid undesirable environmental impacts such as ground vibration, flyrock and back-break. Therefore, proper predicting and subsequently optimizing these impacts may reduce damage on facilities and equipment. In this study, an artificial neural network (ANN) was developed to predict flyrock and back-break resulting from blasting. To do this, 97 blasting works in Delkan iron mine, Iran, were investigated and required blasting parameters were collected. The most influential parameters on flyrock and back-break, i.e. burden, spacing, hole length, stemming, and powder factor were considered as model inputs. Results of absolute error (Ea) and root mean square error (RMSE) (0.0137 and 0.063 for Ea and RMSE, respectively) reveal that ANN as a powerful tool can predict flyrock and back-break with high degree of accuracy. In addition, this paper presents a new metaheuristic approximation approach based on the ant colony optimization (ACO) for solving the problem of flyrock and back-break in Delkan iron mine. Considering changeable parameters of the ACO algorithm, blasting pattern parameters were optimized to minimize results of flyrock and back-break. Eventually, implementing ACO algorithm, reductions of 61 and 58 % were observed in flyrock and back-break results, respectively.</description><identifier>ISSN: 0177-0667</identifier><identifier>EISSN: 1435-5663</identifier><identifier>DOI: 10.1007/s00366-015-0415-0</identifier><language>eng</language><publisher>London: Springer London</publisher><subject>Algorithms ; Ant colony optimization ; Artificial neural networks ; Blasting (explosive) ; CAE) and Design ; Calculus of Variations and Optimal Control; Optimization ; Classical Mechanics ; Computer Science ; Computer-Aided Engineering (CAD ; Control ; Environmental impact ; Ground motion ; Heuristic methods ; Impact damage ; Iron and steel plants ; Math. Applications in Chemistry ; Mathematical and Computational Engineering ; Neural networks ; Original Article ; Predictions ; Rocks ; Root-mean-square errors ; Systems Theory</subject><ispartof>Engineering with computers, 2016-04, Vol.32 (2), p.255-266</ispartof><rights>Springer-Verlag London 2015</rights><rights>Engineering with Computers is a copyright of Springer, 2016.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c427t-b687006515b7afca053e2141610536d72e63cf2ca4fb15da5cbaa93815d842013</citedby><cites>FETCH-LOGICAL-c427t-b687006515b7afca053e2141610536d72e63cf2ca4fb15da5cbaa93815d842013</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/s00366-015-0415-0$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00366-015-0415-0$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27903,27904,41467,42536,51297</link.rule.ids></links><search><creatorcontrib>Saghatforoush, Amir</creatorcontrib><creatorcontrib>Monjezi, Masoud</creatorcontrib><creatorcontrib>Shirani Faradonbeh, Roohollah</creatorcontrib><creatorcontrib>Jahed Armaghani, Danial</creatorcontrib><title>Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and back-break induced by blasting</title><title>Engineering with computers</title><addtitle>Engineering with Computers</addtitle><description>Blasting is the process of use of explosives to excavate or remove the rock mass. The main objective of blasting operation is to provide proper rock fragmentation and to avoid undesirable environmental impacts such as ground vibration, flyrock and back-break. Therefore, proper predicting and subsequently optimizing these impacts may reduce damage on facilities and equipment. In this study, an artificial neural network (ANN) was developed to predict flyrock and back-break resulting from blasting. To do this, 97 blasting works in Delkan iron mine, Iran, were investigated and required blasting parameters were collected. The most influential parameters on flyrock and back-break, i.e. burden, spacing, hole length, stemming, and powder factor were considered as model inputs. Results of absolute error (Ea) and root mean square error (RMSE) (0.0137 and 0.063 for Ea and RMSE, respectively) reveal that ANN as a powerful tool can predict flyrock and back-break with high degree of accuracy. In addition, this paper presents a new metaheuristic approximation approach based on the ant colony optimization (ACO) for solving the problem of flyrock and back-break in Delkan iron mine. Considering changeable parameters of the ACO algorithm, blasting pattern parameters were optimized to minimize results of flyrock and back-break. Eventually, implementing ACO algorithm, reductions of 61 and 58 % were observed in flyrock and back-break results, respectively.</description><subject>Algorithms</subject><subject>Ant colony optimization</subject><subject>Artificial neural networks</subject><subject>Blasting (explosive)</subject><subject>CAE) and Design</subject><subject>Calculus of Variations and Optimal Control; Optimization</subject><subject>Classical Mechanics</subject><subject>Computer Science</subject><subject>Computer-Aided Engineering (CAD</subject><subject>Control</subject><subject>Environmental impact</subject><subject>Ground motion</subject><subject>Heuristic methods</subject><subject>Impact damage</subject><subject>Iron and steel plants</subject><subject>Math. Applications in Chemistry</subject><subject>Mathematical and Computational Engineering</subject><subject>Neural networks</subject><subject>Original Article</subject><subject>Predictions</subject><subject>Rocks</subject><subject>Root-mean-square errors</subject><subject>Systems Theory</subject><issn>0177-0667</issn><issn>1435-5663</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp1kM1OxCAUhYnRxHH0AdyRuK5e2gLt0kz8SyZxo2tCKYzMtFChE1MfwyeWSV3owsXl3nC_cwgHoUsC1wSA30SAgrEMCM2gPBxHaEHKgmaUseIYLYBwngFj_BSdxbgFIAVAvUBfK9831snReoe9wU7vg-xSGz982GHp2lQjVr7zbsJ-GG1vP2dadhsf7PjWR2x8wEPQrVXzJqn-oMnYdFPwanZspNplTdByh61r90qnqwk3nYyjdZtzdGJkF_XFT1-i1_u7l9Vjtn5-eFrdrjNV5nzMGlZxAEYJbbg0SgItdE5KwkiaWMtzzQplciVL0xDaSqoaKeuiSnNV5un_S3Q1-w7Bv-91HMXW74NLTwpS53Vd5cDLRJGZUsHHGLQRQ7C9DJMgIA7Rizl6kaIXh-gFJE0-a2Ji3UaHX87_ir4BiM2JPA</recordid><startdate>20160401</startdate><enddate>20160401</enddate><creator>Saghatforoush, Amir</creator><creator>Monjezi, Masoud</creator><creator>Shirani Faradonbeh, Roohollah</creator><creator>Jahed Armaghani, Danial</creator><general>Springer London</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SC</scope><scope>7TB</scope><scope>7XB</scope><scope>8AL</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KR7</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0N</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>Q9U</scope></search><sort><creationdate>20160401</creationdate><title>Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and back-break induced by blasting</title><author>Saghatforoush, Amir ; Monjezi, Masoud ; Shirani Faradonbeh, Roohollah ; Jahed Armaghani, Danial</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c427t-b687006515b7afca053e2141610536d72e63cf2ca4fb15da5cbaa93815d842013</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Algorithms</topic><topic>Ant colony optimization</topic><topic>Artificial neural networks</topic><topic>Blasting (explosive)</topic><topic>CAE) and Design</topic><topic>Calculus of Variations and Optimal Control; Optimization</topic><topic>Classical Mechanics</topic><topic>Computer Science</topic><topic>Computer-Aided Engineering (CAD</topic><topic>Control</topic><topic>Environmental impact</topic><topic>Ground motion</topic><topic>Heuristic methods</topic><topic>Impact damage</topic><topic>Iron and steel plants</topic><topic>Math. Applications in Chemistry</topic><topic>Mathematical and Computational Engineering</topic><topic>Neural networks</topic><topic>Original Article</topic><topic>Predictions</topic><topic>Rocks</topic><topic>Root-mean-square errors</topic><topic>Systems Theory</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Saghatforoush, Amir</creatorcontrib><creatorcontrib>Monjezi, Masoud</creatorcontrib><creatorcontrib>Shirani Faradonbeh, Roohollah</creatorcontrib><creatorcontrib>Jahed Armaghani, Danial</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</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>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</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>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</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 China</collection><collection>Engineering Collection</collection><collection>ProQuest Central Basic</collection><jtitle>Engineering with computers</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Saghatforoush, Amir</au><au>Monjezi, Masoud</au><au>Shirani Faradonbeh, Roohollah</au><au>Jahed Armaghani, Danial</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and back-break induced by blasting</atitle><jtitle>Engineering with computers</jtitle><stitle>Engineering with Computers</stitle><date>2016-04-01</date><risdate>2016</risdate><volume>32</volume><issue>2</issue><spage>255</spage><epage>266</epage><pages>255-266</pages><issn>0177-0667</issn><eissn>1435-5663</eissn><abstract>Blasting is the process of use of explosives to excavate or remove the rock mass. The main objective of blasting operation is to provide proper rock fragmentation and to avoid undesirable environmental impacts such as ground vibration, flyrock and back-break. Therefore, proper predicting and subsequently optimizing these impacts may reduce damage on facilities and equipment. In this study, an artificial neural network (ANN) was developed to predict flyrock and back-break resulting from blasting. To do this, 97 blasting works in Delkan iron mine, Iran, were investigated and required blasting parameters were collected. The most influential parameters on flyrock and back-break, i.e. burden, spacing, hole length, stemming, and powder factor were considered as model inputs. Results of absolute error (Ea) and root mean square error (RMSE) (0.0137 and 0.063 for Ea and RMSE, respectively) reveal that ANN as a powerful tool can predict flyrock and back-break with high degree of accuracy. In addition, this paper presents a new metaheuristic approximation approach based on the ant colony optimization (ACO) for solving the problem of flyrock and back-break in Delkan iron mine. Considering changeable parameters of the ACO algorithm, blasting pattern parameters were optimized to minimize results of flyrock and back-break. Eventually, implementing ACO algorithm, reductions of 61 and 58 % were observed in flyrock and back-break results, respectively.</abstract><cop>London</cop><pub>Springer London</pub><doi>10.1007/s00366-015-0415-0</doi><tpages>12</tpages></addata></record> |
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| subjects | Algorithms Ant colony optimization Artificial neural networks Blasting (explosive) CAE) and Design Calculus of Variations and Optimal Control Optimization Classical Mechanics Computer Science Computer-Aided Engineering (CAD Control Environmental impact Ground motion Heuristic methods Impact damage Iron and steel plants Math. Applications in Chemistry Mathematical and Computational Engineering Neural networks Original Article Predictions Rocks Root-mean-square errors Systems Theory |
| title | Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and back-break induced by blasting |
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