Developing a new hybrid-AI model to predict blast-induced backbreak
Abstract Drilling and blasting is the predominant rock excavation method in mining and tunneling projects. Back-break (BB) is one of the most undesirable by-products of blasting and causing rock mine wall instability, increasing blasting cost as well as decreasing performance of the blasting. In thi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Hasanipanah, Mahdi [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag London 2016 |
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| Übergeordnetes Werk: |
Enthalten in: Engineering with computers - London : Springer, 1985, 33(2016), 3 vom: 20. Aug., Seite 349-359 |
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| Übergeordnetes Werk: |
volume:33 ; year:2016 ; number:3 ; day:20 ; month:08 ; pages:349-359 |
| Links: |
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| DOI / URN: |
10.1007/s00366-016-0477-7 |
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SPR004523679 |
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| 245 | 1 | 0 | |a Developing a new hybrid-AI model to predict blast-induced backbreak |
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| 520 | |a Abstract Drilling and blasting is the predominant rock excavation method in mining and tunneling projects. Back-break (BB) is one of the most undesirable by-products of blasting and causing rock mine wall instability, increasing blasting cost as well as decreasing performance of the blasting. In this research work, a practical new hybrid model to predict the blast-induced BB is proposed. The model is based on particle swarm optimization (PSO) in combination with adaptive neuro-fuzzy inference system (ANFIS). In this regard, a database including 80 datasets was collected from blasting operations of the Shur river dam region, Iran, and the values of four effective parameters on BB, i.e., burden, spacing, stemming and powder factor were precisely measured. In addition, the values of the BB for the whole 80 blasting events were measured. The accuracy of the proposed PSO-ANFIS model was also compared with the multiple linear regression (MLR). Median absolute error, coefficient of determination (R2) and root mean squared error, as three statistical functions, were used to evaluate the performance of the predictive models. The results achieved indicate that the PSO-ANFIS model has strong potential to indirect prediction of BB with high degree of accuracy. The R2 equal to 0.922 suggests the superiority of the PSO-ANFIS model in predicting BB, while this value was obtained as 0.857 for MLR model. | ||
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| 700 | 1 | |a Shahnazar, Azam |4 aut | |
| 700 | 1 | |a Arab, Hossein |4 aut | |
| 700 | 1 | |a Golzar, Saeid Bagheri |4 aut | |
| 700 | 1 | |a Amiri, Maryam |4 aut | |
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10.1007/s00366-016-0477-7 doi (DE-627)SPR004523679 (SPR)s00366-016-0477-7-e DE-627 ger DE-627 rakwb eng Hasanipanah, Mahdi verfasserin aut Developing a new hybrid-AI model to predict blast-induced backbreak 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2016 Abstract Drilling and blasting is the predominant rock excavation method in mining and tunneling projects. Back-break (BB) is one of the most undesirable by-products of blasting and causing rock mine wall instability, increasing blasting cost as well as decreasing performance of the blasting. In this research work, a practical new hybrid model to predict the blast-induced BB is proposed. The model is based on particle swarm optimization (PSO) in combination with adaptive neuro-fuzzy inference system (ANFIS). In this regard, a database including 80 datasets was collected from blasting operations of the Shur river dam region, Iran, and the values of four effective parameters on BB, i.e., burden, spacing, stemming and powder factor were precisely measured. In addition, the values of the BB for the whole 80 blasting events were measured. The accuracy of the proposed PSO-ANFIS model was also compared with the multiple linear regression (MLR). Median absolute error, coefficient of determination (R2) and root mean squared error, as three statistical functions, were used to evaluate the performance of the predictive models. The results achieved indicate that the PSO-ANFIS model has strong potential to indirect prediction of BB with high degree of accuracy. The R2 equal to 0.922 suggests the superiority of the PSO-ANFIS model in predicting BB, while this value was obtained as 0.857 for MLR model. Blasting (dpeaa)DE-He213 Back-break (dpeaa)DE-He213 PSO-ANFIS (dpeaa)DE-He213 MLR (dpeaa)DE-He213 Shahnazar, Azam aut Arab, Hossein aut Golzar, Saeid Bagheri aut Amiri, Maryam aut Enthalten in Engineering with computers London : Springer, 1985 33(2016), 3 vom: 20. Aug., Seite 349-359 (DE-627)253722551 (DE-600)1459031-1 1435-5663 nnns volume:33 year:2016 number:3 day:20 month:08 pages:349-359 https://dx.doi.org/10.1007/s00366-016-0477-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 33 2016 3 20 08 349-359 |
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10.1007/s00366-016-0477-7 doi (DE-627)SPR004523679 (SPR)s00366-016-0477-7-e DE-627 ger DE-627 rakwb eng Hasanipanah, Mahdi verfasserin aut Developing a new hybrid-AI model to predict blast-induced backbreak 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2016 Abstract Drilling and blasting is the predominant rock excavation method in mining and tunneling projects. Back-break (BB) is one of the most undesirable by-products of blasting and causing rock mine wall instability, increasing blasting cost as well as decreasing performance of the blasting. In this research work, a practical new hybrid model to predict the blast-induced BB is proposed. The model is based on particle swarm optimization (PSO) in combination with adaptive neuro-fuzzy inference system (ANFIS). In this regard, a database including 80 datasets was collected from blasting operations of the Shur river dam region, Iran, and the values of four effective parameters on BB, i.e., burden, spacing, stemming and powder factor were precisely measured. In addition, the values of the BB for the whole 80 blasting events were measured. The accuracy of the proposed PSO-ANFIS model was also compared with the multiple linear regression (MLR). Median absolute error, coefficient of determination (R2) and root mean squared error, as three statistical functions, were used to evaluate the performance of the predictive models. The results achieved indicate that the PSO-ANFIS model has strong potential to indirect prediction of BB with high degree of accuracy. The R2 equal to 0.922 suggests the superiority of the PSO-ANFIS model in predicting BB, while this value was obtained as 0.857 for MLR model. Blasting (dpeaa)DE-He213 Back-break (dpeaa)DE-He213 PSO-ANFIS (dpeaa)DE-He213 MLR (dpeaa)DE-He213 Shahnazar, Azam aut Arab, Hossein aut Golzar, Saeid Bagheri aut Amiri, Maryam aut Enthalten in Engineering with computers London : Springer, 1985 33(2016), 3 vom: 20. Aug., Seite 349-359 (DE-627)253722551 (DE-600)1459031-1 1435-5663 nnns volume:33 year:2016 number:3 day:20 month:08 pages:349-359 https://dx.doi.org/10.1007/s00366-016-0477-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 33 2016 3 20 08 349-359 |
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10.1007/s00366-016-0477-7 doi (DE-627)SPR004523679 (SPR)s00366-016-0477-7-e DE-627 ger DE-627 rakwb eng Hasanipanah, Mahdi verfasserin aut Developing a new hybrid-AI model to predict blast-induced backbreak 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2016 Abstract Drilling and blasting is the predominant rock excavation method in mining and tunneling projects. Back-break (BB) is one of the most undesirable by-products of blasting and causing rock mine wall instability, increasing blasting cost as well as decreasing performance of the blasting. In this research work, a practical new hybrid model to predict the blast-induced BB is proposed. The model is based on particle swarm optimization (PSO) in combination with adaptive neuro-fuzzy inference system (ANFIS). In this regard, a database including 80 datasets was collected from blasting operations of the Shur river dam region, Iran, and the values of four effective parameters on BB, i.e., burden, spacing, stemming and powder factor were precisely measured. In addition, the values of the BB for the whole 80 blasting events were measured. The accuracy of the proposed PSO-ANFIS model was also compared with the multiple linear regression (MLR). Median absolute error, coefficient of determination (R2) and root mean squared error, as three statistical functions, were used to evaluate the performance of the predictive models. The results achieved indicate that the PSO-ANFIS model has strong potential to indirect prediction of BB with high degree of accuracy. The R2 equal to 0.922 suggests the superiority of the PSO-ANFIS model in predicting BB, while this value was obtained as 0.857 for MLR model. Blasting (dpeaa)DE-He213 Back-break (dpeaa)DE-He213 PSO-ANFIS (dpeaa)DE-He213 MLR (dpeaa)DE-He213 Shahnazar, Azam aut Arab, Hossein aut Golzar, Saeid Bagheri aut Amiri, Maryam aut Enthalten in Engineering with computers London : Springer, 1985 33(2016), 3 vom: 20. Aug., Seite 349-359 (DE-627)253722551 (DE-600)1459031-1 1435-5663 nnns volume:33 year:2016 number:3 day:20 month:08 pages:349-359 https://dx.doi.org/10.1007/s00366-016-0477-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 33 2016 3 20 08 349-359 |
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10.1007/s00366-016-0477-7 doi (DE-627)SPR004523679 (SPR)s00366-016-0477-7-e DE-627 ger DE-627 rakwb eng Hasanipanah, Mahdi verfasserin aut Developing a new hybrid-AI model to predict blast-induced backbreak 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2016 Abstract Drilling and blasting is the predominant rock excavation method in mining and tunneling projects. Back-break (BB) is one of the most undesirable by-products of blasting and causing rock mine wall instability, increasing blasting cost as well as decreasing performance of the blasting. In this research work, a practical new hybrid model to predict the blast-induced BB is proposed. The model is based on particle swarm optimization (PSO) in combination with adaptive neuro-fuzzy inference system (ANFIS). In this regard, a database including 80 datasets was collected from blasting operations of the Shur river dam region, Iran, and the values of four effective parameters on BB, i.e., burden, spacing, stemming and powder factor were precisely measured. In addition, the values of the BB for the whole 80 blasting events were measured. The accuracy of the proposed PSO-ANFIS model was also compared with the multiple linear regression (MLR). Median absolute error, coefficient of determination (R2) and root mean squared error, as three statistical functions, were used to evaluate the performance of the predictive models. The results achieved indicate that the PSO-ANFIS model has strong potential to indirect prediction of BB with high degree of accuracy. The R2 equal to 0.922 suggests the superiority of the PSO-ANFIS model in predicting BB, while this value was obtained as 0.857 for MLR model. Blasting (dpeaa)DE-He213 Back-break (dpeaa)DE-He213 PSO-ANFIS (dpeaa)DE-He213 MLR (dpeaa)DE-He213 Shahnazar, Azam aut Arab, Hossein aut Golzar, Saeid Bagheri aut Amiri, Maryam aut Enthalten in Engineering with computers London : Springer, 1985 33(2016), 3 vom: 20. Aug., Seite 349-359 (DE-627)253722551 (DE-600)1459031-1 1435-5663 nnns volume:33 year:2016 number:3 day:20 month:08 pages:349-359 https://dx.doi.org/10.1007/s00366-016-0477-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 33 2016 3 20 08 349-359 |
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10.1007/s00366-016-0477-7 doi (DE-627)SPR004523679 (SPR)s00366-016-0477-7-e DE-627 ger DE-627 rakwb eng Hasanipanah, Mahdi verfasserin aut Developing a new hybrid-AI model to predict blast-induced backbreak 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2016 Abstract Drilling and blasting is the predominant rock excavation method in mining and tunneling projects. Back-break (BB) is one of the most undesirable by-products of blasting and causing rock mine wall instability, increasing blasting cost as well as decreasing performance of the blasting. In this research work, a practical new hybrid model to predict the blast-induced BB is proposed. The model is based on particle swarm optimization (PSO) in combination with adaptive neuro-fuzzy inference system (ANFIS). In this regard, a database including 80 datasets was collected from blasting operations of the Shur river dam region, Iran, and the values of four effective parameters on BB, i.e., burden, spacing, stemming and powder factor were precisely measured. In addition, the values of the BB for the whole 80 blasting events were measured. The accuracy of the proposed PSO-ANFIS model was also compared with the multiple linear regression (MLR). Median absolute error, coefficient of determination (R2) and root mean squared error, as three statistical functions, were used to evaluate the performance of the predictive models. The results achieved indicate that the PSO-ANFIS model has strong potential to indirect prediction of BB with high degree of accuracy. The R2 equal to 0.922 suggests the superiority of the PSO-ANFIS model in predicting BB, while this value was obtained as 0.857 for MLR model. Blasting (dpeaa)DE-He213 Back-break (dpeaa)DE-He213 PSO-ANFIS (dpeaa)DE-He213 MLR (dpeaa)DE-He213 Shahnazar, Azam aut Arab, Hossein aut Golzar, Saeid Bagheri aut Amiri, Maryam aut Enthalten in Engineering with computers London : Springer, 1985 33(2016), 3 vom: 20. Aug., Seite 349-359 (DE-627)253722551 (DE-600)1459031-1 1435-5663 nnns volume:33 year:2016 number:3 day:20 month:08 pages:349-359 https://dx.doi.org/10.1007/s00366-016-0477-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 33 2016 3 20 08 349-359 |
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Hasanipanah, Mahdi @@aut@@ Shahnazar, Azam @@aut@@ Arab, Hossein @@aut@@ Golzar, Saeid Bagheri @@aut@@ Amiri, Maryam @@aut@@ |
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Back-break (BB) is one of the most undesirable by-products of blasting and causing rock mine wall instability, increasing blasting cost as well as decreasing performance of the blasting. In this research work, a practical new hybrid model to predict the blast-induced BB is proposed. The model is based on particle swarm optimization (PSO) in combination with adaptive neuro-fuzzy inference system (ANFIS). In this regard, a database including 80 datasets was collected from blasting operations of the Shur river dam region, Iran, and the values of four effective parameters on BB, i.e., burden, spacing, stemming and powder factor were precisely measured. In addition, the values of the BB for the whole 80 blasting events were measured. The accuracy of the proposed PSO-ANFIS model was also compared with the multiple linear regression (MLR). Median absolute error, coefficient of determination (R2) and root mean squared error, as three statistical functions, were used to evaluate the performance of the predictive models. The results achieved indicate that the PSO-ANFIS model has strong potential to indirect prediction of BB with high degree of accuracy. 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Hasanipanah, Mahdi |
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Hasanipanah, Mahdi misc Blasting misc Back-break misc PSO-ANFIS misc MLR Developing a new hybrid-AI model to predict blast-induced backbreak |
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Developing a new hybrid-AI model to predict blast-induced backbreak Blasting (dpeaa)DE-He213 Back-break (dpeaa)DE-He213 PSO-ANFIS (dpeaa)DE-He213 MLR (dpeaa)DE-He213 |
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developing a new hybrid-ai model to predict blast-induced backbreak |
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Developing a new hybrid-AI model to predict blast-induced backbreak |
| abstract |
Abstract Drilling and blasting is the predominant rock excavation method in mining and tunneling projects. Back-break (BB) is one of the most undesirable by-products of blasting and causing rock mine wall instability, increasing blasting cost as well as decreasing performance of the blasting. In this research work, a practical new hybrid model to predict the blast-induced BB is proposed. The model is based on particle swarm optimization (PSO) in combination with adaptive neuro-fuzzy inference system (ANFIS). In this regard, a database including 80 datasets was collected from blasting operations of the Shur river dam region, Iran, and the values of four effective parameters on BB, i.e., burden, spacing, stemming and powder factor were precisely measured. In addition, the values of the BB for the whole 80 blasting events were measured. The accuracy of the proposed PSO-ANFIS model was also compared with the multiple linear regression (MLR). Median absolute error, coefficient of determination (R2) and root mean squared error, as three statistical functions, were used to evaluate the performance of the predictive models. The results achieved indicate that the PSO-ANFIS model has strong potential to indirect prediction of BB with high degree of accuracy. The R2 equal to 0.922 suggests the superiority of the PSO-ANFIS model in predicting BB, while this value was obtained as 0.857 for MLR model. © Springer-Verlag London 2016 |
| abstractGer |
Abstract Drilling and blasting is the predominant rock excavation method in mining and tunneling projects. Back-break (BB) is one of the most undesirable by-products of blasting and causing rock mine wall instability, increasing blasting cost as well as decreasing performance of the blasting. In this research work, a practical new hybrid model to predict the blast-induced BB is proposed. The model is based on particle swarm optimization (PSO) in combination with adaptive neuro-fuzzy inference system (ANFIS). In this regard, a database including 80 datasets was collected from blasting operations of the Shur river dam region, Iran, and the values of four effective parameters on BB, i.e., burden, spacing, stemming and powder factor were precisely measured. In addition, the values of the BB for the whole 80 blasting events were measured. The accuracy of the proposed PSO-ANFIS model was also compared with the multiple linear regression (MLR). Median absolute error, coefficient of determination (R2) and root mean squared error, as three statistical functions, were used to evaluate the performance of the predictive models. The results achieved indicate that the PSO-ANFIS model has strong potential to indirect prediction of BB with high degree of accuracy. The R2 equal to 0.922 suggests the superiority of the PSO-ANFIS model in predicting BB, while this value was obtained as 0.857 for MLR model. © Springer-Verlag London 2016 |
| abstract_unstemmed |
Abstract Drilling and blasting is the predominant rock excavation method in mining and tunneling projects. Back-break (BB) is one of the most undesirable by-products of blasting and causing rock mine wall instability, increasing blasting cost as well as decreasing performance of the blasting. In this research work, a practical new hybrid model to predict the blast-induced BB is proposed. The model is based on particle swarm optimization (PSO) in combination with adaptive neuro-fuzzy inference system (ANFIS). In this regard, a database including 80 datasets was collected from blasting operations of the Shur river dam region, Iran, and the values of four effective parameters on BB, i.e., burden, spacing, stemming and powder factor were precisely measured. In addition, the values of the BB for the whole 80 blasting events were measured. The accuracy of the proposed PSO-ANFIS model was also compared with the multiple linear regression (MLR). Median absolute error, coefficient of determination (R2) and root mean squared error, as three statistical functions, were used to evaluate the performance of the predictive models. The results achieved indicate that the PSO-ANFIS model has strong potential to indirect prediction of BB with high degree of accuracy. The R2 equal to 0.922 suggests the superiority of the PSO-ANFIS model in predicting BB, while this value was obtained as 0.857 for MLR model. © Springer-Verlag London 2016 |
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Developing a new hybrid-AI model to predict blast-induced backbreak |
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https://dx.doi.org/10.1007/s00366-016-0477-7 |
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Shahnazar, Azam Arab, Hossein Golzar, Saeid Bagheri Amiri, Maryam |
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Shahnazar, Azam Arab, Hossein Golzar, Saeid Bagheri Amiri, Maryam |
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10.1007/s00366-016-0477-7 |
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2024-07-04T01:27:13.985Z |
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| score |
7.397874 |