Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling
Abstract The potential surface settlement, especially in urban areas, is one of the most hazardous factors in subway and other infrastructure tunnel excavations. Therefore, accurate prediction of maximum surface settlement (MSS) is essential to minimize the possible risk of damage. This paper presen...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Hasanipanah, Mahdi [verfasserIn] |
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| Format: |
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 - Springer London, 1985, 32(2016), 4 vom: 28. März, Seite 705-715 |
|---|---|
| Übergeordnetes Werk: |
volume:32 ; year:2016 ; number:4 ; day:28 ; month:03 ; pages:705-715 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00366-016-0447-0 |
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OLC2064362274 |
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| 520 | |a Abstract The potential surface settlement, especially in urban areas, is one of the most hazardous factors in subway and other infrastructure tunnel excavations. Therefore, accurate prediction of maximum surface settlement (MSS) is essential to minimize the possible risk of damage. This paper presents a new hybrid model of artificial neural network (ANN) optimized by particle swarm optimization (PSO) for prediction of MSS. Here, this combination is abbreviated using PSO-ANN. To indicate the performance capacity of the PSO-ANN model in predicting MSS, a pre-developed ANN model was also developed. To construct the mentioned models, horizontal to vertical stress ratio, cohesion and Young’s modulus were set as input parameters, whereas MSS was considered as system output. A database consisting of 143 data sets, obtained from the line No. 2 of Karaj subway, in Iran, was used to develop the predictive models. The performance of the predictive models was evaluated by comparing performance prediction parameters, including root mean square error (RMSE), variance account for (VAF) and coefficient correlation (R2). The results indicate that the proposed PSO-ANN model is able to predict MSS with a higher degree of accuracy in comparison with the ANN results. In addition, the results of sensitivity analysis show that the horizontal to vertical stress ratio has slightly higher effect of MSS compared to other model inputs. | ||
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| 700 | 1 | |a Khamesi, Hossein |4 aut | |
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10.1007/s00366-016-0447-0 doi (DE-627)OLC2064362274 (DE-He213)s00366-016-0447-0-p DE-627 ger DE-627 rakwb eng 004 600 VZ Hasanipanah, Mahdi verfasserin aut Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2016 Abstract The potential surface settlement, especially in urban areas, is one of the most hazardous factors in subway and other infrastructure tunnel excavations. Therefore, accurate prediction of maximum surface settlement (MSS) is essential to minimize the possible risk of damage. This paper presents a new hybrid model of artificial neural network (ANN) optimized by particle swarm optimization (PSO) for prediction of MSS. Here, this combination is abbreviated using PSO-ANN. To indicate the performance capacity of the PSO-ANN model in predicting MSS, a pre-developed ANN model was also developed. To construct the mentioned models, horizontal to vertical stress ratio, cohesion and Young’s modulus were set as input parameters, whereas MSS was considered as system output. A database consisting of 143 data sets, obtained from the line No. 2 of Karaj subway, in Iran, was used to develop the predictive models. The performance of the predictive models was evaluated by comparing performance prediction parameters, including root mean square error (RMSE), variance account for (VAF) and coefficient correlation (R2). The results indicate that the proposed PSO-ANN model is able to predict MSS with a higher degree of accuracy in comparison with the ANN results. In addition, the results of sensitivity analysis show that the horizontal to vertical stress ratio has slightly higher effect of MSS compared to other model inputs. Tunneling Surface settlement PSO-ANN Hybrid model Noorian-Bidgoli, Majid aut Jahed Armaghani, Danial aut Khamesi, Hossein aut Enthalten in Engineering with computers Springer London, 1985 32(2016), 4 vom: 28. März, Seite 705-715 (DE-627)129175404 (DE-600)51529-2 (DE-576)014455536 0177-0667 nnns volume:32 year:2016 number:4 day:28 month:03 pages:705-715 https://doi.org/10.1007/s00366-016-0447-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 GBV_ILN_4318 GBV_ILN_4323 AR 32 2016 4 28 03 705-715 |
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10.1007/s00366-016-0447-0 doi (DE-627)OLC2064362274 (DE-He213)s00366-016-0447-0-p DE-627 ger DE-627 rakwb eng 004 600 VZ Hasanipanah, Mahdi verfasserin aut Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2016 Abstract The potential surface settlement, especially in urban areas, is one of the most hazardous factors in subway and other infrastructure tunnel excavations. Therefore, accurate prediction of maximum surface settlement (MSS) is essential to minimize the possible risk of damage. This paper presents a new hybrid model of artificial neural network (ANN) optimized by particle swarm optimization (PSO) for prediction of MSS. Here, this combination is abbreviated using PSO-ANN. To indicate the performance capacity of the PSO-ANN model in predicting MSS, a pre-developed ANN model was also developed. To construct the mentioned models, horizontal to vertical stress ratio, cohesion and Young’s modulus were set as input parameters, whereas MSS was considered as system output. A database consisting of 143 data sets, obtained from the line No. 2 of Karaj subway, in Iran, was used to develop the predictive models. The performance of the predictive models was evaluated by comparing performance prediction parameters, including root mean square error (RMSE), variance account for (VAF) and coefficient correlation (R2). The results indicate that the proposed PSO-ANN model is able to predict MSS with a higher degree of accuracy in comparison with the ANN results. In addition, the results of sensitivity analysis show that the horizontal to vertical stress ratio has slightly higher effect of MSS compared to other model inputs. Tunneling Surface settlement PSO-ANN Hybrid model Noorian-Bidgoli, Majid aut Jahed Armaghani, Danial aut Khamesi, Hossein aut Enthalten in Engineering with computers Springer London, 1985 32(2016), 4 vom: 28. März, Seite 705-715 (DE-627)129175404 (DE-600)51529-2 (DE-576)014455536 0177-0667 nnns volume:32 year:2016 number:4 day:28 month:03 pages:705-715 https://doi.org/10.1007/s00366-016-0447-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 GBV_ILN_4318 GBV_ILN_4323 AR 32 2016 4 28 03 705-715 |
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10.1007/s00366-016-0447-0 doi (DE-627)OLC2064362274 (DE-He213)s00366-016-0447-0-p DE-627 ger DE-627 rakwb eng 004 600 VZ Hasanipanah, Mahdi verfasserin aut Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2016 Abstract The potential surface settlement, especially in urban areas, is one of the most hazardous factors in subway and other infrastructure tunnel excavations. Therefore, accurate prediction of maximum surface settlement (MSS) is essential to minimize the possible risk of damage. This paper presents a new hybrid model of artificial neural network (ANN) optimized by particle swarm optimization (PSO) for prediction of MSS. Here, this combination is abbreviated using PSO-ANN. To indicate the performance capacity of the PSO-ANN model in predicting MSS, a pre-developed ANN model was also developed. To construct the mentioned models, horizontal to vertical stress ratio, cohesion and Young’s modulus were set as input parameters, whereas MSS was considered as system output. A database consisting of 143 data sets, obtained from the line No. 2 of Karaj subway, in Iran, was used to develop the predictive models. The performance of the predictive models was evaluated by comparing performance prediction parameters, including root mean square error (RMSE), variance account for (VAF) and coefficient correlation (R2). The results indicate that the proposed PSO-ANN model is able to predict MSS with a higher degree of accuracy in comparison with the ANN results. In addition, the results of sensitivity analysis show that the horizontal to vertical stress ratio has slightly higher effect of MSS compared to other model inputs. Tunneling Surface settlement PSO-ANN Hybrid model Noorian-Bidgoli, Majid aut Jahed Armaghani, Danial aut Khamesi, Hossein aut Enthalten in Engineering with computers Springer London, 1985 32(2016), 4 vom: 28. März, Seite 705-715 (DE-627)129175404 (DE-600)51529-2 (DE-576)014455536 0177-0667 nnns volume:32 year:2016 number:4 day:28 month:03 pages:705-715 https://doi.org/10.1007/s00366-016-0447-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 GBV_ILN_4318 GBV_ILN_4323 AR 32 2016 4 28 03 705-715 |
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10.1007/s00366-016-0447-0 doi (DE-627)OLC2064362274 (DE-He213)s00366-016-0447-0-p DE-627 ger DE-627 rakwb eng 004 600 VZ Hasanipanah, Mahdi verfasserin aut Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2016 Abstract The potential surface settlement, especially in urban areas, is one of the most hazardous factors in subway and other infrastructure tunnel excavations. Therefore, accurate prediction of maximum surface settlement (MSS) is essential to minimize the possible risk of damage. This paper presents a new hybrid model of artificial neural network (ANN) optimized by particle swarm optimization (PSO) for prediction of MSS. Here, this combination is abbreviated using PSO-ANN. To indicate the performance capacity of the PSO-ANN model in predicting MSS, a pre-developed ANN model was also developed. To construct the mentioned models, horizontal to vertical stress ratio, cohesion and Young’s modulus were set as input parameters, whereas MSS was considered as system output. A database consisting of 143 data sets, obtained from the line No. 2 of Karaj subway, in Iran, was used to develop the predictive models. The performance of the predictive models was evaluated by comparing performance prediction parameters, including root mean square error (RMSE), variance account for (VAF) and coefficient correlation (R2). The results indicate that the proposed PSO-ANN model is able to predict MSS with a higher degree of accuracy in comparison with the ANN results. In addition, the results of sensitivity analysis show that the horizontal to vertical stress ratio has slightly higher effect of MSS compared to other model inputs. Tunneling Surface settlement PSO-ANN Hybrid model Noorian-Bidgoli, Majid aut Jahed Armaghani, Danial aut Khamesi, Hossein aut Enthalten in Engineering with computers Springer London, 1985 32(2016), 4 vom: 28. März, Seite 705-715 (DE-627)129175404 (DE-600)51529-2 (DE-576)014455536 0177-0667 nnns volume:32 year:2016 number:4 day:28 month:03 pages:705-715 https://doi.org/10.1007/s00366-016-0447-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 GBV_ILN_4318 GBV_ILN_4323 AR 32 2016 4 28 03 705-715 |
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10.1007/s00366-016-0447-0 doi (DE-627)OLC2064362274 (DE-He213)s00366-016-0447-0-p DE-627 ger DE-627 rakwb eng 004 600 VZ Hasanipanah, Mahdi verfasserin aut Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2016 Abstract The potential surface settlement, especially in urban areas, is one of the most hazardous factors in subway and other infrastructure tunnel excavations. Therefore, accurate prediction of maximum surface settlement (MSS) is essential to minimize the possible risk of damage. This paper presents a new hybrid model of artificial neural network (ANN) optimized by particle swarm optimization (PSO) for prediction of MSS. Here, this combination is abbreviated using PSO-ANN. To indicate the performance capacity of the PSO-ANN model in predicting MSS, a pre-developed ANN model was also developed. To construct the mentioned models, horizontal to vertical stress ratio, cohesion and Young’s modulus were set as input parameters, whereas MSS was considered as system output. A database consisting of 143 data sets, obtained from the line No. 2 of Karaj subway, in Iran, was used to develop the predictive models. The performance of the predictive models was evaluated by comparing performance prediction parameters, including root mean square error (RMSE), variance account for (VAF) and coefficient correlation (R2). The results indicate that the proposed PSO-ANN model is able to predict MSS with a higher degree of accuracy in comparison with the ANN results. In addition, the results of sensitivity analysis show that the horizontal to vertical stress ratio has slightly higher effect of MSS compared to other model inputs. Tunneling Surface settlement PSO-ANN Hybrid model Noorian-Bidgoli, Majid aut Jahed Armaghani, Danial aut Khamesi, Hossein aut Enthalten in Engineering with computers Springer London, 1985 32(2016), 4 vom: 28. März, Seite 705-715 (DE-627)129175404 (DE-600)51529-2 (DE-576)014455536 0177-0667 nnns volume:32 year:2016 number:4 day:28 month:03 pages:705-715 https://doi.org/10.1007/s00366-016-0447-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 GBV_ILN_4318 GBV_ILN_4323 AR 32 2016 4 28 03 705-715 |
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Hasanipanah, Mahdi Noorian-Bidgoli, Majid Jahed Armaghani, Danial Khamesi, Hossein |
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Hasanipanah, Mahdi |
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feasibility of pso-ann model for predicting surface settlement caused by tunneling |
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Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling |
| abstract |
Abstract The potential surface settlement, especially in urban areas, is one of the most hazardous factors in subway and other infrastructure tunnel excavations. Therefore, accurate prediction of maximum surface settlement (MSS) is essential to minimize the possible risk of damage. This paper presents a new hybrid model of artificial neural network (ANN) optimized by particle swarm optimization (PSO) for prediction of MSS. Here, this combination is abbreviated using PSO-ANN. To indicate the performance capacity of the PSO-ANN model in predicting MSS, a pre-developed ANN model was also developed. To construct the mentioned models, horizontal to vertical stress ratio, cohesion and Young’s modulus were set as input parameters, whereas MSS was considered as system output. A database consisting of 143 data sets, obtained from the line No. 2 of Karaj subway, in Iran, was used to develop the predictive models. The performance of the predictive models was evaluated by comparing performance prediction parameters, including root mean square error (RMSE), variance account for (VAF) and coefficient correlation (R2). The results indicate that the proposed PSO-ANN model is able to predict MSS with a higher degree of accuracy in comparison with the ANN results. In addition, the results of sensitivity analysis show that the horizontal to vertical stress ratio has slightly higher effect of MSS compared to other model inputs. © Springer-Verlag London 2016 |
| abstractGer |
Abstract The potential surface settlement, especially in urban areas, is one of the most hazardous factors in subway and other infrastructure tunnel excavations. Therefore, accurate prediction of maximum surface settlement (MSS) is essential to minimize the possible risk of damage. This paper presents a new hybrid model of artificial neural network (ANN) optimized by particle swarm optimization (PSO) for prediction of MSS. Here, this combination is abbreviated using PSO-ANN. To indicate the performance capacity of the PSO-ANN model in predicting MSS, a pre-developed ANN model was also developed. To construct the mentioned models, horizontal to vertical stress ratio, cohesion and Young’s modulus were set as input parameters, whereas MSS was considered as system output. A database consisting of 143 data sets, obtained from the line No. 2 of Karaj subway, in Iran, was used to develop the predictive models. The performance of the predictive models was evaluated by comparing performance prediction parameters, including root mean square error (RMSE), variance account for (VAF) and coefficient correlation (R2). The results indicate that the proposed PSO-ANN model is able to predict MSS with a higher degree of accuracy in comparison with the ANN results. In addition, the results of sensitivity analysis show that the horizontal to vertical stress ratio has slightly higher effect of MSS compared to other model inputs. © Springer-Verlag London 2016 |
| abstract_unstemmed |
Abstract The potential surface settlement, especially in urban areas, is one of the most hazardous factors in subway and other infrastructure tunnel excavations. Therefore, accurate prediction of maximum surface settlement (MSS) is essential to minimize the possible risk of damage. This paper presents a new hybrid model of artificial neural network (ANN) optimized by particle swarm optimization (PSO) for prediction of MSS. Here, this combination is abbreviated using PSO-ANN. To indicate the performance capacity of the PSO-ANN model in predicting MSS, a pre-developed ANN model was also developed. To construct the mentioned models, horizontal to vertical stress ratio, cohesion and Young’s modulus were set as input parameters, whereas MSS was considered as system output. A database consisting of 143 data sets, obtained from the line No. 2 of Karaj subway, in Iran, was used to develop the predictive models. The performance of the predictive models was evaluated by comparing performance prediction parameters, including root mean square error (RMSE), variance account for (VAF) and coefficient correlation (R2). The results indicate that the proposed PSO-ANN model is able to predict MSS with a higher degree of accuracy in comparison with the ANN results. In addition, the results of sensitivity analysis show that the horizontal to vertical stress ratio has slightly higher effect of MSS compared to other model inputs. © Springer-Verlag London 2016 |
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Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling |
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