Improving the Performance of Intelligent Back Analysis for Tunneling Using Optimized Fuzzy Systems: Case Study of the Karaj Subway Line 2 in Iran
AbstractTunnels are often designed by uncertain geotechnical data. In order to reduce these uncertainties, back analysis is commonly selected to re-estimate the assumed parameters. This paper presents a novel, intelligent back analysis method combining fuzzy systems, imperialistic competitive algori...
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| Autor*in: |
Torabi, Seyed Rahman [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Rechteinformationen: |
Nutzungsrecht: © 2014 American Society of Civil Engineers |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Journal of computing in civil engineering - New York, NY : ASCE, 1987, 29(2015), 6 |
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| Übergeordnetes Werk: |
volume:29 ; year:2015 ; number:6 |
| Links: |
|---|
| DOI / URN: |
10.1061/(ASCE)CP.1943-5487.0000421 |
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| Katalog-ID: |
OLC1957696044 |
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| 520 | |a AbstractTunnels are often designed by uncertain geotechnical data. In order to reduce these uncertainties, back analysis is commonly selected to re-estimate the assumed parameters. This paper presents a novel, intelligent back analysis method combining fuzzy systems, imperialistic competitive algorithm, and numerical analysis. The proposed methodology comprises three phases. First, a database of a real case study and numerical analysis are used to develop the training and testing data of the study. In the second phase, the nonlinear relationship of two sets of parameters, including geomechanical parameters of the soil mass and the zone stress conditions, with surface settlement is investigated by three fuzzy models. These models are designed by three methods including particle swarm optimization, imperialistic competitive algorithm, and integration of nearest neighborhood clustering with gradient descent training. In the last phase, imperialistic competitive algorithm is employed one more time to implement the back analysis procedure in the three tuned fuzzy models. Finally, verification of the models is done with the numerical analysis on the results of back analysis, and then the results are compared with the measured values of settlements. The results introduced the particle swarm optimization tuned fuzzy model as the most accurate intelligent model. | ||
| 540 | |a Nutzungsrecht: © 2014 American Society of Civil Engineers | ||
| 650 | 4 | |a Case Study | |
| 650 | 4 | |a Case Studies | |
| 650 | 4 | |a Tunneling (Physics) | |
| 650 | 4 | |a Sensitivity analysis | |
| 650 | 4 | |a Fuzzy logic | |
| 650 | 4 | |a Fuzzy algorithms | |
| 650 | 4 | |a Research | |
| 650 | 4 | |a Fuzzy systems | |
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| 700 | 1 | |a Khamesi, Hossein |4 oth | |
| 700 | 1 | |a Ghadiri, Zakarya |4 oth | |
| 700 | 1 | |a Mirzaei-Nasirabad, Hossein |4 oth | |
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10.1061/(ASCE)CP.1943-5487.0000421 doi PQ20160617 (DE-627)OLC1957696044 (DE-599)GBVOLC1957696044 (PRQ)a1811-5d2d40bffb786d736578e75eb5e683b03df279911f5e2f50a31fb50e902e97b20 (KEY)0159946120150000029000600000improvingtheperformanceofintelligentbackanalysisfo DE-627 ger DE-627 rakwb eng 690 ZDB 56.03 bkl Torabi, Seyed Rahman verfasserin aut Improving the Performance of Intelligent Back Analysis for Tunneling Using Optimized Fuzzy Systems: Case Study of the Karaj Subway Line 2 in Iran 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier AbstractTunnels are often designed by uncertain geotechnical data. In order to reduce these uncertainties, back analysis is commonly selected to re-estimate the assumed parameters. This paper presents a novel, intelligent back analysis method combining fuzzy systems, imperialistic competitive algorithm, and numerical analysis. The proposed methodology comprises three phases. First, a database of a real case study and numerical analysis are used to develop the training and testing data of the study. In the second phase, the nonlinear relationship of two sets of parameters, including geomechanical parameters of the soil mass and the zone stress conditions, with surface settlement is investigated by three fuzzy models. These models are designed by three methods including particle swarm optimization, imperialistic competitive algorithm, and integration of nearest neighborhood clustering with gradient descent training. In the last phase, imperialistic competitive algorithm is employed one more time to implement the back analysis procedure in the three tuned fuzzy models. Finally, verification of the models is done with the numerical analysis on the results of back analysis, and then the results are compared with the measured values of settlements. The results introduced the particle swarm optimization tuned fuzzy model as the most accurate intelligent model. Nutzungsrecht: © 2014 American Society of Civil Engineers Case Study Case Studies Tunneling (Physics) Sensitivity analysis Fuzzy logic Fuzzy algorithms Research Fuzzy systems Usage Khamesi, Hossein oth Ghadiri, Zakarya oth Mirzaei-Nasirabad, Hossein oth Enthalten in Journal of computing in civil engineering New York, NY : ASCE, 1987 29(2015), 6 (DE-627)12938383X (DE-600)166033-0 (DE-576)014770865 0887-3801 nnns volume:29 year:2015 number:6 http://dx.doi.org/10.1061/(ASCE)CP.1943-5487.0000421 Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2014 GBV_ILN_4700 56.03 AVZ AR 29 2015 6 |
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10.1061/(ASCE)CP.1943-5487.0000421 doi PQ20160617 (DE-627)OLC1957696044 (DE-599)GBVOLC1957696044 (PRQ)a1811-5d2d40bffb786d736578e75eb5e683b03df279911f5e2f50a31fb50e902e97b20 (KEY)0159946120150000029000600000improvingtheperformanceofintelligentbackanalysisfo DE-627 ger DE-627 rakwb eng 690 ZDB 56.03 bkl Torabi, Seyed Rahman verfasserin aut Improving the Performance of Intelligent Back Analysis for Tunneling Using Optimized Fuzzy Systems: Case Study of the Karaj Subway Line 2 in Iran 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier AbstractTunnels are often designed by uncertain geotechnical data. In order to reduce these uncertainties, back analysis is commonly selected to re-estimate the assumed parameters. This paper presents a novel, intelligent back analysis method combining fuzzy systems, imperialistic competitive algorithm, and numerical analysis. The proposed methodology comprises three phases. First, a database of a real case study and numerical analysis are used to develop the training and testing data of the study. In the second phase, the nonlinear relationship of two sets of parameters, including geomechanical parameters of the soil mass and the zone stress conditions, with surface settlement is investigated by three fuzzy models. These models are designed by three methods including particle swarm optimization, imperialistic competitive algorithm, and integration of nearest neighborhood clustering with gradient descent training. In the last phase, imperialistic competitive algorithm is employed one more time to implement the back analysis procedure in the three tuned fuzzy models. Finally, verification of the models is done with the numerical analysis on the results of back analysis, and then the results are compared with the measured values of settlements. The results introduced the particle swarm optimization tuned fuzzy model as the most accurate intelligent model. Nutzungsrecht: © 2014 American Society of Civil Engineers Case Study Case Studies Tunneling (Physics) Sensitivity analysis Fuzzy logic Fuzzy algorithms Research Fuzzy systems Usage Khamesi, Hossein oth Ghadiri, Zakarya oth Mirzaei-Nasirabad, Hossein oth Enthalten in Journal of computing in civil engineering New York, NY : ASCE, 1987 29(2015), 6 (DE-627)12938383X (DE-600)166033-0 (DE-576)014770865 0887-3801 nnns volume:29 year:2015 number:6 http://dx.doi.org/10.1061/(ASCE)CP.1943-5487.0000421 Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2014 GBV_ILN_4700 56.03 AVZ AR 29 2015 6 |
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10.1061/(ASCE)CP.1943-5487.0000421 doi PQ20160617 (DE-627)OLC1957696044 (DE-599)GBVOLC1957696044 (PRQ)a1811-5d2d40bffb786d736578e75eb5e683b03df279911f5e2f50a31fb50e902e97b20 (KEY)0159946120150000029000600000improvingtheperformanceofintelligentbackanalysisfo DE-627 ger DE-627 rakwb eng 690 ZDB 56.03 bkl Torabi, Seyed Rahman verfasserin aut Improving the Performance of Intelligent Back Analysis for Tunneling Using Optimized Fuzzy Systems: Case Study of the Karaj Subway Line 2 in Iran 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier AbstractTunnels are often designed by uncertain geotechnical data. In order to reduce these uncertainties, back analysis is commonly selected to re-estimate the assumed parameters. This paper presents a novel, intelligent back analysis method combining fuzzy systems, imperialistic competitive algorithm, and numerical analysis. The proposed methodology comprises three phases. First, a database of a real case study and numerical analysis are used to develop the training and testing data of the study. In the second phase, the nonlinear relationship of two sets of parameters, including geomechanical parameters of the soil mass and the zone stress conditions, with surface settlement is investigated by three fuzzy models. These models are designed by three methods including particle swarm optimization, imperialistic competitive algorithm, and integration of nearest neighborhood clustering with gradient descent training. In the last phase, imperialistic competitive algorithm is employed one more time to implement the back analysis procedure in the three tuned fuzzy models. Finally, verification of the models is done with the numerical analysis on the results of back analysis, and then the results are compared with the measured values of settlements. The results introduced the particle swarm optimization tuned fuzzy model as the most accurate intelligent model. Nutzungsrecht: © 2014 American Society of Civil Engineers Case Study Case Studies Tunneling (Physics) Sensitivity analysis Fuzzy logic Fuzzy algorithms Research Fuzzy systems Usage Khamesi, Hossein oth Ghadiri, Zakarya oth Mirzaei-Nasirabad, Hossein oth Enthalten in Journal of computing in civil engineering New York, NY : ASCE, 1987 29(2015), 6 (DE-627)12938383X (DE-600)166033-0 (DE-576)014770865 0887-3801 nnns volume:29 year:2015 number:6 http://dx.doi.org/10.1061/(ASCE)CP.1943-5487.0000421 Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2014 GBV_ILN_4700 56.03 AVZ AR 29 2015 6 |
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10.1061/(ASCE)CP.1943-5487.0000421 doi PQ20160617 (DE-627)OLC1957696044 (DE-599)GBVOLC1957696044 (PRQ)a1811-5d2d40bffb786d736578e75eb5e683b03df279911f5e2f50a31fb50e902e97b20 (KEY)0159946120150000029000600000improvingtheperformanceofintelligentbackanalysisfo DE-627 ger DE-627 rakwb eng 690 ZDB 56.03 bkl Torabi, Seyed Rahman verfasserin aut Improving the Performance of Intelligent Back Analysis for Tunneling Using Optimized Fuzzy Systems: Case Study of the Karaj Subway Line 2 in Iran 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier AbstractTunnels are often designed by uncertain geotechnical data. In order to reduce these uncertainties, back analysis is commonly selected to re-estimate the assumed parameters. This paper presents a novel, intelligent back analysis method combining fuzzy systems, imperialistic competitive algorithm, and numerical analysis. The proposed methodology comprises three phases. First, a database of a real case study and numerical analysis are used to develop the training and testing data of the study. In the second phase, the nonlinear relationship of two sets of parameters, including geomechanical parameters of the soil mass and the zone stress conditions, with surface settlement is investigated by three fuzzy models. These models are designed by three methods including particle swarm optimization, imperialistic competitive algorithm, and integration of nearest neighborhood clustering with gradient descent training. In the last phase, imperialistic competitive algorithm is employed one more time to implement the back analysis procedure in the three tuned fuzzy models. Finally, verification of the models is done with the numerical analysis on the results of back analysis, and then the results are compared with the measured values of settlements. The results introduced the particle swarm optimization tuned fuzzy model as the most accurate intelligent model. Nutzungsrecht: © 2014 American Society of Civil Engineers Case Study Case Studies Tunneling (Physics) Sensitivity analysis Fuzzy logic Fuzzy algorithms Research Fuzzy systems Usage Khamesi, Hossein oth Ghadiri, Zakarya oth Mirzaei-Nasirabad, Hossein oth Enthalten in Journal of computing in civil engineering New York, NY : ASCE, 1987 29(2015), 6 (DE-627)12938383X (DE-600)166033-0 (DE-576)014770865 0887-3801 nnns volume:29 year:2015 number:6 http://dx.doi.org/10.1061/(ASCE)CP.1943-5487.0000421 Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2014 GBV_ILN_4700 56.03 AVZ AR 29 2015 6 |
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10.1061/(ASCE)CP.1943-5487.0000421 doi PQ20160617 (DE-627)OLC1957696044 (DE-599)GBVOLC1957696044 (PRQ)a1811-5d2d40bffb786d736578e75eb5e683b03df279911f5e2f50a31fb50e902e97b20 (KEY)0159946120150000029000600000improvingtheperformanceofintelligentbackanalysisfo DE-627 ger DE-627 rakwb eng 690 ZDB 56.03 bkl Torabi, Seyed Rahman verfasserin aut Improving the Performance of Intelligent Back Analysis for Tunneling Using Optimized Fuzzy Systems: Case Study of the Karaj Subway Line 2 in Iran 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier AbstractTunnels are often designed by uncertain geotechnical data. In order to reduce these uncertainties, back analysis is commonly selected to re-estimate the assumed parameters. This paper presents a novel, intelligent back analysis method combining fuzzy systems, imperialistic competitive algorithm, and numerical analysis. The proposed methodology comprises three phases. First, a database of a real case study and numerical analysis are used to develop the training and testing data of the study. In the second phase, the nonlinear relationship of two sets of parameters, including geomechanical parameters of the soil mass and the zone stress conditions, with surface settlement is investigated by three fuzzy models. These models are designed by three methods including particle swarm optimization, imperialistic competitive algorithm, and integration of nearest neighborhood clustering with gradient descent training. In the last phase, imperialistic competitive algorithm is employed one more time to implement the back analysis procedure in the three tuned fuzzy models. Finally, verification of the models is done with the numerical analysis on the results of back analysis, and then the results are compared with the measured values of settlements. The results introduced the particle swarm optimization tuned fuzzy model as the most accurate intelligent model. Nutzungsrecht: © 2014 American Society of Civil Engineers Case Study Case Studies Tunneling (Physics) Sensitivity analysis Fuzzy logic Fuzzy algorithms Research Fuzzy systems Usage Khamesi, Hossein oth Ghadiri, Zakarya oth Mirzaei-Nasirabad, Hossein oth Enthalten in Journal of computing in civil engineering New York, NY : ASCE, 1987 29(2015), 6 (DE-627)12938383X (DE-600)166033-0 (DE-576)014770865 0887-3801 nnns volume:29 year:2015 number:6 http://dx.doi.org/10.1061/(ASCE)CP.1943-5487.0000421 Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2014 GBV_ILN_4700 56.03 AVZ AR 29 2015 6 |
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| author |
Torabi, Seyed Rahman |
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690 ZDB 56.03 bkl Improving the Performance of Intelligent Back Analysis for Tunneling Using Optimized Fuzzy Systems: Case Study of the Karaj Subway Line 2 in Iran Case Study Case Studies Tunneling (Physics) Sensitivity analysis Fuzzy logic Fuzzy algorithms Research Fuzzy systems Usage |
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Improving the Performance of Intelligent Back Analysis for Tunneling Using Optimized Fuzzy Systems: Case Study of the Karaj Subway Line 2 in Iran |
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Improving the Performance of Intelligent Back Analysis for Tunneling Using Optimized Fuzzy Systems: Case Study of the Karaj Subway Line 2 in Iran |
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improving the performance of intelligent back analysis for tunneling using optimized fuzzy systems: case study of the karaj subway line 2 in iran |
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Improving the Performance of Intelligent Back Analysis for Tunneling Using Optimized Fuzzy Systems: Case Study of the Karaj Subway Line 2 in Iran |
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AbstractTunnels are often designed by uncertain geotechnical data. In order to reduce these uncertainties, back analysis is commonly selected to re-estimate the assumed parameters. This paper presents a novel, intelligent back analysis method combining fuzzy systems, imperialistic competitive algorithm, and numerical analysis. The proposed methodology comprises three phases. First, a database of a real case study and numerical analysis are used to develop the training and testing data of the study. In the second phase, the nonlinear relationship of two sets of parameters, including geomechanical parameters of the soil mass and the zone stress conditions, with surface settlement is investigated by three fuzzy models. These models are designed by three methods including particle swarm optimization, imperialistic competitive algorithm, and integration of nearest neighborhood clustering with gradient descent training. In the last phase, imperialistic competitive algorithm is employed one more time to implement the back analysis procedure in the three tuned fuzzy models. Finally, verification of the models is done with the numerical analysis on the results of back analysis, and then the results are compared with the measured values of settlements. The results introduced the particle swarm optimization tuned fuzzy model as the most accurate intelligent model. |
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AbstractTunnels are often designed by uncertain geotechnical data. In order to reduce these uncertainties, back analysis is commonly selected to re-estimate the assumed parameters. This paper presents a novel, intelligent back analysis method combining fuzzy systems, imperialistic competitive algorithm, and numerical analysis. The proposed methodology comprises three phases. First, a database of a real case study and numerical analysis are used to develop the training and testing data of the study. In the second phase, the nonlinear relationship of two sets of parameters, including geomechanical parameters of the soil mass and the zone stress conditions, with surface settlement is investigated by three fuzzy models. These models are designed by three methods including particle swarm optimization, imperialistic competitive algorithm, and integration of nearest neighborhood clustering with gradient descent training. In the last phase, imperialistic competitive algorithm is employed one more time to implement the back analysis procedure in the three tuned fuzzy models. Finally, verification of the models is done with the numerical analysis on the results of back analysis, and then the results are compared with the measured values of settlements. The results introduced the particle swarm optimization tuned fuzzy model as the most accurate intelligent model. |
| abstract_unstemmed |
AbstractTunnels are often designed by uncertain geotechnical data. In order to reduce these uncertainties, back analysis is commonly selected to re-estimate the assumed parameters. This paper presents a novel, intelligent back analysis method combining fuzzy systems, imperialistic competitive algorithm, and numerical analysis. The proposed methodology comprises three phases. First, a database of a real case study and numerical analysis are used to develop the training and testing data of the study. In the second phase, the nonlinear relationship of two sets of parameters, including geomechanical parameters of the soil mass and the zone stress conditions, with surface settlement is investigated by three fuzzy models. These models are designed by three methods including particle swarm optimization, imperialistic competitive algorithm, and integration of nearest neighborhood clustering with gradient descent training. In the last phase, imperialistic competitive algorithm is employed one more time to implement the back analysis procedure in the three tuned fuzzy models. Finally, verification of the models is done with the numerical analysis on the results of back analysis, and then the results are compared with the measured values of settlements. The results introduced the particle swarm optimization tuned fuzzy model as the most accurate intelligent model. |
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Improving the Performance of Intelligent Back Analysis for Tunneling Using Optimized Fuzzy Systems: Case Study of the Karaj Subway Line 2 in Iran |
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http://dx.doi.org/10.1061/(ASCE)CP.1943-5487.0000421 |
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Khamesi, Hossein Ghadiri, Zakarya Mirzaei-Nasirabad, Hossein |
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