Inverse Analysis of Deep Excavation Using Differential Evolution Algorithm
Summary This paper presents the applications of the differential evolution (DE) algorithm in back analysis of soil parameters for deep excavation problems. A computer code, named Python‐based DE, is developed and incorporated into the commercial finite element software ABAQUS, with a parallel comput...
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| Published in | International journal for numerical and analytical methods in geomechanics Vol. 39; no. 2; pp. 115 - 134 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Bognor Regis
Blackwell Publishing Ltd
10.02.2015
Wiley Subscription Services, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0363-9061 1096-9853 1096-9853 |
| DOI | 10.1002/nag.2287 |
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| Abstract | Summary
This paper presents the applications of the differential evolution (DE) algorithm in back analysis of soil parameters for deep excavation problems. A computer code, named Python‐based DE, is developed and incorporated into the commercial finite element software ABAQUS, with a parallel computing technique to run an FE analysis for all trail vectors of one generation in DE in multiple cores of a cluster, which dramatically reduces the computational time. A synthetic case and a well‐instrumented real case, that is, the Taipei National Enterprise Center (TNEC) project, are used to demonstrate the capability of the proposed back‐analysis procedure. Results show that multiple soil parameters are well identified by back analysis using a DE optimization algorithm for highly nonlinear problems. For the synthetic excavation case, the back‐analyzed parameters are basically identical to the input parameters that are used to generate synthetic response of wall deflection. For the TNEC case with a total of nine parameters to be back analyzed, the relative errors of wall deflection for the last three stages are 2.2, 1.1, and 1.0%, respectively. Robustness of the back‐estimated parameters is further illustrated by a forward prediction. The wall deflection in the subsequent stages can be satisfactorily predicted using the back‐analyzed soil parameters at early stages. Copyright © 2014 John Wiley & Sons, Ltd. |
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| AbstractList | Summary
This paper presents the applications of the differential evolution (DE) algorithm in back analysis of soil parameters for deep excavation problems. A computer code, named Python‐based DE, is developed and incorporated into the commercial finite element software ABAQUS, with a parallel computing technique to run an FE analysis for all trail vectors of one generation in DE in multiple cores of a cluster, which dramatically reduces the computational time. A synthetic case and a well‐instrumented real case, that is, the Taipei National Enterprise Center (TNEC) project, are used to demonstrate the capability of the proposed back‐analysis procedure. Results show that multiple soil parameters are well identified by back analysis using a DE optimization algorithm for highly nonlinear problems. For the synthetic excavation case, the back‐analyzed parameters are basically identical to the input parameters that are used to generate synthetic response of wall deflection. For the TNEC case with a total of nine parameters to be back analyzed, the relative errors of wall deflection for the last three stages are 2.2, 1.1, and 1.0%, respectively. Robustness of the back‐estimated parameters is further illustrated by a forward prediction. The wall deflection in the subsequent stages can be satisfactorily predicted using the back‐analyzed soil parameters at early stages. Copyright © 2014 John Wiley & Sons, Ltd. This paper presents the applications of the differential evolution (DE) algorithm in back analysis of soil parameters for deep excavation problems. A computer code, named Python-based DE, is developed and incorporated into the commercial finite element software ABAQUS, with a parallel computing technique to run an FE analysis for all trail vectors of one generation in DE in multiple cores of a cluster, which dramatically reduces the computational time. A synthetic case and a well-instrumented real case, that is, the Taipei National Enterprise Center (TNEC) project, are used to demonstrate the capability of the proposed back-analysis procedure. Results show that multiple soil parameters are well identified by back analysis using a DE optimization algorithm for highly nonlinear problems. For the synthetic excavation case, the back-analyzed parameters are basically identical to the input parameters that are used to generate synthetic response of wall deflection. For the TNEC case with a total of nine parameters to be back analyzed, the relative errors of wall deflection for the last three stages are 2.2, 1.1, and 1.0%, respectively. Robustness of the back-estimated parameters is further illustrated by a forward prediction. The wall deflection in the subsequent stages can be satisfactorily predicted using the back-analyzed soil parameters at early stages. Copyright copyright 2014 John Wiley & Sons, Ltd. Summary This paper presents the applications of the differential evolution (DE) algorithm in back analysis of soil parameters for deep excavation problems. A computer code, named Python-based DE, is developed and incorporated into the commercial finite element software ABAQUS, with a parallel computing technique to run an FE analysis for all trail vectors of one generation in DE in multiple cores of a cluster, which dramatically reduces the computational time. A synthetic case and a well-instrumented real case, that is, the Taipei National Enterprise Center (TNEC) project, are used to demonstrate the capability of the proposed back-analysis procedure. Results show that multiple soil parameters are well identified by back analysis using a DE optimization algorithm for highly nonlinear problems. For the synthetic excavation case, the back-analyzed parameters are basically identical to the input parameters that are used to generate synthetic response of wall deflection. For the TNEC case with a total of nine parameters to be back analyzed, the relative errors of wall deflection for the last three stages are 2.2, 1.1, and 1.0%, respectively. Robustness of the back-estimated parameters is further illustrated by a forward prediction. The wall deflection in the subsequent stages can be satisfactorily predicted using the back-analyzed soil parameters at early stages. Copyright © 2014 John Wiley & Sons, Ltd. This paper presents the applications of the differential evolution (DE) algorithm in back analysis of soil parameters for deep excavation problems. A computer code, named Python‐based DE, is developed and incorporated into the commercial finite element software ABAQUS, with a parallel computing technique to run an FE analysis for all trail vectors of one generation in DE in multiple cores of a cluster, which dramatically reduces the computational time. A synthetic case and a well‐instrumented real case, that is, the Taipei National Enterprise Center (TNEC) project, are used to demonstrate the capability of the proposed back‐analysis procedure. Results show that multiple soil parameters are well identified by back analysis using a DE optimization algorithm for highly nonlinear problems. For the synthetic excavation case, the back‐analyzed parameters are basically identical to the input parameters that are used to generate synthetic response of wall deflection. For the TNEC case with a total of nine parameters to be back analyzed, the relative errors of wall deflection for the last three stages are 2.2, 1.1, and 1.0%, respectively. Robustness of the back‐estimated parameters is further illustrated by a forward prediction. The wall deflection in the subsequent stages can be satisfactorily predicted using the back‐analyzed soil parameters at early stages. Copyright © 2014 John Wiley & Sons, Ltd. |
| Author | Wang, J. H. Chen, J. J. Zhang, L. L. Zhao, B. D. Jeng, D. S. |
| Author_xml | – sequence: 1 givenname: B. D. surname: Zhao fullname: Zhao, B. D. organization: Department of Civil and Architectural Engineering, City University of Hong Kong, Hong Kong, China – sequence: 2 givenname: L. L. surname: Zhang fullname: Zhang, L. L. email: Correspondence to: L. L. Zhang, State Key Laboratory of Ocean Engineering, Civil Engineering Department, Shanghai Jiaotong University, Shanghai, 200240, China., lulu_zhang@sjtu.edu.cn organization: State Key Laboratory of Ocean Engineering, Civil Engineering Department, Shanghai Jiaotong University, 200240, Shanghai, China – sequence: 3 givenname: D. S. surname: Jeng fullname: Jeng, D. S. organization: State Key Laboratory of Ocean Engineering, Civil Engineering Department, Shanghai Jiaotong University, 200240, Shanghai, China – sequence: 4 givenname: J. H. surname: Wang fullname: Wang, J. H. organization: State Key Laboratory of Ocean Engineering, Civil Engineering Department, Shanghai Jiaotong University, 200240, Shanghai, China – sequence: 5 givenname: J. J. surname: Chen fullname: Chen, J. J. organization: State Key Laboratory of Ocean Engineering, Civil Engineering Department, Shanghai Jiaotong University, 200240, Shanghai, China |
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Journal of Geotechnical and Geoenvironmental Engineering, ASCE 2012; 138(1):69-88. 2010; 34 2011; 137 1998; 48 2009; 24 1996; 18 2013; 48 2010; 37 2005; 131 2012 1999; 49 1999; 23 2008 2006; 132 2008; 35 2006 1996; 122 2008; 32 2005 2001; 28 1992 2011; 15 2002 1989; 26 2012; 36 1991; 117 2011; 38 2007; 34 1993; 15 2009; 33 2009; 36 2009; 35 1997; 11 2000; 37 2010; 136 2007; 133 2013; 139 2002; 22 2005; 32 2003; 29 2012; 28 2013 2007; 44 2010; 5 1998; 124 2012; 138 2005; 55 1989; 39 1994; 31 e_1_2_8_28_1 Price KV (e_1_2_8_43_1) 2005 e_1_2_8_24_1 Ou CY (e_1_2_8_53_1) 2006 Hashash YMA (e_1_2_8_4_1) 1996; 122 e_1_2_8_26_1 e_1_2_8_9_1 e_1_2_8_20_1 e_1_2_8_22_1 e_1_2_8_45_1 Vardakos S (e_1_2_8_47_1) 2012; 28 e_1_2_8_41_1 Chan C (e_1_2_8_37_1) 2009; 35 e_1_2_8_17_1 e_1_2_8_19_1 e_1_2_8_36_1 e_1_2_8_15_1 e_1_2_8_38_1 Kung GTC (e_1_2_8_7_1) 2007; 44 Ng CWW (e_1_2_8_5_1) 1999; 49 e_1_2_8_11_1 e_1_2_8_34_1 Amorim EP (e_1_2_8_46_1) 2012 Finno RJ (e_1_2_8_2_1) 1991; 117 e_1_2_8_30_1 Fourie AB (e_1_2_8_50_1) 1989; 39 e_1_2_8_29_1 e_1_2_8_25_1 Ou CY (e_1_2_8_55_1) 2000; 37 e_1_2_8_27_1 e_1_2_8_48_1 Lampinen J (e_1_2_8_49_1) 2002 Kemeny J (e_1_2_8_32_1) 2003; 29 Bica AVD (e_1_2_8_51_1) 1998; 48 e_1_2_8_6_1 e_1_2_8_8_1 e_1_2_8_21_1 e_1_2_8_42_1 e_1_2_8_23_1 e_1_2_8_44_1 e_1_2_8_40_1 e_1_2_8_18_1 e_1_2_8_39_1 e_1_2_8_14_1 e_1_2_8_35_1 e_1_2_8_16_1 Lim A (e_1_2_8_12_1) 2010; 5 Ou CY (e_1_2_8_3_1) 1994; 31 Ou CY (e_1_2_8_13_1) 1998; 124 e_1_2_8_10_1 e_1_2_8_31_1 e_1_2_8_56_1 e_1_2_8_33_1 e_1_2_8_54_1 e_1_2_8_52_1 |
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This paper presents the applications of the differential evolution (DE) algorithm in back analysis of soil parameters for deep excavation problems. A... This paper presents the applications of the differential evolution (DE) algorithm in back analysis of soil parameters for deep excavation problems. A computer... Summary This paper presents the applications of the differential evolution (DE) algorithm in back analysis of soil parameters for deep excavation problems. A... |
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| SubjectTerms | Algorithms Cam-clay model Computer programs Deflection differential evolution Excavation excavations Finite element method inverse analysis Mathematical analysis Mathematical models optimization algorithms Soil (material) Soil analysis Walls |
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| Title | Inverse Analysis of Deep Excavation Using Differential Evolution Algorithm |
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