Back-Analysis and Parameter Identification for Deep Excavation Based on Pareto Multiobjective Optimization
Publication: Journal of Aerospace Engineering
Volume 28, Issue 6
Abstract
In this paper, a back-analysis method of deep excavation based on the Pareto multiobjective optimization is proposed and a multiobjective optimization algorithm multialgorithm genetically adaptive multiobjective method (AMALGAM) is implemented in a commercial FEM software to identify soil parameters based on multiple types of field observations. The proposed method is applied to a well-instrumented deep excavation, i.e., the Taipei National Enterprise Center (TNEC) project. The observed wall deflection and ground surface settlement at Stage 3 of the excavation are simultaneously used to estimate the nine soil parameters of the modified Cam-clay (MCC) model for three clay layers. The Pareto front in the biobjective space exhibits a rectangular shape, which implies that the simultaneous minimization of both objectives can be achieved. The back-analyzed soil parameters of the compromise solution from the biobjective back-analysis can reasonably simulate both the wall deflection and ground surface settlement for Stage 3. The differences of the predictions and the actual observations for Stages 4 to 7 using the back-analyzed soil parameters from Stage 3 are mainly because the inadequacy of the MCC model to simulate the small strain soil behaviors of excavation.
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Acknowledgments
The work in this paper was substantially supported by the National Basic Research Program of China (973 Program, Project No. 2014CB049100) and the Natural Science Foundation of China (Project Nos. 41172252 and 41372275). The authors are grateful for the supports from the BaJian Talent Program by the Organization Department of the Central Committee of the CPC and the Shanghai Rising-Star Program (Project No. 12QA1401800).
References
ABAQUS version 6.7 [Computer software]. Pawtucket, RI, Standard user’s manual, Hibbitt, Karlsson & Sorenson.
Ang, A. H. S., and Tang, W. H. (2012). Probability concepts in engineering: Emphasis on applications to civil and environmental engineering, 2nd Ed., Vol. 1, Wiley, New York.
Branke, J., Deb, K., Dierolf, H., and Osswald, M. (2004). “Finding knees in multi-objective optimization.” Parallel problem solving from nature-PPSN VIII, Springer, Berlin, 722–731.
Chiampi, M., Ragusa, C., and Repetto, M. (1996). “Fuzzy approach for multiobjective optimization in magnetics.” IEEE Trans. Magn., 32(3), 1234–1237.
Cohon, J. L. (1978). Multiobjective programming and planning, Academic, New York.
Dasaka, S. M., and Zhang, L. M. (2012). “Spatial variability of in-situ weathered soils.” Geotechnique, 62(5), 375–384.
Deb, K. (2001). Multi-objective optimization using evolutionary algorithms, Wiley, Chichester, U.K.
Deb, K. (2011). “Multi-objective optimisation using evolutionary algorithms: An introduction.” Multi-objective evolutionary optimisation for product design and manufacturing, Springer, London.
Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. (2002). “A fast and elitist multiobjective genetic algorithm: NSGA-II.” IEEE Trans. Evol. Comput., 6(2), 182–197.
Edgeworth, F. Y. (1881). Mathematical physics, C. Kegan Paul, London.
Finno, R. J., and Calvello, M. (2005). “Supported excavations: Observational method and inverse modeling.” J. Geotech. Geoenviron. Eng., 826–836.
Finno, R. J., and Harahap, I. S. (1991). “Finite element analyses of HDR-4 excavation.” J. Geotech. Geoenviron. Eng., 1590–1609.
Fonseca, C., and Fleming, P. (1993). “Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization.” Proc., 5th Int. Conf. on Genetic Algorithms, Morgan Kaufmann, San Mateo, CA, 416–423.
Haario, H., Saksman, E., and Tamminen, J. (2001). “An adaptive metropolis algorithm.” Bernoulli, 7(2), 223–242.
Hashash, Y. M. A., Levasseur, S., Osouli, A., Finno, R., and Malecot, Y. (2010). “Comparison of two inverse analysis techniques for learning deep excavation response.” Comput. Geotech., 37(3), 323–333.
Hashash, Y. M. A., Marulanda, C., Ghaboussi, J., and Jung, S. (2006). “Novel approach to integration of numerical modeling and field observations for deep excavations.” J. Geotech. Geoenviron. Eng., 1019–1031.
Hashash, Y. M. A., and Whittle, A. J. (1996). “Ground movement prediction for deep excavations in soft clay.” J. Geotech. Geoenviron. Eng., 474–486.
Horn, J., Nafpliotis, N., and Goldberg, D. E. (1994). “A niched Pareto genetic algorithm for multiobjective optimization.” Vol. 1, Proc., 1st IEEE Conf. on Evolutionary Computation, 1994 IEEE World Congress on Computational Intelligence, IEEE, New York, 82–87.
Hou, Y. M., Wang, J. H., and Zhang, L. L. (2009). “Finite-element modeling of a complex deep excavation in Shanghai.” Acta Geotechnica, 4(1), 7–16.
Hsieh, P. G., Ou, C. Y., and Lim, A. (2010). “Use of the total stress undrained model to the analysis of deep excavation.” Vol. 1, Proc., 17th Southeast Asian Geotechnical Conf., Southeast Asian Geotechnical Society (SEAGS), Pathumthani, Thailand, 227–230.
Juang, C. H., Luo, Z., Atamturktur, S., and Huang, H. (2013). “Bayesian updating of soil parameters for braced excavations using field observations.” J. Geotech. Geoenviron. Eng., 395–406.
Ledesma, A., Gens, A., and Alonso, E. E. (1996). “Estimation of parameters in geotechnical backanalysis—I. Maximum likelihood approach.” Comput. Geotech., 18(1), 1–27.
Levasseur, S., Malecot, Y., Boulon, M., and Flavigny, E. (2009). “Statistical inverse analysis based on genetic algorithm and principal component analysis: Method and developments using synthetic data.” Int. J. Numer. Anal. Methods Geomech., 33(12), 1485–1511.
Levasseur, S., Malecot, Y., Boulon, M., and Flavigny, E. (2010). “Statistical inverse analysis based on genetic algorithm and principal component analysis: Applications to excavation problems and pressuremeter tests.” Int. J. Numer. Anal. Methods Geomech., 34(5), 471–491.
Levasseur, S., Malécot, Y., Boulon, M., and Flavigny, E. (2008). “Soil parameter identification using a genetic algorithm.” Int. J. Numer. Anal. Methods Geomech., 32(2), 189–213.
Lim, A., Ou, C. Y., and Hsieh, P. G. (2010). “Evaluation of clay constitutive models for analysis of deep excavation under undrained conditions.” J. Geoeng., 5(1), 9–20.
Lloret-Cabot, M., Fenton, G. A., and Hicks, M. A. (2014). “On the estimation of scale of fluctuation in geostatistics.” Georisk: Assess. Manage. Risk Eng. Syst. Geohazards, 8(2), 129–140.
Mertens, J., Stenger, R., and Barkle, G. F. (2006). “Multiobjective inverse modeling for soil parameter estimation and model verification.” Vadose Zone J., 5(3), 917–933.
Murata, T., and Ishibuchi, H. (1995). “MOGA: Multi-objective genetic algorithms.” Proc., 1995 IEEE Int. Conf. on Evolutionary Computation, IEEE, New York.
Navarro, V., Candel, M., Barenca, A., Yustres, A., and Garcia, B. (2007). “Optimisation procedure for choosing cam clay parameters.” Comput. Geotech., 34(6), 524–531.
Nikolinakou, M., Whittle, A. J., Savidis, S., and Schran, U. (2011). “Prediction and interpretation of the performance of a deep excavation in Berlin sand.” J. Geotech. Geoenviron. Eng., 1047–1061.
Ou, C. Y. (2006). Deep excavation: Theory and practice, Taylor & Francis, London.
Ou, C. Y., and Lai, C. H. (1994). “Finite-element analysis of deep excavation in layered sandy and clayey soil deposits.” Can. Geotech. J., 31(2), 204–214.
Ou, C. Y., Shiau, B. Y., and Wang, I. W. (2000). “Three-dimensional deformation behavior of the Taipei national enterprise center (TNEC) excavation case history.” Can. Geotech. J., 37(2), 438–448.
Papon, A., Riou, Y., Dano, C., and Hicher, P. Y. (2012). “Single- and multi-objective genetic algorithm optimization for identifying soil parameters.” Int. J. Numer. Anal. Methods Geomech., 36(5), 597–618.
Pareto, V. (1897). Political economy courses, Rouge, Lausanne, France.
Peng, M., Li, X. Y., Li, D. Q., Jiang, S. H., and Zhang, L. M. (2014). “Slope safety evaluation by integrating multi-source monitoring information.” Struct. Saf., 49, 65–74.
Rechea, C., Levasseur, S., and Finno, R. (2008). “Inverse analysis techniques for parameter identification in simulation of excavation support systems.” Comput. Geotech., 35(3), 331–345.
Schoups, G., Hopmans, J. W., Young, C. A., Vrugt, J. A., and Wallender, W. W. (2005). “Multi-criteria optimization of a regional spatially-distributed subsurface water flow model.” J. Hydrol., 311(1), 20–48.
Storn, R., and Price, K. (1997). “Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces.” J. Global Optim., 11(4), 341–359.
Suppapitnarm, A., Seffen, K. A., Parks, G. T., and Clarkson, P. J. (2000). “A simulated annealing algorithm for multiobjective optimization.” Eng. Optim., 33(1), 59–85.
Tang, Y. G., and Kung, G. T. C. (2009). “Application of nonlinear optimization technique to back analyses of deep excavation.” Comput. Geotech., 36(1–2), 276–290.
Vrugt, J. A., and Robinson, B. A. (2007). “Improved evolutionary optimization from genetically adaptive multimethod search.” Proc. Natl. Acad. Sci., 104(3), 708–711.
Wang, J. H., Xu, Z. H., and Wang, W. D. (2009). “Wall and ground movements due to deep excavations in Shanghai soft soils.” J. Geotech. Geoenviron. Eng., 985–994.
Yapo, P. O., Gupta, H. V., and Sorooshian, S. (1998). “Multi-objective global optimization for hydrologic models.” J. Hydrol., 204(1–4), 83–97.
Yin, Z. Y., and Hicher, P. Y. (2008). “Identifying parameters controlling soil delayed behaviour from laboratory and in situ pressuremeter testing.” Int. J. Numer. Anal. Methods Geomech., 32(12), 1515–1535.
Zentar, R., Hicher, P. Y., and Moulin, G. (2001). “Identification of soil parameters by inverse analysis.” Comput. Geotech., 28(2), 129–144.
Zhang, J., Tang, W. H., and Zhang, L. M. (2009). “Efficient probabilistic back-analysis of slope stability model parameters.” J. Geotech. Geoenviron. Eng., 99–109.
Zhang, J. F., Chen, J. J., Wang, J. H., and Zhu, Y. F. (2013a). “Prediction of tunnel displacement induced by adjacent excavation in soft soil.” Tunnelling and Underground Space Technol., 36, 24–33.
Zhang, L., and Chen, J. J. (2012). “Effect of spatial correlation of standard penetration test (SPT) data on bearing capacity of driven piles in sand.” Can. Geotech. J., 49(4), 394–402.
Zhang, L. L., Zhang, J., Zhang, L. M., and Tang, W. H. (2010). “Back analysis of slope failure with Markov chain Monte Carlo simulation.” Comput. Geotech., 37(7–8), 905–912.
Zhang, L. L., Zuo, Z. B., Ye, G. L., Jeng, D. S., and Wang, J. H. (2013b). “Probabilistic parameter estimation and predictive uncertainty based on field measurements for unsaturated soil slope.” Comput. Geotech., 48(1), 72–81.
Zhao, B. D., Zhang, L. L., Jeng, D. S., Wang, J. H., and Chen, J. J. (2014). “Inverse analysis of deep excavation using differential evolution algorithm.” Int. J. Numer. Anal. Methods Geomech., in press.
Zitzler, E., Deb, K., and Thiele, L. (2000). “Comparison of multiobjective evolutionary algorithms: Empirical results.” Evol. Comput., 8(2), 173–195.
Zitzler, E., and Thiele, L. (1998). “An evolutionary algorithm for multiobjective optimization: The stength Pareto approach.” Computer Engineering and Communication Network Lab (TIK), Swiss Federal Institute of Technology (ETH), Zurich, Switzerland.
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Journal of Aerospace Engineering
Volume 28 • Issue 6 • November 2015
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© 2014 American Society of Civil Engineers.
History
Received: Feb 15, 2014
Accepted: Aug 25, 2014
Published online: Oct 14, 2014
Discussion open until: Mar 14, 2015
Published in print: Nov 1, 2015
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