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Erschienen in: Structural and Multidisciplinary Optimization 2/2017

08.07.2016 | RESEARCH PAPER

An adaptive surrogate model based on support vector regression and its application to the optimization of railway wind barriers

verfasst von: Huoyue Xiang, Yongle Li, Haili Liao, Cuijuan Li

Erschienen in: Structural and Multidisciplinary Optimization | Ausgabe 2/2017

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Abstract

This study provides evidence supporting the use of the update strategies for the support vector regression (SVR) model. Firstly, the fitting and interpolation method (FIM) is presented to select SVR parameters, and three infill strategies are adopted to search for update points. Secondly, the infill strategy and parameter selection method are illustrated by test functions that illustrate their dependability. The distribution of update points, the sample density and the proportion of update points are discussed. Finally, the adaptive SVR surrogate model is applied to optimize the protective effect of railway wind barriers. The result shows that the parameter selection method has high stability. On the whole, the accuracy of the adaptive SVR model using a suitable infill strategy will be improved with an increasing proportion of update points if the final number of training points is identical. The optimization result shows an optimal porosity of 0.117 when the height of the railway wind barrier is 2.05 m (full scale).

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Literatur
Zurück zum Zitat Aich U, Banerjee S (2014) Modeling of EDM responses by support vector machine regression with parameters selected by particle swarm optimization. Appl Math Model 38:2800–2818CrossRef Aich U, Banerjee S (2014) Modeling of EDM responses by support vector machine regression with parameters selected by particle swarm optimization. Appl Math Model 38:2800–2818CrossRef
Zurück zum Zitat Basudhar A (2012) Selection of anisotropic kernel parameters using multiple surrogate information. 12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSM, Indianapolis, Indiana Basudhar A (2012) Selection of anisotropic kernel parameters using multiple surrogate information. 12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSM, Indianapolis, Indiana
Zurück zum Zitat Ben-Israel A, Greville T (2003) Generalized inverses, 2nd edn. Springer, New YorkMATH Ben-Israel A, Greville T (2003) Generalized inverses, 2nd edn. Springer, New YorkMATH
Zurück zum Zitat Bourdin P, Wilson JD (2008) Windbreak aerodynamics: is computational fluid dynamics reliable? Bound-Layer Meteorol 126(2):181–208CrossRef Bourdin P, Wilson JD (2008) Windbreak aerodynamics: is computational fluid dynamics reliable? Bound-Layer Meteorol 126(2):181–208CrossRef
Zurück zum Zitat Chapelle O, Vapnik V, Bousquet O, Mukherjee S (2002) Choosing multiple parameters for support vector machines. Mach Learn 46(1):131–159CrossRefMATH Chapelle O, Vapnik V, Bousquet O, Mukherjee S (2002) Choosing multiple parameters for support vector machines. Mach Learn 46(1):131–159CrossRefMATH
Zurück zum Zitat Charuvisit S, Kimura K, Fujino Y (2004) Effects of wind barrier on a vehicle passing in the wake of a bridge tower in cross wind and its response. J Wind Eng Ind Aerodyn 92(7–8):609–639CrossRef Charuvisit S, Kimura K, Fujino Y (2004) Effects of wind barrier on a vehicle passing in the wake of a bridge tower in cross wind and its response. J Wind Eng Ind Aerodyn 92(7–8):609–639CrossRef
Zurück zum Zitat Chu C, Chang C, Huang C et al (2013) Windbreak protection for road vehicles against crosswind. J Wind Eng Ind Aerodyn 116:61–69CrossRef Chu C, Chang C, Huang C et al (2013) Windbreak protection for road vehicles against crosswind. J Wind Eng Ind Aerodyn 116:61–69CrossRef
Zurück zum Zitat Coleman SA, Baker CJ (1992) Reduction of accident risk for high sided road vehicles in cross winds. J Wind Eng Ind Aerodyn 44–44:2685–2695CrossRef Coleman SA, Baker CJ (1992) Reduction of accident risk for high sided road vehicles in cross winds. J Wind Eng Ind Aerodyn 44–44:2685–2695CrossRef
Zurück zum Zitat Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, CambridgeCrossRefMATH Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, CambridgeCrossRefMATH
Zurück zum Zitat Dixon L and Szegö G (1978) Towards global optimization 2. North Holland, Amsterdam, the Netherlands Dixon L and Szegö G (1978) Towards global optimization 2. North Holland, Amsterdam, the Netherlands
Zurück zum Zitat Dong ZB, Luo WY, Qian GQ et al (2007) A wind tunnel simulation of the mean velocity fields behind upright porous fences. Agric For Meteorol 146(1–2):82–93CrossRef Dong ZB, Luo WY, Qian GQ et al (2007) A wind tunnel simulation of the mean velocity fields behind upright porous fences. Agric For Meteorol 146(1–2):82–93CrossRef
Zurück zum Zitat Fan HY, Dulikravich GS, Han ZX (2005) Aerodynamic data modeling using support vector machines. Inverse Prob Sci Eng 13(3):261–278MathSciNetCrossRefMATH Fan HY, Dulikravich GS, Han ZX (2005) Aerodynamic data modeling using support vector machines. Inverse Prob Sci Eng 13(3):261–278MathSciNetCrossRefMATH
Zurück zum Zitat Forrester A, Keane A (2009) Recent advances in surrogate-based optimization. Prog Aerosp Sci 45(1):50–79CrossRef Forrester A, Keane A (2009) Recent advances in surrogate-based optimization. Prog Aerosp Sci 45(1):50–79CrossRef
Zurück zum Zitat Forrester A, Sóbester A and Keane A (2008) Engineering design via surrogate modelling: a practical guide. A John Wiley and Sons, Ltd Forrester A, Sóbester A and Keane A (2008) Engineering design via surrogate modelling: a practical guide. A John Wiley and Sons, Ltd
Zurück zum Zitat Goel T, Haftka R, Shyy W, Queipo N (2007) Ensemble of surrogates. Struct Multidiscip Optim 33:199–216CrossRef Goel T, Haftka R, Shyy W, Queipo N (2007) Ensemble of surrogates. Struct Multidiscip Optim 33:199–216CrossRef
Zurück zum Zitat Green DW and Perry RH (2008) Perry’s chemical engineers handbook (8th edn.). McGraw-Hill Green DW and Perry RH (2008) Perry’s chemical engineers handbook (8th edn.). McGraw-Hill
Zurück zum Zitat Gunn SR (1998) Support vector machines for classification and regression. Image speech Intelligent Systems Group Gunn SR (1998) Support vector machines for classification and regression. Image speech Intelligent Systems Group
Zurück zum Zitat Guo WW, Wang YJ, Xia H, Lu S (2015) Wind tunnel test on aerodynamic effect of wind barriers on train-bridge system. Sci China Technol Sci 58(2):219–225CrossRef Guo WW, Wang YJ, Xia H, Lu S (2015) Wind tunnel test on aerodynamic effect of wind barriers on train-bridge system. Sci China Technol Sci 58(2):219–225CrossRef
Zurück zum Zitat He XH, Zhou YF, Wang HF, Han Y, Shi K (2014) Aerodynamic characteristics of a trailing rail vehicles on viaduct based on still wind tunnel experiments. J Wind Eng Ind Aerodyn 135:22–33CrossRef He XH, Zhou YF, Wang HF, Han Y, Shi K (2014) Aerodynamic characteristics of a trailing rail vehicles on viaduct based on still wind tunnel experiments. J Wind Eng Ind Aerodyn 135:22–33CrossRef
Zurück zum Zitat Hsu CW, Chang CC and Lin CJ (2003) A practical guide to support vector classification. Technical report, University of National Taiwan, Department of Computer Science and Information Engineering, 1–12 Hsu CW, Chang CC and Lin CJ (2003) A practical guide to support vector classification. Technical report, University of National Taiwan, Department of Computer Science and Information Engineering, 1–12
Zurück zum Zitat Huang CL, Wang CJ (2006) A GA-based feature selection and parameters optimization for support vector machines. Expert Syst Appl 31:231–240CrossRef Huang CL, Wang CJ (2006) A GA-based feature selection and parameters optimization for support vector machines. Expert Syst Appl 31:231–240CrossRef
Zurück zum Zitat Jie HX, Wu YZ, Ding JW (2014) An adaptive metamodel-based global optimization algorithm for black-box type problems. Eng Optim 46:1–24CrossRef Jie HX, Wu YZ, Ding JW (2014) An adaptive metamodel-based global optimization algorithm for black-box type problems. Eng Optim 46:1–24CrossRef
Zurück zum Zitat Jones D, Schonlau M, Welch W (1998) Efficient global optimization of expensive black-box functions. J Glob Optim 13:455–492MathSciNetCrossRefMATH Jones D, Schonlau M, Welch W (1998) Efficient global optimization of expensive black-box functions. J Glob Optim 13:455–492MathSciNetCrossRefMATH
Zurück zum Zitat Keerthi S, Lin C (2003) Asymptotic behaviors of support vector machines with Gaussian Kernel. Neural Comput 15(7):1667–1689CrossRefMATH Keerthi S, Lin C (2003) Asymptotic behaviors of support vector machines with Gaussian Kernel. Neural Comput 15(7):1667–1689CrossRefMATH
Zurück zum Zitat Kozmar H, Procino L, Borsani L, Bartoli G (2012) Sheltering efficiency of wind barriers on bridges. J Wind Eng Ind Aerodyn 107–108:274–284CrossRef Kozmar H, Procino L, Borsani L, Bartoli G (2012) Sheltering efficiency of wind barriers on bridges. J Wind Eng Ind Aerodyn 107–108:274–284CrossRef
Zurück zum Zitat Kwon SD, Kim DH, Lee SH et al (2011) Design criteria of wind barriers for traffic. Part 1: wind barrier performance. Wind Struct 14(1):55–70CrossRef Kwon SD, Kim DH, Lee SH et al (2011) Design criteria of wind barriers for traffic. Part 1: wind barrier performance. Wind Struct 14(1):55–70CrossRef
Zurück zum Zitat Lee SJ, Park KC, Park CW (2002) Wind tunnel observations about the shelter effect of porous fences on the sand particle movements. Atmos Environ 36:1453–1463CrossRef Lee SJ, Park KC, Park CW (2002) Wind tunnel observations about the shelter effect of porous fences on the sand particle movements. Atmos Environ 36:1453–1463CrossRef
Zurück zum Zitat Lin SW, Ying KC, Chen SC (2008) Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert Syst Appl 35:1817–1824CrossRef Lin SW, Ying KC, Chen SC (2008) Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert Syst Appl 35:1817–1824CrossRef
Zurück zum Zitat Liu YL, Chen WL, Ding LP et al (2013) Response surface methodology based on support vector regression for polygon blank shape optimization design. Int J Adv Manuf Technol 66:1391–1405 Liu YL, Chen WL, Ding LP et al (2013) Response surface methodology based on support vector regression for polygon blank shape optimization design. Int J Adv Manuf Technol 66:1391–1405
Zurück zum Zitat McKay M, Conover W, Beckman R (1979) A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21(2):239–245MathSciNetMATH McKay M, Conover W, Beckman R (1979) A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21(2):239–245MathSciNetMATH
Zurück zum Zitat Mullur A, Messac A (2005) Extended radial basis functions: more flexible and effective metamodeling. AIAA J 43(6):1306–1315CrossRef Mullur A, Messac A (2005) Extended radial basis functions: more flexible and effective metamodeling. AIAA J 43(6):1306–1315CrossRef
Zurück zum Zitat Nakayama H, Arakawa M, Washino K (2003) Using support vector machines in optimization for black-box objective functions. IEEE Proc Int Joint Conf 2:1617–1622MATH Nakayama H, Arakawa M, Washino K (2003) Using support vector machines in optimization for black-box objective functions. IEEE Proc Int Joint Conf 2:1617–1622MATH
Zurück zum Zitat Pan F, Zhu P, Zhang Y (2010) Metamodel-based lightweight design of B-pillar with TWB structure via support vector regression. Comput Struct 88:36–44CrossRef Pan F, Zhu P, Zhang Y (2010) Metamodel-based lightweight design of B-pillar with TWB structure via support vector regression. Comput Struct 88:36–44CrossRef
Zurück zum Zitat Richard B, Cremona C, Adelaide L (2012) A response surface method based on support vector machines trained with an adaptive experimental design. Struct Saf 39:14–21CrossRef Richard B, Cremona C, Adelaide L (2012) A response surface method based on support vector machines trained with an adaptive experimental design. Struct Saf 39:14–21CrossRef
Zurück zum Zitat Richards PJ, Norris SE (2011) Appropriate boundary conditions for computational wind engineering models revisited. J Wind Eng Ind Aerodyn 99(4):257–266CrossRef Richards PJ, Norris SE (2011) Appropriate boundary conditions for computational wind engineering models revisited. J Wind Eng Ind Aerodyn 99(4):257–266CrossRef
Zurück zum Zitat Sack J, Welch WJ, Mitchell TJ et al (1989) Design and analysis of computer experiments (with discussion). Stat Sci 4:409–435CrossRef Sack J, Welch WJ, Mitchell TJ et al (1989) Design and analysis of computer experiments (with discussion). Stat Sci 4:409–435CrossRef
Zurück zum Zitat Smola AJ (1998) Learning with kernels. PhD Thesis, Technische Unverisity Berlin Smola AJ (1998) Learning with kernels. PhD Thesis, Technische Unverisity Berlin
Zurück zum Zitat Sóbester A, Leary S, Keane A (2005) On the design of optimization strategies based on global response surface approximation models. J Glob Optim 33:31–59MathSciNetCrossRefMATH Sóbester A, Leary S, Keane A (2005) On the design of optimization strategies based on global response surface approximation models. J Glob Optim 33:31–59MathSciNetCrossRefMATH
Zurück zum Zitat Song H, Choi K, Lee I et al (2013) Adaptive virtual support vector machine for reliability analysis of high-dimensional problems. Struct Multidiscip Optim 47:479–491MathSciNetCrossRefMATH Song H, Choi K, Lee I et al (2013) Adaptive virtual support vector machine for reliability analysis of high-dimensional problems. Struct Multidiscip Optim 47:479–491MathSciNetCrossRefMATH
Zurück zum Zitat Tan XH, Bi WH, Hou XL et al (2011) Reliability analysis using radial basis function networks and support vector machines. Comput Geotech 2011(38):178–186CrossRef Tan XH, Bi WH, Hou XL et al (2011) Reliability analysis using radial basis function networks and support vector machines. Comput Geotech 2011(38):178–186CrossRef
Zurück zum Zitat Vapnik V, Golowich S, Smola A (1997) Support method for prediction approximation regression estimation, and signal processing. Advance in neural information processing system 9. MIT Press, Cambridge, MA Vapnik V, Golowich S, Smola A (1997) Support method for prediction approximation regression estimation, and signal processing. Advance in neural information processing system 9. MIT Press, Cambridge, MA
Zurück zum Zitat Wan XT, Pekny J, Reklaitis G (2005) Simulation-based optimization with surrogate models-application to supply chain management. Comput Chem Eng 29:1317–1328CrossRef Wan XT, Pekny J, Reklaitis G (2005) Simulation-based optimization with surrogate models-application to supply chain management. Comput Chem Eng 29:1317–1328CrossRef
Zurück zum Zitat Wang C, Duan QY, Gong W, Ye AZ, Di ZH, Miao CY (2014) An evaluation of adaptive surrogate modeling based optimization with two benchmark problems. Environ Model Softw 60:167–179CrossRef Wang C, Duan QY, Gong W, Ye AZ, Di ZH, Miao CY (2014) An evaluation of adaptive surrogate modeling based optimization with two benchmark problems. Environ Model Softw 60:167–179CrossRef
Zurück zum Zitat Wu K, Wang S (2009) Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space. Pattern Recogn 42:710–717CrossRefMATH Wu K, Wang S (2009) Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space. Pattern Recogn 42:710–717CrossRefMATH
Zurück zum Zitat Xiang HY, Li YL, Chen B et al (2014) Protection effect of railway wind barrier on running safety of train under cross winds. Adv Struct Eng 17(8):1176–1187 Xiang HY, Li YL, Chen B et al (2014) Protection effect of railway wind barrier on running safety of train under cross winds. Adv Struct Eng 17(8):1176–1187
Zurück zum Zitat Xiang HY, Li YL, Wang B, Liao HL (2015) Numerical simulation of the protective effect of railway wind barriers under crosswinds. Int J Rail Transp 3(3):151–163CrossRef Xiang HY, Li YL, Wang B, Liao HL (2015) Numerical simulation of the protective effect of railway wind barriers under crosswinds. Int J Rail Transp 3(3):151–163CrossRef
Zurück zum Zitat Zhang J, Chouwdhury S, Messac A (2012) An adaptive hybrid surrogate model. Struct Multidiscip Optim 46:233–238CrossRef Zhang J, Chouwdhury S, Messac A (2012) An adaptive hybrid surrogate model. Struct Multidiscip Optim 46:233–238CrossRef
Zurück zum Zitat Zhu P, Pan F, Chen W et al (2012) Use of support vector regression in structural optimization: application to vehicle crashworthiness design. Math Comput Simul 86:21–31MathSciNetCrossRef Zhu P, Pan F, Chen W et al (2012) Use of support vector regression in structural optimization: application to vehicle crashworthiness design. Math Comput Simul 86:21–31MathSciNetCrossRef
Metadaten
Titel
An adaptive surrogate model based on support vector regression and its application to the optimization of railway wind barriers
verfasst von
Huoyue Xiang
Yongle Li
Haili Liao
Cuijuan Li
Publikationsdatum
08.07.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
Structural and Multidisciplinary Optimization / Ausgabe 2/2017
Print ISSN: 1615-147X
Elektronische ISSN: 1615-1488
DOI
https://doi.org/10.1007/s00158-016-1528-9

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