Top

Structural and Multidisciplinary Optimization

Published in:

08-07-2016 | RESEARCH PAPER

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

Authors: Huoyue Xiang, Yongle Li, Haili Liao, Cuijuan Li

Published in: Structural and Multidisciplinary Optimization | Issue 2/2017

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

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).

previous article Reliability-based design optimization of multidisciplinary system under aleatory and epistemic uncertainty

next article Topology optimization of reflector antennas based on integrated thermal-structural-electromagnetic analysis

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

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

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

Ben-Israel A, Greville T (2003) Generalized inverses, 2nd edn. Springer, New YorkMATH

Bourdin P, Wilson JD (2008) Windbreak aerodynamics: is computational fluid dynamics reliable? Bound-Layer Meteorol 126(2):181–208CrossRef

Chapelle O, Vapnik V, Bousquet O, Mukherjee S (2002) Choosing multiple parameters for support vector machines. Mach Learn 46(1):131–159CrossRefMATH

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

Chu C, Chang C, Huang C et al (2013) Windbreak protection for road vehicles against crosswind. J Wind Eng Ind Aerodyn 116:61–69CrossRef

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

Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, CambridgeCrossRefMATH

Dixon L and Szegö G (1978) Towards global optimization 2. North Holland, Amsterdam, the Netherlands

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

Fan HY, Dulikravich GS, Han ZX (2005) Aerodynamic data modeling using support vector machines. Inverse Prob Sci Eng 13(3):261–278MathSciNetCrossRefMATH

Forrester A, Keane A (2009) Recent advances in surrogate-based optimization. Prog Aerosp Sci 45(1):50–79CrossRef

Forrester A, Sóbester A and Keane A (2008) Engineering design via surrogate modelling: a practical guide. A John Wiley and Sons, Ltd

Goel T, Haftka R, Shyy W, Queipo N (2007) Ensemble of surrogates. Struct Multidiscip Optim 33:199–216CrossRef

Green DW and Perry RH (2008) Perry’s chemical engineers handbook (8th edn.). McGraw-Hill

Gunn SR (1998) Support vector machines for classification and regression. Image speech Intelligent Systems Group

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

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

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

Huang CL, Wang CJ (2006) A GA-based feature selection and parameters optimization for support vector machines. Expert Syst Appl 31:231–240CrossRef

Jie HX, Wu YZ, Ding JW (2014) An adaptive metamodel-based global optimization algorithm for black-box type problems. Eng Optim 46:1–24CrossRef

Jones D (2001) A taxonomy of global optimization methods based on response surfaces. J Glob Optim 21:345–383MathSciNetCrossRefMATH

Jones D, Schonlau M, Welch W (1998) Efficient global optimization of expensive black-box functions. J Glob Optim 13:455–492MathSciNetCrossRefMATH

Keerthi S, Lin C (2003) Asymptotic behaviors of support vector machines with Gaussian Kernel. Neural Comput 15(7):1667–1689CrossRefMATH

Kleijnen J (2009) Kriging metamodeling in simulation: a review. Eur J Oper Res 192:707–716MathSciNetCrossRefMATH

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

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

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

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

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

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

Mullur A, Messac A (2005) Extended radial basis functions: more flexible and effective metamodeling. AIAA J 43(6):1306–1315CrossRef

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

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

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

Richards PJ, Norris SE (2011) Appropriate boundary conditions for computational wind engineering models revisited. J Wind Eng Ind Aerodyn 99(4):257–266CrossRef

Sack J, Welch WJ, Mitchell TJ et al (1989) Design and analysis of computer experiments (with discussion). Stat Sci 4:409–435CrossRef

Smola AJ (1998) Learning with kernels. PhD Thesis, Technische Unverisity Berlin

Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14:199–222MathSciNetCrossRef

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

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

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

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

Wan XT, Pekny J, Reklaitis G (2005) Simulation-based optimization with surrogate models-application to supply chain management. Comput Chem Eng 29:1317–1328CrossRef

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

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

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, 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

Zhang J, Chouwdhury S, Messac A (2012) An adaptive hybrid surrogate model. Struct Multidiscip Optim 46:233–238CrossRef

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

Title: An adaptive surrogate model based on support vector regression and its application to the optimization of railway wind barriers
Authors: Huoyue Xiang
Yongle Li
Haili Liao
Cuijuan Li
Publication date: 08-07-2016
Publisher: Springer Berlin Heidelberg
Published in: Structural and Multidisciplinary Optimization / Issue 2/2017
Print ISSN: 1615-147X
Electronic ISSN: 1615-1488
DOI: https://doi.org/10.1007/s00158-016-1528-9

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Other articles of this Issue 2/2017

Stability-ensured topology optimization of boom structures with volume and stress considerations

A unified aggregation and relaxation approach for stress-constrained topology optimization

Thanks to our Reviewers

Spatial orientation and topology optimization of modular trusses

Shape optimization of energy storage flywheel rotor

Reliability-based design optimization for problems with interval distribution parameters

Premium Partners