An Improvement of the Response Surface Method Based on Successive Linear Interpolation

Abstract:

The response surface method (RSM) is widely used to alleviate the computational burden of engineering analyses. For reliability analysis, the common approach in the RSM is to use a linear interpolation technique. However, the experimental points are all arranged using the classical RSM in each process of interpolation. Therefore, the number of experimental points is large that resulting in the efficiency is lower. In this study, an improvement of the RSM for structural reliability analysis has been proposed based on the technique of successive linear interpolation. As seen from the example, the proposed method yields better results than those of the classical RSM, and the number of experimental points using the proposed method is less than that of classical RSM. It seems that the proposed method improves the convergence speed and reduces the computational effort.

• Periodical: Advanced Materials Research (Volumes 532-533)
• Pages: 724-727
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.532-533.724
• Online since: June 2012
• Authors: Wei Tao Zhao, Tian Jun Yu, Yi Yang
• Keywords: Response Surface Method (RSM), Structural Reliability, Successive Linear Interpolation

References

[1] Thoft-Christensen P, Murotsu Y: Application of Structural Systems Reliability Theory (Springer, Berlin 1986).

DOI: 10.1007/978-3-642-82764-8

[2] An WG: Structural systems reliability and optimization design based on reliability (National Defence Industry Press, Beijing 1997).

[3] An WG: The reliability analysis and optimization design of stochastic structural systems (Beijing University of aeronautics and astronautics Press, Beijing 2005).

[4] Bucher CG: Structural Safety, Vol. 7 (1990), p.57.

[5] Rajashekhar M R, Ellingwood B R: Structural Safety, Vol. 12 (1993), p.205.

[6] Kim S, Na S: Structural Safety Vol. 19(1997), p.3.

[7] Das PK, Zheng Y: Probabilistic Engineering Mechanics, Vol. 15 (2000), p.309.

[8] Gayton N, Bourinet JM, Lemaire M: Structural safety, Vol. 25 (2003), p.99.

[9] Kaymaz I, McMahon CA: Probabilistic Engineering Mechanics, Vol. 20 (2005), p.11.

[10] Duprat F, Sellier A: Probabilistic Engineering Mechanics, Vol. 21(2006), p.207.

[11] Youn BD, Choi KK: Computers & Structures, Vol. 82(2004), p.241.

[12] Lancaster P, Salkauskas K: Mathematics of Computation, Vol. 155(1981), p.141.

[13] Breitkpf P, Naceur H, Rassineux A, Villon P: Computers & Structures, Vol. 83(2005), p.1411.

[14] Bucher CG, Most T: Probabilistic Engineering Mechanics, Vol. 23(2008), p.154.

[15] Ditlevsen O, Madsen HO: Structural reliability methods. (Wiley, Chischester, 1996).

[16] Shinozuka M: Journal of Structural Engineering, ASCE, Vol. 109(1983), p.721.

[17] Bjerager P: Journal of Engineering Mechanics , ASCE, Vol. 114(1988), p.285.

[18] Der Kiureghian A, Dakessian T: Structural Safety, Vol. 20(1998), p.37.

[19] Rubinstein RY: Simulation and the Monte Carlo Method. (Wiley, New York, 1981).

[20] Melchers RE: Structural Safety, Vol. 6(1989), p.3.

[21] Au SK, Beck JL: Probabilistic Engineering Mechanics, Vol. 16(2001), p.263.

[22] Schueller GI, Pradlwarter HW: Probabilistic Engineering Mechanics, Vol. 19( 2004), p.463.

[23] Rabitz H, Alis OF, Shorter J: Computer Physics Communications, Vol. 117(1999), p.11.

[24] Rabitz H, Alis OF: Journal of Mathematical Chemistry, Vol. 25(1999), p.197.

[25] Alis OF, Rabitz H: Journal of Mathematical Chemistry, Vol. 29(2001), p.127.

[26] Li G, Rosenthal C, Rabitz H: Journal of Physical Chemistry, Vol. 105(2001), p.7765.

[27] Li G, Wang SW, Rabitz H: Journal of Mathematical Chemistry, Vol. 30(2001), p.1.

[28] Li G, Wang SW, Rabitz H: Chemical Engineering Science, Vol. 57(2002), p.4445.