Structural reliability analysis using response surface method with improved genetic algorithm

Fang, Yongfeng; Tee, Kong Fah

doi:10.12989/sem.2017.62.2.139

Cited by 7 publications

(2 citation statements)

References 13 publications

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“…In RSM, the polynomial basis functions for approximation are chosen a priori. RSMs have been widely used in structural optimization problems like helicopter rotor, 3 truss, 4,5 stiffened plates, 6 marine structures, 7 lateral stability of arch bridge, 8 FRP composite deck, [9][10][11][12] composite plates 13 and shells 14,15 and so on.…”

Section: Introductionmentioning

confidence: 99%

Search for accurate RSM metamodels for structural engineering

Kalita

Dey

Haldar

2019

Journal of Reinforced Plastics and Composites

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With rapid advancement in computing power and development of numerical tools and scientific theories in fields like structural engineering, simple experiments can now be carried out in-silico. However, simulating many real-life phenomena in analytical fields still remains largely intractable or requires huge computational resources. A number of researchers have developed suitable metamodels to reduce the computational time needed to solve complex structural problems. As such, response surface method has become quite popular due to its versatility and ability to reduce even the most hard-to-model engineering problems into a simple polynomial form. The number and type of sampling points needed for building the response surface approximation are selected by design of experimentation techniques like Box-Behnken design, central composite design, D-optimal design, etc. One design may be appropriate for some particular problems, while a different design would perform better on others. To assess the performance of such metamodels, statistical measures like R 2 and error-based metrics like root-mean-squared error have been commonly used by researchers. In this paper, extensive numerical experiments are performed to gauge the performance of Box-Behnken design, central composite design and D-optimal designs as a metamodeling tool in structural engineering. The insufficiency of classical statistical accuracy measures like R 2 , R 2 adj and R 2 pred is demonstrated, and the need for measures based on external test data such as Q 2 F1 , Q 2 F2 or Q 2 F3 is stressed. Subsequently, some recommendations are also made on building metamodels for structural engineering problems.

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Section: Introductionmentioning

confidence: 99%

Search for accurate RSM metamodels for structural engineering

Kalita

Dey

Haldar

2019

Journal of Reinforced Plastics and Composites

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“…Allaix and Carbone [17] used an improved RSM to evaluate structural reliability. Fang and Tee [18] proposed a novel method for structural reliability analysis based on RSM and improved genetic algorithm. Li et al [19] applied the RSM to fulfill the seismic reliability evaluation of steeltimber hybrid shear wall systems.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Reliability Design of Multicomponent Structure with Improved Weighted Regression Distributed Collaborative Surrogate Model Method

Dong

Zhu

et al. 2018

Advances in Materials Science and Engineering

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For dynamic reliability design of complex structures with multiple components, an improved weighted regression distributed collaborative surrogate model method (IWRDCSMM) is developed from the extremum response surface method (ERSM), decomposed-coordinated thought, and improved weighted regression principle. e ERSM is used to address the dynamic reliability and sensitivity analyses of multicomponent structures and enhance the computing efficiency. e decomposedcoordinated thought is applied to handle the relationship among multiple components. e improved weighted regression method is used to find the efficient samples with smaller errors to improve the modeling accuracy. e proposed method is first introduced for dynamic probabilistic analysis (including reliability analysis and sensitivity analysis) of multicomponent structures. e method is then mathematically modeled by adopting the efficient samples selected based on the improved weighted regression method. Finally, the radial deformation dynamic probabilistic analysis of an aeroengine turbine blisk assembled by blade and disk is accomplished, in respect of the IWRDCSMM, fluid-thermal-structure interaction, and the randomness of input parameters within the time domain [0, T]. e results illustrate that the reliability degree of turbine blisk radial deformation is 0.9951 when the allowable value u allow is 2.30 × 10 −3 m, and all the input parameters affecting the turbine blisk radial deformation are gas temperature, angular speed, inlet velocity, outlet pressure, material density, and inlet pressure, successively. As revealed by the comparison of different methods, the IWRDCSMM has high fitting speed and simulation efficiency with the guarantee of accuracy. e efforts of this study provide a promising dynamic probabilistic analysis technique for complex structures with multiple components and enrich mechanical reliability theory.

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