System reliability-based optimisation for truss structures using genetic algorithm and neural network

Optimum structures must have adequate resistance against external random loads. Since most truss structures involve a series of failure processes, it is necessary to develop system reliability analyses for the optimum design of truss structures. In this paper, a hybrid method of system reliability-based design optimisation (SRBDO) is proposed by combining genetic algorithms (GAs) and radial basis functions (RBFs) neural networks. The proposed method is applied to truss structures, and then the validity is demonstrated through two specific examples. Detailed discussions for the failure sequences such as buckling failure and bending failure are presented. It is concluded that the structural weight increases significantly with the increase of the target system reliability index or the coefficient of variation of design parameters. Results of two optimisation schemes of the steel truss girder show that the cross-sectional areas of the beams are decreased and those of web members are increased.

[1]  Yu Liu,et al.  Enhanced sequential optimization and reliability assessment for reliability-based design optimization , 2012 .

[2]  Min Chul Jeong,et al.  Simple Approaches for Efficiently Reliability-Based Design Optimization of Bridges , 2011 .

[3]  George I. N. Rozvany,et al.  DCOC: An optimality criteria method for large systems Part II: Algorithm , 1993 .

[4]  Uri Kirsch,et al.  Optimal topologies of truss structures , 1989 .

[5]  Woo-Sang Lee,et al.  Probabilistic optimal safety with minimum life-cycle cost based on stochastic finite element analysis of steel cable-stayed bridges , 2011 .

[6]  Alaa Chateauneuf,et al.  Reliability-based optimization of structural systems by adaptive target safety – Application to RC frames , 2008 .

[7]  Jin Cheng,et al.  Optimum design of steel truss arch bridges using a hybrid genetic algorithm , 2010 .

[8]  Dan M. Frangopol,et al.  Optimization of lifetime maintenance strategies for deteriorating structures considering probabilities of violating safety, condition, and cost thresholds , 2006 .

[9]  Guan-Chun Luh,et al.  Optimal design of truss-structures using particle swarm optimization , 2011 .

[10]  Hyun-Moo Koh,et al.  System reliability analysis using dominant failure modes identified by selective searching technique , 2013, Reliab. Eng. Syst. Saf..

[11]  Wei Wang,et al.  Reliability analysis using radial basis function networks and support vector machines , 2011 .

[12]  O. Hasançebi,et al.  Layout optimization of trusses using simulated annealing , 2000 .

[13]  O. Hasançebi,et al.  An exponential big bang-big crunch algorithm for discrete design optimization of steel frames , 2012 .

[14]  Fuat Erbatur,et al.  Layout optimisation of trusses using simulated annealing , 2002 .

[15]  Palle Thoft-Christensen,et al.  Reliability of Structural Systems with Correlated Elements , 1982 .

[16]  Ru-Cheng Xiao,et al.  Serviceability reliability analysis of cable-stayed bridges , 2005 .

[17]  Li Xiang,et al.  Improved wavelet neural network combined with particle swarm optimization algorithm and its application , 2006 .

[18]  Fred Moses.  Problems and prospects of reliability-based optimization , 1997 .

[19]  D. M. Peng,et al.  Optimal design of arch bridges by integrating genetic algorithms and the mechanism method , 1999 .

[20]  Zhan Kang,et al.  Reliability-based design optimization of adhesive bonded steel–concrete composite beams with probabilistic and non-probabilistic uncertainties , 2011 .

[21]  Yoshisada Murotsu,et al.  An approach to reliability-based optimization of redundant structures , 1994 .