Dual Target Optimization of Two-Dimensional Truss Using Cost Efficiency and Structural Reliability Sufficiency

Document Type: Regular Article

Authors

1 M.Sc. Graduated, Department of Mechanical Engineering, Semnan Branch, Islamic Azad University, Semnan, Iran

2 Department of Civil and Environmental Engineering, Washington State University, Pullman, United States

3 M.Sc. Graduated, Faculty of Civil, Water and Environment Engineering, Shahid Beheshti University, Tehran, Iran

Abstract

The main contribution of this study is to open a discussion regarding the structural optimization associated with the cost efficiency and structural reliability sufficiency consideration. To do so, several various optimization approaches are investigated to deliberate both cost and reliability concerns. Particularly, particle swarm optimization is highlighted as a reliable optimization approach. Accordingly, an illustrative example is rendered to compare the feasibility of the considered optimization approaches. The feasibility of the investigated approaches is evaluated using the cost and reliability analysis. For the considered example, it was observed that the PSO optimization algorithm has multiple advantages such as easy realization, fast convergence, and promising performance in nonlinear performance optimization. The PSO optimization algorithm can be successfully applied in various fields of civil engineering. This popularity is due to the understandable performance of the PSO as well as its simplicity. In this paper, first, the literature on the subject has been described by two-dimensional truss analysis using the finite element method and optimized using the PSO particle swarm algorithm. A comparison of the results with this reference indicates the accuracy of this particle swarm algorithm in truss optimization. Indeed, this study ignites two main insights in structural optimizations assessment. The first illustration is related to how to establish a framework for structural system reliability analysis associated with the different degrees of indeterminacies. And the second illustration is related to making a decision problem concerning the structural optimization while both cost and reliability metric are two main parameters for the construction point of the view.

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[1]       Cui Z, Gao X. Theory and applications of swarm intelligence. Neural Comput Appl 2012;21:205–6. doi:10.1007/s00521-011-0523-8.
[2]       Dorigo M, Blum C. Ant colony optimization theory: A survey. Theor Comput Sci 2005;344:243–78. doi:10.1016/j.tcs.2005.05.020.
[3]       Antoniou P, Pitsillides A, Blackwell T, Engelbrecht A. Employing the flocking behavior of birds for controlling congestion in autonomous decentralized networks. 2009 IEEE Congr. Evol. Comput., IEEE; 2009, p. 1753–61. doi:10.1109/CEC.2009.4983153.
[4]       Antoniou P, Pitsillides A, Blackwell T, Engelbrecht A, Michael L. Congestion control in wireless sensor networks based on bird flocking behavior. Comput Networks 2013;57:1167–91. doi:10.1016/j.comnet.2012.12.008.
[5]       Karaboga D, Basturk B. On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 2008;8:687–97. doi:10.1016/j.asoc.2007.05.007.
[6]       Karaboga D, Basturk B. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 2007;39:459–71. doi:10.1007/s10898-007-9149-x.
[7]       Li XL, Qian JX. Studies on artificial fish swarm optimization algorithm based on decomposition and coordination techniques. J Circuits Syst 2003;1:1–6.
[8]       Neshat M. FAIPSO: fuzzy adaptive informed particle swarm optimization. Neural Comput Appl 2013;23:95–116. doi:10.1007/s00521-012-1256-z.
[9]       Chen D, Zhao C, Zhang H. An improved cooperative particle swarm optimization and its application. Neural Comput Appl 2011;20:171–82. doi:10.1007/s00521-010-0503-4.
[10]     Kennedy J, Eberhart R. Particle swarm optimization (PSO). Proc. IEEE Int. Conf. Neural Networks, Perth, Aust., 1995, p. 1942–8.
[11]     Eberhart R, Kennedy J. A new optimizer using particle swarm theory. MHS’95. Proc. Sixth Int. Symp. Micro Mach. Hum. Sci., IEEE; n.d., p. 39–43. doi:10.1109/MHS.1995.494215.
[12]     Hajihassani M, Jahed Armaghani D, Kalatehjari R. Applications of Particle Swarm Optimization in Geotechnical Engineering: A Comprehensive Review. Geotech Geol Eng 2018;36:705–22. doi:10.1007/s10706-017-0356-z.
[13]     Zhu H, Wang Y, Wang K, Chen Y. Particle Swarm Optimization (PSO) for the constrained portfolio optimization problem. Expert Syst Appl 2011;38:10161–9.
[14]     Shi Y, Eberhart R. A modified particle swarm optimizer. 1998 IEEE Int. Conf. Evol. Comput. proceedings. IEEE world Congr. Comput. Intell. (Cat. No. 98TH8360), IEEE; 1998, p. 69–73.
[15]     Poitras G, Lefrançois G, Cormier G. Optimization of steel floor systems using particle swarm optimization. J Constr Steel Res 2011;67:1225–31.
[16]     Kalatehjari R. An improvised three-dimensional slope stability analysis based on limit equilibrium method by using particle swarm optimization 2013.
[17]     Rajeev S, Krishnamoorthy CS. Discrete optimization of structures using genetic algorithms. J Struct Eng 1992;118:1233–50.
[18]     Ghasemi SH, Nowak AS. Reliability index for non-normal distributions of limit state functions. Struct Eng Mech 2017;62:365–72.
[19]     Ghasemi SH, Nowak AS. Target reliability for bridges with consideration of ultimate limit state. Eng Struct 2017;152:226–37.
[20]     Ghasemi SH, Nowak AS. Reliability analysis of circular tunnel with consideration of the strength limit state. Geomech Eng 2018;15:879–88.
[21]     Ghasemi SH, Lee JY. Reliability-Based Indicator for Post-Earthquake Traffic Flow Capacity of a Highway Bridge. Structural Safety, under publiction. 2020.