Meta-heuristic design optimization of steel moment resisting frames subjected to natural frequency constraints

Zakian, Pooya

doi:10.1016/j.advengsoft.2019.102686

Cited by 26 publications

(7 citation statements)

References 34 publications

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“…Due to the random nature of meta-heuristics, three algorithms including the PSO, the GWO, and the IGWO are selected for the optimization. These three population-based algorithms are used because the PSO is a well-established algorithm with successful applications (Eberhart and Kennedy, 1995; Esfandiari et al, 2018; Zakian, 2019), and the GWO and the IGWO are two recently developed algorithms with remarkable performances (Kaveh and Zakian, 2018; Mirjalili et al, 2014; Zakian, 2019). In order to compare the solutions of these algorithms with the same computational cost, number of agents and the maximum number of iterations for each algorithm are taken as 50 and 200, respectively.…”

Section: Design Examplementioning

confidence: 99%

“…Pipe racks are considered as industrial non-building structures (ASCE, 2010) which support pipes and other relevant mechanical or electrical equipment in petrochemical and power plants. Only some studies exist on design concepts and/or design optimization of pipe rack structures (Arya et al, 2003; Bedair, 2015; Bhattacharya et al, 2008; di Roseto et al, 2017; Di Sarno and Karagiannakis, 2019; Lee et al, 2015; Villaverde, 1997), while there are many works on optimal design of building frames (Dogan and Ozyuksel Ciftcioglu, 2020; Doğan and Saka, 2012; Gholizadeh, 2015; Gholizadeh and Moghadas, 2014; Kaveh and Bakhshpoori, 2013; Saka, 2009; Wang et al, 2020; Zakian, 2019) and trusses (Kaveh and Bakhshpoori, 2016; Kaveh and Zakian, 2014a, 2018), among which a part of them are briefly discussed herein. Arya et al (2003) compared a manually designed pipe rack with an automated design using STRUDL-II software in three different cases of imposing design constraints and concluded that the software design can reduce the structural weight while thoroughly following the requirements of standards.…”

Section: Introductionmentioning

confidence: 99%

“…Due to advances in meta-heuristic algorithms and their simplicity (Gholizadeh and Fattahi, 2019; Livani et al, 2018; Mirjalili et al, 2014), many engineering optimization problems on structural design have successfully been solved by these algorithms (Akin and Saka, 2015; Daryan et al, 2019; Esfandiari et al, 2018; Hassanzadeh and Gholizadeh, 2019; Kaveh and Zakian, 2014b; Zakian and Kaveh, 2018). A part of those algorithms are adaptive charged system search (ACSS) (Zakian and Kaveh, 2018), particle swarm optimization (PSO) (Kaveh et al, 2014), grey wolf optimizer (GWO) (Ghannadi et al, 2020; Kaveh and Zakian, 2018; Zakian, 2019), improved grey wolf optimizer (IGWO) (Kaveh and Zakian, 2018; Zakian, 2019), harmony search (HS) (Akin and Saka, 2015; Degertekin, 2007; Kaveh and Zakian, 2013; Saka, 2009), teaching learning based algorithm (TLBO) (Zakian, 2019), cuckoo search (CS) (Kaveh et al, 2015b), enhanced bat algorithm (EBA) (Kaveh and Zakian, 2014a), water evaporation optimization (WEO) (Kaveh and Bakhshpoori, 2016), Newton meta-heuristic algorithm (NMA) (Gholizadeh et al, 2020), and center of mass optimization (CMO) (Gholizadeh and Ebadijalal, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Optimal design of steel pipe rack structures using PSO, GWO, and IGWO algorithms

Zakian

Ordoubadi

Alavi³

2021

Advances in Structural Engineering

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Design optimization of industrial structures is of great importance for engineers in order to provide a cost-effective structural design. Meanwhile, pipe rack is a skeletal industrial structure subjected to various types of loading such as gravity, seismic, piping, and thermal forces. While there are many studies on design optimization of the most common structures, only a limited number of studies exist on optimal design of industrial structures. In this article, a design optimization problem is proposed for weight minimization of steel pipe rack structures, and then the problem is solved through three meta-heuristic algorithms consisting of a modified particle swarm optimization (PSO), grey wolf optimizer (GWO), and the recently developed improved grey wolf optimizer (IGWO). The optimization problem is in discrete form in order to consider practically available cross-sections for the structural members. Stress ratio, drift, and dimensional constraints are imposed during the optimization. In order to demonstrate the capability and effectiveness of the present design optimization problem, a pipe rack structure is optimized by the proposed algorithms, and the optimized designs are compared to an ordinary design in terms of the structural weight and the status of constraints.

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Section: Design Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal design of steel pipe rack structures using PSO, GWO, and IGWO algorithms

Zakian

Ordoubadi

Alavi³

2021

Advances in Structural Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…In some cases, even the overall puncture may fail because of this phenomenon. In this regard, a mechanical model of the steel puncture needle, which is bent by the fi ber, was established [10][11][12][13]. Moreover, the effects of needle punching position and the fabric thickness on the needle resistance are studied empirically.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Mechanical Behavior of Different Needle Tip Shapes During Puncture of Carbon Fiber Fabric

Yang

Dong

Chen

et al. 2021

Autex Research Journal

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In the present study, the fiber-bending around the needle during the piercing process of the carbon fabric is investigated. In this regard, a mathematical model is established to investigate the bending elongation of the carbon fiber around the needle and the interaction between the carbon fiber and the needle tip. Then the mechanical behavior of the carbon fabric when moving down the tip of the steel needle is analyzed. Based on the performed analysis, a shape curve equation that satisfies the puncture needle tip is established. Furthermore, the influence of different needle tip shapes on the mechanical behavior of the carbon fiber is analyzed. The performance of the needle tip is subjected to different loads, including the puncture template, horizontal tension of the fiber to the needle tip, frictional resistance between the fiber and the needle tip, sliding force, and the bending moment. The performed analysis shows that when the shape of the needle tip assumes the form of curve 10, the downward force, horizontal tension, friction resistance, sliding force, and bending moment are minimized. Accordingly, curve 10 is proposed as the optimal shape for the needle tip. The present study is expected to provide theoretical guidance for selecting overall puncture process parameters.

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“…Truss structure optimization with application diversity is one of the most popular problems in civil engineering [12][13][14][15][16][17][18][19]. Truss optimization includes three types: section shape, geometry, and topology.…”

Section: Introductionmentioning

confidence: 99%

Reducing the Collision Damage Done to the Tips of Steel Needles during Integrated Piercing by Using Shape Optimization with Feature Selection

Yang

Dong

Jiang

et al. 2020

Mathematical Problems in Engineering

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Recent research has shown that, during integrated piercing, the yarn tension can displace the needles from the centers of the holes in the piercing template. To reduce the damage done to the needle tips when the needles and the hole walls collide when the needle displacement is too large, this paper proposes a method for optimizing the needle shape that satisfies the strength constraint while targeting minimum needle displacement. First, the optimal objective function and strength constraint conditions for the tip displacement of the integrated puncture needle are established, which are affected by many factors. Then, the feature selection method of machine learning is used to reduce the dimensionality of the objective function after data reduction, and the feasible region of key features is reduced to avoid falling into the local best point in the optimization iteration. Finally, machine vision is used to measure experimentally the tip displacement of a needle array. The results show that the average tip displacement is reduced by 18.16–31.42% and the collision risk is reduced from 43.14% to 5.54%. It shows that the optimization method of needle shape based on feature selection is effective for reducing needle tip impact damage during integrated piercing.

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Meta-heuristic design optimization of steel moment resisting frames subjected to natural frequency constraints

Cited by 26 publications

References 34 publications

Optimal design of steel pipe rack structures using PSO, GWO, and IGWO algorithms

Optimal design of steel pipe rack structures using PSO, GWO, and IGWO algorithms

Analysis of Mechanical Behavior of Different Needle Tip Shapes During Puncture of Carbon Fiber Fabric

Reducing the Collision Damage Done to the Tips of Steel Needles during Integrated Piercing by Using Shape Optimization with Feature Selection

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