Optimal Design of Steel Curved Roof Frames by Enhanced Vibrating Particles System Algorithm

Authors

  • Ali Kaveh
    Affiliation

    Iran University of Science and Technology

  • Seyed Rohollah Hoseini Vaez
    Affiliation

    University of Qom

  • Pedram Hosseini
    Affiliation

    Mahallat Institute of Higher Education

  • Mohsen Bakhtyari
    Affiliation

    University of Qom

https://doi.org/10.3311/PPci.14812

Abstract

The paper presents an optimal design of steel curved roof frames with its roof being part of a circular arc. The elements of frames are tapered I-section members. In the objective function for optimization, two factors affecting the weight of frames are considered simultaneously. First, the roof slope angle as an effective variable on the values of the structural loading and second, the cross-section of members that are considered as continuous and discrete variables, respectively. In the range of 3 to 70 degrees, the optimum range of roof slope angles for steel curved roof frames, as well as precise value of the best roof slope angle, will be reported. Enhanced Vibrating Particles System (EVPS) algorithm is utilized for the optimal design of steel curved roof frames with tapered members. The performance and efficiency of the EVPS algorithm is compared with six other recently developed optimization algorithms including VPS, GWO, HS, SSA, ECBO and GOA algorithms. The effectiveness and performance of EVPS algorithm is proven. Frames design are performed using ANSI/AISC 360-05 specifications which strength, displacement and stability constraints are imposed on the frames.

Keywords:

curved roof frames, tapered members, structural optimization, meta-heuristic algorithms, enhanced vibrating particles system algorithm

Citation data from Crossref and Scopus

Published Online

2019-09-17

How to Cite

Kaveh, A., Hoseini Vaez, S. R., Hosseini, P., Bakhtyari, M. “Optimal Design of Steel Curved Roof Frames by Enhanced Vibrating Particles System Algorithm”, Periodica Polytechnica Civil Engineering, 63(4), pp. 947–960, 2019. https://doi.org/10.3311/PPci.14812

Issue

Section

Research Article