Seismic Evaluation of Optimal Performance-Based Design of Steel Moment Frames with Metaheuristic Algorithms

Document Type : Original Article

Authors

1 Graduate M.Sc. in Structural Engineering, , Department of Civil Engineering, Faculty of Engineering, University of Zanjan, Zanjan, Iran

2 Associate professor, Department of Civil Engineering, Faculty of Engineering, University of Zanjan, Zanjan, Iran

3 Assistant Professor Department of Civil Engineering, Faculty of Engineering, University of Zanjan, Zanjan, Iran

Abstract

Optimal use of materials in constructing structures is one of the main goals of any design. Construction of structural systems is costly for their builders, so structures and buildings that are economically justifiable, appropriate, and meet the requirements of the criteria are more welcomed. In contrast, maintaining the structural performance in earthquakes is vital to ensuring safety and reducing damage during earthquakes. As a result of optimizing frames sections, the stiffness and strength of components are reduced, and these frames' performance against earthquakes is in question. In this research, the performance level of optimized steel moment frames with metaheuristic algorithms has been evaluated. For this purpose, the seismic performance of five-story steel moment frames with different geometric characteristics has been optimized and seismically evaluated using Particle Swarm Optimization (PSO), Charged System Search (CSS) Ant Colony Algorithm (ACO) and Genetic Algorithm (GA). Study results show that the optimized frame based on the Charged System Search algorithm has lighter sections and lower weight, while the seismic behavior responses of the structures are obtained faster. Furthermore, in terms of performance levels, the total number of collapse plastic joints in the Particle Swarm Optimization (PSO) was higher than other methods. Therefore, this algorithm can also be proposed as a suitable proposal method for the optimal design of similar frames.

Keywords

Main Subjects

References

[1] Kaveh, A., Azar, B. F., Hadidi, A., Sorochi, F. R., & Talatahari, S. (2010). “Performance-based seismic design of steel frames using ant colony optimization”, Journal of Constructional Steel Research, 66(4), 566-574.
[2] Hultman, M. (2010). Weight optimization of steel trusses by a genetic algorithm-size, shape and topology optimization according to Eurocode. TVBK-5176.
[3] Heerman, D. W. (1987). “Computer simulation methods in theoretical physics”, Applied Optics, 26(10), 1818.  
[4] Haupt, R. L., & Haupt, S. E. (2004). Practical genetic algorithms. John Wiley & Sons.
[5] Saka, M. P., & Kameshki, E. S. (1998). “Optimum design of nonlinear elastic framed domes”, Advances in Engineering Software, 29(7-9), 519-528.
[6] Erbatur, F., Hasançebi, O., Tütüncü, I., & Kılıç, H. (2000). “Optimal design of planar and space structures with genetic algorithms”, Computers & Structures, 75(2), 209-224. 
[7] Pezeshk, S., Camp, C. V., & Chen, D. (2000). “Design of nonlinear framed structures using genetic optimization”, Journal of structural engineering, 126(3), 382-388.
[8] Fourie, P. C., & Groenwold, A. A. (2002). “The particle swarm optimization algorithm in size and shape optimization”, Structural and Multidisciplinary Optimization, 23(4), 259-267.
[9] Perez, R. L., & Behdinan, K. (2007). “Particle swarm approach for structural design optimization”, Computers & Structures, 85(19-20), 1579-1588.
[10] Camp, C. V., & Bichon, B. J. (2004). “Design of space trusses using ant colony optimization”, Journal of structural engineering, 130(5), 741-751.
[11] Kaveh, A., & Talatahari, S. (2010). “An improved ant colony optimization for the design of planar steel frames”, Engineering Structures, 32(3), 864-873.
[12] Karimi, F., & Vaez, S. R. H. (2019). “Two-stage optimal seismic design of steel moment frames using the LRFD-PBD method”, Journal of Constructional Steel Research, 155, 77-89.
[13] Fathali, M., Hoseini Vaez, S., Dehghani, E. (2019). “Modeling the link beam behavior to evaluate its performance according to FEMA 356 and calculating the target displacement of performance levels”, Civil Infrastructure Researches, 4(2), 47-60. doi: 10.22091/cer.2018.3193.1118
[14] Kennedy, J., & Eberhart, R. (1995, November). Particle swarm optimization. In Proceedings of ICNN'95-international conference on neural networks (Vol. 4, pp. 1942-1948). IEEE.
[15] Kaveh, A., & Talatahari, S. (2010). “An improved ant colony optimization for the design of planar steel frames”, Engineering Structures, 32(3), 864-873.
[16] Blum, C., & Roli, A. (2003). “Metaheuristics in combinatorial optimization: Overview and conceptual comparison”, ACM computing surveys (CSUR), 35(3), 268-308.
[17] Poli, R., Kennedy, J., & Blackwell, T. (2007). “Particle swarm optimization”, Swarm intelligence, 1(1), 33-57.
[18] Glover, F. (1977). “Heuristics for integer programming using surrogate constraints”, Decision sciences, 8(1), 156-166.
[19] Dorigo, M., & Gambardella, L. M. (1997). “Ant colony system: a cooperative learning approach to the traveling salesman problem”, IEEE Transactions on evolutionary computation, 1(1), 53-66.
[20] Haupt, R. L., & Haupt, S. E. (2004). Practical genetic algorithms with CD-Rom. Wiley-Interscience.
[21] Federal Emergency Management Agency, FEMA-273. NEHRP guideline for the seismic rehabilitation of buildings. Washington (DC): Building Seismic Safety Council; 1997.
[22] Federal Emergency Management Agency, FEMA-350. Recommended seismic design criteria for new steel moment-frame buildings. SAC Joint Venture, USA. 2000.
[23] FEMA-356.: Prestandard and Commentary for the Seismic Rehabilitation of Buildings American Society of Civil Engineers (2000)
[24] ATC-40.: Seismic Evaluation and Retrofit of Reinforced Concrete Buildings: Applied Technology Council (1996)
Send comment about this article
CAPTCHA Image

Files

Share

How to cite

Statistics