پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شماره‌ها | 646 |
| تعداد مقالات | 9,458 |
| تعداد مشاهده مقاله | 68,235,746 |
| تعداد دریافت فایل اصل مقاله | 47,769,400 |
Optimum Robust Design of 2D Steel Moment-Resisting Frames Using Enhanced Vibrating Particles System Algorithm | ||
| Journal of Rehabilitation in Civil Engineering | ||
| مقاله 8، دوره 14، شماره 2 - شماره پیاپی 42، مرداد 2026 اصل مقاله (3.01 M) | ||
| نوع مقاله: Regular Paper | ||
| شناسه دیجیتال (DOI): 10.22075/jrce.2025.2284 | ||
| نویسندگان | ||
| Pedram Hosseini* 1؛ Fazeleh Sadat Lajevardi2؛ Seyed Rohollah Hoseini Vaez3 | ||
| 1Assistant Professor, Faculty of Engineering, Mahallat Institute of Higher Education, Mahallat, Iran | ||
| 2M.Sc. Graduate, Department of Civil Engineering, Faculty of Engineering, University of Qom, Qom, Iran | ||
| 3Professor, Department of Civil Engineering, Faculty of Engineering, University of Qom, Qom, Iran | ||
| تاریخ دریافت: 11 اسفند 1403، تاریخ بازنگری: 23 تیر 1404، تاریخ پذیرش: 27 تیر 1404 | ||
| چکیده | ||
| This study presents a robust design optimization (RDO) approach for 2D steel moment-resisting frames, addressing uncertainties in material properties and external loads. The study considers special moment frames with high ductility capacity (R=8) designed according to American Institute of Steel Construction Load and Resistance Factor Design (AISC-LRFD) specifications. The objective is to minimize both structural weight and the robustness index, defined as the standard deviation of roof displacement. The Enhanced Vibrating Particles System (EVPS) algorithm is employed to solve the optimization problem, while Monte Carlo simulation (MCS) is used to model uncertainties. Three benchmark frames (10, 15, and 24 stories) demonstrate the effectiveness of the proposed methodology. Results show a 50-60% reduction in roof displacement variability compared to deterministic optimization, with only a 20-30% increase in structural weight. For the 10-story frame with β=0.4, the approach achieved a 67% reduction in standard deviation (from 0.484 to 0.159) with a 74% weight increase (from 63,848 lb to 111,701 lb). The robustness index coefficient (β) is identified as a key parameter for controlling the weight-robustness trade-off, allowing designers to tailor solutions based on project requirements. The study provides a practical framework for improving steel frame reliability under real-world conditions. | ||
تازه های تحقیق | ||
| ||
| کلیدواژه‌ها | ||
| Enhanced Vibrating Particles System؛ Robust design optimization؛ Uncertainty؛ Steel moment resisting frame؛ Monte Carlo simulation | ||
| مراجع | ||
|
[1] Nabati M, Gholizadeh S. PERFORMANCE-BASED OPTIMIZATION OF STEEL MOMENT FRAMES BY A MODIFIED NEWTON METAHEURISTIC ALGORITHM. Int J Optim Civ Eng 2023;13. https://doi.org/10.22068/ijoce.2023.13.2.548.
[2] Jahjouh M. An experience based artificial neural network in the design optimization of steel frames. Eng Res Express 2022;4:45031. https://doi.org/10.1088/2631-8695/aca6ce.
[3] Hasançebi O, Azad SK. An exponential big bang-big crunch algorithm for discrete design optimization of steel frames. Comput \& Struct 2012;110:167–79. https://doi.org/10.1016/j.compstruc.2012.07.014.
[4] Kaveh A, Zaerreza A. Reliability-based design optimization of the frame structures using the force method and SORA-DM framework. Structures 2022;45:814–27. https://doi.org/10.1016/j.istruc.2022.09.057.
[5] He J, Lin S, Li Y, Dong X, Chen S. Genetic Algorithm for Optimal Placement of Steel Plate Shear Walls for Steel Frames. Buildings 2022;12. https://doi.org/10.3390/buildings12060835.
[6] Hasançebi O, Carbas S. Bat inspired algorithm for discrete size optimization of steel frames. Adv Eng Softw 2014;67:173–85. https://doi.org/10.1016/j.advengsoft.2013.10.003.
[7] Le Thanh C, Sang-To T, Hoang-Le H-L, Danh T-T, Khatir S, Wahab MA. Combination of intermittent search strategy and an improve particle swarm optimization algorithm (IPSO) for damage detection of steel frame. Fract Struct Integr 2022;16:141–52. https://doi.org/10.3221/IGF-ESIS.59.11.
[8] Kaveh A, Azar BF, Hadidi A, Sorochi FR, Talatahari S. Performance-based seismic design of steel frames using ant colony optimization. J Constr Steel Res 2010;66:566–74. https://doi.org/10.1016/j.jcsr.2009.11.006.
[9] Kale BN, Aydougdu .Ibrahim, Demir E. Performance of the whale optimization algorithm in space steel frame optimization problems. Int. Conf. Harmon. Search Algorithm, 2020, p. 139–54. https://doi.org/10.1007/978-981-15-8603-3_13.
[10] Mam K, Douthe C, Le Roy R, Consigny F. Shape optimization of braced frames for tall timber buildings: Influence of semi-rigid connections on design and optimization process. Eng Struct 2020;216:110692. https://doi.org/10.1016/j.engstruct.2020.110692.
[11] Kociecki M, Adeli H. Shape optimization of free-form steel space-frame roof structures with complex geometries using evolutionary computing. Eng Appl Artif Intell 2015;38:168–82. https://doi.org/10.1016/j.engappai.2014.10.012.
[12] Van Mellaert R, Mela K, Tiainen T, Heinisuo M, Lombaert G, Schevenels M. Mixed-integer linear programming approach for global discrete sizing optimization of frame structures. Struct Multidiscip Optim 2018;57:579–93. https://doi.org/10.1007/s00158-017-1770-9.
[13] Saldaña-Robles AL, Bustos-Gaytán A, la Peña JA, Saldaña-Robles A, Alcántar-Camarena V, Balvant in-Garcia A, et al. Structural design of an agricultural backhoe using TA, FEA, RSM and ANN. Comput Electron Agric 2020;172:105278. https://doi.org/10.1016/j.compag.2020.105278.
[14] Sadeghpour A, Ozay G. Investigating the predictive capabilities of ANN, RSM, and ANFIS in assessing the collapse potential of RC structures. Arab J Sci Eng 2024:1–22. https://doi.org/10.1007/s13369-024-09618-x.
[15] Nouri Y, Ghanbari MA, Fakharian P. An integrated optimization and ANOVA approach for reinforcing concrete beams with glass fiber polymer. Decis Anal J 2024;11:100479. https://doi.org/10.1016/j.dajour.2024.100479.
[16] Maheri MR, Safari D. Topology optimization of bracing in steel structures by genetic algorithm. Fourth Int. Conf. Adv. Steel Struct., 2005, p. 277–82. https://doi.org/10.1016/B978-008044637-0/50040-8.
[17] Ben-Tal A, Brekelmans R, Den Hertog D, Vial J-P. Globalized robust optimization for nonlinear uncertain inequalities. INFORMS J Comput 2017;29:350–66. https://doi.org/10.1287/ijoc.2016.0735.
[18] Takezawa A, Nii S, Kitamura M, Kogiso N. Topology optimization for worst load conditions based on the eigenvalue analysis of an aggregated linear system. Comput Methods Appl Mech Eng 2011;200:2268–81. https://doi.org/10.1016/j.cma.2011.03.008.
[19] Sigmund O. Manufacturing tolerant topology optimization. Acta Mech Sin 2009;25:227–39. https://doi.org/10.1007/s10409-009-0240-zDesign optimization of steel structures considering uncertainties.
[20] Papadrakakis M, Lagaros ND, Plevris V. Design optimization of steel structures considering uncertainties. Eng Struct 2005;27:1408–18. https://doi.org/10.1016/j.engstruct.2005.04.002.
[21] Wang X, Shi Y, Meng Z, Yang B, Long K. Uncertainty-oriented topology optimization of dynamic structures considering hybrid uncertainty of probability and random field. Reliab Eng \& Syst Saf 2025;256:110744. https://doi.org/10.1016/j.ress.2024.110744.
[22] Hosseini P, Kaveh A, Hoseini Vaez SR. ROBUST DESIGN OPTIMIZATION OF SPACE TRUSS STRUCTURES. Int J Optim Civ Eng 2022;12.
[23] Yadav R, Ganguli R. Reliability based and robust design optimization of truss and composite plate using particle swarm optimization. Mech Adv Mater Struct 2022;29:1892–902. https://doi.org/10.1080/15376494.2020.1843743.
[24] Dammak K, Yaich A, Hami A El, Walha L, Haddar M. An efficient optimization based on the robust hybrid method for the coupled acoustic–structural system. Mech Adv Mater Struct 2020;27:1816–26. https://doi.org/10.1080/15376494.2018.1525629.
[25] Kamel A, Dammak K, Hami A El, Jdidia M Ben, Hammami L, Haddar M. A modified hybrid method for a reliability-based design optimization applied to an offshore wind turbine. Mech Adv Mater Struct 2022;29:1229–42. https://doi.org/10.1080/15376494.2020.1811927.
[26] Yarasca J, Mantari JL, Monge JC, Hinostroza MA. A robust five-unknowns higher-order deformation theory optimized via machine learning for functionally graded plates. Mech Adv Mater Struct 2024;31:10420–35. https://doi.org/10.1080/15376494.2024.2344037.
[27] Qu X, Haftka RT. Reliability-based design optimization using probabilistic sufficiency factor. Struct Multidiscip Optim 2004;27:314–25. https://doi.org/10.1007/s00158-004-0390-3.
[28] Ito M, Kim NH, Kogiso N. Conservative reliability index for epistemic uncertainty in reliability-based design optimization. Struct Multidiscip Optim 2018;57:1919–35. https://doi.org/10.1007/s00158-018-1903-9.
[29] Yonekura K, Kanno Y. Global optimization of robust truss topology via mixed integer semidefinite programming. Optim Eng 2010;11:355–79. https://doi.org/10.1007/s11081-010-9107-1.
[30] Hosseini P, Kaveh A, Fathali MA, Vaez SRH. A two-loop RBDO approach for steel frame structures using EVPS, GWO, and Monte Carlo simulation. Mech Adv Mater Struct 2025;32:605–24. https://doi.org/10.1080/15376494.2024.2352800.
[31] Taguchi G, Phadke MS. Quality Engineering through Design Optimization. In: Dehnad K, editor. Qual. Control. Robust Des. Taguchi Method, Boston, MA: Springer US; 1989, p. 77–96. https://doi.org/10.1007/978-1-4684-1472-1_5.
[32] Taguchi G. Introduction to quality engineering: designing quality into products and processes. 1986.
[33] Zaman K, McDonald M, Mahadevan S, Green L. Robustness-based design optimization under data uncertainty. Struct Multidiscip Optim 2011;44:183–97. https://doi.org/10.1007/s00158-011-0622-2.
[34] Parkinson A. Robust mechanical design using engineering models. J Mech Des 1995;117:48–54. https://doi.org/10.1115/1.2836470.
[35] Lagaros ND, Plevris V, Papadrakakis M. Neurocomputing strategies for solving reliability-robust design optimization problems. Eng Comput 2010;27:819–40. https://doi.org/10.1108/02644401011073674.
[36] Lagaros ND, Papadrakakis M. Robust seismic design optimization of steel structures. Struct Multidiscip Optim 2007;33:457–69. https://doi.org/10.1007/s00158-006-0047-5.
[37] Erfani T, Utyuzhnikov S V. Control of robust design in multiobjective optimization under uncertainties. Struct Multidiscip Optim 2012;45:247–56. https://doi.org/10.1007/s00158-011-0693-0.
[38] Chen W, Lewis K. Robust design approach for achieving flexibility in multidisciplinary design. AIAA J 1999;37:982–9. https://doi.org/10.2514/2.805.
[39] Chen W, Allen JK, Tsui K-L, Mistree F. A procedure for robust design: minimizing variations caused by noise factors and control factors. J Mech Des 1996;118:478–85. https://doi.org/10.1115/1.2826915.
[40] Chi H-W, Bloebaum CL. Mixed variable optimization using Taguchi’s orthogonal arrays. Struct Optim 1996;12:147–52. https://doi.org/10.1007/BF01196949.
[41] Lee K-H, Eom I-S, Park G-J, Lee W-I. Robust design for unconstrained optimization problems using the Taguchi method. AIAA J 1996;34:1059–63. https://doi.org/10.2514/3.13187.
[42] Sandgren E, Cameron TM. Robust design optimization of structures through consideration of variation. Comput Struct 2002;80:1605–13. https://doi.org/10.1016/S0045-7949(02)00160-8.
[43] Doltsinis I, Kang Z. Robust design of structures using optimization methods. Comput Methods Appl Mech Eng 2004;193:2221–37. https://doi.org/10.1016/j.cma.2003.12.055.
[44] Lagaros ND, Plevris V, Papadrakakis M. Multi-objective design optimization using cascade evolutionary computations. Comput Methods Appl Mech Eng 2005;194:3496–515. https://doi.org/10.1016/j.cma.2004.12.029.
[45] Beyer H-G, Sendhoff B. Robust optimization--a comprehensive survey. Comput Methods Appl Mech Eng 2007;196:3190–218. https://doi.org/10.1016/j.cma.2007.03.003.
[46] Kang Z, Bai S. On robust design optimization of truss structures with bounded uncertainties. Struct Multidiscip Optim 2013;47:699–714. https://doi.org/10.1007/s00158-012-0868-3.
[47] Liu Z, Atamturktur S, Juang CH. Performance based robust design optimization of steel moment resisting frames. J Constr Steel Res 2013;89:165–74. https://doi.org/10.1016/j.jcsr.2013.07.011.
[48] Zhao Z, Lv G, Xu Y. Facilitate the design of residential PV using reliability-based design optimization. Renew Energy 2025;240:122144. https://doi.org/10.1016/j.renene.2024.122144.
[49] Lyu M-Z, Yang J-S, Chen J-B, Li J. High-efficient non-iterative reliability-based design optimization based on the design space virtually conditionalized reliability evaluation method. Reliab Eng \& Syst Saf 2025;254:110646. https://doi.org/10.1016/j.ress.2024.110646.
[50] Chen J, Chen Z, Jiang W, Guo H, Chen L. A reliability-based design optimization strategy using quantile surrogates by improved PC-kriging. Reliab Eng \& Syst Saf 2025;253:110491. https://doi.org/10.1016/j.ress.2024.110491.
[51] Steinacker C, Paulsen M, Schröder M, Rich J. Robust design of bicycle infrastructure networks. Sci Rep 2025;15:15471. https://doi.org/10.1038/s41598-025-99976-9.
[52] Saffari H, Zahedi MJ, Ebrahimpour N, Soleymani A. The effect of earthquake characteristics on the seismic performance of steel moment resisting frames. Int J Steel Struct 2023;23:1431–46. https://doi.org/10.1007/s13296-023-00769-5.
[53] Bakhshinezhad S, Mansouri E, Jeong S-H. Machine Learning-Aided Performance-Based Optimal Design of Steel Moment-Resisting Frames Using NSGA-II. Nat Hazards Rev 2025;26:4025016. https://doi.org/10.1061/NHREFO.NHENG-2313.
[54] Roohbakhsh H, Vosoughi AR, Razmara Shooli A. Performance-Based Design Optimization of Corroded Special Moment--Resisting RC Frames. J Struct Des Constr Pract 2024;30:4024116. https://doi.org/10.1061/JSDCCC.SCENG-1602.
[55] Do B, Ohsaki M. Gaussian mixture model for robust design optimization of planar steel frames. Struct Multidiscip Optim 2021;63:137–60. https://doi.org/10.1007/s00158-020-02676-3.
[56] Do B, Ohsaki M, Yamakawa M. Bayesian optimization for robust design of steel frames with joint and individual probabilistic constraints. Eng Struct 2021;245:112859. https://doi.org/10.1016/j.engstruct.2021.112859.
[57] Zhang R, Hu S. Optimal design of self-centering braced frames with limited self-centering braces. J Build Eng 2024;88:109201. https://doi.org/10.1016/j.jobe.2024.109201.
[58] Soleymani A, Kontoni D-PN, Jahangir H. Random vibration-based investigation of required separation gap between adjacent buildings. Earthquakes Struct 2024;26:285–97. https://doi.org/10.12989/eas.2024.26.4.285.
[59] Soleymani A, Saffari H. A novel hybrid strong-back system to improve the seismic performance of steel braced frames. J Build Eng 2024;84:108482. https://doi.org/10.1016/j.jobe.2024.108482.
[60] Paknahd M, Hosseini P, Kaveh A, Hakim SJS. A SELF-ADAPTIVE ENHANCED VIBRATING PARTICLE SYSTEM ALGORITHM FOR STRUCTURAL OPTIMIZATION: APPLICATION TO ISCSO BENCHMARK PROBLEMS. Int J Optim Civ Eng 2025;15.
[61] Hosseini P, Kaveh A, Naghian A, Abedi A. OPTIMIZATION OF ARTIFICIAL STONE MIX DESIGN USING MICROSILICA AND ARTIFICIAL NEURAL NETWORKS. Int J Optim Civ Eng 2024;14. https://doi.org/10.22068/ijoce.2024.14.3.602.
[62] MATLAB. Version R2022a. Natick, Massachusetts, MathWorks Inc 2022.
[63] AISC. Resistance factor design specification for structural steel buildings. Am Inst Steel Constr Inc, Chicago, 2001.
[64] Kaveh A, Vaez SRH, Hosseini P. Simplified dolphin echolocation algorithm for optimum design of frame. Smart Struct Syst 2018;21:321–33. https://doi.org/10.12989/sss.2018.21.3.321. | ||
|
آمار تعداد مشاهده مقاله: 64 تعداد دریافت فایل اصل مقاله: 59 |
||