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Farahani, Sina. (1405). A Data-Driven Seismic Design Framework for RC Frames Using Wavelet Transforms and Machine Learning. پژوهشنامه مدیریت اجرایی, (), 1-24. doi: 10.22080/ceas.2026.30950.1073
Sina Farahani. "A Data-Driven Seismic Design Framework for RC Frames Using Wavelet Transforms and Machine Learning". پژوهشنامه مدیریت اجرایی, , , 1405, 1-24. doi: 10.22080/ceas.2026.30950.1073
Farahani, Sina. (1405). 'A Data-Driven Seismic Design Framework for RC Frames Using Wavelet Transforms and Machine Learning', پژوهشنامه مدیریت اجرایی, (), pp. 1-24. doi: 10.22080/ceas.2026.30950.1073
Farahani, Sina. A Data-Driven Seismic Design Framework for RC Frames Using Wavelet Transforms and Machine Learning. پژوهشنامه مدیریت اجرایی, 1405; (): 1-24. doi: 10.22080/ceas.2026.30950.1073

A Data-Driven Seismic Design Framework for RC Frames Using Wavelet Transforms and Machine Learning

Civil Engineering and Applied Solutions
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 11 دی 1405    اصل مقاله (2.61 M)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22080/ceas.2026.30950.1073
نویسنده
Sina Farahani*
Department of Civil Engineering, Faculty of Civil Engineering and Architecture, Shahid Chamran University of Ahvaz, Ahvaz, Iran
تاریخ دریافت: 22 دی 1404،  تاریخ بازنگری: 24 بهمن 1404،  تاریخ پذیرش: 17 اردیبهشت 1405 
چکیده
Nowadays, reinforced concrete (RC) frames are one of the most well-developed lateral load-resisting systems used around the world. However, the complex nonlinear behavior of the composite material system, which combines steel and concrete, makes precise seismic analysis and design particularly challenging. Established methods, such as modal response history analysis and time history analysis, are often difficult to apply. This challenge is compounded when significant soil structure interaction alters the structural response through higher mode effects. Consequently, the development of innovative analytical methods that are both simplified and robust remains a primary challenge in advancing the design of these structural systems. This study presents a novel wavelet transform-based machine learning method (WTMLM) for the seismic design of RC frames. The method incorporates soil-structure interaction (SSI) effects and formulates the dynamic equilibrium of the system. The proposed WTMLM is used to design five RC frames with varying story heights on two different soil types. The performance is evaluated using nonlinear time-history analyses (NTHA) under real ground motions. Finally, a new equation for predicting displacement distribution is developed based on machine learning and median NTHA results. The findings indicate the WTMLM is an effective method for controlling the seismic performance of RC structures.
کلیدواژه‌ها
Reinforced concrete moment-resisting frame؛ Wavelet transform؛ Machine learning-based method؛ Seismic behavior؛ Dynamic analysis
مراجع
  1. American Society of Civil Engineers (ASCE). ASCE/SEI 7-16: Minimum Design Loads and Associated Criteria for Buildings and Other Structures. Reston (VA): ASCE; 2017.
  2. British Standards Institution. CEN EN 1998-1: Eurocode 8: Design of structures for earthquake resistance. Ispra (IT): EN; 1998.
  3. Building and Housing Research Center. Standard No. 2800: Iranian code of practice for seismic resistant design of buildings. Tehran (IR): 2800; 2015.
  4. Farahani, S., Akhaveissy, A. H., Damkilde, L. Towards a modified displacement-based seismic design of braced reinforced concrete frame structures considering soil structure interaction. Structures, 2025; 75: 108443. doi:10.1016/j.istruc.2025.108443.
  5. Priestley, M., Calvi, G., Kowalsky, M. Displacement-based seismic design of structures. 1st ed. Rome (IT): IUSS Press; 2007. doi:10.1002/EQE.807.
  6. Sullivan, T. J. Highlighting Differences between Force-Based and Displacement-Based Design Solutions for Reinforced Concrete Frame Structures. Structural Engineering International, 2013; 23: 122-131. doi:10.2749/101686613X13439149156958.
  7. Sullivan, T. J., Priestley, M. J. N., Calvi, G. M. A Model Code for the Displacement-Based Seismic Design of Structures. 1st ed. Rome (IT): IUSS Press; 2012.
  8. AK, C. Dynamics of structures: theory and applications to earthquake engineering. 1 ed. Prentice Hall (NJ): Upper Saddle River; 2007.
  9. Nazarimofrad, E., Farahani, S., Zahrai, S. M. Multiobjective optimal placement of active tendons to control irregular multistory buildings with soil–structure interaction. The Structural Design of Tall and Special Buildings, 2019; 28: e1581. doi:10.1002/tal.1581.
  10. Imam, M. H., Mohiuddin, M., Shuman, N. M., Oyshi, T. I., Debnath, B., Liham, M. I. M. H. Prediction of seismic performance of steel frame structures: A machine learning approach. Structures, 2024; 69: 107547. doi:10.1016/j.istruc.2024.107547.
  11. Demir, A., Sahin, E. K., Demir, S. Advanced tree-based machine learning methods for predicting the seismic response of regular and irregular RC frames. Structures, 2024; 64: 106524. doi:10.1016/j.istruc.2024.106524.
  12. Farahani, S., Barari, A. A simplified procedure for the prediction of liquefaction-induced settlement of offshore wind turbines supported by suction caisson foundation based on effective stress analyses and an ML-based group method of data handling. Earthquake Engineering & Structural Dynamics, 2023; 52: 5072-5098. doi:10.1002/eqe.4000.
  13. Salajegheh, E., Heidari, A., Saryazdi, S. Optimum design of structures against earthquake by discrete wavelet transform. International Journal for Numerical Methods in Engineering, 2005; 62: 2178-2192. doi:10.1002/nme.1279.
  14. Kamgar, R., Tavakoli, R., Rahgozar, P., Jankowski, R. Application of discrete wavelet transform in seismic nonlinear analysis of soil–structure interaction problems. Earthquake Spectra, 2021; 37: 1980-2012. doi:10.1177/8755293020988027.
  15. Dadkhah, M., Kamgar, R., Heidarzadeh, H. Reducing the Cost of Calculations for Incremental Dynamic Analysis of Building Structures Using the Discrete Wavelet Transform. Journal of Earthquake Engineering, 2022; 26: 3317-3342. doi:10.1080/13632469.2020.1798830.
  16. Kamgar, R., Dadkhah, M., Naderpour, H. Earthquake-induced nonlinear dynamic response assessment of structures in terms of discrete wavelet transform. Structures, 2022; 39: 821-847. doi:10.1016/j.istruc.2022.03.060.
  17. Yang, J., Li, F., Zhou, J., Zhang, Y., Li, P., Henry, R. S. Experimental Investigation of Seismic Damage in RC Frame Structures With Soil-Structure Interaction Using Wavelet Packet Transformation. Earthquake Engineering and Resilience, 2025; 4: 379-399. doi:10.1002/eer2.70018.
  18. Zhang, H., Wang, L., Shi, W. Seismic intelligent retrofitting of aging steel structure using semi-active TMD with LSTM prediction and wavelet transform combined algorithm. Thin-Walled Structures, 2025; 214: 113431. doi:10.1016/j.tws.2025.113431.
  19. Asgari, A., Bagheripour, M. H. Earthquake Response Analysis of Soil Layers Using HFTD Approach. In: editors. Soil Dynamics and Earthquake Engineering. 2012. p. 320-325. doi:10.1061/41102(375)39.
  20. Akbarzadeh, M. R., Naeim, B., Asgari, A., Estekanchi, H. E. Framework for multi-hazard parameterized fragility based uncertainty quantification and sensitivity analysis of offshore wind turbines. Soil Dynamics and Earthquake Engineering, 2026; 201: 109963. doi:10.1016/j.soildyn.2025.109963.
  21. Asgari, A., Ranjbar, F., Bagheri, M. Seismic resilience of pile groups to lateral spreading in liquefiable soils: 3D parallel finite element modeling. Structures, 2025; 74: 108578. doi:10.1016/j.istruc.2025.108578.
  22. Asgari, A., Ahmadtabar Sorkhi, S. F. Wind turbine performance under multi-hazard loads: Wave, wind, and earthquake effects on liquefiable soil. Results in Engineering, 2025; 26: 104647. doi:10.1016/j.rineng.2025.104647.
  23. Idriss Izzat, M., Seed, H. B. Seismic Response of Horizontal Soil Layers. Journal of the Soil Mechanics and Foundations Division, 1968; 94: 1003-1031. doi:10.1061/JSFEAQ.0001163.
  24. Federal Emergency Management Agency (FEMA). Improvement of nonlinear static seismic analysis procedures. Washington, D.C. (US): FEMA; 2005.
  25. Lu, Y., Hajirasouliha, I., Marshall, A. M. An improved replacement oscillator approach for soil-structure interaction analysis considering soft soils. Engineering Structures, 2018; 167: 26-38. doi:10.1016/j.engstruct.2018.04.005.
  26. Agrawal, A. K., He, W. L. A closed-form approximation of near-fault ground motion pulses for flexible structures. In: Engineering Mechanics: Proceedings of the 11th Conference; 1996 May 19-22; Fort Lauderdale, Florida. p.
  27. Makris, N., Black, C. J. Dimensional Analysis of Inelastic Structures Subjected to Near Fault Ground Motions. Berkeley (CA): Earthquake Engineering Research Center, University of California; 2003. Report No.: UCB/EERC 2003-05.
  28. Amiri, J. V., Davoodi, M. R., Sahafi, A. Simulation of Near-Fault Ground Motions With Equivalent Pulses & Compare Their Effects on MRF Structures. In: Proceedings of the 14th World Conference on Earthquake Engineering; 2008 Oct 12-17; Beijing, China. p.
  29. Misiti M, M. Y., Oppenheim G, Poggi JM Wavelet toolbox reference. 1 ed. Natick (MA): The MathWorks; 213.
  30. American Concrete Institute. ACI 318-14: Building code requirements for structural concrete (ACI 318-14). Farmington Hills (MI): ACI; 2014.
  31. Khampanit, A., Leelataviwat, S., Kochanin, J., Warnitchai, P. Energy-based seismic strengthening design of non-ductile reinforced concrete frames using buckling-restrained braces. Engineering Structures, 2014; 81: 110-122. doi:10.1016/j.engstruct.2014.09.033.
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