[1] Hajirasouliha, I., Doostan, A. (2010). A simplified model for seismic response prediction of concentrically braced frames. Advances in Engineering Software, 41(3), 497-505
[2] Miranda, B. (1999). Approximate Seismic Lateral Deformation Demands in Multistory Buildings. Journal of Structural Engineering, 125, 417-425
[3] Kuang, J.S., Huang, K. (2011). Simplified multi-degree-of-freedom model for estimation of seismic response of regular wall-frame structures. The Structural Design of Tall and Special Buildings, 20, 418-432
[4] Lai, X., He, Z., Wu, Y. (2021). Elastic inter-story drift seismic demand estimate of super high-rise buildings using coupled flexural-shear model with mass and stiffness non-uniformities. Engineering Structures, 226, 111378
[5] Bai, J., Zhang, J., Jin, S., Wang, Y. (2021). A simplified computational model for seismic performance evaluation of steel plate shear wall-frame structural systems. Structures, 33, 1677-1689
[6] Azizi, M., Ghasemi, S.A.M., Ejlali, R.G., Talatahari, S. (2019). Optimal tuning of fuzzy parameters for structural motion control using multiverse optimizer. The Structural Design of Tall and Special Buildings, 28(13), e1652
[7] Effati, M., Shahmalekpour, P. (2018).Providing a Method for Predicting the Concrete Slump Based on Adaptive Neuro-Fuzzy Inference System. Journal of Structural and Construction Engineering, 6(1), 127-140. (In Persian)
[8] Naderpour, N., Fakharian, P. (2018). Predicting the torsional strength of reinforced concrete beams strengthened with FRP sheets in terms of artificial neural networks. Journal of Structural and Construction Engineering, 5(1), 20-35. (In Persian)
[9] Naderpour, H., Rafiean, A.H., Fakharian, P. (2018). Compressive strength prediction of environmentally friendly concrete using artificial neural networks. Journal of Building Engineering, 16, 213-219
[10] Mehrabi, P., Honarbari, S., Rafiei, S., Jahandari, S., Bidgoli, M.A. (2021). Seismic response prediction of FRC rectangular columns using intelligent fuzzy‑based hybrid metaheuristic techniques. Journal of Ambient Intelligence and Humanized Computing
[11] Zhang, R., Chen, Z., Chen, S., Zheng, J., Büyüköztürk, O., Sun, H. (2019). Deep long short-term memory networks for nonlinear structural seismic response prediction. Computers and Structures, 220, 55-68
[12] Oh, B.K., Park, Y., Park, H.S. (2020). Seismic response prediction method for building structures using convolutional neural network. Structural Control and Health Monitoring, 27(5), e2519
[13] Peng, H., Yan, J., Yu, Y., Luo, Y. (2021). Time series estimation based on deep Learning for structural dynamic nonlinear prediction. Structures, 29, 1016-1031
[14] Zadeh, L.A. (1965). Fuzzy sets. Information and Control, 8, 338-353
[15] Feng, G. (2010). Analysis and Synthesis of Fuzzy Control Systems A Model-Based Approach. CRC Press
[16] Mamdani, E.H. (1974). Applications of fuzzy algorithms for simple dynamic plants. Procedings of IEEE, 121(12), 1585–1588
[17] Takagi, T., Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions Systems, Man & Cybernetics, 15(1), 116–132
[18] Kukolj, D. (2002). Design of adaptive Takagi-Sugeno-Kang fuzzy models. Applied Soft Computing, 2, 89-103
[19] Acampora, G., (2011). A TSK Neuro-Fuzzy Approach for Modeling Highly Dynamic Systems. IEEE International Conference on Fuzzy Systems, 146-152
[20] Azar, B.F., Veladi, H., Raeesi, F., Talatahari, S. (2020). Control of the nonlinear building using an optimum inverse TSK model of MR damper based on modified grey wolf optimizer. Engineering Structures, 214, 110657
[21] Topcu, I.B., Sarıdemir, M. (2008). Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic. Computational Materials Science, 41, 305-311
[22] Shoorehdeli, M.A., Teshnehlab, M., Sedigh, A.K. (2009). Identification using ANFIS with intelligent hybrid stable learning algorithm approaches. Neural Computing & Applications, 18, 157-174
[23] Dewan, M.W., Huggett, D.J., Liao, T.W., Wahab, M.A., Okeil, A.M. (2016). Prediction of tensile strength of friction stir weld joints with adaptive neuro-fuzzy inference system (ANFIS) and neural network. Materials and Design, 92, 288-299
[24] Harooni, A.B., Marghmaleki, A.N. (2017). Implementing a PSO-ANFIS model for prediction of viscosity of mixed oils. Petroleum Science and Technology, 35(2), 155-162
[25] Yinfeng, D., Yingmin, L., Ming, L., Mingkui, X. (2008). Nonlinear structural response prediction based on support vector machines. Journal of Sound and Vibration, 311, 886-897
[26] Thaler, D., Stoffel, M., Markert, B., Bamer, F. (2021). Machine-learning-enhanced tail end prediction of structural response statistics in earthquake engineering. Earthquake Engineering and Structural Dynamics, 2021, 1-17
[27] Jang, J.S.R. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man and Cybernetics, 23(3), 665-685
[28] Chatterjee, A., Watanabe, K. (2006). An optimized Takagi-Sugeno type neuro-fuzzy system for modeling robot manipulators. Neural Computing & Applications, 15(1), 55-61
[29] Chen, M.S. (1999). A comparative study of learning methods in tuning parameters of fuzzy membership functions. IEEE Transactions on Systems, and Cybernetics, 3, 40-44
[30] Mirjalili, S., Mirjalili, S.M., Hatamlou, A. (2016). Multi-Verse Optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications, 27, 495-513
[31] Newmark, N.M. (1959). A method of computation for structural dynamics. Journal of the engineering mechanics division, 85(3), 67-94
[32] Chopra, A.K. 2012. Dynamics of Structures. Theory and Applications to Earthquake Engineering. 4th Edition, Pearson Education.
[32] Shirgir, S., Azar, B.F., Hadidi, A. (2020), Reliability-based simplification of Bouc-Wen model and parameter identification using a new hybrid algorithm. Structures, 27, 297-308
[33] Yang, J. N., Long, F. X., and Wong, D. (1988). Optimal Control of Nonlinear Structures. Journal of Applied Mechanics, 55(4), 931-938.
[34] Mohebbi, M., Joghataie, A.(2012), Designing optimal tuned mass dampers for nonlinear frames by distributed genetic algorithms. The Structural Design of Tall and Special Buildings, 21(1), 57-76
[35] Joghataie, A., Mohebbi, M. (2012), Optimal control of nonlinear frames by Newmark and distributed genetic algorithms. The Structural Design of Tall and Special Buildings, 21(2), 77-95