The Effect of Intelligent Semi-Active Thermal Exchange- Fuzzy Inference System in Structural Seismic Rehabilitation

Document Type : Regular Paper


1 Department Of Civil Engineering, College of Technical and Engineering, Parand Branch, Islamic Azad University,Parand, Iran.

2 Department of Civil Engineering, East Tehran Branch, Islamic Azad University, Tehran, Iran.


The effect of intelligent semi-active thermal exchange-fuzzy controller in structural rehabilitation by attenuating seismic responses of structural systems is investigated. In the suggested structural controller, MR dampers and their sensors are employed as a semi-active controller. Resultant control forces of MR damper are administrated by providing external voltage supply during the earthquakes and high intensity winds. Moreover, a novel evolutionary algorithm of thermal exchange (TE) is applied to compute the optimal location and the quantity of Magnetorheological (MR) dampers and their sensors with regard to minimizing resultant vibration magnitude. An optimal semi-active Thermal Exchange-Fuzzy Controller (TE-FC) has been suggested to administrate MR damper ingeniously. Results of numerical simulations illustrate the efficiency of suggested control system. The TE-FC can determine the optimal control arrangement and forces during a reasonable number of iterations. In comparison of the performance of various control strategies, the TE-FC demonstrates that economical cost and rehabilitation properties of the building could be optimized simultaneously. The TE-FC managed the optimal control forces online during strong ground motion, to attenuate the excessive responses in several rehabilitated buildings. Consequently, the TE-FC could improve the reliability of rehabilitated structure in comparison with passive and offline controllers. The significant efficiency of optimal arrangement of dampers and sensors over uniformly distribution of damper and sensors is presented as well.


Main Subjects

[1] Sabbagh-Yazdi, S.R., Farhoud, A. & Zabihi-Samani M., Transient Galerkin finite volume solution of dynamic stress intensity factors. Asian Journal of Civil Engineering, 2019. 20(3): p. 371-381.
[2] Spencer, B.F. and M.K. Sain, Controlling buildings: a new frontier in feedback. IEEE Control Systems Magazine, 1997. 17(6): p. 19-35.
[3] Gutierrez Soto, M. and H. Adeli, Semi-active vibration control of smart isolated highway bridge structures using replicator dynamics. Engineering Structures, 2019. 186: p. 536-552.
[4] Zabihi-Samani, M.G.-B., M., Optimal Semi-active Structural Control with a Wavelet-Based Cuckoo-Search Fuzzy Logic Controller. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 2018: p. 1-16.
[5] FEMA 350 - Recommended Seismic Design Criteria for New Steel Moment Frame Buildings. 2000, USA: SAC Joint Venture.
[6] Housner, G.W., et al., Structural Control: Past, Present, and Future. Journal of Engineering Mechanics, 1997. 123(9): p. 897-971.
[7] Ghanooni-Bagha, M., et al., The effect of materials on the reliability of reinforced concrete beams in normal and intense corrosions. Eksploatacja i Niezawodnosc – Maintenance and Reliability, 2017. 19(3): p. 393-402.
[8] Naeiji, A., F. Raji, and I. Zisis, Wind loads on residential scale rooftop photovoltaic panels. Journal of Wind Engineering and Industrial Aerodynamics, 2017. 168: p. 228-246.
[9] Farjoud, A., et al., Magneto-rheological fluid behavior in squeeze mode. Smart Materials and Structures, 2009. 18(9): p. 095001.
[10] Yang, G., et al., Large-scale MR fluid dampers: modeling and dynamic performance considerations. Engineering Structures, 2002. 24(3): p. 309-323.
[11] Rashid, M.M., M.A. Aziz, and M.R. Khan, An Experimental Design of Bypass Magneto-Rheological (MR) damper. IOP Conference Series: Materials Science and Engineering, 2017. 260: p. 012021.
[12] Zemp, R., et al., Development of a long-stroke MR damper for a building with tuned masses. Smart Materials and Structures, 2016. 25(10): p. 105006.
[13] Ding, Y., Zhang, L., Zhu, H. et al., Simplified design method for shear-valve magnetorheological dampers. Earthquake Engineering and Engineering Vibration, 2014. 13(4): p. 637–652.
[15] Symans, M.D., et al., Energy Dissipation Systems for Seismic Applications: Current Practice and Recent Developments. Journal of Structural Engineering, 2008. 134(1): p. 3-21.
[16] Takewaki, I., Optimal damper placement for critical excitation. Probabilistic Engineering Mechanics, 2000. 15(4): p. 317-325.
[17] Zabihi-Samani, M. and M. Ghanooni-Bagha, A fuzzy logic controller for optimal structural control using MR dampers and particle swarm optimization. Journal of Vibroengineering, 2017. 19(3): p. 1901-1914.
[18] Kaveh, A. and A. Dadras, A novel meta-heuristic optimization algorithm. Adv. Eng. Softw., 2017. 110(C): p. 69-84.
[19] Rumelhart, D.E., G.E. Hinton, and R.J. Williams, Learning representations by back-propagating errors. Nature, 1986. 323(6088): p. 533-536.
[20] Ostadali-Makhmalbaf, M., M. Tutunchian, and M. Zabihi-Samani, Optimized fuzzy logic controller for semi-active control of buildings using particle swarm optimization. Advanced Material Research, 2011. 2505(9): p. 255-260.
[21] Khademi, F., et al., Predicting strength of recycled aggregate concrete using Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System and Multiple Linear Regression. International Journal of Sustainable Built Environment, 2016. 5(2): p. 355-369.
[22] Masoud Zabihi-Samani, S.P.M., Farzaneh Raji, Effects of Fly Ash on Mechanical Properties of Concrete. Journal of Applied Engineering Sciences, 2018. 8(2): p. 35-40.
[23] Nomura, Y., H. Furuta, and M. Hirokane, An Integrated Fuzzy Control System for Structural Vibration. Computer-Aided Civil and Infrastructure Engineering, 2007. 22(4): p. 306-316.
[24] Amini, F. and M. Zabihi-Samani, A Wavelet-Based Adaptive Pole Assignment Method for Structural Control. Computer-Aided Civil and Infrastructure Engineering, 2014. 29(6): p. 464-477.
[25] Zabihi-Samani, M. and F. Amini, A cuckoo search controller for seismic control of a benchmark tall building. Journal of Vibroengineering, 2015. 17(3): p. 1382‑1400.
[26] Zisis, I., F. Raji, and J.D. Candelario, Large-Scale Wind Tunnel Tests of Canopies Attached to Low-Rise Buildings. Journal of Architectural Engineering, 2017. 23(1): p. B4016005.
[27] Amini, A. and N. Nikraz, A Method for Constructing Non-Isosceles Triangular Fuzzy Numbers using Frequency Histogram and Statistical Parameters. Journal of Soft Computing in Civil Engineering, 2017. 1(1): p. 65-85.
[28] Muradova, A., G. Tairidis, and G. Stavroulakis, Fuzzy Vibration Suppression of a Smart Elastic Plate using Graphical Computing Environment. Journal of Soft Computing in Civil Engineering, 2018. 2(1): p. 1-17.
[29] Zabihi-Samani, M. and M. Ghanooni-Bagha, An optimal cuckoo search-Fuzzy logic controller for optimal structural control International Journal of Optimization in Civil Engineering, 2018. 8(1): p. 117-135.
[30] Aghajanian, S., et al., Optimal control of steel structures by improved particle swarm. International Journal of Steel Structures, 2014. 14(2): p. 223-230.
[31] Aly, A.M., Vibration Control of Buildings Using Magnetorheological Damper: A New Control Algorithm. Journal of Engineering, 2013. 2013: p. 10.
[32] G. YANG, H.J. JUNG and B.F. SPENCER, Jr, DYNAMIC MODEL OF FULL-SCALE MR DAMPERS FOR CIVIL ENGINEERING APPLICATIONS. US-Japan Workshop on Smart Structures for Improved Seismic Performance in Urban Region, 2001: p. 1-16.
[33] Carrion, J.E.a.S.J., B.F., (), . . Model-based strategies for real-time hybrid testing. NSEL Report Series, Report no.NSEL-006,  Department of Civil and Environmental Engineering. University of Illinois at Urbana-Champaign., 2007.
[34] Spencer, B.F., et al., Phenomenological Model for Magnetorheological Dampers. Journal of Engineering Mechanics, 1997. 123(3): p. 230-238.
[35] MATLAB, Version The Mathworks Inc.
[36] Xue, X., et al., Semi-active Control Strategy using Genetic Algorithm for Seismically Excited Structure Combined with MR Damper. Journal of Intelligent Material Systems and Structures, 2011. 22(3): p. 291-302.