Soft Computing in Earthquake Engineering: a Short Overview


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Abstract


Soft Computing refers to the name for solving the hardest problems with which human are confronted today that tolerates the imprecision, uncertainty, partial truth, and approximation of the solutions. Nature inspired algorithms, like evolutionary algorithms, swarm intelligence, and neural networks become one of the leading methods for solving these problems. The soft computing methods have also been applied for solving the earthquake engineering problems. In this paper, a short review of these methods is presented. In line with this, the problems solved by soft computing algorithms are identified, then, the characteristics of these algorithms are exposed and finally, the applications of the soft computing algorithms are identified. The paper concludes with an overview of the possible directions for further development.
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Keywords


Earthquake Engineering; Optimal Seismic Design; Earthquake Prediction; Data Analysis

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References


A. Elnashai, L. Sarno, Fundamentals of Earthquake Engineering (Wiley, 2010).
http://dx.doi.org/10.1002/9780470024867

R. Day, Geotechnical Earthquake Engineering (MacGraw-Hill, 2012).

V. Gioncu, F. Mazzolani, Earthquake Engineering for Structural Design (CRC Press, 2010).

A.E. Eiben, J.E. Smith, Introduction to Evolutionary Computing (Springer-Verlag, Berlin, 2003).
http://dx.doi.org/10.1017/s0263574704270436

C. Blum, D. Merkle, Swarm Intelligence(Springer-Verlag, Berlin, 2008).
http://dx.doi.org/10.1007/978-3-540-74089-6

M. Hassoun, Fundamentals of Artificial Neural Networks, A Bradford Book, 2003.
http://dx.doi.org/10.1145/272874.1067696

Earthquake Engineering Research 1982, Committee on Earthquake Engineering, Research Commission on Engineering and Technical Systems, National Research Council, National Academy Press, Washington, D.C. 1982.
http://dx.doi.org/10.1080/02630258508970413

C. Arnold, R. Reitherman, Building Configuration and Seismic Design (John Wiley, New York, 1982).

A. Preumont, Vibration Control of Active Structures: An Introduction (Springer-Verlag, Berlin, 2011).
http://dx.doi.org/10.1007/978-94-007-2033-6_1

R. Guclu, H. Yazici, Fuzzy Logic Control of a Non-linear Structural System against Earthquake Induced Vibration, Journal of Vibration and Control, vol. 13, n. 11, 2007, pp. 1535-1551.
http://dx.doi.org/10.1177/1077546307077663

M. D. Symans, F. A. Charney, A. S. Whittaker, M. C. Constantinou, C. A. Kircher, M. W. Johnson, and R. J. McNamara, Energy Dissipation Systems for Seismic Applications: Current Practice and Recent Developments", Journal of Structural Engineering vol. 134, n. 1, 2008, pp. 3-21
http://dx.doi.org/10.1061/(asce)0733-9445(2008)134:1(3)

H. Shayeghi, H. Eimani Kalasar, H. Shayanfar, and A. Shayeghi, PSO based TMD design for vibration control of tall building structures, in Proceedings of the International Conference on Artificial Intelligence (ICAI ’09), 2009.

N. R.Fisco and H. Adeli, Smart structures: Part I – Active and semi-active control. Scientia Iranica, vol.18, 2011, 275–284.
http://dx.doi.org/10.1016/j.scient.2011.05.034

S. Thenozhi, W.Yu,Advances in modeling and vibration control of building structures, Annual Reviews in Control, Elsevier, vol.37, 2013, pp.346–364.
http://dx.doi.org/10.1016/j.arcontrol.2013.09.012

R. L. Mayes, F.Naeim, Design of Structures with Seismic Isolation,chapter 14, pp. 723-756.
http://dx.doi.org/10.1007/978-1-4615-1693-4_14

J. C. Ramallo1, E. A. Johnson, and B. F. Spencer Jr., ‘‘Smart’’ Base Isolation Systems, Journal of Engineering Mechanics, vol. 128, n. 10, 2002, pp. 1088–1099.
http://dx.doi.org/10.1061/(asce)0733-9399(2002)128:10(1088)

O. E. Ozbulut and S. Hurlebaus, Fuzzy control of piezoelectric friction dampers for seismic protection of smart base isolated buildings, Bulletin of Earthquake Engineering, vol. 8, n. 6, 2010, pp. 1435– 1455.
http://dx.doi.org/10.1007/s10518-010-9187-5

T.T. Soong, B.F. Spencer Jr, Supplemental energy dissipation: state-of-the-art and state-of-the practice, Engineering Structures, Elsevier, vol.24, 2002, pp. 243–259.
http://dx.doi.org/10.1016/s0141-0296(01)00092-x

T. T.Soong, A. M.Reinhorn, Y. P.Wang, and R. C.Lin, Full-scale implementation of active control-I: Design and simulation. Journal of Structural Engineering, 1991, pp.3516–3536.
http://dx.doi.org/10.1061/(asce)0733-9445(1991)117:11(3516)

A. Schmidt, The Design of an Active Structural Vibration Reduction System Using a Modified Particle Swarm Optimization, 2010.
http://dx.doi.org/10.1007/978-3-642-15461-4_55

R. Guclu and H. Yazici, “Vibration control of a structure with ATMD against earthquake using fuzzy logic controllers,” Journal of Sound and Vibration, vol. 318, n. 1-2, 2008, pp. 36–49.
http://dx.doi.org/10.1016/j.jsv.2008.03.058

R.J. Geller, Earthquake prediction: a critical review, Geophys. J. Int., vol. 131, 1997, pp. 425-450.
http://dx.doi.org/10.1111/j.1365-246x.1997.tb06588.x

L.A. Zadeh, Foreword, Proceedings of the Second International Conference on Fuzzy Logic and Neural Networks, Iizouka, Yapan, 1992, pp. XIII-XIV.

D.K. Pratihar, Soft Computing: Fundamentals and Applications, (Alpha Science International Ltd., Oxford, 2014).

C. Darwin, On the Origin of Species(Harvard University Press,London, 1859).
http://dx.doi.org/10.1126/science.146.3640.51-b

D. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning (Addison-Wesley, Massachusetts, 1996).

J. Koza, Genetic Programming 2 - Automatic Discovery of Reusable Programs (MIT Press, Cambridge, USA, 1994).
http://dx.doi.org/10.1145/239616.1066351

T. Bäck, Evolutionary Algorithms in Theory and Practice - Evolution Strategies, Evolutionary Programming, Genetic Algorithms(University Press, Oxford, 1996).
http://dx.doi.org/10.1108/k.1998.27.8.979.4

L. Fogel, A. Owens, M. Walsh, Artificial Intelligence through Simulated Evolution(John Willey, New York, US, 1966).

R. Storn, K. Price, Differential Evolution: A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization, vol. 11 no. 4, 1997, pp. 341-359.
http://dx.doi.org/10.1023/a:1008202821328

J. Kennedy, R. Eberhart, The Particle Swarm Optimization; Social Adaptation in Information Processing. In D. Corne, M. Dorigo, F. Glover, New Ideas in Optimization, McGraw Hill, London, UK,1999, pp. 379-387.

D. Karaboga, B. Bastruk, A Powerful and Efficient Algorithm for Numerical Function Optimization: Artificial Bee Colony (ABC) Algorithm. Journal of Global Optimization, vol. 39 n. 3, 2007,pp. 459-471.
http://dx.doi.org/10.1007/s10898-007-9149-x

X.-S. Yang, Firefly Algorithm. In X.-S. Yang, Nature-Inspired Metaheuristic Algorithms,Luniver Press, London, UK, 2008, pp. 79-90.

I. Fister, I. Jr. Fister, X.-S. Yang, J. Brest, A comprehensive review of firefly algorithms, Swarm and Evolutionary Computation, vol. 13, 2013, pp. 34-46.
http://dx.doi.org/10.1016/j.swevo.2013.06.001

Z.W.Geem, Recent Advances in Harmony Search Algorithm (Springer-Verlag, Berlin, 2012).
http://dx.doi.org/10.1007/978-3-642-04317-8

M. Dorigo, G. Di Caro, The Ant Colony Optimization Meta-heuristic. In D. Corne, M. Dorigo, F. Glover, New Ideas in Optimization,McGraw Hill, London, UK, 1999,pp. 11-32.
http://dx.doi.org/10.1109/cec.1999.782657

D. Rumelhart, J. McClelland, Parallel Distributed Processing (MIT Press, Cambridge, 1986).
http://dx.doi.org/10.1017/s0263574700016404

R.J. Williams, A learning algorithm for continually running fully recurrent neural networks, Neural Computation, MIT Press, vol. 1 n. 2, 2008, pp. 270-280.
http://dx.doi.org/10.1162/neco.1989.1.2.270

E. Wong, Stochastic Neural Networks, Algorithmica, Springer Berlin Heilderberg, vol. 6 n. 1-6, 1991, pp. 466-478.
http://dx.doi.org/10.1007/bf01759054

B. Happel, J. Murre, The Design and Evolution of Modular Network Architecture, Neural Networks, vol. 7, 1994, pp. 985-1004.
http://dx.doi.org/10.1016/s0893-6080(05)80155-8

P. Auer, H. Burgsteiner, W. Maass, A learning rule for very single universal approximators consisting of a single layer perceptrons, Neural Networks, vol. 21 n. 5, 2008, pp. 786-795.
http://dx.doi.org/10.1016/j.neunet.2007.12.036

D.S. Brookmhead, D. Lowe, Multivariable functional interpolation and adaptive network, Complex Systems, vol. 2, 1988, pp. 321-355.

T. Kohonen, Self-Organized Formulation of Topological Correct Feature Maps, Biological Cybernetics, vol. 43 n. 1, pp. 59-69.
http://dx.doi.org/10.1007/bf00337288

R. Hecht-Nielsen, Neurocomputer applications, Neural Computers, Springer Verlag, Berlin, Heidelberg, 1988, pp. 445-453.
http://dx.doi.org/10.1007/978-3-642-83740-1_45

D.F. Specht, Probabilistic neural networks, Neural Networks, vol. 3 n. 1, 1990, pp. 109-118.
http://dx.doi.org/10.1016/0893-6080(90)90049-q

S. Kirkpatrick, C.J. Gellat, M. Veechi, Optimization by Simulated Annealing. Science, vol. 220 n. 4578, 1983, pp. 671-680.
http://dx.doi.org/10.1126/science.220.4598.671

C. Cortes, V.N. Vapnik, Support-Vector Networks, Machine Learning, Kluwer Academic Publisher, vol. 20 n. 3, 1995, pp. 273-297.
http://dx.doi.org/10.1007/bf00994018

T.K. Caugney, Nonlinear Theory of Random Vibrations, Advances in Applied Mechanics, Acadamic Press, vol. 11, 1971, pp. 209-253.
http://dx.doi.org/10.1016/s0065-2156(08)70343-0

T.K. Caugney, Equivalent Linearization Techniques, TheJournal of the Acoustical Society of America, California Institute of Technology, Pasadena, vol. 35 n. 11, 1963, pp. 1706-1711.

K. Jármai, J. Farkas, Y. Kurobane,Optimum seismic design of a multi-storey steel frame, Engineering Structures, vol. 28 n. 7, 2006, pp. 1038 – 1048.
http://dx.doi.org/10.1016/j.engstruct.2005.11.011

AYT. Leung, H. Zhang, CC. Cheng, YY. Lee, Particle swarm optimization of TMD by non-stationary base excitation during earthquake, Earthquake Engineering & Structural Dynamics, Wiley Online Library, vol. 37 n. 9, 2008, pp. 1223 - 1246.
http://dx.doi.org/10.1002/eqe.811

S. Gholizadeh, E. Salajegheh,Optimal seismic design of steel structures by an efficient soft computing based algorithm,Journal of Constructional Steel Research, Elsevier, vol. 66 n. 1, 2010, pp. 85 – 95.
http://dx.doi.org/10.1016/j.jcsr.2009.07.006

SM. Seyedpoor, J. Salajegheh, E. Salajegheh, S. Gholizadeh, Optimal design of arch dams subjected to earthquake loading by a combination of simultaneous perturbation stochastic approximation and particle swarm algorithms, Applied Soft Computing, Elsevier, vol. 11 n. 1, 2011, pp. 39 – 48.
http://dx.doi.org/10.1016/j.asoc.2009.10.014

N. Xiao, L. Su, Y. Wang, Utilization of Particle Swarm Optimization in Equivalent Linearization Method Applied to Earthquake Engineering,Advances in Structural Engineering, Multi-Science, vol. 14 n. 2, 2011, pp. 179 – 188.
http://dx.doi.org/10.1260/1369-4332.14.2.179

S. Gharehbaghia, M.J. Fadaee, Design Optimization of RC Frames under Earthquake Loads, Int. J. Optim. Civil Eng, vol. 2 n. 4, 2012, pp. 459 – 477.

M. Khatibinia, M.J. Fadaee, J. Salajegheh, E, Salajegheh, Seismic reliability assessment of RC structures including soil-structure interaction using wavelet weighted least squares support vector machine,Reliability Engineering & System Safety, Elsevier, 2012.
http://dx.doi.org/10.1016/j.ress.2012.09.006

K. Ye, Z. Chen, H. Zhu, A proposed strategy for the application of the modified harmony search algorithm to code-based selection and scaling of ground motions,Journal of Computing in Civil Engineering, American Society of Civil Engineers, 2012.
http://dx.doi.org/10.1061/(asce)cp.1943-5487.0000261

A.Kaveh, P. Zakian,Optimal design of steel frames under seismic loading using two meta-heuristic algorithms,Journal of Constructional Steel Research, Elsevier, vol. 82, 2013, pp. 111 – 130.
http://dx.doi.org/10.1016/j.jcsr.2012.12.003

M. Sonmez, E. Aydin, T. Karabork, Using an artificial bee colony algorithm for the optimal placement of viscous dampers in planar building frames,Structural and Multidisciplinary Optimization, Springer Berlin Heidelberg, vol. 48 n. 2, 2013, pp. 395 – 409.
http://dx.doi.org/10.1007/s00158-013-0892-y

E. Salajegheh, S. Gholizadeh, M. Khatibinia, Optimal design of structures for earthquake loads by a hybrid RBF-BPSO method, Earthquake Engineering and Engineering Vibration, Springer Berlin Heidelberg, vol. 7 n. 1, 2008, pp. 13 – 24.
http://dx.doi.org/10.1007/s11803-008-0778-y

S. Gholizadeh, E. Salajegheh, Optimal design of structures subjected to time history loading by swarm intelligence and an advanced metamodel, Computer Methods in Applied Mechanics and Engineering, Elsevier , vol. 198 n. 37, 2009, pp. 2936 – 2949.
http://dx.doi.org/10.1016/j.cma.2009.04.010

D. Han, G. Wang,Application of particle swarm optimization to seismic location,Genetic and Evolutionary Computing, 2009. WGEC'09. 3rd International Conference on, IEEE, 2009, pp. 641 – 644.
http://dx.doi.org/10.1109/wgec.2009.48

K. Deep, A. Yadav, S. Kumar,Improving local and regional earthquake locations using an advance inversion Technique: Particle swarm optimization,World Journal of Modelling and Simulation,vol. 8 n. 2, 2012, pp. 135 – 141.

H. Adeli, A. Panakkat, A probabilistic neural network for earthquake magnitude prediction, Neural Networks, Elsevier, vol. 22, 2009, pp. 1018 – 1024.
http://dx.doi.org/10.1016/j.neunet.2009.05.003

A.F. Santos, H.F. Campos Velho, E.F.P. Luz, S.R, Freitas, G. Grell, M.A. Gan, Firefly optimization to determine the precipitation field on South America,Inverse Problems in Science and Engineering, Taylor & Francis, vol. 21, n. 3, 2013, pp. 451 – 466.
http://dx.doi.org/10.1080/17415977.2012.712531

H. Shah, R. Ghazali, N. Mohd Nawi, Using artificial bee colony algorithm for MLP training on earthquake time series data prediction,Journal of Computing, vol. 3 n. 6, 2011, pp. 135 – 142.

A.H. Alavi, A.H. Gandomi, Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing, Computers & Structures, Elsevier, vol. 89 n. 23, 2011, pp. 2176 – 2194.
http://dx.doi.org/10.1016/j.compstruc.2011.08.019

A.H. Gandomi, A.H. Alavi, M. Mousavi, S.M. Tabatabaei, A hybrid computational approach to derive new ground-motion prediction equations, Engineering Applications of Artificial Intelligence, Elsevier, vol. 24 n. 4, 2011, pp. 717 – 732.
http://dx.doi.org/10.1016/j.engappai.2011.01.005

Soft Computing, 2014, http://en.wikipedia.org/wiki/ Soft Computing.

K.M. Saridakis, A.J. Dentsoras, Soft computing in engineering design – A review, Advanced Engineering Informatics, vol. 22, 2008, pp. 202–221.
http://dx.doi.org/10.1016/j.aei.2007.10.001

Subrata Chakraborty, Gautam Bhattacharya "Proceedings of the International Symposium on Engineering under Uncertainty"
http://dx.doi.org/10.1007/978-81-322-0757-3

P. Lu, S. Chen, Y. Zheng, Artificial Intelligence in Civil Engineering, Mathematical Problems in Engineering Volume 2012, Article ID 145974, 22 pages
http://dx.doi.org/10.1155/2012/145974


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