Determining the Natural Frequency of Cantilever Beams Using ANN and Heuristic Search

References

• Allen, D. E., and T. M. Murra. 1993. Design criterion for vibrations due to walking. Engineering Journal - American Institute of Steel Construction 117–29. Illinois.
   Google Scholar
• Alvis, S. R. 2001. An Experimental and Analytical Investigation of Floor Vibrations. Master’s Thesis, Faculty of the Virginia Polytechnic Institute and State University, April 2001, Blacksburg, Virginia.
   Google Scholar
• Atashpaz-Gargari, E., and C. Lucas. 2007. Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition. Proceedings of IEEE congress on evolutionary computation, September 25-28, Singapore, pp 4661–67.
   Google Scholar
• Atashpaz-Gargari, E., R. Rajabioun, F. Hashemzadeh, and F. L. Salmasi. 2009. A decentralized PID controller based on optimal shrinkage of Gershgorin bands and PID tuning using colonial competitive algorithm. International Journal of Innovative Computing, Information and Control (IJICIC) 5 10(A):3227–40.
   Google Scholar
• Bağdatlı, S. M., E. Özkaya, H. R. Özygit, and A. Tekin. 2009. Non-linear vibrations of stepped beam system using artificial neural networks. Structural Engineering and Mechanics 33 (1):15–30. doi:10.12989/sem.2009.33.1.015.
   Web of Science ®Google Scholar
• Baker, J. E. 1987. Reducing bias and inefficiency in the selection algorithm, genetic algorithms and their applications. Proceedings of the Second International Conference on Genetic Algorithms (ICGA), Cambridge, Massachusetts, USA. July 28-31. pp 14–21.
   Google Scholar
• Beavers, T. A. 1998. Fundamental Natural Frequency of Steel Joist-Supported Floors. Master’s Thesis, Virginia Polytechnic Institute and State University, Blacksburg, Virginia.
   Google Scholar
• Behnia, A., H. K. Chai, N. Ranjbar, and N. Behnia. 2013. Finite element analysis of the dynamic response of composite floors subjected to walking induced vibrations. Advances in Structural Engineering 6 (5):959–74. doi:10.1260/1369-4332.16.5.959.
   Google Scholar
• Bishop, C. 2006. Pattern recognition and machine learning. Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA.
   Google Scholar
• Chen, M.-H., S.-H. Chen, and P.-C. Chang. 2015. Imperial competitive algorithm with policy learning for the traveling salesman proble. Soft Computing. doi:10.1007/s00500-015-1886-z.
   PubMed Web of Science ®Google Scholar
• Clerc, M. 2012. Standard particle swarm optimisation, particle swarm central. Technical Report, Available online at: http://clerc.maurice.free.fr/pso/SPSOdescriptions.pdf, accessed 24 August 2014.
   Google Scholar
• CSA-Standard. 1999. Canadian standards association, guide for floor vibrations (steel structures for buildings-limit state design). CSA Standard CAN 3-S16, pp 1–89.
   Google Scholar
• Da Silva, J. G. S., P. C. :. G. Da S Vellasco, S. A. L. De Andrade, F. J. Da CPSoeiro, and R. N. Werneck. 2003. An evaluation of the dynamical performance of composite slabs. Computers and Structtures 81 (18–19):1905–13. doi:10.1016/S0045-7949(03)00210-4.
   Web of Science ®Google Scholar
• Dash, T. 2015. A study on intrusion detection using neural networks trained with evolutionary algorithms. Soft Computing. doi:10.1007/s00500-015-1967-z.
   PubMedGoogle Scholar
• Ellis, B. R. 2001. Serviceability evaluation of floors induced by walking. The Structural Engineer 79 (21):30–36.
   Google Scholar
• Gerami, M., A. Sivandi Pour, and A. Dalvand. 2012. Determination of the natural frequency of the moment connections steel beams for controlling vibration of floors by artificial neural networks. Modares Civil Engineering Journal (M.C.E.L) 12 (2):113–25.
   Google Scholar
• Ghorban Pour, A. H., and M. Ghassemieh. 2009. Vertical vibration of composite floor by neural network analysis. Civil Engineering Infrastructures Journal (CEIJ) 43 (1):117–26.
   Google Scholar
• Goldberg, D. E. 1989. Genetic algorithms in search, optimization, and machine learning. Boston, MA, USA: Addison-Wesley Longman Publishing Co., Inc.
   Google Scholar
• Hadzima-Nyarko, M., D. Morić, H. Draganić, and E. K. Nyarko. 2012. New direction based (fundamental) periods of RC frames using genetic algorithms. Proceedings: 15WCEE, Lisboa, Portugal, No. 2493.
   Google Scholar
• Haykin, S. 1999. Neural networks: A comprehensive foundation, 2nd ed. Prentice Hall PTR Upper Saddle River, NJ, USA.
   Google Scholar
• Holland, J. H. 1992. Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence. Ann Arbor: The University of Michigan Press. 1975. Reprinted by MIT Press, April 1992, NetLibrary, Inc
   Google Scholar
• Housner, G. W. 1975. Dynamics of structures. R. W. Clough, and J. Penzien (eds.) New York: McGraw-Hill. wiley. doi:10.1002/eqe.4290040510
   Google Scholar
• Hudson, M. J., and P. Reynolds. 2012. Implementation considerations for active vibration control in the design of floor structures. Engineering Structures 44:334–58. doi:10.1016/j.engstruct.2012.05.034.
   Web of Science ®Google Scholar
• Karlik, B., E. Özkaya, S. Aydin, and M. Pakdemirli. 1998. Vibrations of a beam-mass systems using artificial neural networks. Computers and Structures 69 (3):339–47. doi:10.1016/S0045-7949(98)00126-6.
   Google Scholar
• Kazemi, M. A., F. Nazari, M. Karimi, S. Baghalian, M. A. Rahbarikahjogh, and A. M. Khodabandelou. 2011. Detection of multiple cracks in beams using particle swarm optimization and artificial neural network. 4th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO), Kuala Lumpur.
   Google Scholar
• Kennedy, J. 1999. Small worlds and mega-minds: Effects of neighborhood topology on particle swarm performance. Proceedings of the 1999 Congress on Evolutionary Computation, Washington, DC, USA, Vol. 3, p. 1938.
   Google Scholar
• Kennedy, J., and R. Eberhart. 1995. Particle swarm optimization. Proceedings of IEEE international conference on neural networks, pp 1942–48.
   Google Scholar
• Kennedy, J., R. Eberhart, and Y. Shi. 2001. Swarm intelligence. San Francisco, CA, USA.: Morgan Kaufmann Publishers Inc.
   Google Scholar
• Kennedy, J., and R. Mendes. 2002. Population structure and particle swarm performance. Proceedings of the 2002 Congress on Evolutionary Computation, CEC ’02, Honolulu, HI, USA, pp. 1671–76.
   Google Scholar
• Kuru, L., A. Ozturk, E. Kuru, and S. Cobanli. 2016. Active power loss minimization in electric power systems through chaotic artificial bee colony algorithm. Technical Gazzete 23 (2):491–98.
   Google Scholar
• Liang, J. J., A. K. Qin, P. N. Suganthan, and S. Baskar. 2006. Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE T Evolut Comput 10 (3):281–95.
   Web of Science ®Google Scholar
• Liu, L., X. Lai, K. Song, and D. Lao. 2015. Intelligent prediction model based on genetic algorithm and support vector machine for evaluation of mining-induced building damage. Tehnical Gazzete 22 (3):743–53.
   Google Scholar
• Marinaki, M., Y. Marinakis, and G. E. Stavroulakis. 2010. Vibration control of beams with piezoelectric sensors and actuators using particle swarm optimization. Expert System with Application 38 (6):6872–83. doi:10.1016/j.eswa.2010.12.037.
   Web of Science ®Google Scholar
• Martell, J. L., A. Ebrahimpour, and M. P. Schoen. 2005. Intelligent approach to floor vibration control. Proceedings: ASME 2005 International Mechanical Engineering Congress and Exposition, Dynamic Systems and Control, Parts A and B, Orlando, Florida, USA. ISBN: 0-7918-4216-9.
   Google Scholar
• Middleton, C. J., and J. M. W. Brownjohn. 2010. Response of high frequency floors: A literature review. Engineering Structures 32:337–52. doi:10.1016/j.engstruct.2009.11.003.
   Web of Science ®Google Scholar
• Morley, L. J., and T. M. Murray. 1993. Predicting floor response due to human activity. Proceedings of the IABSE International Colloquium on Structural Serviceability of Buildings, Gothenburg, Sweden, pp. 297–302.
   Google Scholar
• Murray, T. M., D. E. Allen, and E. E. Ungar. 1997. Steel design guide series 11: Floor vibrations due to human activity. Chicago, Illinois: American Institute of Steel Construction, Inc. (AISC).
   Google Scholar
• Nikoo, M., F. Ramezani, M. Hadzima-Nyarko, E. K. Nyarko, and M. Nikoo. 2016. Flood-routing modeling with neural network optimized by social-based algorithm. Natural Hazards. doi:10.1007/s11069-016-2176-5.
   PubMed Web of Science ®Google Scholar
• Nikoo, M., F. Torabian Moghadam, and L. Sadowski. 2015. Prediction of concrete compressive strength by evolutionary artificial neural networks. Advances in Materials Science and Engineering. doi:10.1155/2015/849126.
   Web of Science ®Google Scholar
• Özkaya, E., and M. Pakdemirli. 1999. Nonlinear vibrations of a beam-mass system with both ends clamped. Journal of Sound and Vibration 221 (3):491–503. doi:10.1006/jsvi.1998.2003.
   Web of Science ®Google Scholar
• Przybylski, J. 2009. Non-linear vibrations of a beam with a pair of piezoceramic actuators. Engineering Structures 31 (11):2687–95. doi:10.1016/j.engstruct.2009.06.019.
   Web of Science ®Google Scholar
• Ramezani, F., M. Nikoo, and M. Nikoo. 2015. Artificial neural network weights optimization based on social-based algorithm to realize sediment over the river. Soft Computing 19:375–87. doi:10.1007/s00500-014-1258-0.
   Web of Science ®Google Scholar
• Sadowski, L., and M. Nikoo. 2014. Corrosion current density prediction in reinforced concrete by imperialist competitive algorithm. Neural Computing and Application 25:1627–38. doi:10.1007/s00521-014-1645-6.
   PubMed Web of Science ®Google Scholar
• S-H., L., J.-G. Hsieh, J.-Y. Chang, and C.-T. Lin. 2015. Training neural networks via simplified hybrid algorithm mixing Nelder–Mead and particle swarm optimization methods. Soft Computing 19:679–89. doi:10.1007/s00500-014-1292-y.
   Web of Science ®Google Scholar
• Sladki, M. J. 1999. Prediction of Floor Vibration Response using the Finite Element Method. Master’s Thesis, Faculty of the Virginia Polytechnic Institute and State University, Blacksburg, Virginia.
   Google Scholar
• Srinivas, M., and L. Patnaik. 1994. Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE. T Syst Man Cyb 24 (4):656–67. doi:10.1109/21.286385.
   Web of Science ®Google Scholar
• Waszczyszyn, Z., and L. Ziemiański. 2001. Neural networks in mechanics of structures and materials – new results and prospects of applications. Computers and Structures 79 (22–25):2261–76. doi:10.1016/S0045-7949(01)00083-9.
   Web of Science ®Google Scholar
• Zambrano-Bigiarini, M., M. Clerc, and R. Rojas. 2013. Standard Particle Swarm Optimisation 2011 at CEC-2013: A baseline for future PSO improvements. IEEE Congress on Evolutionary Computation (CEC), Cancun, Mexico, pp. 2337–2344.
   Google Scholar