Neural networks for the analysis of mine-induced building vibrations

• Autorzy: Kuźniar, K.
• Języki publikacji: EN

Abstrakty

EN

A study of the capabilities of arti?cial neural networks in respect of selected problems of the analysis of mine-induced building vibrations is presented. Neural network technique was used for the prediction of building fundamental natural period, mapping of mining tremors parameters into response spectra from ground vibrations, soil-structure interaction analysis, simulation of building response to seismictype excitation. On the basis of the experimental data obtained from the measurements of kinematic excitations and dynamic responses of actual structures, training and testing patterns of neural networks were formulated. The obtained results lead to a conclusion that the neural technique gives possibility of e?cient, accurate enough for engineering, analysis of structural dynamics problems related to mineinduced excitations.

Słowa kluczowe

PL

sieci neuronowe; symulacja neuronowa; kompresja danych; wstępne przetwarzanie danych; wstrząs górniczy; dane eksperymentalne

EN

neural network; neural simulation; data compression; data pre-processing; mining tremors; experimental data

• Czasopismo: Computer Assisted Mechanics and Engineering Sciences
• Rocznik: 2011
• Tom: Vol. 18, no. 3
• Strony: 147-159
• Opis fizyczny: Bibliogr. 20 poz., rys., tab., wykr.

Twórcy

autor

Kuźniar, K.

• Pedagogical University of Cracow, Institute of Technology Podchorążych 2, 30-084 Kraków, Poland,

Bibliografia

• [1] C.M. Bishop. Neural Networks for Pattern Recognition. Oxford: Clarendon Press, 1996.
• [2] R. Ciesielski, K. Kuźniar, E. Maciąg, T. Tatara. Empirical formulae for fundamental natural periods of buildings with load bearing walls. Archives Civil Engineering, 38(4): 291–299, 1992.
• [3] R. Ciesielski, K. Kuźniar, E. Maciąg, T. Tatara. Damping of vibration in precast buildings with bearing concrete walls. Archives of Civil Engineering. 41(3): 329–341, 1995.
• [4] S. Haykin. Neural networks – a comprehensive foundation. 2nd Edition, Prentice Hall Intern. Inc., Upper Saddle River, NY, 1999.
• [5] J.-S. Jang, Ch.-T.Sun, E. Mizutani. Neuro-fuzzy and soft computing. A computational approach to learning and machine intelligence, Prentice Hall Intern. Inc., Upper Saddle River, NY, 1997.
• [6] K. Kuźniar. Analysis of vibrations of medium-height buildings with load-bearing walls subjected to mining tremors using neural networks. Series: Civil Engineering, Monograph 310, Publishing House CUT, Cracow, 2004, (in Polish).
• [7] K. Kuźniar. Soil-Structure Interaction Analysis using Neural Networks and Data Compression. Proc. of the Seventh International Conference on Engineering Computational Technology, B.H.V. Topping, J.M. Adam, F.J. Pallares, R. Bru and M.L.Romero, [Eds.], Civil-Comp Press, Stirlingshire, Scotland, paper 145, 2010.
• [8] K. Kuźniar. Neural Network prediction of response spectra from mining tremors with the problem decomposition. Archives of Civil Engineering. 51(3): 311–321, 2005.
• [9] K. Kuźniar, E. Maciąg. Neural network analysis of soil-structure interaction in case of mining tremors. In D. Doolin, A. Kammerer, T. Nogami, R.B. Seed & I. Towhata, [Eds.], Proc. 11th International Conference on Soil Dynamics and Earthquake Engineering and the 3rd International Conference on Earthquake Geotechnical Engineering, Vol. 2, Berkeley, USA, 2004, 829–836.
• [10] K. Kuźniar, E. Maciąg, T. Tatara. Prediction of building foundation response spectra from mining-induced vibrations using neural networks (in Polish). Scientific Works of Central Mining Institute. Mining & Environment, 4(4): 50–64, 2010.
• [11] K. Kuźniar, E. Maciąg, Z. Waszczyszyn. Computation of natural periods of vibrations of medium-height prefabricated buildings by neural networks. Archives of Civil Engineering, 46: 515–523, 2000.
• [12] K. Kuźniar, Z. Waszczyszyn. Neural analysis of vibration problems of real flat buildings and data pre-processing. Engineering Structures, 24: 1327–1335, 2002.
• [13] K. Kuźniar, Z. Waszczyszyn. Neural simulation of dynamic response of prefabricated buildings subjected to paraseismic excitations. Computers & Structures, 81(24–25): 2353–2360, 2003.
• [14] K. Kuźniar, Z. Waszczyszyn. Neural networks and principal component analysis for identification of building natural periods, Journal of Computing in Civil Engineering, 20(6): 431–436, 2006.
• [15] K. Kuźniar, Z. Waszczyszyn. Neural networks for the simulation and identification analysis of buildings subjected to paraseismic excitations. Chapter XVI in: N. Lagaros and Y. Tsompanakis, [Eds.], Intelligent computational paradigms in earthquake engineering, Idea Group, Hershey, PA, USA, 2007.
• [16] E. Maciąg. Experimental evaluation of changes of dynamic properties of buildings on different grounds. Earth. Eng. Struct. Dyn., 14: 925–932, 1986.
• [17] S. Osowski. Neural networks–an algorithmic approach (in Polish). WNT, Warsaw 1996.
• [18] PN-80/B-03040 Foundation and Machine Support Structures. Analysis and Design (in Polish). Polish Code Committee.
• [19] Z. Waszczyszyn, L. Ziemiański. Neural networks in the identification analysis of structural mechanics problems. Chapter 7 in: Z. Mróz and G. Stavroulakis, [Eds.], Parameter identification of materials and structures, CISM Lecture Notes No. 469, Springer, Wien-NY, 2005.
• [20] Z. Zembaty. Rockburst induced ground motion – a comparative study. Soil Dynamics & Earthquake Engineering, 24(1): 11–23, 2004.