Bayesian regression approaches on example of concrete fatigue failure prediction

• Autorzy: Słoński M.
• Konferencja: Polish Conference on Computer Methods in Mechanics (16 ; 21-24.06.2005 ; Częstochowa, Poland
• Języki publikacji: EN

Abstrakty

EN

The focus of this paper is the application of two nonlinear regression models in the context of Bayesian inference to the problem of failure prediction of concrete specimen under repeated loads based on experimental data. These two models are compared with an empirical formulae. Results on testing data show that both models give better point predictions than empirical formulae. Moreover, Bayesian regression approach makes it possible to calculate prediction intervals (error bars) describing the reliability of the models predictions.

Słowa kluczowe

EN

Bayesian inference; concrete; fatigue failure; Gaussian process regression; feedforward neural network

PL

beton; pęknięcie zmęczeniowe; regresja

• Wydawca: Instytut Podstawowych Problemów Techniki PAN
• Czasopismo: Computer Assisted Mechanics and Engineering Sciences
• Rocznik: 2006
• Tom: Vol. 13, No. 4
• Strony: 655--668
• Opis fizyczny: Bibliogr. 15 poz., tab., wykr.

Twórcy

autor

Słoński M.

• Cracow University of Technology, Institute for Computational Civil Engineering ul. Warszawska 2A, 31-155 Kraków, Poland

Bibliografia

• [1] C.A.L. Bailer-Jones, T.J. Sabin, D.J.C. MacKay, P.J. Withers. Prediction of deformed and annealed microstructures using Bayesian neural networks and Gaussian processes. In: Proc. of the Australia-Pacific Forum on Intelligent Processing and Manufacturing of Materials, 1997.
• [2] C.M. Bishop. Neural Networks for Pattern Recognition. Oxford University Press, Oxford, 1995.
• [3] C.M. Bishop. Pattern Recognition and Machine Learning. Springer, New York, 2006.
• [4] C.M. Bishop, M.E. Tipping. Bayesian regression and classification. In: J. Suykens, G. Horvath, S. Basu, C. Micchelli, J. Vandewalle, eds., Advances in Learning Theory: Methods, Models and Applications, NATO Science Series III: Computer and Systems Sciences, 267-285. IOS Press, 2003.
• [5] K. Furtak. Strength of the concrete under multiple repeated loads, (in Polish). Arch. Civil Engrg., 30, 1984.
• [6] M. Jakubek, Z. Waszczyszyn. Neural analysis of concrete fatigue durability by the neuro-fuzzy FWNN. In: L. Rutkowski, J. Siekmann, R. Tadeusiewicz, L.A. Zadeh, eds., Proc. Artificial Intelligence and Soft Computing ICAISC2004 - 7th Int. Conf, 1075-1080. T.U. of Częstochowa, Springer, Częstochowa/Zakopane, 2004.
• [7] K. Jin-Keun, K. Yun-Yong. Experimental study of the fatigue behavior of high strength concrete. Cement and Concrete Research, 26(10): 1513-1523, 1996.
• [8| J. Kaliszuk, A. Urbańska. Z Waszczyszyn, K. Furtak. Neural analysis of concrete fatigue durability on the basis of experimental evidence. Arch. Civil Engrg., 38, 2001.
• [9] J. Lampinen, A. Vehtari. Bayesian approach for neural networks review and case studies. Neural Networks, 14(3): 7-24. 2001. (In vi ted article).
• [10] D..I.C. MacKay. Information Theory, Inference and Learning Algorithms. Cambridge University Press, 2003.
• [11| I.T. Nabney. Netlab: Algorithms for Pattern Recogmtwn. Springer-Verlag, London, 2002.
• [12] CE. Rasmussen, CK.I. Williams. Gaussian Processcs for Machmc Learning. The MIT Press, Cambridge, Massachusetts, 2006.
• [13] M. Słoński. Prediction of concrete fatigue durability using Bayesian neural networks. Computer Assisted Mech. Eng. Sd., 12: 259-265, 2005.
• [14] M.E. Tipping. Bayesian inference: An introduction to principles and practice m machine learning. In: O. Bousquet, U. von Luxburg, , G. Ratsch, eds., Aduanced Lectures on Machine Learning, vol. 3176 of Lecture Notes in Computer Science, 41-62. Springer, 2004.
• [15] Z. Waszczyszyn, L. Ziemiański. Neurocomputing in the analysis of selected inverse problems of mechanics of structures and materials. Computer Assisted Mech. Engrg. Sci.. 13: 125-159, 2006.