Artificial neural networks in civil engineering: another five years of research in Poland

• Autorzy: Waszczyszyn Z.
• Języki publikacji: EN

Abstrakty

EN

This state-of-the-art-paper is a resum´e of research activity of a non-formal Research Group on ArticialNeural Networks (RGANN) applications in Civil Engineering (CE). RGANN has been working at the Cracow University of Technology, Poland, since 1996 under the supervision of the author of this paper.Ten years 1996–2005 of the research and teaching activity of RGANN was reported in paper [61]. Thepresent paper brie?y reports on the activities originated in the ten year period and their continuation after2005. The main attention is focused on new research carried out in the five year period 2006–2011. The paper discusses some selected problems which are included in fourteen supplementary papers, marked in references of these papers as published in this CAMES Special Issue. The attention is focused on: Hybrid Computational Systems, development of modifications of ANNs and methods of their learning, Bayesian neural networks and Bayesian inference methods, damage identi?cation in CE structures, structure health monitoring, applications of ANNs in mechanics of structures and materials, joining of ANNs with measurements on laboratory models and real structures, developmentof new non-destructive measurement methods, applications of ANNs in health structure monitoring andrepair, applications of ANNs in geotechnics and geodesy. The paper is based on the supplementary papers which were presented at the Special Session on Applications of ANNs at the 57th Polish Civil EngineeringConference in Krynica, 2011, see [74].

Słowa kluczowe

EN

neural network; finite element method; Bayesian network

PL

sieci neuronowe; sieci bayesowskie; metoda elementów skończonych

• Wydawca: Instytut Podstawowych Problemów Techniki PAN
• Czasopismo: Computer Assisted Mechanics and Engineering Sciences
• Rocznik: 2011
• Tom: Vol. 18, no. 3
• Strony: 131--146
• Opis fizyczny: Bibliogr. 76 poz., wykr.

Twórcy

autor

Waszczyszyn Z.

• Department of Structural Mechanics Rzeszów University of Technology W. Pola 2, 35-959 Rzeszów, Poland,

Bibliografia

• [1] C.M. Bishop. Neural Networks for Pattern Recognition. Oxford University Press, London, 1995.
• [2] C.M. Bishop. Pattern Recognition and Machine Learning. Springer Science&Business Media, LLC, 2006.
• [3] A. Borowiec. Detection of Damage in Structural Systems with Utilization of Changes in the Structure Models (in Polish), Ph.D. Thesis. Rzeszów University of Technology, 2008.
• [4] L. Buda-Ożóg. Diagnosis of Technical State of Concrete Elements by Means of Dynamic Tests (in Polish), Ph.D. Thesis. Rzeszów University of Technology, 2007.
• [5] H. Demuth, M. Beale. Neural Network Toolbox for Use with MATLAB. User’s Guide, Version 4, The Mathworks Inc., 2000.
• [6] M. Gajzler. Neural networks in the advisory system for repairs of industrial floor. Computer Assisted Mech. Eng. Sci., 18(4): 255–263, 2011.
• [7] J. Ghaboussi, D.A. Pecknold, M. Zhang, R.M. Haj-Ali. Autoprogressive training of neural network constitutive models. Int. J. Num. Mech. Eng., 42: 105–126, 1998.
• [8] J. Ghaboussi. Advances in neural networks in computational mechanics and engineering. In Ch. 4 of [62], 191–237, 2010.
• [9] J. Gil. Examples of Applications of Neural Networks in Geodesy (in Polish). Oficyna Wyd. Uniwersytetu Zielonogórskiego, Zielona Góra, Poland, 2006.
• [10] Y.M. Hashash, S. Jung, J. Ghaboussi. Numerical implementatrion of a neural network based material model in finite element analysis. Int. J. Num. Mech. Eng., 59: 989–1005, 2004.
• [11] S. Haykin. Neural Networks – A Comprehensible Foundations, 2nd edition. Prentice-Hall Upper Saddle River, NJ, 1999.
• [12] M. Jakubek. Application of Artificial Neural Networks to the Analysis of Selected Problems of Experimental Mechanics of Structures and Materials (in Polish). Cracow University of Technology, Cracow, Poland, 2007.
• [13] M. Jakubek. Fuzzy weight neural network in the analysis of concrete specimens and R/C column buckling tests. Computer Assisted Mech. Eng. Sci., 18(4): 243–254, 2011.
• [14] Ł. Kaczmarczyk. Numerical Analysis of Selected Problems of Nonhomogeneous Continua, (in Polish) Ph.D. Thesis. Cracow University of Technology, Cracow, Poland, 2006.
• [15] Ł. Kaczmarczyk, Z. Waszczyszyn. Identification of characteristic length of microstructure for second order continuum multiscale model by Bayesian neural networks, Computer Assisted Mech. Eng. Sci., 14: 183–196, 2007.
• [16] J. Kaliszuk. Reliability Analysis of Structures and Structural Elements by Means of Artificial Neural Networks (in Polish), Ph.D. Thesis. University of Zielona Góra, Poland, 2005.
• [17] J. Kaliszuk. Hybrid Monte Carlo Method in the reliability analysis of structures. Computer Assisted Mech. Eng. Sci., 18(3): 205–216, 2011.
• [18] J. Kaliszuk, J. Marcinowski, Z. Waszczyszyn. Laboratory tests and numerical modelling of large displacements of an elasto-plastic cylindrical shell. In: W. Pietraszkiewicz, Cz. Szymczak [Eds.], Shell Structures: Theory and Applications, 477–480. Taylor & Francis Group, 2005.
• [19] A. Krok. The analysis of Selected Problems of Mechanics of Structures and Materials by ANNs and Kalman Filters (in Polish), Ph.D. Thesis. Cracow University of Technology, Cracow, Poland, 2007.
• [20] A. Krok. An improved Neural Kalman Filtering Algorithm in the analysis of cyclic behaviour of concrete specimens. Computer Assisted Mech. Eng. Sci., 18(4): 275–282, 2011.
• [21] M. Kłos, M.J. Sulewska, Z. Waszczyszyn. Neural identification of compaction characteristics for granular soils. Computer Assisted Mech. Eng. Sci., 18(4): 265–273, 2011.
• [22] M. Kłos, Z. Waszczyszyn. Modal analysis and modified cascade neural networks in identification of geometrical parameters of circular arches. Computers & Structures, 89: 581–589, 2011.
• [23] K. Kuźniar. Analysis of Vibrations of Medium-Height Buildings with Load Bearing Walls Subjected to Mining Tremors Using Neural Networks (in Polish), D.Sc. Dissertation, Monograph No. 310, Series on Structural Engineering. Cracow University of Technology, Cracow, Poland, 2004.
• [24] K. Kuźniar. Neural networks for the analysis of mine-induced building vibrations. Computer Assisted Mech. Eng. Sci., 18(3): 147–159, 2011.
• [25] K. Kuźniar, Z. Waszczyszyn. Neural networks and principal component analysis for identification of building natural periods. J. Computing Civil Eng., 20(6): 431–441, 2006.
• [26] K. Kuźniar, Z. Waszczyszyn. Neural Networks for the Simulation and Identification Analysis of Buildings Subjected to Paraseismic Excitations. Ch.XVI in [27], 393–432, 2007.
• [27] N. Lagaros and Y. Tsdompanakis [Eds.], Intelligent Computational Paradigms in Earthquake Engineering. Idea Group. Hershey, PA, USA, 2007.
• [28] C.-C.J. Lin, J. Ghaboussi. Generating multiple spectrum compatible accelerograms using neural networks. Int. J. Earthquake Eng. Stru. Dynamics, 30: 1021–1042, 2001.
• [29] W. Łakota. Detection and Localization of Damage in Beam Structures (in Polish). Oficyna Wyd. Politechniki Rzeszowskiej, Rzeszów, 1999.
• [30] D.J.C. MacKay. Bayesian interpolation. Neural Computation, 4(3): 415–447, 1992.
• [31] B. Miller. Updating of a Mathematical Model of Structures to Physical Models (in Polish), Ph.D. Thesis. Rzeszów University of Technology, 2001.
• [32] B. Miller. Application of neural networks for structure updating. Computer Assisted Mech. Eng. Sci., 18(3): 191–203, 2011.
• [33] B. Miller, Z. Waszczyszyn, L. Ziemiański. Identification of load parameters for an elastic-plastic beam basing on dynamic characteristic changes. In: L. Rutkowski et al., Artificial Intelligence and Soft Computing, LNAI 6114, P.II, 590–597. Springer, 2010.
• [34] M. Mrówczyńska. The Accuracy and Efficiency of Mapping the Terrain by Using Neural Networks (in Polish), Ph.D. Thesis. Warsaw University of Technology, Warsaw, Poland, 2005.
• [35] M. Mrówczyńska. Neural networks and neuro-fuzzy systems applied to the analysis of selected problems of geodesy. Computer Assisted Mech. Eng. Sci., 18(3): 161–173, 2011.
• [36] Z. Mróz, G.E. Stavroulakis (Eds.). Parameter Identification of Materials and Structures, CISM Courses and Lectures No. 404. Springer, Wien – New York, 2005.
• [37] I.T. Nabney. Netlab – Algorithms for Pattern Recognition. Springer, London, 2004.
• [38] P.Nazarko. Estimation of the Technical State of Structures and Detection of Their Damage (in Polish), Ph.D. Thesis. Rzeszów University of Technology, 2008.
• [39] P. Nazarko. Assessment of Structures Technical State (in Polish). Oficyna Wyd. Politechniki Rzeszowskiej, Rzeszów, 1999.
• [40] P. Nazarko, L. Ziemiański. Application of artificial neural networks in the damage identification of structural elements. Computer Assisted Mech. Eng. Sci., 18(3): 175–189, 2011.
• [41] E. Pabisek. Hybrid Systems Integrating FEM and ANN in the Analysis of Selected Problems of Mechanics of Structures and Materials (in Polish), D.Sc. Dissertation, Monograph No. 369, Series on Structural Engineering. Cracow University of Technology, Cracow, Poland, 2008.
• [42] E. Pabisek. Self-learning FEM/ANN approach to identification of equivalent material in real structures. Archives of Civ. Eng., 54(2): 395–404, 2008.
• [43] E. Pabisek. Self-learning FEM/ANN approach to identification of equivalent material models for plane stress problems. Computer Assisted Mech. Eng. Sci., 15: 67–78, 2008.
• [44] E. Pabisek. Identification of an equivalent model for granular soils by FEM/CNN/p-EMP hybrid system. Computer Assisted Mech. Eng. Sci., 18(4): 283–290, 2011.
• [45] E. Pabisek, M. Jakubek, and Z. Waszczyszyn. A fuzzy network for the analysis of experimental structural engineering problems. In: L. Rutkowski and J. Kacprzyk [Eds.], Neural Networks and Soft Computing, 772–777. Physica-Verlag, A Springer-Verlag Company, 2003.
• [46] E. Pabisek, J. Kaliszuk, Z. Waszczyszyn. Neural and finite element analysis of a plane steel frame reliability by the Classical Monte Carlo Method, In: L. Rutkowski et al., [Eds.]. Lecture Notes on Artificial Intelligence, LNAI 3070, 1081–1086. Springer-Verlag, Berlin - Heidelberg, 2004.
• [47] T. Paez. Neural networks in mechanical system simulation, identification and assessment. Shock and Vibration, 1: 177–199, 1993.
• [48] D.T. Pham, X. Liu. Neural Networks for Identification, Prediction and Control. Springer, 1995.
• [49] G. Piątkowski. Detection of Damage in Structural Elements by Means of Artificial Neural Networks (in Polish), Ph.D. Thesis. Rzeszów University of Technology, 2003.
• [50] G. Piątkowski, Z. Waszczyszyn. Identification problems of Recurrent Cascade Neural Network application in prediction of an additional mass location. Computer Assisted Mech. Eng. Sci., 18(3): 217–228, 2011.
• [51] H.S. Shin, G.N. Pande. On self-learning finite element codes based on monitored response of structure. Computers and Geotechnics, 27: 161–178, 2000.
• [52] H.S. Shin, G.N. Pande. Identification of elastic constants for orthotropic materials from a structural test. Computers and Geotechnics, 30: 571–577, 2003.
• [53] M.J. Sulewska. Artificial Neural Networks in the Evaluation of Non-cohesive Soil Compaction Parameters (in Polish). Proceedings in Civil Engineering No 64. Institute of Fundamental Technological Research, Warsaw – Białystok, Poland 2009.
• [54] M.J. Sulewska. Applying artificial neural networks for analysis of geotechnical problems. Computer Assisted Mech. Eng. Sci., 18(4): 231–241, 2011.
• [55] M. Słoński. Prediction of concrete fatigue durability using Bayesian neural networks. Computer Assisted Mec. Eng. Sci., 12: 259–265, 2005.
• [56] M. Słoński. Bayesian regression approaches in example of concrete fatigue failure prediction. Computer Assisted Mec. Eng. Sci., 13: 655–668, 2006.
• [57] M. Słonski. HPC strength prediction using Bayesian neural networks. Computer Assisted Mec. Eng. Sci., 14: 345–352, 2007.
• [58] M. Słoński. A comparison of model selection methods for compressive strength prediction of high performance concrete using neural networks. Computers & Structures, 88(21–22): 1248–1253, 2010.
• [59] M. Słoński. Bayesian neural networks and Gauss Processes in identification of concrete properties. Computer Assisted Mech. Eng. Sci., 18(4): 291–302, 2011.
• [60] Z. Waszczyszyn [Ed.]. Neural Networks in the Analysis and Design of Structures. CISM Courses and Lectures No. 404. Springer, Wien – New York, 1999.
• [61] Z. Waszczyszyn. Artificial neural networks in civil and structural engineering. Ten years of research in Poland. Computer Assisted Mech. Eng. Sci., 13: 489–512, 2006.
• [62] Z. Waszczyszyn [Ed.]. Advances of Soft Computing in Engineering, CISM Courses and Lectures, vol. 512. Springer, Wien – New York, 2010.
• [63] Z. Waszczyszyn, M. Bartczak. Neural prediction of critical loads in compressed cylindrical shells with geometrical imperfections, Non-linear Mechanics, 37: 763–775, 2003.
• [64] Z. Waszczyszyn, M. Słoński. Bayesian neural networks for prediction of response spectra. Foundations Civil Envir. Eng., 7: 343–361, 2006.
• [65] Z. Waszczyszyn, M. Słoński. Criterion of maximum marginal likelihood instead of cross-validation method for design of artificial neural networks (in Polish). Proc. Rzeszów Univ. Technol., No 243, 173–185. Rzeszów University of Technology, Rzeszów, 2007.
• [66] Z. Waszczyszyn, M. Słoński. Maximum of Marginal Likelihood Criterion instead of Cross-Validation for designing of artificial neural networks. In: L. Rutkowski et al., [Eds.]. Artificial Intelligence and Soft Computing – ICAISC 2008, LNAI 5097, 186–194. Springer, Berlin – Heidelberg – New York, 2008.
• [67] Z. Waszczyszyn, M. Słoński. Selected problem of artificial neural networks development. Ch. 5 in [62], 237–316, 2010.
• [68] Z. Waszczyszyn, L. Ziemiański. Neural networks in the identification analysis of structural mechanics problems. Ch. 7 in: [36], 265–340, 205.
• [69] Z. Waszczyszyn, L. Ziemiański. Neurocomputing in the analysis of selected inverse problems of mechanics of structures and materials, Computer Assisted Mech. Eng. Sci., 13: 125–159, 2006.
• [70] M. Wojciechowski. Feed-Forward Artificial Neural Networks and Their Derivatives - Application Possibilities in Engineering and Geotechnics (in Polish), Ph.D. Thesis. Łódź University of Technology, 2009.
• [71] M. Wojciechowski. Application of artificial neural network in soil parameter identification for deep excavation numerical model. Computer Assisted Mech. Eng. Sci., 18(4): 303–311, 2011.
• [72] L. Ziemiański. Neural Networks in Structural Dynamics (in Polish). Oficyna Wyd. Politechniki Rzeszowskiej, Rzeszów, 1999.
• [73] L. Ziemiański, B. Miller, G. Piątkowski. Application of neurocomputing to parametric identification using dynamical responses. Ch.15 in [27], 362–392, 2007.
• [74] L. Ziemiański, A. Kozłowski, S. Woliński [Eds]. 57th Annual Conference onScientific Problems of Civil Engineering, Rzeszów-Krynica, 18–22 Sept. 2011. Proceedings, Special Session on ANN in Civil Engineering, 292–293. Oficyna Wyd. Politechniki Rzeszowskiej, Rzeszów, 2011.
• [75] ECCOMAS Thematic Conference IPM 2009. International Symposium on Inverses Problems in Mechanics of Structures and Materials. Book of Abstracts, 23–25 April 2009, Rzeszów-Łańcut, Poland, 2009.
• [76] ECCOMAS Thematic Conference IPM 2011. 2nd International Conference on Inverses Problems in Mechanics of Structures and Materials. Conference Proceedings, 27–30 April 2011, Rzeszów-Sieniawa, Poland, 2011.