Neural Networks in the Identification Analysis of Structural Mechanics Problems

The Chapter is related to research carried out by the authors’ teams. The Chapter contains seven Sections and list of References. Section 1 concerns basics of selected neural networks. The main attention is paid to the Back-Propagation NNs, which are mostly applied in the analysis of engineering problems. Modifications of this NN (replicator, cascade NN, Fuzzy Weight NN) and some other NNs (Radial Basis Function NN and Adaptive Neuro-Fuzzy Inference System) are discussed in short. Data preprocessing, design problems of these NNs and approximation errors are considered as well. Section 2 is related to the application of NNs for simulating trials in the Classical Monte Carlo Method. Patterns generated by an FE program are used for the NN training and testing. A great numerical efficiency of this approach is presented on an example of the reliability analysis of an elastoplastic plane frame. Section 3 deals with the identification problems of real buildings subjected to paraseismic excitations. Section 4 is related to the application of dynamic response (eigenfrequencies excited by impulse loadings or wave propagation measurements) to the parameter identification of structural elements with defects. In Section 5 the problem of FEM models updating is considered. A hybrid approach is discussed as a sequence of the application of an initially formulated FE model with control parameters, which are identified by an NN. The calibration and verification of the updated FE model is performed on the base of laboratory tests. Section 6 discusses applications of a modification of a standard NN (Fuzzy Weight NN) to the analysis of problems from experimental structural mechanics that give fuzzy results. Section 7 deals with so-called implicit modelling (i.e. model-free, data-related NNs) of physical relationships. In References, besides basic literature, also papers written by the authors and their associates are quoted3,4.

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