A detailed study of the capabilities and powerfulness of soft computing techniques such as artificial neural network with respect to the identification of structural parameters and structural responses are presented. This chapter includes the definition of neural architectures and system identification of multistory structure. An efficient identification algorithm for the multistory structure subject to initial condition and ground displacement is presented. Response identification subject to real earthquake data has also been discussed. Several example problems are incorporated to show the efficiency and reliability of the proposed algorithm.

Introduction

System identification methods in structural dynamics, in general are formulated as inverse vibration problems to identify properties of a structure from measured data. The dynamic behaviour of complicated systems often needs to be investigated by system identification, since it usually has to meet certain requirements. The use of computers and efficient mathematical tools allow an identification of the process dynamics by evaluating the input and output signals of the system. The result of such process identification is usually a mathematical model, by which the dynamic behaviour can be estimated or predicted.

As regards the publications by Natke (1982), Masri, Bekey, Sassi, and Caughey (1982), Masri, Sassi, and Caughey (1982), and Schoukens and Pintelon (1991) presented various methodologies for different type of problems in system identification. Ibanez (1979) has reviewed various techniques for improving structural dynamic models and Datta, Shrikhande, and Paul (1998) reviewed problems related to system identification of buildings done until that date. Some...