Identification of nonlinearities in structural dynamics by using artificial neural networks and optimization


Thesis Type: Postgraduate

Institution Of The Thesis: Orta Doğu Teknik Üniversitesi, Faculty of Engineering, Department of Mechanical Engineering, Turkey

Approval Date: 2013

Student: ANIL KOYUNCU

Supervisor: HASAN NEVZAT ÖZGÜVEN

Abstract:

Many real life engineering structures exhibit nonlinear behavior in practice. Although there are sophisticated methods including the effect of nonlinearities on dynamic response of structures in literature, uncertainties about nonlinear elements make further investigation necessary for modeling nonlinearity, and this is usually achieved by using experimental data taken from real systems. Therefore, identification of nonlinearities -determining location, type and parameters of the nonlinear elements- is critical in dynamical structures. In this study, a new approach is proposed for identification of structural nonlinearities by employing neural networks. Linear finite element model of the system and frequency response functions measured at arbitrary locations of the system are used in this approach. Using the finite element model, a training data set is created, which appropriately spans the possible nonlinear configurations space of the system. A classification neural network trained on these data sets then localizes and determines the type of nonlinearity associated with the corresponding degree of freedom in the system. A new training data set spanning the parametric space associated with the determined nonlinearities is created to facilitate parametric identification. Utilizing this data set, a feed forward regression neural network is trained, which parametrically identifies the related nonlinearity. The proposed approach does not require data collection from the degrees of freedoms related with nonlinear elements, and furthermore, the proposed approach is sufficiently accurate even in the presence of measurement noise. Identified parameters are improved utilizing optimization. The application of the proposed approach is demonstrated on an example system with nonlinear elements and a real life experimental setup with a local nonlinearity.