A Novel Neuro-Computing Strategy for Damage Evaluation using Forced Vibration Time Series


A novel neural networks based strategy is proposed and developed for the direct identification of structural parameters (stiffness and damping coefficients) from the time-domain forced vibration responses of object structures without any eigenvalue analysis and optimization process that is required in many identification algorithms for inverse analysis. Two back-propagation neural networks are constructed to facilitate the process of damage evaluation in parametric format. The first one, called emulator neural network, is to model the dynamic behavior of a reference structure that has the same overall dimension and topology as the object structure to be identified. The trained emulator neural network can be used as a nonparametric model of the reference structure to forecast its dynamic response with sufficient accuracy. However, when the parameters of the reference structure are modified to form a so-called associated structure, the vibration responses forecast by the emulator neural network will differ from the simulated responses of the associated structure. Their difference can be assessed with a proposed root mean square (RMS) difference vector for both velocity and displacement responses. With the associated structural parameters and their corresponding RMS difference vectors, another network, called parametric evaluation neural network, can be trained. In this study, several 5-storey frames are considered as example object structures with simulated displacement and velocity time histories that mimic the measured dynamic responses in practice. The performance of the proposed strategy has been demonstrated quite satisfactorily. The proposed strategy is extremely efficient in computation and thus has potential of becoming a practical tool for near real time monitoring of civil infrastructures.

Meeting Name

7th International Conference on Motion and Vibration Control (2004: Aug. 8-11, St. Louis, MO)


Civil, Architectural and Environmental Engineering

Document Type

Article - Conference proceedings

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