Joint damage assessment of framed structures using a neural networks technique

Abstract

A method is proposed to estimate the joint damages of a steel structure from modal data using a neural networks technique. The beam-to-column connection in a steel frame structure is represented by a zero-length rotational spring at the end of the beam element, and the joint fixity factor is defined from the rotational stiffness so that the factor may be in the range of 0–1.0. The severity of joint damage is then defined as the reduction ratio of the connection fixity factor. Several advanced techniques are employed to develop the robust damage identification technique using neural networks. The concept of substructural identification is used for the localized damage assessment in a large structure. The noise-injection learning algorithm is used to reduce the effects of the noise in the modal data. The data perturbation scheme is also employed to assess the confidence in the estimated damages based on a few sets of actual measurement data. The feasibility of the proposed method is examined through a numerical simulation study on a 2-bay 10-story structure and an experimental study on a 2-story structure. It is found that joint damages can be reasonably estimated even for the case where the measured modal vectors are limited to a localized substructure and the data are severely corrupted with noise.