Damage detection for a frame structure model using vibration displacement measurement

Abstract

A structural parameter identification and damage detection approach using displacement measurement time series is proposed, and the performance of the approach is validated experimentally with a frame structure model in a healthy condition and with joint connection damages. The dynamic displacement response of the frame structure under base excitation is measured by noncontact laser displacement sensors. The proposed approach is carried out using two neural networks: one is called displacement-based neural network emulator (DNNE) and the other is called parametric evaluation neural network (PENN). First, the theoretical basis and the selection of the input and output of the DNNE and the PENN are explained, and second, an identification index called root mean square (RMS) of prediction displacement difference vector (RMSPDDV) is defined. The performance of the proposed methodology for damage detection of the frame structure model with different joint damage scenarios introduced by loosening the bolts connecting the beams and columns is investigated with the direct use of displacement measurement under base excitations. The results from the proposed time domain displacement-based identification approach are compared with them based on the extracted frequencies and show that the proposed time domain methodology can identify the variation of interstory stiffness due to the joint damage with acceptable accuracy without any modal shapes and frequencies extracted from a dynamic test. The proposed approach provides an alternative way for damage detection of engineering structures by the direct use of structural dynamic displacement measurements.