Monitoring of a Frame Structure Model for Damage Identification using Artificial Neural Networks

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 proposed approach is carried out using two neural networks: one is called time-delay neural network (TDNN) and the other is called traditional neural network (TNN). The theoretical basis and the selection of the input and output of the TDNN and the TNN are explained. The performance of the proposed methodology for damage detection of the frame structure model with different joint damage scenarios is introduced by direct use of displacement measurement under base excitations. A simulation study has been carried out for the incomplete measurement data. The proposed approach provides an alternative way for damage detection of engineering structures by direct use of structural dynamic displacement measurements.