Structural damage detection using measured dynamic data for pattern recognition is a promising approach. These pattern recognition techniques utilize artificial neural networks and genetic algorithm to match pattern features. In this study, an artificial neural network–based damage detection method using frequency response functions is presented, which can effectively detect nonlinear damages for a given level of excitation. The main objective of this article is to present a feasible method for structural vibration–based health monitoring, which reduces the dimension of the initial frequency response function data and transforms it into new damage indices and employs artificial neural network method for detecting different levels of nonlinearity using recognized damage patterns from the proposed algorithm. Experimental data of the three-story bookshelf structure at Los Alamos National Laboratory are used to validate the proposed method. Results showed that the levels of nonlinear damages can be identified precisely by the developed artificial neural networks. Moreover, it is identified that artificial neural networks trained with summation frequency response functions give higher precise damage detection results compared to the accuracy of artificial neural networks trained with individual frequency response functions. The proposed method is therefore a promising tool for structural assessment in a real structure because it shows reliable results with experimental data for nonlinear damage detection which renders the frequency response function–based method convenient for structural health monitoring.

This paper addresses the problem of identifying a system of forces from vehicle crossing a guideway using only the vibration responses caused by the forces as the input without knowledge of the vehicle characteristics. The vehicle is modeled as a single axle and two-axle loads with fixed axle spacing moving on a simply supported beam with viscous damping. The equations of motion of the beam are obtained through modal coordinate transformation, and the resulting set of equations relating the Fourier transforms of the responses and the moving forces are converted into time domain by a new method proposed by the authors. Correctness of the identified forces are checked by the correlation between the measured responses and the responses reconstructed with the identified forces moving on the beam. Experimental result shows that the method is effective to give good correlation when both measured bending moment and acceleration are used, and it is faster and it gives more accurate estimate of the total mass of the vehicle than an existing method.