A method for simultaneous identification of moving masses and damages of the supporting structure from measured responses is presented. The interaction forces between the masses and the structure are used as excitation. Masses and damage extents are used as the optimization variables; compared to the approaches based on identification of the interaction forces, it allows ill-conditioning to be avoided and decreases the number of required sensors. The virtual distortion method is used; the damaged structure is modeled by the intact structure subjected to response-coupled virtual distortions and moving forces. These are related to the optimization variables via a linear system, which allows the optimization variables of both kinds to be treated in a unified way. A moving dynamic influence matrix is introduced to reduce the numerical costs. The adjoint variable method is used for fast sensitivity analysis. A numerical experiment of a three-span beam with 10% rms measurement error and three types of model errors is presented.

This paper describes an effective method of substructure isolation for local structural health monitoring (SHM). In practice, often only a small part of a larger structure is critical and needs monitoring. However, typical low-frequency SHM methods require modeling and analysis of the global structure, which can be costly, time-consuming and error-prone. The proposed approach is based on the virtual distortion method (VDM) and uses force distortions to model fixed supports in the boundary nodes to isolate the considered substructure from influences of the rest of the structure. Therefore, given an excitation of the substructure and the measured response of the global structure, the response of the substructure treated as fixed supported can be computed. Local-only monitoring is then possible using virtually any of the existing methods. However, consistently with the isolation methodology, strain distortions are used here for modeling of damages of the isolated substructure. The discrete adjoint variable method is used for the first time within the framework of the VDM in order to perform fast analytical sensitivity analysis and improve the computational effectiveness of the damage identification by one order of magnitude. A numerical example of a frame-truss with 5 and 10% noise level and an experiment of a cantilever beam are presented to validate the isolation methodology. Copyright © 2010 John Wiley & Sons, Ltd.

Road roughness is an important factor in road network maintenance and ride quality. This paper proposes a road-roughness estimation method using the frequency response function (FRF) of a vehicle. First, based on the motion equation of the vehicle and the time shift property of the Fourier transform, the vehicle FRF with respect to the displacements of vehicle–road contact points, which describes the relationship between the measured response and road roughness, is deduced and simplified. The key to road roughness estimation is the vehicle FRF, which can be estimated directly using the measured response and the designed shape of the road based on the least-squares method. To eliminate the singular data in the estimated FRF, the shape function method was employed to improve the local curve of the FRF. Moreover, the road roughness can be estimated online by combining the estimated roughness in the overlapping time periods. Finally, a half-car model was used to numerically validate the proposed methods of road roughness estimation. Driving tests of a vehicle passing over a known-sized hump were designed to estimate the vehicle FRF, and the simulated vehicle accelerations were taken as the measured responses considering a 5% Gaussian white noise. Based on the directly estimated vehicle FRF and updated FRF, the road roughness estimation, which considers the influence of the sensors and quantity of measured data at different vehicle speeds, is discussed and compared. The results show that road roughness can be estimated using the proposed method with acceptable accuracy and robustness.