When using the analysis of vibration measurements as a tool for health monitoring of bridges, the problem arises of separating abnormal changes from normal changes in the dynamic behaviour. Normal changes are caused by varying environmental conditions such as humidity, wind and most important, temperature. The temperature may have an impact on the boundary conditions and the material properties. Abnormal changes on the other hand are caused by a loss of sti ness somewhere along the bridge. It is clear that the normal changes should not raise an alarm in the monitoring system (i.e. a false positive), whereas the abnormal changes may be critical for the structure's safety. In the frame of the European SIMCES-project, the Z24-Bridge in Switzerland was monitored during almost one year before it was artiÿcially damaged. Black-box models are determined from the healthy-bridge data. These models describe the variations of eigenfrequencies as a function of temperature. New data are compared with the models. If an eigenfrequency exceeds certain conÿdence intervals of the model, there is probably another cause than the temperature that drives the eigenfrequency variations, for instance damage.The paper is organized as follows. Next section describes the bridge and the monitoring system. Section 3 explains how the modal parameters are automatically extracted from the massive amount of vibration data. In Section 4, we attempt to construct black-box models that explain the variation of the eigenfrequencies due to changing environmental in uences. The models are found by applying system identiÿcation techniques to the data of the undamaged bridge. In Section 5, the models are validated by using fresh data. These validation data also contain the artiÿcial damage events. Finally some conclusions and recommendations are given in Section 6.
This paper reviews stochastic system identification methods that have been used to estimate the modal parameters of vibrating structures in operational conditions. It is found that many classical input-output methods have an output-only counterpart. For instance, the Complex Mode Indication Function (CMIF) can be applied both to Frequency Response Functions and output power and cross spectra. The Polyreference Time Domain (PTD) method applied to impulse responses is similar to the Instrumental Variable (IV) method applied to output covariances. The Eigensystem Realization Algorithm (ERA) is equivalent to stochastic subspace identification.
This paper deals with the problem of damage detection using output-only vibration measurements under changing environmental conditions. Two types of features are extracted from the measurements: eigenproperties of the structure using an automated stochastic subspace identification procedure and peak indicators computed on the Fourier transform of modal filters. The effects of environment are treated using factor analysis and damage is detected using statistical process control with the multivariate Shewhart-T control charts.A numerical example of a bridge subject to environmental changes and damage is presented. The sensitivity of the damage detection procedure to noise on the measurements, environment and damage is studied. An estimation of the computational time needed to extract the different features is given, and a table is provided to summarize the advantages and drawbacks of each of the features studied. r
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