Vibration based damage identification (VBDI) techniques rely on the fact that damage in a structure reduces its stiffness and alters its global vibration characteristics. Measurement of changes in the vibration characteristics can therefore be used to determine the damage in the structure. The VBDI technique does not depend on a-priori information about the damage site; the vicinity of the damage need not be accessible; and often a limited number of sensors can be used to localize and quantify the damage. Unfortunately, most of the available damage identification algorithms fail when applied to practical structures due to the effect of measurement errors, uncertainties induced by environmental and boundary condition, the need to use incomplete mode shapes, mode truncation, and the non-unique nature of the solutions. Damage detection based on changes in modal characteristics can be treated as a pattern recognition problem. Artificial neural networks provide an ideal means of obtaining a solution to such a problem. This paper presents a new robust two-step algorithm for detecting the location and magnitude of damage. The technique uses principles of structural dynamics and artificial neural networks. A modal energy based vibration property, known as the damage index vector, is used as the input to the network. The proposed algorithm is used to detect simulated damage in a simple finite element model of a slab and girder bridge. The result shows that the proposed algorithm is quite effective in identifying the location and magnitude of damage, even in the presence of measurement errors in the input data.
Under the auspices of the Canadian Network of the Centers of Excellence on Intelligent Sensing of Innovative Structures ( ISIS) a number of bridges incorporating innovative materials and/or innovative structural concepts have been instrumented with a view to monitoring their health. ISIS has initiated a project to create a central archive for the long-term collection and maintenance of the data obtained from the instrumented bridges. Typical instrumentation consists of sensors of various kinds (temperature, strain, displacement and acceleration). Data may be collected at regular intervals under normal traffic conditions using an automated or semi-automated data acquisition system. As well, static and dynamic load tests may be scheduled and performed from time to time. The data received from the various sources will be converted to a common format and will reside in a central relational database. A world-wide-web interface to the archive has been provided. The interface will allow authorized bridge managers to submit data to the archive. It will also allow other researchers to explore the archive, and to extract data from it, using many common formats.
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