Abstract
One of the greatest challenges affecting the use of structural health monitoring is the establishment of effective and reliable techniques for processing and management of the accumulated measurement data. This article presents results achieved in the use of an artificial neural computing approach applied to this problem. The unsupervised neural learning algorithm known as frequency sensitive competitive learning is employed in the processing of sensor data from three civil engineering structures. It is shown that the algorithm is capable of learning the normal response of the structure and provides effective means of identifying novel features in the sensor record thereafter. This permits further detailed study of these specific noteworthy events. Events are identified using a relative novelty index computed by the neural network architecture. Examples demonstrate the identification of vehicle traffic on one bridge, seismic activity on a second and the response to wind loading on a feature statue.
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© 2005 Springer
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McNeill, D.K., Card, L. (2005). Adaptive Event Detection for Shm System Monitoring. In: Ansari, F. (eds) Sensing Issues in Civil Structural Health Monitoring. Springer, Dordrecht. https://doi.org/10.1007/1-4020-3661-2_31
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DOI: https://doi.org/10.1007/1-4020-3661-2_31
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-3660-6
Online ISBN: 978-1-4020-3661-3
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