Abstract
Structural Health Monitoring (SHM) has enabled the condition of large structures, like bridges, to be evaluated in real time. In order to monitor behavioral changes, it is essential to identify parameters of the structure that are sensitive enough to capture damage as it develops while being stable enough during ambient behavior of the structure. Research has shown that monitoring the neutral axis (N.A.) position satisfies the first criterion of sensitivity; however, monitoring N.A. location is challenging because its position is affected by the loads applied to the structure. The motivation behind this research comes from the greater than expected impact of various load characteristics on observed N.A. location. This paper develops an indirect way to estimate the characteristics of vehicular loads (magnitude and lateral position of the load) and uses a data mining approach to predict the expected location of the N.A. Instead of monitoring the behavior of the N.A., in the proposed method the residuals between the monitored and predicted N.A. location are monitored. Using actual SHM data collected from a cable-stayed bridge, over a 2-year period, the paper presents the steps to be followed for creating a data mining model to predict N.A. location, the use of monthly sample residuals of N.A. to capture behavioral changes, the ability of the method to distinguish between changes in the load characteristics from behavioral changes of the structure (e.g. change in response due to cracking, bearings becoming frozen, cables losing tension, etc.), and the high sensitivity of the method that allows capturing of minor changes.
Highlights
The objective of Structural Health Monitoring (SHM) is to provide a diagnosis of the condition of a structure in at, or near real time
The variability of the N.A. position for the same truck passes over different tests shows the effect of the noise and the difficulty it would present in using N.A. location measured from a load test as an indication of changes in the bridge
Both controlled load tests and response from ambient traffic loads have been used to show that N.A. location depends on the magnitude of the vehicle loads and lateral position of the applied load
Summary
The objective of Structural Health Monitoring (SHM) is to provide a diagnosis of the condition of a structure in at, or near real time. The variability of the N.A. position for the same truck passes over different tests shows the effect of the noise and the difficulty it would present in using N.A. location measured from a load test as an indication of changes in the bridge. A data mining model was trained using the data collected from the first 3 months (March 2015 to May 2015) to predict the N.A. location based on the magnitude (West ) and the lateral position (P) of the load as described earlier (Figure 8). The use of data mining techniques to find the correlations of N.A. location with magnitude and lateral position of the load reduced the variability of the residuals of the N.A. noise is still a problem that can hide small changes. This consistency is even clearer where small changes are simulated in the N.A. position
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