Abstract
In structural health monitoring (SHM), various sensors are installed at critical locations of a structure. The signals from sensors are either continuously or periodically analyzed to determine the state and performance of the structure. An objective comparison of the sensor data at different time ranges is essential for assessing the structural condition or excessive load experienced by the structure which leads to potential damage in the structure. The objectives of the current study are to establish a relationship between the data from various sensors to estimate the reliability of the data and potential damage using the statistical pattern matching techniques. In order to achieve these goals, new methodologies based on statistical pattern recognition techniques have been developed. The proposed methodologies have been developed and validated using sensor data obtained from an instrumented bridge and road test data from heavy vehicles. The application of statistical pattern matching techniques are relatively new in SHM data interpretation and current research demonstrates that it has high potential in assessing structural conditions, especially when the data are noisy and susceptible to environmental disturbances.
Highlights
In a study by Mirza and Haider [1], it was found that more than 40% of the bridges in service in Canada are over 30 years old
In a similar study conducted by Chase and Washer [2] on the bridge infrastructure in the United States found that about 187,000 bridges representing more than 25% of all bridges were deficient at that time, and about 5,000 bridges were becoming deficient every year
A later study by Research and Innovative Technology Administration [3] puts the number of deficient bridges to about 12% of the total US National and State bridges
Summary
In a study by Mirza and Haider [1], it was found that more than 40% of the bridges in service in Canada are over 30 years old. Noman et al [6] and Islam et al [7] developed statistical pattern based techniques based on the above concepts to interpret the senor data from structural monitoring systems. The objective of this paper is to utilize statistical methods to develop a set of techniques to compare a pair of time history signal blocks corresponding to a sensor. Such a pair of signal blocks could be either obtained from a sensor at different times or one of the signals may represent the real signal and the others simulated using a mathematical model of the system being monitored. The developed techniques have been applied to a case study with strain data from an instrumented bridge pier
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