Abstract

In Structural Health Monitoring (SHM), various sensors are installed in the critical locations of a structure. The signals from sensors are either continuously or periodically analyzed to determine the state and performance of the structure. The objective of this thesis is to apply statistical pattern recognition techniques to determine the relation among signals or engineering data from various sensors installed on a structure. An objective comparison of the sensor data at different time ranges is essential for assessing the structural condition, detect any malfunction of sensors, or excessive load experienced by the structure which leads to potential damage in the structure. The objectives of the current research are to establish a relationship between the data from various sensors to estimate the reliability of the data, and to determine defective sensor using the statistical pattern matching techniques. In order to achieve these goals, new methodologies based on statistical pattern recognition techniques have been developed and implemented using the MATLAB environment. The proposed methodologies have been developed and validated using sensor data obtained from an instrumented bridge and road test data from industry. The statistical pattern matching techniques are quite new in SHM data interpretation and current research demonstrate that it has high potential in assessing structural conditions, especially when the data is noisy and susceptible to environmental disturbances.