Time series-based damage detection and localization algorithm with application to the ASCE benchmark structure
Section snippets
Introduction and motivation
Structural health monitoring has become of great significance in the civil engineering community in the last few years [1]. There is a need to continuously monitor the level of performance and safety of structures, while subjected to everyday loads as well as earthquakes, hurricanes and other extreme events, due to increased safety requirements and financial implications. Recent research has demonstrated that wireless sensing networks can be successfully used for structural health monitoring [2]
Description of the damage algorithm
Structural damage affects the dynamic properties of a structure, resulting in a change in the statistical characteristics of the measured acceleration time histories. Thus, damage detection can be performed using time series analysis of vibration signals measured from a structure before and after damage. In this study, we use the ARMA time series to model the vibration data obtained from the sensor. The analysis is limited to linear vibration data (before and after the event) and the assumption
Application results
In order to test the validity of the algorithm, results from the numerical simulation and laboratory experiments of the ASCE benchmark structure are used. The structure is a four story, two-bay×two-bay steel-braced frame, illustrated in Fig. 7 [9]. The location of the accelerometers and the loading on the structure are also identified in Fig. 8 [10]. It can be seen that the structure is subjected to shaking at the top story. Damage is simulated by removing braces in various combinations,
Conclusions
In this paper, a damage-detection algorithm based on time series modeling is discussed. A damage sensitive feature, DSF, which is a function of the first three auto regressive (AR) components, is also discussed. A hypothesis test involving the t-test is used to obtain a damage decision. Two localization indices, LI1 and LI2, defined in the AR coefficient space are also introduced. The damage detection and localization methodologies were tested on the analytical and experimental results of the
Acknowledgements
The present research study is supported by the National Science Foundation through Grant No. CMS-0121841. We greatly appreciate their continued support. The first author is supported by the John A. Blume Graduate Research Fellowship. The authors would like to acknowledge the ASCE Benchmark Committee for providing the relevant MATLAB codes.
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