Abstract
Detecting structural damage in real time is important and challenging for bridge structural health monitoring systems, especially when large amount of time series monitoring data are collected for continuous monitoring and evaluation of abnormal conditions. Conventional approaches fail to efficiently process such large-scale data in real time due to high storage and processing cost. In this paper, we present an efficient real-time bridge structural condition evaluation based on data compression. We introduce an efficient time series representation to compress sensor data into symbol streams by applying symbolic aggregate approximation (SAX), which transforms sensing data into symbolic representation to reduce dimension while preserving important features and guaranteeing low-bounding distance. Upon receiving sensing data in real time, we compress raw data into SAX representation before evaluation. Then, we evaluate bridge structural condition by performing classification based on compressed data efficiently. The proposed method is evaluated using a typical real bridge data set from SMC. Compared with the prediction results on original data using existing methods, our approach reduces the processing time from hours to several seconds with improved accuracy, showing that the proposed method is effective in improving both efficiency and accuracy of bridge structural condition evaluation in real time.
This research is supported by National Natural Science Foundation of China (No. 51608070), Chongqing general project of basic science and advanced technology (No. cstc2016jcyjA0022), USA NSF CISE SaTC grant (No. 1564097), an IBM faculty award, and Fundamental Research Funds for the Central Universities (No. 2019CDXYJSJ0021).
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Dan, J., Liu, L., Wang, Y., Chen, J., Huang, X. (2019). Real-Time Bridge Structural Condition Evaluation Based on Data Compression. In: Guo, S., Liu, K., Chen, C., Huang, H. (eds) Wireless Sensor Networks. CWSN 2019. Communications in Computer and Information Science, vol 1101. Springer, Singapore. https://doi.org/10.1007/978-981-15-1785-3_11
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DOI: https://doi.org/10.1007/978-981-15-1785-3_11
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