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Data Compression Algorithms for Bridge Health Monitoring
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Ken CHEN1, Ai-ping ZHONG1, Yu-xuan WEN2, 3, Yang YANG1, Wei LI1, Shan ZENG2, 3, Jun WANG1, Qu-shan TAN1, Liu YANG2, 3, *
Science Technology and Engineering | 2025, 25(8) : 3304 - 3315
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Science Technology and Engineering | 2025, 25(8): 3304-3315
Automation and Computational Technology
Data Compression Algorithms for Bridge Health Monitoring
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Ken CHEN1, Ai-ping ZHONG1, Yu-xuan WEN2, 3, Yang YANG1, Wei LI1, Shan ZENG2, 3, Jun WANG1, Qu-shan TAN1, Liu YANG2, 3, *
Affiliations
  • 1 Sichuan Digital Transpotation Tech Co., Ltd. Chengdu 610095 China
  • 2 National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu 611756 China
  • 3 School of Information Science and Technology Southwest Jiaotong University Chengdu 611756 China
Published: 2025-03-18 doi: 10.12404/j.issn.1671-1815.2401129
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The bridge health monitoring system based on sensor data acquisition has become standard for new bridge construction. However, this scenario presents challenges due to the massive volume of monitoring data that is difficult to store. Therefore, focusing on the time-series characteristics of bridge monitoring data, compression schemes were explored for bridge monitoring data. Differential compression was investigated based on the arithmetic progression properties of bridge monitoring timestamps and floating-point exclusive OR(XOR) compression based on the low frequency of changes in monitoring value data. Compared to the Gorilla time series database algorithm, the XOR compression method added control bits to avoid degradation of compression results. Experimental analysis reveals that both algorithms exhibit varying degrees of superiority over common compressors. The differential compression of timestamp sequences demonstrates superior compression rates compared to common compressors, achieving a compression rate of 0.015 6 for timestamp sequences that conform to arithmetic progression characteristics, approaching the compression limit value. Compression and decompression speeds are above average, and the method is insensitive to monitoring type. On the other hand, the XOR compression method performs well on datasets with low frequency of change, achieving compression rates of 0. 302 8 for bridge data and 0. 662 8 for non-bridge data, indicating sensitivity of the XOR compression method to monitoring type. In practical applications of bridge monitoring, suitable compression storage schemes can be selected based on the characteristics of the bridge monitoring dataset.

bridge monitoring  /  time series data  /  differential compression  /  exclusive OR(XOR) compression
Ken CHEN, Ai-ping ZHONG, Yu-xuan WEN, Yang YANG, Wei LI, Shan ZENG, Jun WANG, Qu-shan TAN, Liu YANG. Data Compression Algorithms for Bridge Health Monitoring[J]. Science Technology and Engineering, 2025 , 25 (8) : 3304 -3315 . DOI: 10.12404/j.issn.1671-1815.2401129
Year 2025 volume 25 Issue 8
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Article Info
doi: 10.12404/j.issn.1671-1815.2401129
  • Receive Date:2024-02-20
  • Online Date:2025-07-29
  • Published:2025-03-18
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History
  • Received:2024-02-20
  • Revised:2024-12-15
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Affiliations
    1 Sichuan Digital Transpotation Tech Co., Ltd. Chengdu 610095 China
    2 National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu 611756 China
    3 School of Information Science and Technology Southwest Jiaotong University Chengdu 611756 China
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表12种不同金属材料的力学参数

Family
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Number of
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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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