The rapid development of science and technology has promoted the leap in bridge engineering technology. The bridge construction technology is becoming more and more mature and mature. The bridges constructed by various new materials and new technologies are emerging endlessly, and the forms and functions of bridge structures are becoming increasingly complex. Over time, after the bridge is completed and opened to traffic, any bridge will become an old bridge. After long-term use, the bridge structure will inevitably cause various damage to the structure due to various reasons, thereby reducing the safety of the bridge and even affecting vehicles safe operation, endangering people's lives. How to carry out quality inspection and safety monitoring of bridge structures has become a hotspot in academic and engineering circles at home and abroad. In order to ensure the safety of bridges and to provide early warning of dangers, this paper specifically studies the method of bridge health detection data storage and early warning based on big data. This paper obtains an appropriate method by performing a multi-factor analysis of the bridge's many attribute data, the potential correlation collapse between each attribute, and the use of appropriate regression modeling. After the data correlation analysis is completed, select the characteristic index to generate a bridge service performance evaluation model, set the optimal numerical interval of the bridge attributes, and provide real-time warning when the bridge monitoring data shows abnormal values. It is hoped that the research in this paper can contribute to bridge safety and provide reliable data basis for future bridge safety development.

Jianhua Zhang. Method for Storage and Early Warning of Bridge Health Detection Data Based on Big Data. International Journal of Engineering Technology and Construction (2020), Vol. 1, Issue 3: 39-53. https://doi.org/10.38007/IJETC.2020.010304.

[1] C. Sun, Y. Zhang. Research on Automatic Early Warning Method for Rail Flaw Based on Intelligent Identification and Periodic Detection. Tiedao Xuebao/Journal of the China Railway Society, 2018, 40(11):140-146. DOI: 10.3969/j.issn.1001-8360.2018.11.020

[2] Thomas Le Guenan, Farid Smai, Annick Loschetter. Accounting for end-user preferences in earthquake early warning systems. Bulletin of Earthquake Engineering, 2016, 14(1):297-319. DOI: 10.1007/s10518-015-9802-6

[3] Sheikh K. Ghafoor, Ryan Marshall, Faisal Hossain. LiquidEarth - river. Acm Sigcas Computers & Society, 2015, 45(2):42-42.

[4] Preeti Verma, Soma Sarkar, Singh Poonam. Devising a method towards development of early warning tool for detection of malaria outbreak. Indian Journal of Medical Research, 2018, 146(5):612-621.

[5] Jian Li, Baoqiang Yan, Mingjiang Zhang. Long-Range Raman Distributed Fiber Temperature Sensor with Early Warning Model for Fire Detection and Prevention. IEEE Sensors Journal, 2019, PP(99):1-1. DOI: 10.1109/jsen.2019.2895735

[6] C.-C. Ma, T.-B. Li, H. Zhang. An evaluation and early warning method for rockburst based on EMS microseismic source parameters. Rock & Soil Mechanics, 2018, 39(2):765-774.

[7] Shuli Fan, Liang Ren, Hongnan Li. Real-Time Monitoring and Early Warning Method Utilizing FBG Sensors in the Retrofitting Process of Structures. International Journal of Distributed Sensor Networks, 2015, 2015(11):1-9. DOI: 10.1155/2015/359549

[8] Yixuan Yuan, Dengwang Li, Max Q.-H. Meng. Automatic Polyp Detection via a Novel Unified Bottom-Up and Top-Down Saliency Approach. IEEE Journal of Biomedical & Health Informatics, 2017, PP(99):1-1.

[9] Yifan Yu, Student Member, IEEE. Delta-Connected Cascaded H-Bridge Multilevel Converters for Large-Scale Photovoltaic Grid Integration. IEEE Transactions on Power Electronics, 2016, PP(99):1-1.

[10] Mohamed G. ElBanan, Ahmed M. Amer, Pascal O. Zinn. Imaging Genomics of Glioblastoma: State of the Art Bridge Between Genomics and Neuroradiology. Neuroimaging Clinics of North America, 2015, 25(1):141-153.

[11] Sébastien Schmerber, O. Deguine, M. Marx. Safety and effectiveness of the Bonebridge transcutaneous active direct-drive bone-conduction hearing implant at 1-year device use:. Archiv für Klinische und Experimentelle Ohren- Nasen- und Kehlkopfheilkunde, 2016, 274(4):1-17. DOI: 10.1007/s00405-016-4228-6

[12] C.-Y. Xia, N. Zhang, H. Xia. Dynamic analysis of a train-bridge system under vessel collision and running safety evaluation of its high-speed train. Zhendong Yu Chongji/journal of Vibration & Shock, 2015, 34(6):155-161.

[13] Xiao-xiao Lai, Hua Lin, Yi-ni Luo. [Summary and analysis of safety warning on clinical application of anti-cold Chinese patent medicine preparations]. Zhongguo Zhong Yao Za Zhi, 2015, 40(8):1594-1600.

[14] Guangrui Tang. Analysis and Study on Safety Warning Distance of Human Body Near The Transmission Lines. IOP Conference Series Materials Science and Engineering, 2018, 392(6):062182.

[15] Sabrina S. Adler, Ian E. Mclaughlin, Seth E. Mermin. You Want a Warning with That? Sugar-Sweetened Beverages, Safety Warnings, and the Constitution. Food & Drug Law Journal, 2016, 71(3):482-518.

[16] Jessica K. Pepper, Margaret J.M. Cress, Doris G. Gammon. Battery Safety Information and Warnings on E-cigarette Packages and Online. Tobacco Regulatory Science, 2018, 4(1):605-613.

[17] Jun Xu, Kaiyuan Lv, Cheng Ma. Rehabilitation of old three-hinged steel arch bridges - A case study of Ling Bridge. Stahlbau, 2016, 85(8):543-551.

[18] Jun Li Wang. Application of Analytic Hierarchy Process in Bridge Safety Comprehensive Evaluation. Applied Mechanics & Materials, 2015, 744-746(4574):744-748.

[19] S. Cui, P. Liu, Y. Cao. Simulation Study on Multiline Vehicle-Bridge Coupled Vibration. Journal of Southwest Jiaotong University, 2017, 52(5):835-843.

[20] Lv, Yisheng, Duan, Yanjie, Kang, Wenwen. Traffic Flow Prediction With Big Data: A Deep Learning Approach. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(2):865-873.