Academic Journal of Architecture and Geotechnical Engineering, 2025, 7(2); doi: 10.25236/AJAGE.2025.070212.
Yiying Liu1, Shenghui Zhou1, Lin Teng1, Chuanwei Du1, Yanzhao Fu2
1Qilu Institute of Technology, Jinan, Shandong, 250200, China
2Jinan Municipal Engineering Design and Research Institute (Group) Co., Ltd., Jinan, Shandong, 250003, China
The integration of big data into steel structure operation and maintenance management is pivotal to contemporary construction development. By leveraging big data and machine learning, safety condition assessment and prediction for in-service steel bridges can effectively mitigate human error stemming from reliance on experience. Simultaneously, this approach optimises resource allocation, preventing wastage and other inefficiencies. Building upon this foundation, analysis of vast datasets, further combined with the diversity of smart bridges, provides scientific inspection for subsequent management and evaluation.
Machine Learning; in-Service Steel Structures; Bridge Safety Condition; Assessment and Prediction
Yiying Liu, Shenghui Zhou, Lin Teng, Chuanwei Du, Yanzhao Fu. Machine Learning-Based Assessment and Prediction of the Safety Condition of in-Service Steel Bridges. Academic Journal of Architecture and Geotechnical Engineering (2025), Vol. 7, Issue 2: 90-94. https://doi.org/10.25236/AJAGE.2025.070212.
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