발간년도 : [2023]
| 논문정보 |
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| 논문명(한글) |
[Vol.18, No.5] Machine Learning-based Bridge Safety Monitoring System Model |
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| 논문투고자 |
Yeong-Hwi Ahn |
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| 논문내용 |
Expansion joint devices in bridges are devices that ensure durability and safety by smoothly accommodating the movement and rotation of the upper deck due to temperature changes, live loads, earthquakes, and drying shrinkage due to the concrete age of the bridge. However, accidents occur as expansion joints are damaged due to various reasons. In general, bridges are maintained through regular inspections, but most inspections are conducted through visual inspection. The proposed model is an artificial intelligence-based bridge expansion joints monitoring system that collects data measuring the displacement of the bridge's suspension device. To test the proposed model, a simulated bridge and a simulated vehicle were manufactured, data in normal and abnormal states were collected, and the collected data was refined and used for machine learning. For the performance analysis and selection of the artificial intelligence model using simulated bridge data, 80% of the simulated bridge learning data and 20% of the test data were used to evaluate the predicted performance score using XGBoost. The average accuracy of predicting the occurrence of steps and damage in expansion joints was 96.2%. However, these results were tested on a simulated bridge, and have the limitation that the results are based on data from an actual bridge. It is expected that if data on various variables that occur in actual bridges are collected and applied, accidents due to damage to expansion joints can be prevented in advance. |
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