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pp.9-16
As pavements age and traffic loads increase, the importance of reliably evaluating pavement deterioration has increased. Therefore, this study proposes an Artificial Intelligence(AI)-based rating-classification method that integrates Falling Weight Deflectometer(FWD) deflection, the elastic modulus, and the crack rate obtained through coring to construct a continuous comprehensive index and classifies pavement deterioration into four stages based on the index. For this analysis, the deflection and elastic modulus obtained from the FWD test (a nondestructive test) and the crack rate calculated through coring (a destructive test) were converted into a continuous normalization index. Subsequently, a comprehensive index graph was constructed using a weighted integration method, and an AI-based slope change analysis was performed to determine the threshold value for classifying the pavement conditions. The step-by-step scoring-based and continuous comprehensive index-based analyses showed similar overall results. However, the continuous comprehensive index-based analysis was more effective for mitigating the stepwise distribution effect and reflecting the pavement deterioration trend in more detail. In both analysis methods, the slope change point was confirmed as the threshold value. Using this value, the pavement state can be classified into four stages, which reflect the deterioration characteristics of each pavement type. However, this study has several limitations. To improve the reliability of the system, additional detailed data, such as crack location, crack shape, and other field information used in the comprehensive index calculation, should be continuously incorporated. This approach enables the establishment of an evaluation system that integrates nondestructive and destructive test data.
1. 서론
1.1. 연구배경 및 목적
2. 조사
2.1. 비파괴 검사
2.2. 파괴 검사
3. 분석
3.1. 처짐량과 균열률에 기반한 분석
3.2. 탄성계수, 처짐량 및 균열률에 기반한 분석
4. 결론
REFERENCES
