한국도로학회논문집 제22권 제6호 (p.9-15)

소음 기반 포장상태등급 평가 인공지능 고도화 연구

Improvement of Artificial Intelligence for Acoustic-based Pavement Condition Grade Evaluation
키워드 :
pavement,condition grade,monitoring,tire-surface friction noise,artificial intelligence

목차

ABSTRACT
1. 서론
2. 문헌고찰
   2.1. 선행연구 고찰
   2.2. 패턴인식 인공지능 유형 고찰
3. 방법론
4. 실증연구
   4.1. 현장 조사
   4.2. 인공지능 초기학습 및 평가 결과
   4.3. 평가등급 조정을 통한 인공지능 학습 최적화
5. 결론
REFERENCES

초록

PURPOSES : The purpose of this study is to enhance the reliability of artificial intelligence for a noise-based pavement condition rating system (to a target performance of 95 %).
METHODS : By comparing four types of pattern recognition artificial intelligence, this work acquires high-quality learning data and optimizes data learning through analysis of error characteristics. RESULTS : The system reliability improved up to 97 % (82 % in a prior study). In addition, 100 % was achieved for the E(F) condition grade, which has a direct impact on maintenance decision making. CONCLUSIONS : KNN-DTW (K-nearest neighbor dynamic time warping) is judged to be the most suitable type of artificial intelligence for a noise-based pavement condition rating system; a 4-grade system is the most suitable for classifying pavement condition.