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.

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

• 한대석(한국건설기술연구원 노후인프라센터) | Han Daeseok
• 김영록(한국건설기술연구원 복합재난대응연구센터) | Kim Young Rok 교신저자