PURPOSES : This study aims to determine whether machine learning techniques based on the results of chemical analysis experiments can be rationally applied to evaluate the aging of various asphalt binders used throughout the country. METHODS : We conducted chemical experiments such as FT-IR, H-NMR, C- NMR, and GPC for the three-stage aging levels of eight types of asphalt binders used in the country and utilized two artificial neural network models to determine valid chemical experimentation and conditions for the use of neural modeling through predictions. RESULTS : The M-prop model, which combined the findings from each neural network model into a single artificial neural network model, demonstrated superior predictive performance compared with the M-base model, which assessed aging using two cluster layers. In addition, the minimum amount of data required to achieve 100% predictive accuracy for the target asphalt binders, regardless of the artificial neural network model, was 18, and the amount of training data decreased to less than 50%. CONCLUSIONS : The predictive accuracy of the aging of asphalt binders was significantly enhanced when GPC data was used, indicating that GPC should be prioritized in evaluating the aging of asphalt binders.

1. 연구배경 및 목적
2. 아스팔트 바인더의 조성 및 분석 방법
    2.1. 아스팔트 바인더의 화학적 조성
    2.2. 노화 아스팔트 바인더의 조성 및 분자구조
    2.3. 아스팔트 바인더의 화학적 분석 방법
3. 화학적 분석 실험 결과 및 노화도 예측
    3.1. 화학적 분석 실험 조건 및 결과
    3.2. 노화도 예측을 위한 학습 데이터 구성
4. 인공 신경망을 이용한 노화도 예측
    4.1. 인공 신경망 모형과 노화도 예측 방법의 특징
    4.2. 노화도 예측 결과
5. 결론
    5.1. 결론 및 요약
감사의 글

• 심승보(정회원 · 한국건설기술연구원 수석연구원) | Shim Seungbo
• 윤태영(정회원 · 한국건설기술연구원 연구위원) | Yun Tae Young 교신저자