아스팔트 혼합물의 골재 간극률 예측을 위한 기계학습 프레임워크
Machine Learning Framework for Predicting Voids in the Mineral Aggregation in Asphalt Mixtures
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초록
골재 간극률은 구조적 강도, 내구성, 배수 및 투수성 등 다양한 아스팔트의 특성에 직접적인 영향을 미친다. 따라서 아스팔트포장이 사용되는 위치, 기후, 환경 등에 적절하도록 골재 간극률이 설계되어야한다. 하지만 골재 간극률은 다양한 요인들에의해 영향을 받으므로 그 설계가 쉽지 않다. 예를 들어 골재 입자의 크기 분포, 구성이나 아스팔트 바인더의 양, 다짐 수준등 다양한 영향인자가 존재한다. 본 연구에서는 골재 간극률에 영향을 미치는 요인들로부터 골재 간극률을 예측하고자 하였다. 이를 위해 다양한 기계학습 모델 방법을 적용하였고 단일 기계학습 모델을 적용했을 때보다 높은 정확도로 골재 간극률을예측할 수 있음을 보였다. 본 연구의 결과는 경험과 노동집약적인 실험에 의존하는 골재 간극률 예측에 데이터 기반의 접근방법을 적용할 수 있음을 보였으며 향후 최적 골재 간극률 설계 등에 활용 가능할 것으로 기대된다.
Abstract
The Voids in the Mineral Aggregate (VMA) within asphalt mixtures play a crucial role in defining the mixture’s structural integrity, durability, and resistance to environmental factors. Accurate prediction and optimization of VMA are essential for enhancing the performance and longevity of asphalt pavements, particularly in varying climatic and environmental conditions. This study introduces a novel machine learning framework leveraging ensemble machine learning model for predicting VMA in asphalt mixtures. By analyzing a comprehensive set of variables, including aggregate size distribution, binder content, and compaction levels, our framework offers a more precise prediction of VMA than traditional single-model approaches. The use of advanced machine learning techniques not only surpasses the accuracy of conventional empirical methods but also significantly reduces the reliance on extensive laboratory testing. Our findings highlight the effectiveness of a data-driven approach in the field of asphalt mixture design, showcasing a path toward more efficient and sustainable pavement engineering practices. This research contributes to the advancement of predictive modeling in construction materials, offering valuable insights for the design and optimization of asphalt mixtures with optimal void characteristics.
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