All Issue

2024 Vol.23, Issue 1 Preview Page

Research Article

Machine Learning Framework for Predicting Voids in the Mineral Aggregation in Asphalt Mixtures

아스팔트 혼합물의 골재 간극률 예측을 위한 기계학습 프레임워크

Hyemin Park1
,
Ilho Na2
,
Hyunhwan Kim3
,
Bongjun Ji4*
박 혜민1
,
나 일호2
,
김 현환3
,
지 봉준4*
1Non-Member, Graduate Student, Department of Regional Infrastructure Engineering, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon-si, Gangwon-do 24341, Republic of Korea
2Non-Member, Team Manager, Management Planning Dept., Korea Petroleum Industries Company, 166 Ichon-ro, Yongsan-gu, Seou, 04427, Republic of Korea
3Non-Member, Assistant Professor, Department of Engineering Technology, Texas State University, San Marcos, Texas, USA
4Member, Assistant Professor, Department of Regional Infrastructure Engineering, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon-si, Gangwon-do 24341, Republic of Korea
*Corresponding Author
30 March 2024. pp. 17-25
PDF XML Citation
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.

Keywords
Voids in the mineral aggregate
Pavement
Construction material
Machine learning
Ensemble

골재 간극률은 구조적 강도, 내구성, 배수 및 투수성 등 다양한 아스팔트의 특성에 직접적인 영향을 미친다. 따라서 아스팔트 포장이 사용되는 위치, 기후, 환경 등에 적절하도록 골재 간극률이 설계되어야한다. 하지만 골재 간극률은 다양한 요인들에 의해 영향을 받으므로 그 설계가 쉽지 않다. 예를 들어 골재 입자의 크기 분포, 구성이나 아스팔트 바인더의 양, 다짐 수준 등 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 간극률을 예측할 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.

References
1
Anderson, R. M. and Bentsen, R. A. (2001), "Influence of voids in the mineral aggregate (VMA) on the mechanical properties of coarse and fine asphalt mixtures", Journal of the Association of Asphalt Paving Technologists, Vol.70.
2
Chadbourn, B. A., Skok Jr, E. L., Newcomb, D. E., Crow, B. L. and Spindle, S. (1999), The effect of voids in mineral aggregate (VMA) on hot-mix asphalt pavements, Minnesota Department of Transportation.
3
Chen, T. and Guestrin, C. (2016), "Xgboost: A scalable tree boosting system", In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.785-794. 10.1145/2939672.2939785
4
Cortes, C. and Vapnik, V. (1995), "Support-vector networks", Machine Learning, Vol.20, pp.273-297. 10.1007/BF00994018
5
Foreman, J. K. (2008), Effect of voids in the mineral aggregate on laboratory rutting behavior of asphalt mixtures, University of Arkansas.
6
Freund, Y. and Schapire, R. E. (1997), "A decision-theoretic generalization of on-line learning and an application to boosting", Journal of Computer and System Sciences, Vol.55, No.1, pp.119-139. 10.1006/jcss.1997.1504
7
Friedman, J. H. (2001), "Greedy function approximation: a gradient boosting machine", Annals of Statistics, pp.1189-1232. 10.1214/aos/1013203451
8
Goodfellow, I., Bengio, Y. and Courville, A. (2016), Deep learning. MIT press.
9
Ho, T. K. (1995), "Random decision forests", In Proceedings of 3rd International Conference on Document Analysis and Recognition, Vol.1, pp.278-282. 10.1109/ICDAR.1995.598994
10
Inoue, T., Gunji, Y. and Akagi, H. (2004), "Rational design method of hot mix asphalt based on calculated VMA", In Proceedings of the 3rd Eurasphalt and Eurobitume Congress held in Vienna, Vol.2.
11
Ji, B. (2021), "Machine Learning-based Concrete Crack Detection Framework for Facility Maintenance", Journal of the Korean Geo-Environmental Society, Vol.22, No.10, pp.5-12.
12
Ji, B., Bhattarai, S. S., Na, I. H. and Kim, H. (2023), "A Bayesian deep learning approach for rheological properties prediction of asphalt binders considering uncertainty of output", Construction and Building Materials, Vol.408, pp.133671. 10.1016/j.conbuildmat.2023.133671
13
Kandhal, P. S. and Chakraborty, S. (1996), Evaluation of voids in the mineral aggregate for HMA paving mixtures, Report No.9, National Center for Asphalt Technology.
14
Kandhal, P. S., Foo, K. Y. and Mallick, R. B. (1998), "Critical review of voids in mineral aggregate requirements in Superpave", Transportation Research Record, No.1609, pp.21-27. 10.3141/1609-03
15
Lavin, P. (2003), Asphalt pavements: a practical guide to design, production and maintenance for engineers and architects, CRC Press. 10.1201/9781482267716
16
Li, X. F., Doh, Y. S., and Kim, K. W. (2004), "Estimation of Rutting based on Volumetric Properties of Asphalt Mixture", International Journal of Highway Engineering, Vol.6, No.3, pp.79-90.
17
Malekloo, A., Ozer, E., AlHamaydeh, M. and Girolami, M. (2022), "Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights", Structural Health Monitoring, Vol.21, No.4, pp.1906-1955. 10.1177/14759217211036880
18
Opitz, D. and Maclin, R. (1999), "Popular ensemble methods: An empirical study", Journal of Artificial Intelligence Research, Vol.11, pp.169-198. 10.1613/jair.614
19
Sengoz, B. and Topal, A. (2007), "Minimum voids in mineral aggregate in hot-mix asphalt based on asphalt film thickness", Building and Environment, Vol.42, No.10, pp.3629-3635. 10.1016/j.buildenv.2006.10.005
20
Shen, S. and Yu, H. (2011), "Analysis of aggregate gradation and packing for easy estimation of hot-mix-asphalt voids in mineral aggregate", Journal of Materials in Civil Engineering, ASCE, Vol.23, No.5, pp.664-672. 10.1061/(ASCE)MT.1943-5533.0000224
21
Wu, B., Pei, Z., Luo, C., Xia, J., Chen, C. and Wang, M. (2023), "Review and evaluation of the prediction methods for voids in the mineral aggregate in asphalt mixtures", Journal of Materials in Civil Engineering, ASCE, Vol.35, No.3, pp.04022455. 10.1061/(ASCE)MT.1943-5533.0004561
22
Zhang, H. (2009), Asphalt mixture volume index and anti-rutting performance unified prediction model and its application, Master's thesis, School of Civil Engineering, Shandong University
Information
  • Publisher :Korean Geosythetics Society
  • Publisher(Ko) :한국지반신소재학회
  • Journal Title :Journal of the Korean Geosynthetics Society
  • Journal Title(Ko) :한국지반신소재학회 논문집
  • Volume : 23
  • No :1
  • Pages :17-25
  • Received Date : 2024-02-17
  • Revised Date : 2024-03-04
  • Accepted Date : 2024-03-05
  • DOI :https://doi.org/10.12814/jkgss.2024.23.1.017
Journal Informaiton Journal of the Korean Geosynthetics Society Journal of the Korean Geosynthetics Society
  • NRF
  • KOFST
  • KISTI Current Status
  • KISTI Cited-by
  • crosscheck
  • orcid
  • open access
  • ccl
Journal Informaiton Journal Informaiton - close