Acknowledgement
This work was supported by the Technology development Program(RS-2023-00256583) funded by the Ministry of SMEs and Startups(MSS, Korea)
References
- 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.
- 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.
- 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.
- Cortes, C. and Vapnik, V. (1995), "Support-vector networks", Machine Learning, Vol.20, pp.273-297. https://doi.org/10.1007/BF00994018
- Foreman, J. K. (2008), Effect of voids in the mineral aggregate on laboratory rutting behavior of asphalt mixtures, University of Arkansas.
- 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. https://doi.org/10.1006/jcss.1997.1504
- Friedman, J. H. (2001), "Greedy function approximation: a gradient boosting machine", Annals of Statistics, pp.1189-1232.
- Goodfellow, I., Bengio, Y. and Courville, A. (2016), Deep learning. MIT press.
- Ho, T. K. (1995), "Random decision forests", In Proceedings of 3rd International Conference on Document Analysis and Recognition, Vol.1, pp.278-282.
- 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.
- 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.
- 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.
- 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.
- 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.
- Lavin, P. (2003), Asphalt pavements: a practical guide to design, production and maintenance for engineers and architects, CRC Press.
- 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.
- 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. https://doi.org/10.1177/14759217211036880
- Opitz, D. and Maclin, R. (1999), "Popular ensemble methods: An empirical study", Journal of Artificial Intelligence Research, Vol.11, pp.169-198. https://doi.org/10.1613/jair.614
- 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. https://doi.org/10.1016/j.buildenv.2006.10.005
- 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. https://doi.org/10.1061/(ASCE)MT.1943-5533.0000224
- 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.
- Zhang, H. (2009), Asphalt mixture volume index and antirutting performance unified prediction model and its application, Master's thesis, School of Civil Engineering, Shandong University