Application of machine learning techniques to predict the temperature distribution in semi-rigid pavement with a cement-treated base
Author:
Funder
Đại học Đà Nẵng
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s41062-024-01363-2.pdf
Reference31 articles.
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2. Adwan I, Milad A, Memon ZA, Widyatmoko I, Zanuri NA, Memon NA, Yusoff NIM (2021) Asphalt pavement temperature prediction models: a review. Appl Sci (Switzerland). https://doi.org/10.3390/app11093794
3. Asefzadeh A, Hashemian L, Bayat A (2017) Development of statistical temperature prediction models for a test road in Edmonton, Alberta, Canada. Int J Pav Res Technol 10(5):369–382. https://doi.org/10.1016/j.ijprt.2017.05.004
4. Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1007/978-3-030-62008-0_35
5. Dietterich TG (2000) An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Mach Learn 40:139–157
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