Machine Learning Models for Bedrock Condition Classification in Pavement Structure Evaluation: A Comparative Study
Author:
Funder
the National Natural Science Foundation of China
Urumqi Transportation Research Project
Shanxi Province Transportation Research Project
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10921-024-01048-x.pdf
Reference58 articles.
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2. Fathi, S., Mehravar, M., Rahman, M.: Development of FWD based hybrid back-analysis technique for railway track condition assessment. Transp. Geotech. 38, 100894 (2023). https://doi.org/10.1016/j.trgeo.2022.100894
3. Zheng, L., Hai-lin, Y., Wan-ping, W., Ping, C.: Dynamic stress and deformation of a layered road structure under vehicle traffic loads: experimental measurements and numerical calculations. Soil Dyn. Earthq. Eng. 39, 100–112 (2012). https://doi.org/10.1016/j.soildyn.2012.03.002
4. Park, B., Cho, S., Rahbar-Rastegar, R., Nantung, T.E., Haddock, J.E.: Prediction of critical responses in full-depth asphalt pavements using the falling weight deflectometer deflection basin parameters. Constr. Build. Mater. 318, 126019 (2022). https://doi.org/10.1016/j.conbuildmat.2021.126019
5. Al-Hunaidi, M.O.: Nondestructive evaluation of pavements using spectral analysis of surface waves in the frequency wave-number domain. J. Nondestruct. Eval. 15(2), 71–82 (1996). https://doi.org/10.1007/BF00729136
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