CBR Prediction of Pavement Materials in Unsoaked Condition Using LSSVM, LSTM-RNN, and ANN Approaches-Reference-Cited by-同舟云学术

CBR Prediction of Pavement Materials in Unsoaked Condition Using LSSVM, LSTM-RNN, and ANN Approaches

Published:2023-02-13 Issue: Volume: Page:
ISSN:1996-6814
Container-title:International Journal of Pavement Research and Technology
language:en
Short-container-title:Int. J. Pavement Res. Technol.

Author:

Khatti Jitendra^ORCID,Grover Kamaldeep Singh^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Mechanics of Materials,Civil and Structural Engineering

Link

https://link.springer.com/content/pdf/10.1007/s42947-022-00268-6.pdf

Reference96 articles.

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2. Harini, H., & Naagesh, S. (2014). Predicting CBR of fine grained soils by artificial neural network and multiple linear regression. International Journal of Civil Engineering, 5(2), 119–126.

3. Agarwal, K.B., & Ghanekar, K.D. (1970). Prediction of CBR from plasticity characteristics of soil. In: Proceeding of 2nd south-east Asian conference on soil engineering, Singapore (pp. 571–576).

4. Karunaprema, K. (2002). A laboratory study to establish some useful relationships for the use of dynamic cone penetrometer. Electronic Journal of Geotechnical Engineering, 7, 17.

5. Kumar, P. P., Pasricha, R., Sani, R. P., Bharadwaj, A. K., Chadha, R., & Rao, P. S. K. M. (2000). An indigenous impact tester for measuring in situ CBR of pavement materials. Highway Research Bulletin, IRC, 63, 13–22.

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