Complementary Modeling of Gravel Road Traffic-Generated Dust Levels Using Bayesian Regularization Feedforward Neural Networks and Binary Probit Regression-Reference-Cited by-同舟云学术

Complementary Modeling of Gravel Road Traffic-Generated Dust Levels Using Bayesian Regularization Feedforward Neural Networks and Binary Probit Regression

Published:2020-02-12 Issue:3 Volume:13 Page:255-262
ISSN:1996-6814
Container-title:International Journal of Pavement Research and Technology
language:en
Short-container-title:Int. J. Pavement Res. Technol.

Author:

Albatayneh Omar,Moomen Milhan,Farid Ahmed,Ksaibati Khaled

Publisher

Springer Science and Business Media LLC

Subject

Mechanics of Materials,Civil and Structural Engineering

Link

http://link.springer.com/content/pdf/10.1007/s42947-020-0261-3.pdf

Reference41 articles.

1. O. Albatayneh, L. Forslöf, K. Ksaibati, Developing and validating an image processing algorithm for evaluating gravel road dust, Inter. J. Pavement Res. Technol. 12 (3) (2019) 288–296.

2. T. Henning, G. J. Giummarra, D. C. Roux, The development of gravel deterioration models for adoption in a New Zealand gravel road management system, Wellington, N.Z.: Land Transport New Zealand, No. 332, 2008, p. 1–96.

3. K. Linard, A system dynamics modelling approach to gravel road maintenance management, In ARRB Conference, ARRB Group Ltd. Melbourne, Australia, 2010.

4. K. T. Linard, Application of System Dynamics to Unsealed Pavement Maintenance, In International Conference on Systems Thinking in Management, Melbourne, Australia, 2008.

5. Federal Highway Administration (FHWA), Highway Statistics 2012, Washington, DC, USA 2012.

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