PREDICTION OF THE FATIGUE LIFE OF ASPHALT MIXTURES USING ARTIFICIAL NEURAL NETWORKS

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

TAHERKHANI H.; EBRAHIMI MOGHADDAM A.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Journal of Transportation Engineering Year:2012 | Volume:4 | Issue:1 (13) Page(s): 45-58

Download Full-Text

Persian Verion

View:

694

Download:

Cites:

Information Journal Paper

Title

PREDICTION OF THE FATIGUE LIFE OF ASPHALT MIXTURES USING ARTIFICIAL NEURAL NETWORKS

Author(s)

TAHERKHANI H. | EBRAHIMI MOGHADDAM A. | Issue Writer Certificate

Keywords

FATIGUE LIFEQ2

ASPHALTIC MIXTURESQ2

ARTIFICIAL NEURAL NETWORKQ2

Abstract

Prediction of the FATIGUE LIFE of asphaltic concrete is required for design and management of pavements.The prediction of the FATIGUE LIFE of asphaltic concrete using ARTIFICIAL NEURAL NETWORK has been investigated in this research. Sufficient experimental data containing fatigue test conducted on asphaltic concrete over a wide range of conditions was needed for modeling. Therefore, reviewing the literature, the results of the fatigue tests on Kansas State asphalt mixtures were found to be appropriate for this research. The parameters of asphalt viscosity, strain level, stiffness of mixture, asphalt content, air voids content and gradation were selected as input variables, while the FATIGUE LIFE was set as the only output variable. Progressive multilayer perceptron ARTIFICIAL NEURAL NETWORK modeling has been used for prediction of the FATIGUE LIFE, with back propagation training algorithm and numerical optimization technique of Levenberg-Marquardt. Modeling was performed using the neural network tool box and programming in MATLAB, and the results have been compared. It is shown that the programming, can better predict the FATIGUE LIFE than directly utilizing the tool box. It is also found that ARTIFICIAL NEURAL NETWORK can predict the FATIGUE LIFE of the asphaltic concrete than the regression equation.

Cites

No record.

References

No record.

Cite

APA: Copy

TAHERKHANI, H., & EBRAHIMI MOGHADDAM, A.. (2012). PREDICTION OF THE FATIGUE LIFE OF ASPHALT MIXTURES USING ARTIFICIAL NEURAL NETWORKS. JOURNAL OF TRANSPORTATION ENGINEERING, 4(1 (13)), 45-58. SID. https://sid.ir/paper/224039/en

Vancouver: Copy

TAHERKHANI H., EBRAHIMI MOGHADDAM A.. PREDICTION OF THE FATIGUE LIFE OF ASPHALT MIXTURES USING ARTIFICIAL NEURAL NETWORKS. JOURNAL OF TRANSPORTATION ENGINEERING[Internet]. 2012;4(1 (13)):45-58. Available from: https://sid.ir/paper/224039/en

IEEE: Copy

H. TAHERKHANI, and A. EBRAHIMI MOGHADDAM, “PREDICTION OF THE FATIGUE LIFE OF ASPHALT MIXTURES USING ARTIFICIAL NEURAL NETWORKS,” JOURNAL OF TRANSPORTATION ENGINEERING, vol. 4, no. 1 (13), pp. 45–58, 2012, [Online]. Available: https://sid.ir/paper/224039/en

Related Journal Papers