NISCAIR Online Periodicals Repository

Research Journals >
Indian Journal of Engineering and Materials Sciences (IJEMS) >
IJEMS Vol.16 [2009] >
IJEMS Vol.16(4) [August 2009] >

Title: Artificial neural networks for predicting low temperature performances of modified asphalt mixtures
Authors: Tasdemir, Yuksel
Keywords: Artificial neural network
General linear model
Thermal stress restrained specimen test
Fracture temperature
Fracture strength
Issue Date: Aug-2009
Publisher: CSIR
Abstract: In this study, the estimation of the low temperature performance of modified asphalt mixtures is investigated by using multi-layer perceptrons (MLP) which is one of the artificial neural networks (ANNs) techniques and general linear model (GLM). The fastest MLP training algorithm, that is the Levenberg-Marquardt algorithm, is used for optimization of the network weights. The ANN test results are compared to GLM results. GLM has, historically, been used to model the low temperature performance (fracture temperature and fracture strength) of asphalt pavements. The data used in the ANN model and GLM are arranged in a format of four input parameters that cover additive type, asphalt binder content, aging level and air void content, and output parameters which are the fracture temperature and the fracture strength. Based on the comparisons, it is found that the ANN generally gives better fracture temperature and fracture strength estimates than the GLM technique.
Page(s): 237-244
ISSN: 0975-1017 (Online); 0971-4588 (Print)
Source:IJEMS Vol.16(4) [August 2009]

Files in This Item:

File Description SizeFormat
IJEMS 16(4) 237-244.pdf251.75 kBAdobe PDFView/Open
 Current Page Visits: 85 
Recommend this item


Online Submission of Articles |  NISCAIR Website |  National Knowledge Resources Consortium |  Contact us |  Feedback

Disclaimer: NISCAIR assumes no responsibility for the statements and opinions advanced by contributors. The editorial staff in its work of examining papers received for publication is helped, in an honorary capacity, by many distinguished engineers and scientists.

CC License Except where otherwise noted, the Articles on this site are licensed under Creative Commons License: CC Attribution-Noncommercial-No Derivative Works 2.5 India

Copyright © 2015 The Council of Scientific and Industrial Research, New Delhi. All rights reserved.

Powered by DSpace Copyright © 2002-2007 MIT and Hewlett-Packard | Compliant to OAI-PMH V 2.0

Home Page Total Visits: 170565 since 01-Sep-2015  Last updated on 30-Jun-2016Webmaster: