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Indian Journal of Engineering and Materials Sciences (IJEMS) >
IJEMS Vol.17 [2010] >
IJEMS Vol.17(6) [December 2010] >
| Title: | Neural network model for temperature sensitivity of emulsified asphalt mixtures |
| Authors: | Oruc, Seref |
| Keywords: | Emulsified asphalt mixture
Temperature sensitivity
Neural networks
Resilient modulus
Regression analysis |
| Issue Date: | Dec-2010 |
| Publisher: | NISCAIR-CSIR, India |
| Abstract: | This study intends to investigate temperature sensitivity of emulsified
asphalt mixtures (cold-mix) and to establish a methodology that would provide
an economic and rapid means for future experimental researches. Temperature
sensitivity of the mixtures is investigated for 0ºC, 5ºC, 15ºC, 25ºC and 40ºC. The samples
are prepared for three residual asphalt contents (4.2%, 5.2% and 6.2%).
Portland cement is substituted for mineral filler in different ratios
from 1% to 6%. A neural network (NN) model is developed for predicting, with
sufficient approximation, relationship between the factors affecting resilient
modulus (inputs; temperature, cement and asphalt content) and the resilient
modulus (output) of emulsified asphalt mixture. A backpropagation neural
network of three layers is employed. First resilient modulus data is obtained
by conducting laboratory resilient modulus tests on emulsified asphalt samples,
and then the results are used to train the neural network. The effectiveness of
different neural network configurations is investigated. Effect of parameters
such as temperature, cement addition level and residual asphalt content that
influence the resilient modulus is explored. The prediction capability of the
NN model is also compared to the traditional regression approach. Results
indicate that NN predicts the resilient modulus with high accuracy. It is also
demonstrated that NN is an excellent method that can reduce time consumed and
can be used as an important tool in evaluating the factors affecting resilient
modulus of the mixtures for the design process. |
| Page(s): | 438-448 |
| CC License: |  CC Attribution-Noncommercial-No Derivative Works 2.5 India |
| ISSN: | 0975-1017 (Online); 0971-4588 (Print) |
| Source: | IJEMS Vol.17(6) [December 2010]
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