EFFECTS OF RUBBER AND BINDER RATIO ON MARSHALL STABILITY OF
RUBBERIZED ASPHALT USING ARTIFICIAL NEURAL NETWORK
1.1 BACKGROUND OF STUDY
Improving the performance of bitumen in order to increase the service life of bituminous
mixture additives has been the focus of researchers and engineers due to the poor performance
and premature failure of some bituminous mixtures. The usage of bitumen admixtures is the idea
behind the usage of scrap tires – with the same aim in application for highway pavement. Rubber
and asphalt materials were extensively studied by Charles H. McDonald in the mid-1960s that
lead him in producing rubberized asphalt. Rubberized Asphalt is used as a pavement material
made up from conventional asphalt concrete mixed with crumb rubber obtained from recycled
tires. Rubbers are modified in the asphalt mixture through wet or dry process.
Introducing scrap tires into asphalt concrete has the potential to solve the waste problem
and has effectively improved the properties of asphalt. Stability and flow of asphalt concrete
determines the performance of the highway pavement and can be obtained through Marshall Test
Method. Marshall stability of the mix is defined as the maximum load carried by the specimen at
a standard temperature of 60oC. On the other hand, flow value is the deformation that the test
specimen undergoes loading up to the maximum load. Low stability in asphalt concrete may lead
to various types of distress in asphalt pavements. These distresses, such as permanent
deformation, low- temperature cracking and bleeding, reduce the lifespan of asphalt pavements.
Asphalt is known for its complex behavior. Different ratios of its components and other
factors such as loadings and temperatures could greatly affect its properties. The various ratios
and amounts of these parameters leads to improving a certain property but worsen another
property. For other researchers to attain the suitable job mix formula for asphalt, number of
GROUP 2 : ASCAÑO, BREIVA, MARINAY, SARMIENTO
APRIL 24, 2017
EFFECTS OF RUBBER AND BINDER RATIO ON MARSHALL STABILITY OF
RUBBERIZED ASPHALT USING ARTIFICIAL NEURAL NETWORK
experiments are necessary which significantly increases the experimental cost and time. To
efficiently yield the best job mix formula without conducting future experiments, models are
needed.
Being able to predict the stability and flow of the modified asphalt, the use of rubberized
asphalt in pavement roads of our country will possibly be implemented. Neural networks are one
approach for efficiently solving complex problems. The researchers aim to develop models to
predict the said properties of the rubber modified asphalt mixture with the help of an artificial
neural network (ANN). Artificial neural network is an adaptive model-free estimator that has
been successfully applied to different areas of civil engineering.
In this study, set of data from conducting the Marshall Stability test is needed to enable
the researches to yield an input-output relationship. From the data gathered, 85% from it will be
used as training samples and the other 15% as test samples. The researchers will train the ANN
models’ set of data repeatedly to improve its performance and reduce errors. The use of ANN
will help determine the best job mix formula of rubberized asphalt so that it will not be prone to
distresses such as rutting and cracking. Knowing the optimum tire rubber ratio, binder ratio and
mixing temperature of rubber and binder, the lifespan of asphalt can be extended. These data
from ANN analysis will be later tested on MATLAB and help future researchers such as DPWH
and other private sectors.
GROUP 2 : ASCAÑO, BREIVA, MARINAY, SARMIENTO
APRIL 24, 2017
EFFECTS OF RUBBER AND BINDER RATIO ON MARSHALL STABILITY OF
RUBBERIZED ASPHALT USING ARTIFICIAL NEURAL NETWORK
1.2 SIGNIFICANCE OF STUDY
SOCIETY
ENGINEERING
Due to the complex behaviour of asphalt rubber materials under various loading
conditions, pavement structure, and mixing conditions, accurately predicting stability and flow of
modified-asphalt pavement is difficult. Through this study, ANN will calculate the stability and
flow of asphalt rubber considering the rubber content, bitumen content and mixing condition of
rubber and bitumen.
HIGHWAY SECTORS/LGU/DPWH
Government and non-local government will be benefited through minimizing their
proposal regarding with the stability and flow performance of their designed mixture. It also
gives more factual percentage of the modifier to be added in the binder resulting to more definite
proportioning of the component of the modified asphalt. Through this study, proportioning of
rubber-asphalt mixture will be optimized thus, it significantly decreases the experimental cost
and time.
GROUP 2 : ASCAÑO, BREIVA, MARINAY, SARMIENTO
APRIL 24, 2017
EFFECTS OF RUBBER AND BINDER RATIO ON MARSHALL STABILITY OF
RUBBERIZED ASPHALT USING ARTIFICIAL NEURAL NETWORK
1.3 OBJECTIVES
The primary aim of this paper is to formulate an equation using Artificial Neural Network
(ANN) that will be used to predict Marshall stability of rubberized- asphalt. To complete the
final base product of the project, the sub-objectives are listed as follows:
•
To derive a model that could predict stability and flow of rubber asphalt specimen
•
To test the accuracy of the model using Pearson R value.
•
To determine the effect of each parameter using parametric test.
1.4 SCOPE AND LIMITATIONS
•
Rubber contents from 0 to 15% by weight of binder will be used in the study.
•
Binder contents from 4.5 to 6.5% (by total weight of specimen tama ba?)
•
Mixing temperature
•
Ambient method will be used in making a crumbed rubber tire. (pero kung bibilin nating
yung rubber di dapat natin lagay ‘to pati kung historical data kasi dipende sa inispecify
nila)
•
This study limits the number of test samples to 40 samples.
• This study does not cover any type of rubber other than scrap tire rubber
GROUP 2 : ASCAÑO, BREIVA, MARINAY, SARMIENTO
APRIL 24, 2017
EFFECTS OF RUBBER AND BINDER RATIO ON MARSHALL STABILITY OF
RUBBERIZED ASPHALT USING ARTIFICIAL NEURAL NETWORK
1.5 CONCEPTUAL FRAMEWORK
INPUT
PROCESS
OUTPUT
PARAMETERS
•
•
•
Rubber %
Binder %
Mixing
Temperature
ANN MODELLING
• ANN MODEL
• PEARSON R
• PARAMETRIC
TEST
IMPORT DATA
For the input parameters amount of rubber and binder, and mixing time are specified. The
researchers can obtain these data through conducting laboratory experiments. After gathering the
required data, it will be encoded and analyzed with the help of MATLAB. The produced result of
the program pertains to the weights and biases that will be used to derive a formula for predicting
the effects of different rate of scrap tires on marshall stability and flow of rubberized asphalt.
GROUP 2 : ASCAÑO, BREIVA, MARINAY, SARMIENTO
APRIL 24, 2017