Optimizing Marshall Test Parameters on Asphalt Concrete Using Hybrid Neural Network - Genetic Algorithm Approach

Optimizing Marshall Test Parameters on Asphalt

Concrete Using Hybrid Neural Network - Genetic

Algorithm Approach

Achmad Baroqah Pohan

English Department ABA BSI Jakarta, Indonesia [email protected]

Tati Mardiana

Informatics Management Department AMIK BSI

Bandung, Indonesia [email protected]

Nining Suryani

AMIK BSI Karawang, Indonesia

[email protected]

Hilda Amalia

Informatics Management Department AMIK BSI

Jakarta, Indonesia [email protected]

Yunita

Informatics Management Department AMIK BSI Jakarta, Indonesia [email protected]

Umi Fadillah

Rachmat Adi Purnama

AMIK BSI Tegal, Indonesia [email protected]

Frengki Pernando

STMIK Nusa Mandiri Jakarta, Indonesia [email protected]

Abstract — The design of the street should be applied the knowledge of the engineer principles for the density of traffic flow and rapidity in order to reduce the accident. A dilapidated mix-aggregate estimation will cause the reducing the street’s quality. Marshall test is technique to test and discover out the level aggregate in mix-construction of asphalt. Both Marshall Stability and Marshall Flow are resulting of the tested to discover how maximum of load will be used by the asphalt. However, it needs a guarantee by the accuracy of the values test of marshall with computing method such as Neural Network. This means to solve the issue of accuracy toward some various data’s and it is not linear. An optimization Artificial Neural Network tested to produce the exact values, to apply the Genetic Algorithm. It purposes to rise the exact being generated by Artificial Neural Network. This experiment has been done to get the optimization of the architecture and to produce the exact more high. The best model can be standardized as initialization stages of design software application based mobile application system.

Keywords: Marshall Stability, Computing Method, Asphalt Concrete

I. INTRODUCTION

Nowadays, the number of traffic light accident has increase rapidly. This has been international concern togetherness. The United Nation has given the data which every year around 1, 3 people or almost three thousand

people per day died due to accident on the street. It is about 90% the death caused by the accident. It is occurring in development countries by the range of five to forty-four years old. If there is not effective effort to press down of its accident, so the death caused by accident will fifth place of accident in the world. The estimation of accident is about 2, 4 million of the death every year [1]

Indonesia is one of development country also having this issue by the manner of death caused by traffic light accident. Based on the Coordinator Police of Traffic Light in 2011, the number of death of traffic light accident reached 31.185 souls with loss material goes to 286 billion. Along 2012 occurred 109.038 cases with 27.441 people died. However, it will bring the disadvantages for social and economy sectors about 203 to 217 billion rupiahs per year. Meanwhile, the accident in 2013 happened 93.578 cases of traffic light accident with 23.385 people death and 234 billion rupiahs for the loss materials [2].

Generally, the traffic light accident happened because of various factors such as human error, careless driving, less optimal of traffic light officer, and bad condition of the street due to less accurate of the system construction of the street. If the street in good condition can reduce the number of the traffic light accident in Indonesia. With the result that, it needs a good measure to build the accuracy power and the period of the street can be acquired quickly and correctly.

This means increasing and pleasuring is priority target to be applied for traffic users.

The goal of the designing of the street is to apply the knowledge of engineer principles for the density of traffic light and rapidity in minimalize of the accident probability of security context, cost, comfortable, economy parameter, and environment[3]. The damage of the street, of course, rather different compared to damage of civil building such as the bridge. The broken street causes of the load continuously. It means if the load of heavy vehicles cross once away possibility will not broke the street. On the contrary, if this happen keeps continue with same content-loaded, then that street might tiredness also. The tiredness of the street such as cracks has been caused by traffic light recurring which is one of the general modus the failure roughness of asphalt on field [4]. This happens because the quality of the street is less. This less is caused how bad mix-aggregate asphalt. In building a new street either increasing or maintaining in Indonesia most of street is using mix-concrete asphalt [5]. Although, most the street in Indonesia always having damage before reaching its period. This condition can be affected by several factors appear such as processing, quality of the materials, the heavy-load traffic light, and environment area [6].

Based on Ozgan [7] Marshall Method of specified of mix-concrete warm asphalt tends to rational approach to choose and proportion two materials, the cement of asphalt and the mineral aggregate. These are used to get certain property decided of the form and structure the asphalt concrete needed. Marshall test is meant to decide an endurance or stability toward flow melt of the mix-asphalt. Marshall design procedure aims to get an optimum level of the asphalt. In guaranteed the level accuracy predicted of the Marshall test nowadays have many researches done by using any computing method with any data of the aggregate mix-concrete asphalt. For the researches can be sum up that the best individual method know as Artificial Neural Network (ANN) to predict the estimation of Marshall Stability, Marshall Flow, and Marshall Quotient [8]. ANN prediction has been less optimal, it would be accurate if the parameter such as the number of hidden layer and learning rate are working well and correct. In order to figure out its efficiency parameter required Genetic Algorithm. This has main purpose to be able to find out the best accuracy percentage by ANN model with maximum parameter [9].

Based on the information above, the research has purpose to figure out an appropriate model of ANN-GA to get the new algorithm in Marshall Testing on concrete-asphalt. So, it can be implemented inside the application mobile basis through every single step to develop software. This performs for reaching quality of the application plus it can used properly of its functions.

II. METODE & MATERIAL A. Asphalt Concrete

Asphalt concrete is mix-concrete consisting of rugged aggregate, soft aggregate, filler, and certain proportion asphalt. These layers must be waterproof, structural value, and well preserved. The layer asphalt concrete divided into 3 kind of combination and it based on the function [5]:

1) Asphalt Concrete-Wearing Course, AC-WC 2) Asphalt Concrete-Binder Course, AC-BC 3) Asphalt Concrete-base, AC-Base

Asphalt Concrete-Wearing Course, AC-WC is a kind of layer which have direct interaction by the loaded and environment area, so it needs planning and designing of the asphalt concrete AC-WC based on the specification. This layer will turn to waterproof, hold out the weather, and high stability.

B. Marshall Test

Marshall Test method is a general method used to standardized in American Society and Material 1997 [10]. In this method has three mainly parameters, there are maximum loaded that can be lift before its crash known as Marshall stability and permanent deformation of before its crash know as Marshall flow with its descendant compared between both of them known as Marshall Quotient (MQ). This means as the power of value to increase (Speedo Stiffness) shown endurances of mix asphalt concrete toward permanent deformation.

C. Research design

The research uses experinmental research. It means involving observation of treatment on the paramather or variable depend on its research and using the test reined by its own researcher. The researcher make two steps as method of research on figure 1

.

Fig. 1.

D. Collecting Data

The data is used in this research take of the dataset of Marshall Test of mix asphalt concrete. The kind of data collected as a follow:

1) Primary Data

This is a data found directly from the source, such as dataset of Marshall Test of mix asphalt concrete by input model. The data found to show the produce of the fickle such as unit, minimal, maximal, mean, Varian and Deviation Standard of input variable toward class variable.

2) Secondary Data

This is a data found indirectly, such as documentation, literature surf, books, journals, and any information regarding to this research.

E. Initial Data Processing

Initial processing of the data include data cleansing, transforming the data into the required shape and grouping and determination of data attributes. This is a follow-up to data collection, by normalizing data. Data normalization is carried out according to the activation function used, in this study the binary sigmoid function is used, the data must be normalized in the range 0 to 1 [12]. Furthermore, the data do transform existing synoptic data by the interval [0.1,0.9], with the following formula, as in :

= . ( )+ 0.1 (1) Explanation : = Transform value X = Initial value a = Minimum value b = Maximum value F. Method of Recommended

This recommendation goes to Applied Genetic Algorithm. It optimizes the parameter for Neural Network. This is also to estimate the produce of Marshall Asphalt concrete. By divided dataset and 10 cross validation method there are training data, then processed used Genetic Algorithm and Neural Network. Thus, it produces Evaluation method that measured by 10 folds x-validations and ROC curve. It can be seen on figure 2

.

Fig. 2

Method of recommended G. Evaluation and Testing

The evaluation of formed model can be done by accurately measuring and area under curve. An accuracy measure is used confusion matrix, and AUC value will be measured by ROC Curve. The Confusion matrix will describe the producing accuracy begin with correct positive prediction, a false-positive prediction, correct negative-prediction, and false-negative prediction. These models formed and can be tested directly with the data randomly separated by 10 folds cross validation. With confusion matrix, the value accuracy of the model will compare between formed model with neural network algorithm and neural network algorithm had optimized. After the quality of the model produced, it can be seen ROC curve had made and AUC value as measurable of the model formed

.

H. Implementation Model and Design Software

In this study, the design of software that defines the model that has been found. Software must be designed according to user requirements and functional in presenting the results predicted marshall stability in the form of mobile-based applications using AppInventor

III. FINDING AND RESULT A. Data normalization and Process

There is more data set and complex of the research previously taken from data set testing of Marshall of Indonesia Ministry of Public Work which is consist of 225 recording. Input and output data are used to qualified. This qualification is to count Marshall Test to get an output of Marshall Stability. This is the following figure 3:

Fig. 3

Marshall Test Parameters

Before that, the premier data should be normalisazed once to purpose the processing training can work rapidly and able to be applied on the research. The similiraty as a follow:

= . ( )+ 0.1 Note : = Transform value X = Initial value a = Minimum value b = Maximum value

Based on the sample randomly of the output or class of Marshall Stability (VSS) as a follow :

Notification:

Minimum value (a) = 405.500 Maximum value (b) = 1254.770 The data shall transform (x) =823.67

Fig. 4

Transform Value B. Algorithm Neural Network

The making of neural network model will be performed at Marshal Test data set. There are sixteen attributes of Marshall Stability by class 1 or 0 (standard or non-standard). It taken one of the experiment for Marshall Stability by using Learning Rate 0,3 and 0,2 as momentum consist of sixteen knots, there are 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15, and 16. These are known as predictor attribute. The following are the stepping in calculating of Neural Network (Backpropagation):

Fig. 5

Neural Network Architecture

1) Initialize the random initial weights

Fig. 6 Initial Weight Value

Tabel 1

Table of First Refraction Value

1.212 2.236 -5.459 5.459 2) Counting Input, Output, Error

=( * ) + ( * ) + ( * ) + ( * )+ ( * )+ ( * )+ ( * )+ ( * )+ ( * )+ ( * )+ ( * )+ ( * )+ ( * )+ ( * )+ ( * )+ ( * )+ =(-0.0698) + (-0.1047) + (-6.62831) + 1.15584+0.91044+0.0902)+ 1.3211) + (-1.21304) + 0.3755 + (-0.61754) +0.03795 +0.18262 + 3.01962 + 0.46197 + 8.2676 + 0.14484 + (1.212) =5.72369 = 1/(1 + . _{) = 0.003257} = 4.38547 = 1/(1 + . ) = 0.012304 = ( * ) + ( * ) + = (0.003257*10.602) +

(0.012304 * 7.679) + (-5.459) = - 5.33002 = 1/(1 + . _{) = 0.995179} = 5.32999 = 1/(1 + . ) = 0.004821 = *(1- ) * (Outputtarget - ) = 0.995179 * (1 - 0.995179) * ( 1 - 0.995179) = 0.00002313 = 0.00477463 = *(1- )* * = 0.0000079 Fig. 7

Neural Network Model Views on Rapidminer C. Optimization of genetic Algorithm

With Learning rate of 0.3, Momentum 0.2, Training cycles of 500, and Hidden Layer as much as 2 layers on Neural Network. And in the Genetic Algorithm included value P_Mutation -1.0, P_Initialize 0.5 and P_Acrossover 0.5. existing ANN + GA model run. The resulting accuracy is 97.37% and the AUC value is 0.975.

Fig. 8

ANN Optimization Model with GA on Rapid Miner

D.

Testing Result by Confusion Matrix

It is formed of matrix consist of True Positive or Tupel Positive and True Negative or Tupel Negative. At Table 2, the accuracy NN has 93,83% whereas the total of True Positive has 64, True False has 147, False Positive has 8 and True Negative has 6.

Tabel 2

Result of Confusion Matrix NN

True Standar True non-standard Class Precision Pred Standar 147 8 94.84% Pred non-standard 6 64 91.43% Class recall 96.08% 88.89% - Accuracy 93.83%

Next, the optimazed is used Genetic Alghorithm and can be seen the increasing accuracy has 97,3% at table 3. The table shows the TP has 70, FN has 149, FP has 2, and TN has 4 with the total of hidden layer has 8.

Tabel 3

Result of Confusion Matrix NN+GA

True Standar True non-standard Class Precision Pred Standar 149 2 98.68% Pred non-standard 4 70 94.59% Class recall 97.39% 97.22% - Accuracy 97.37%

E. Evaluation ROC Curve

The figure 9 shows the graphs of ROC with value AUC (Area Under Curve) has 0.975 and the level of diagnose Excellent Classification

Fig. 9

The Value of AUC Neural Network in ROC Curve

After had been optimazed now there is appearing the increasing as can be seen at the figure 10. This shows the graphs of ROC and the value of AUC has 0,992 by the level of diagnose Excellent Classification

.

Figure 10

Value AUC Neural Network in ROC Curve

F. Implementation Model and Design Software

Software Design in this study based on the best model that has been formed. Software Design in this research based on Stage Capability Maturity Model Integration (CMMI) which apply best computation model from ANN-GA into mobile application.

Figure 11 ANN-GA Model

In designing mobile apps to implement the ANN-GA model using AppInventor . The following software design to implement the ANN-GA model results have been found.

Figure 12 Design Software

IV. CONCLUSION

This research has been make a model testing by using neural network and optimazed with genetic alghorithm. This is to predict the result of Marshal test at the stability asphalt-concrete aggregate. Some experiments made to get the best result. The result of this research proves individual utilizing of Neural Network Method bringing the accuracy of 93,83%. In the other side the utilizing Hybrid of Neural Network method and Genetic Alghorithm are proved to increase the accuracy of 97,37%. Therefore the value of AUC NN has

0,975 raised to be 0,992 if neural network optimazed used genetic alghorithm. The following are some benefits can be taken of this research:

1. This simplify research can be applied and designed at Dirjen Bina Marga as a new option developing the circumstance of the street in making of National Medium Term Development Plan (RPJMN).

2. The benefit of policy of this research can be used as matter of considering in making of decision to how strong the street is.

3. The benefit of theoretic hopes this research is able to give contribution of thoughts for developing theoretic that related to the combination between Genetic Algorithm Method and Artificial Neural Network. 4. The benefit of this research for people is giving simplify

to the Staff of Bina Marga in predicting the street strengthens by MARSMELO application without any limitation time and space

.

V. REFERENCE

[1] WHO, Global Plan for the Decade of Action for Road Safety 2011-2020. Geneva, 2011

[2] Pudji.Hartanto, “Jadilah Pelopor Keselamatan Berlalu lintas dan Budayakan Keselamatan sebagai Kebutuhan “,Korlantas Mabes Polri, 2012.

[3] QDTMR “Road planning and design manual, design philosophy” Queensland Department of Transport and Main Roads,(QDtMR), Chapter 2. DOI=http://www.tmr.qld.gov.au/Business-and- industry/Technical-standards-and-publications/Road-planning-and-design-manual.aspx. Retrieved November 1,2010

[4] Reza and Mansour.Fakhri “Prediction of frequency for simulation of asphalt mix fatique test Using MARS and ANN” Department of civil Engineering, Toosi Universitas Of Technology, Iran, 2014.

[5] S.Sukirman, “Beton Aspal Campuran Panas”, Granit, Bandung, 2003.

[6] AASHTO,“Guide for design of pavement structure”, Washington DC, USA,1993.

[7] Ozgan.Ercan, “Fuzzy logic and statistical-based modeling of the Marshall Stability of asphalt concrete under varying temparatures and exposure times”, Duzce University, Turkey, 2009.

[8] Tapkin, Sercan, Abdulkadir.Cevik and Un.Usa, “Prediction of Marshall test results for polypropylene modified dense bituminous mixtures using neural networks”, Expert System with application 37, Elsevier , Turkey, 2010.

[9] Whitcombe, J.M., Cropp, R.A., Braddock, R.D., Agranovski, I.E., ”The use of sensitivity analysis and genetic algorithms for the management of catalystemissions from oil refi neries” Math. Comput. Model. 4 4, 430 e 438, 2006.

[10] ASTM, “Road and paving materials vehicle – pavement systems”, published by the American society of testing material officials, Washington DC, 1997.

[11] Heaton, ”Introduction to Neural Network With java” Second Edition, Heaton Research.Inc, USA,2008.

[12] Jong, Jek Siang (2009), “ Jaringan Syaraf Tiruan dan Pemrogramannya menggunakan MATLAB”, Penerbit Andi, Yogjakarta