International Journal of Civil Engineering and Technology (IJCIET)
Volume 10, Issue 04, April 2019, pp. 1674–1681, Article ID: IJCIET_10_04_175
Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJCIET&VType=10&IType=4
ISSN Print: 0976-6308 and ISSN Online: 0976-6316
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ROAD DAMAGE ASSESMENT FOR FLEXIBLE
PAVEMENT USING DIGITAL IMAGE
PROCESSING
Ida Ayu Ari Angreni
Doctoral Course Student of Civil Engineerin Department
Hasanuddin University, Jl. Poros Malino Km. 6, Gowa, Indonesia
Sakti Adji Adisasmita
Professor,Civil Engineering Department,
Hasanuddin University, Jl. Poros Malino Km. 6, Gowa, Indonesia
M. Isran Ramli
Associate Professor, Civil Engineering Department
Hasanuddin University, Jl. Poros Malino Km. 6, Gowa, Indonesia
Sumarni Hamid
Associate Professor, Civil Engineering Department
Hasanuddin University, Jl. Poros Malino Km. 6, Gowa, Indonesia.
ABSTRACT
The assesment of the road pavement condition using mechanical tools collided
with the problem of funds, because the price of these tools is quite expensive and for
one type of equipment only measures one particular condition, the method of visual
inspection is a good solution, because it is sufficient practical, simple and efficient,
but there are still weaknesses in the visual assessment of road damage. The visual
assessment method is very subjective, depending on the assessor. Considering the
existence of weaknesses in the method of visually evaluating road damage, it is
necessary to make an algorithm or method to detect and calculate the value of damage
to the road quickly and precisely. This study aims calculating the value of road
damage with the Dirgolaksono and Mochtar visual methods (D & M method), to make
a model of road damage based assessment algorithms based on digital imagery, and
to apply digital image methods to the road sections reviewed. The initial step of the
algorithm process is taking pictures with a type of digital camera, so that digital
images are generated and then processed using Matlab R2016a. It resulted a
classification of road damage and the value of damage from the road section. The
results obtained are visually damaged road values and road damage values in digital
imagery, getting values that are almost the same for the same road segment. It is
proven that there is a strong correlation between the value of damage visually with the
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Road Damage Assesment for Flexible Pavement Using Digital Image Processing
value of damage digitally or it can be said there is no difference between the value of
damage visually with the value of damage in digital image
Key words: Road Damage, Value of road damage, Visual Inspection, Digital Image.
Cite this Article: Ida Ayu Ari Angreni, Sakti Adji Adisasmita, M. Isran Ramli,
Sumarni Hamid, Road Damage Assesment for Flexible Pavement Using Digital Image
Processing, International Journal of Civil Engineering and Technology 10(4), 2019,
pp. 1674–1681.
http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=10&IType=4
1. INTRODUCTION
The road functions as the main sector in the development of activities in other sectors
(agriculture, industry, trade, education, health, tourism, etc.). Physical conditions condition of
roads are the main thing which needs to be considered [1]. Road damage is a common
problem. Many roads in big cities are damaged or in the process of being damaged. This
condition has been an issue for almost every major city in Indonesia. Roads with minor
damage often do not get attention so that the damage gets worse and results in reducing the
capacity of roads. Thus, a particular method is needed to detect the damage of the road
before it becomes severe. These efforts can be carried
The pavement conditions of a road can be detected through functional analysis and
structural analysis [2]. Functional analysis is carried out by examining road conditions in two
ways, mechanical and visual, which results in disruption to road users' safety and comfort so
the vehicle operating costs increased. Structural analysis includes pavement failure or damage
from one or more pavement components which causes the pavement can no longer bear the
traffic load [3].
The assesment of the road pavement using a mechanical tool is collided with the problem
of funds, because the price of these tools is quite expensive and for one type of equipment
only measures one particular condition, such as bending, surface hardness and others. The
visual inspection method is one of good planning, because it is quite practical, simple and
efficient.
There are several methods of assessing the level of visual damages that are often used so
far, one of which is the method of Dirgolaksono and Mochtar (1990) or what is called the D
& M method [4]. This method is a refinement of the existing methods of road damage
assessment. The improvement is that the D & M method divides roads in segments so that the
assessment becomes more precise. The level of road damage is grouped into three levels,
namely light, moderate, and severe according to the parameters of each damage level.
Each type of damage is grouped in categories related to the damage factors that occur.
There are 4 categories, namely: category I, is the type of damage with the biggest damage
factor and the multiplier factor is 6. This category I includes the type of damage to potholes.
Category II with multiplier factor 2, category II includes the type of raveling wethering
damage, alligator cracking and depression. Category III with multiplier 1 includes transverse
cracking damage, longitudinal cracking and block cracking. Category IV with a multiplier of
0.25, including shrinkage cracking, rutting, patching, edge deteriorations and asphalt excees.
Unfortunately, the visual damage assessment of the road still have some weaknesses. The
visual assessment method is very subjective, depending on the assessor. The assessment of an
assessor can differ greatly from the results of assesments from others for the same road
segment. In addition, visual assessment is subjective by considering the fatigue from human
eyes so that the assessment of road damage becomes less precise [5]. Considering the
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weaknesses of the visual damage assessment method, an algorithm or method is needed to
detect and calculate the value of damage to the road quickly and precisely
2. RELATED WORK
There have been some researches conducted pertaining digital image processing for crack
damage [6]. [7] conducted a research on a crack pattern on the pavement surface, while [8]
identified a crack pattern from the linear tracking device (LINTRACK) with photos to obtain
crack length, percentage and crack area. In 2010, [9] used a method with an initial image
segmentation algorithm to calculate the area and length of road surface damage or defects.
[10] conducted research with image processing techniques to extract features from images.
Neural Networks approach was used to detect defective image areas. [11] conducted edge
assesment in the process for the identification and classification of cracks.
Other researchers, [12], [13], [14], [15] used more detailed assesments in detecting
pavement cracks. [16] conducted analysis in digital image processing using bilinear
interpolation to obtain image based correction on the threshold of segmentation with
statistical analysis.
Previous research carried out with digital image was only at the stage of detecting cracks,
not yet at the stage of assessing road damage based on detected road damage. Based the
review of the previous research, this research aims at conducting a visual assessment of road
damage based on the Dirgolaksono and Mochtar methods, modelling an algorithms of road
damage based on digital image processing and applying the digital image method on the road
sections being assessed. This article particularly discusses the assessment of crack damage,
cracked crocodile skin and holes.
3. RESEARCH METHODS
3.1. Location and Time of Research
The research began with data collection in the form of images from road damage on ten road
sections located in Depok. Canon 550D digital camera and a tripod with a height of 80 cm are
used, with an angle of 60o. The data obtained is then processed by extracting the road damage
image, using MATLAB R2016a after the extraction process the classification process will be
carried out to determine the type of damages to the road. This research was carried out for 6
months.
3.2. Materials and Tools
The data used in this study are image data or road damage photos. Total of data 565 (five
hundred sixty five) photos for all three types of road damage. The types of damages
comprises; crocodile crack damage, crack and hole. The data is obtained by taking pictures
using a Canon EOS 550D digital camera and a tripod with a height of 80 cm, with an angle of
600.
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Road Damage Assesment for Flexible Pavement Using Digital Image Processing
3.3. Stages of Research Methods
The stages of the research method are illustrated in the following flow diagram:
START
Road Damage Image
Pre Processing
Shape Extraction
Classification
The Value of Road Damage
FINISH
Figure 3.3. Flowchart of Research Method Stages
3.4. Image Extraction Stages
The first step is to develop the application of road damage image extraction is to study the
morphological method for extracting road damage images. In this study the image used is
road damage images. In addition, the data used were five hundred and sixty-five photos of
road damage.
3.5. The Stages of the Image Classification Process
The process of classification of road damage images.
3.6. The Value of Road Damage
The value of road damage obtained is a visual damage value using the Dirgolaksono and
Mochtar methods (D & M method) and the value of road damage based on digital images.
The assessment of road damage with digital images follows the stages below:
a. Segment Classification Algorithm
This segment classification program aims to find out what types of damage are contained in a
segment that is inputted, along with the value of each type of damage, as well as the total
value of the damage. In the database, a segment is a directory (folder) which contains road
damage images along the segment.
By the time the program runs, it required the directory input segment to be classified.
Then the program will ask for the input percentage of the real damage value in the segment
(percent_ damage). The percentage of damage to the estate is the total percentage of the
damaged part of the road segment. Furthermore, each image in the segment will be classified
one by one using the radial vector algorithm as explained earlier. The program will record the
classification results of each image to be used in the next stage, namely the scoring stage.
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After each image in the segment is fully classified, the next step is calculating the score.
Scores are calculated separately for each type of damage, so there are 6 types of scores,
namely:
1) Cracked score,2) Moderate Aligator Score 3) Aligator Severe's score, 4) Pothole Slight
Score,
5) Pothole Moderate Score, 6) Pothole Severe score
The six scores are then accumulated based on their weight so that the total score is
generated. The seven scores are then presented as program output. For more details, see
Figure 3.4.
START
Segment Directory Input
Input percent_ damage
Classification of Types of
Damage to Each Image in the
Segment Directory
Calculate Score
-Crack Scores
- Moderate Aligator scores
- Aligator Severe score
- Pothole Slight score
- Pothole Moderate scores
- Pothole Severe's score
- Total score
FINISH
Figure 3.4. Road Damage Assessment Flowchart
4. ANALYSIS AND DISCUSSION
4.1. The Assessment of Visual Road Damage
The survey was carried out by preparing the SNR (Strategic National Road) road object for 10
road segments. Visually assessed the damage to the road based on the D & M method. Road
damage that is seen is: cracks, crocodile skin cracks, and holes, conduct road damage
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assessment using the DM method, The results of the assessment are shown in the following
Table.4.1.
Table 4.1. The Value of Road Damage Based on D & M Method
No.
1.
2.
3.
4.
5.
6.
7.
8.
9.
10
Roads
Jl. Meruyung Raya
Jl. Jambore
Jl. Raya Sawangan
Jl. Cilodong Raya
Jl. Arief Rahman
Jl. Radar Auri
Jl. Nusantara Raya
Jl. Teratai Raya
Jl. Taman Bunga
Jl. Muchtar Raya
Damage Value
182
38
70
308
54
292
52
88
38
102
4.2. Digital Image Damage Assessment
The value of road damage is based on digital imagery for 10 road segments, only 5 roads with
valid data, because there are data that cannot be read for example: the shadow of a legible leaf
is damaged, the last night's rain is also legibly damaged. The five roads can be seen in
following Table 4.2.
Table 4.2. The Value of Road Damage by Digital Image
No
1.
2.
3.
4.
5.
The Name of Roads
Jl. Cilodong Raya
Jl. Jambore
Jl. Meruyung Raya
Jl. Arief Rachman
Jl. Raya Sawangan
The Value of Digital Image Damage
236,16
42,56
162,24
41,28
66,2
4.3. Validation
To find out whether the value of damage based on a digital image is valid, a statistical test
will be conducted with Spss. The test used is a different pair of tests. We can see in Table 4.3.
that the value of road damage visually and damage value of road based on digital image.
Table 4.3. The Value of Road Damage by Visual and Digital Image
The Name of Roads
The Value of Visual Damage
Jl. Cilodong Raya
Jl. Jambore
Jl. Meruyung Raya
Jl. Arief Rachman
Jl. Raya Sawangan
308
38
182
54
70
The Value of Digital
Image Damage
236,16
42,56
162,24
41,28
53,44
The average damage value visually is 110.4, while the digital damage average value is
107.136. There is a strong correlation between the value of damage visually and the value of
damage digitally. The correlation coefficient is 0.876 and significant. It means that there is no
difference between the value of damage visually and the value of damage digitally.
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5. CONCLUSIONS
The conclusions from the research are:
a). Analyzing road damage by the D & M visual method was obtained as follows: for the Arif
Rahman road, the damage value was 57; Meruyung Raya road segment is 182; the Jamboree
road segment is 38; Cilodong Raya road segment is 308 and for the Sawangan Highway, the
damage value is 70. From these 5 road sections based on visual assessment, it can be
concluded that this road has minor damage.
b). Create a digital image-based road damage assessment algorithm model. By going through
the extraction process, classification and looking for the value of road damage. Obtained road
damage value for 5 (five) road segments, namely: (1) Arif Rahman road the damage value is
41.28, the road segment.(2) Roaring is 162.24. (3) Jambore road segment is 42.56; roads. (4)
Cilodong Raya is 236.16; (5) Sawangan highway section is 53.44. This indicates that the
value of damage obtained by using a digital image is smaller in value than
visually, although the amount of damage obtained is more, due to the image. This digital can
detect the type of damage more sensitive than that visual method.
c). By using SPSS, it can be known the validation, how valid is the value of road damage in
digital image against the value of damage visually. There is a strong correlation between the
value of damage visually with the value of damage digitally. The correlation coefficient is
0.998 and significant.
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