E-ISSN: 2528-388X
P-ISSN: 0213-762X
INERSIA
Vol.
No.
December 2023 The Evaluation of Pavement Condition Assessment Methods for Road Assets in Coastal Areas Clarence Deborah Teopilus and Mukhammad Rizka Fahmi Amrozi* Department of Civil and Environmental Engineering.
Faculty of Engineering.
Universitas Gadjah Mada.
Yogyakarta 55281.
Indonesia
ABSTRACT
Keywords:
Road Pavement Distress
Road Asset Management SDI
PCI
PASER
The Daendels road is a vital provincial road asset that facilitates the distribution of goods and services, enhances tourism access, and promotes socio-economic development in the southern region of Java Island.
The deteriorated condition of the Daendels road pavement has the potential to escalate both the likelihood of accidents and vehicle operating costs.
In Indonesia, road distress is measured using the Surface Distress Index (SDI), but certain types of distress are not yet incorporated into the Therefore, this study aims to identify the typical road distress in the coastal region and then to evaluate and compare several visual methods for evaluating the functional condition of road pavements, i.
, the SDI.
Pavement Condition Index (PCI), and Pavement Surface Evaluation and Rating (PASER).
Pavement conditions for Daendels Road have different analysis results depending on the method used.
The average value of PCI is 50.
lightly damage.
, the SDI is 164 .
everely damage.
, and the PASER is 4 .
lightly damage.
The statistical analyses indicate that both the SDI-PCI and SDI-PASER methods have a very strong relationship.
The SDI-PCI method has a higher correlation and coefficient of determination value (R= -0,929.
RA= 0,8.
than SDI-PASER (R= -0,807.
RA= 0,.
The PCI method is more applicable than the SDI dan PASER as it considers a wider range of pavement distress .
and more accurately represents the typical distress encountered on the South Coast of Java Island.
The pavement condition of Daendels Road is classified as severely damaged with typical distress involving cracking .
ongitudinal, transversal, alligator, and block.
, patching, and pothole.
Hence, a comprehensive plan for road maintenance was suggested, encompassing major rehabilitation using a hot mix asphalt overlay.
This is an open access article under the CCAeBY license.
Introduction Roads serve as the primary infrastructure for land transportation and have a substantial influence on the economic, social, cultural, and political aspects of a region .
According to Government Regulation No.
34/2006,
roads are defined as land transportation infrastructure that includes all components of the road, including associated structures and equipment used for vehicular movement, excluding trains, trams, and cable cars .
Over time, the continued use of roads will inevitably result in road deterioration and a decrease in the quality performance of the road surface, affecting both its functionality and structural integrity.
Consequently, this will have negative consequences for road users .
*Corresponding author.
E-mail: fahmi.
amrozi@ugm.
https://dx.
org/10.
21831/inersia.
Received August 7th 2023.
Revised December 22nd 2023.
Accepted December 27th 2023 Available online December 31st 2023 Daendels Road is one of the provincial roads that connects the southern part regions of Java, stretches from the east of Cilacap to the border area of Jogja.
Wates.
Daendels Road plays a crucial role in facilitating the distribution of products and services, providing access to tourism on the southern coast of Java Island, and driving the socioeconomic growth of the region.
Thus, the roads should be managed and preserved in good condition .
To provide optimal service performance, it is crucial to promptly identify and diagnose the typical distress of road This allows for proactive road maintenance to be carried out, effectively minimizing additional damage at a reduced expense.
Several studies were conducted to evaluate and compare road damage methods .
However, there have not been many case studies found that focus on road pavement distress in coastal areas.
Clarence Deborah Teopilus and Mukhammad Rizka Fahmi Amrozi Therefore, this study aims to determine the typical road damage in coastal areas and evaluate and compare several visual survey methods for evaluating the functional condition of road pavements.
INERSIA.
Vol.
No.
December 2023 The study was conducted on 1.
2 km length of Daendels road (STA 29 775 Ae 30 .
as shown in Figure 2.
Daendels Road uses flexible pavement and has a width of 7 m.
The roads were subsequently partitioned into sample units to comply with the specified range of sample unit area outlined by the three methods.
, for the PCI method, the length of the sample unit is 25 meters .
sample unit.
, while for the SDI and PASER methods is 100 meters .
sample unit.
Pavement condition evaluation can be used to determine optimal road handling alternatives.
There are various methods to evaluate road conditions, including the Pavement Condition Index (PCI), the International Roughness Index (IRI), and the Surface Distress Index (SDI) method.
In this study.
PCI.
SDI, and Pavement Surface Evaluation and Rating (PASER) methods were used as visual survey procedures to assess the condition of Daendels Road pavement and propose appropriate road repair or maintenance options.
Furthermore, correlation and determination analyses were conducted to ascertain the extent of the relationship and influence of the assessment results of the SDI method .
he standard method used by Bina Marg.
on the PCI and PASER The research was conducted by a visual survey and required tools, including: .
survey form.
measuring tape.
handphone camera, .
reflective vest and light stick.
Daendles Road Method In this study.
PCI.
SDI, and PASER methods were used to assess the condition of Daendels Road pavement following the flowchart in Figure 1.
Figure 2.
Location of the Daendels Road .
1 The PCI Method Pavement Condition Index (PCI) is a method to assess pavement conditions based on the type, density, and level of damage .
that occurs on the pavement surface.
The Pavement Condition Index (PCI) assessment uses values ranging from 0 to 100 and has criteria for excellent, very good, good, fair, poor, very poor, and failed .
Density is a type of damage to the area of a sample unit measured in mA or meters of length and produced as a The density of damage is expressed by Equations 1 and Equation 2.
yayceycuycycnycyc = yaycc yayceycuycycnycyc = yaycc yayc yayc y 100% .
y 100% .
where yaycc is the total area of a type of distress for each severity level .
A), yayc is the area of unit sample .
A), yaycc is total length of distress type at each severity level .
The deduct value against density graph is utilized to ascertain the reduction score for each category of distress based on the relationship between density and deduct Once the density value has been obtained, it is Figure 1.
Research flow chart.
INERSIA.
Vol.
No.
December 2023 Clarence Deborah Teopilus and Mukhammad Rizka Fahmi Amrozi necessary to plot the deduct value graph based on the severity and type of damage.
To calculate the yc value, the sum of the individual deduct values is reduced by the value of any damage that exceeds 5 for airports or unpaved roads and exceeds 2 for paved roads.
If a sample unit does not have any reduction values greater than 2, then all reduction values can be utilized as Corrected Deduct Values (CDV).
However, if there are two or more reduction values, the maximum ycAycEycN is determined as with ycEyayayc is Pavement Condition Index for each unit, yayaycO is Corrected Deduct Value for each unit.
After obtaining the PCI value of each sample unit, the next thing to do is calculate the PCI value on 1 road section .
using Equation 5.
PCI =
yuycEyaya.
ycu where ycEyaya as the pavement condition index for each unit and ycu as the number of sample units.
All deduct values are arranged sequentially from largest to smallest.
Determine the maximum number of deduct values allowed by using Equation 3.
yco = 1 ( 9 ) .
Oe ycAycaycuyaycO) .
Where yco is the number of permit reductions for sample units.
MaxDV is the highest deduct value.
The number of individuals yaycO is subtracted according to the yco value of the calculation result.
If the sum of the subtraction values is less than yco, then all the subtraction values are used to determine the maximum yayaycO .
The deduct value is added so that the Total Deduct Value .
cNyaycO ) is obtained.
yc iterates with yc being the number of individuals deduct values > 2.
Figure 4.
Pavement conditions based on the PCI method .
2 The SDI Methods The yayaycO value is generated based on the ycNyaycO and yc In Figure 3, the ycNyaycO value can be plotted by adjusting the q value in the calculation.
If the yayaycO value is less than the highest deduction value, then the yayaycO value used is the highest individual deduct value.
The Surface Distress Index (SDI) is an official method to evaluate pavement conditions in Indonesia.
Based on the Bina Marga SMO-03/RCS Guideline, the SDI calculation requires 4 measurement factors, i.
, the percentage of crack area, average crack width, number of potholes/km, and average rutting depth of wheel ruts .
The assessment for each SDI factor is specified in Table 1 to Table 4 while the pavement conditions presented in Table .
Cracks area Table 1.
SDI 1 .
Percentage (%) SDI value 1
None
<10
>30
Figure 3.
Graphic CDV .
The pavement conditions based on the PCI can be seen on Figure 4 while the PCI value for each sample unit calculated by Equation 4.
ycEyayayc = 100 Oe yayaycOycoycaycu .
Crack width Table 2.
SDI assessment 2 .
Width .
SDI value 2
None
SDI 1
End, <1 SDI 1
Medium, 1-5
SDI 1
Wide, >5 1 x 2 SDI Clarence Deborah Teopilus and Mukhammad Rizka Fahmi Amrozi .
Number of potholes INERSIA.
Vol.
No.
December 2023 Result and Discussion Table 3.
SDI 3 rating .
Quantity/km SDI value 3
None
SDI 2
<10
SDI 2 15
SDI 2 75
>50
SDI 2 225
1 The PCI The PCI values and conditions vary for each sample unit .
ee Table .
The PCI calculation revealed that the road condition at the starting station of the sample is more deteriorated compared to those at the end.
The common type of road distress identified in the Daendels road includes patching, longitudinal and transverse cracking, alligator cracking, potholes, block cracking, and elevation differences between the edge of the pavement and the shoulder of the road .
ane/shoulder drop-of.
Rutting depth of rutting ruts Table 4.
SDI 4 rating .
Quantity/km SDI value 4
None
SDI 3
SDI 3 .
x 0,.
SDI 3 .
SDI 3 20 Table 5.
Pavement conditions based on the SDI values .
SDI value Condition <50 Good 50 Ae 100 Medium 100 Ae 150 Light Damage >150 Heavy Damage Table 6.
Pavement assessment based on the PCI method Sample PCI value Condition 3 The PASER Method The Pavement Surface Evaluation and Rating (PASER) method is a visual road condition survey developed in the United States and Canada by identifying and assessing the quality of the road surface.
In the PASER method, four main parameters need to be considered, i.
aveling, fatness, wea.
, surface deformation .
utting, shoving collapse, and frost heav.
, cracking .
ransverse, slippage, longitudinal, block, alligator, and reflectio.
, potholes and patches .
The assessment of the PASER uses a scale of 1 to 10.
The PASER value of 1 indicates the condition of the pavement is severely damaged or failed .
orst conditio.
, while value 10 indicates the condition of the road pavement is excellent like new .
est conditio.
The assessment process and identification of PASER assessment methods are described as follows:
Pavement Condition Survey The surveyor observes the condition of the road pavement by dividing the road segment per 100 m length then measuring the quantity and dimension of road distress.
Assessment using the PASER Method Categorize the rating or quality assessment of road surface distress based on general pavement condition and visible distress in road segment following the PASER Manual During the assessment, it is important to acknowledge that each individual road segment as sample unit may not exhibit all the specified categories of distress for a certain rating.
They may possess only one or two types of distress.
Very poor Very poor Fair Serious Poor Fair Very poor Fair Poor Poor Serious Serious Serious Very poor Poor Poor Fair Poor Very poor Poor Poor Serious Serious Serious Failed Failed Fair Fair Poor Fair Satisfactory Good Satisfactory Satisfactory Good Satisfactory Satisfactory Satisfactory Satisfactory Poor Good Satisfactory Satisfactory Satisfactory Satisfactory Very poor Fair Satisfactory INERSIA.
Vol.
No.
December 2023 Clarence Deborah Teopilus and Mukhammad Rizka Fahmi Amrozi The example of PCI calculation procedures is illustrated as follows:
Deduct Value The deduct value is obtained from the plot of the density relationship graph with the deduct value as shown in Figure 5.
Density (%) Patch = 3.
84% (L)
Elongated or transverse crack = 2.
91% (L)
Elongated or transverse crack = 1.
03% (M)
Crocodile skin crack = 2.
43% (L)
Crocodile skin crack = 1.
85% (L)
Pothole = 2.
29% (L)
Pothole = 2.
86% (M)
Pothole = 2.
86% (H)
Block crack = 1.
95% (L)
Lane/shoulder drop off = 2.
91% (L)
Lane/shoulder drop off = 4.
46% (M)
Maximum Corrected Value The example of the CDV calculation procedures for unit sample 25 is presented in Table 7.
The CDV value is obtained from the graph plot of the relationship between TDV and the yc value as shown in Figure 6.
Table 7.
The CDV calculation for unit sample 25 Iteration Deduct Value ycycyc yee ycycyc Figure 5.
Deduct value for crocodile crack .
Figure 6.
The CDV for unit sample 25 .
171,24
Clarence Deborah Teopilus and Mukhammad Rizka Fahmi Amrozi .
PCI value The determination of the corrected reducing value (NPT) has been obtained, so the largest NPT value with a value of 92 is taken, then the PCI value obtained is 8.
Accordingly, pavement conditions with PCI value 8 are classified as failed.
INERSIA.
Vol.
No.
December 2023 Table 8 and Table 9 indicate a significant difference in the results of pavement conditions.
This is owing to the different survey timeframes, with the Bina Marga conducting a road condition investigation in July while primary data gathering in December.
As the survey was conducted using a visual survey, the subjectivity of the assessment will also have a significant impact on the assessment outcomes.
2 The SDI Table 8.
The SDI analysis results using primary data Unit sample SDI value Pavement Conditions The analysis for the SDI method utilized two distinct types of data, i.
, primary data from a visual survey and secondary data sourced from the Bina Marga.
The calculation of the SDI assessment for sample number 7 is demonstrated as follows.
SDI Value Calculation 1 Sample unit 7 total crack area of 91.
85 mA with a sample unit width of 7 m.
Then the crack percentage value is as 85 m2 y 100% = 13.
100 y 7 m Then the value of SDI 1 based on Table 1 is 20 % yaycycaycaycoyc = Heavy damaged Heavy damaged Heavy damaged Heavy damaged Heavy damaged Heavy damaged Heavy damaged Medium Good Good Good Medium Table 9.
The SDI analysis results using secondary data Unit sample SDI value Pavement Conditions .
SDI Value Calculation 2 The average crack width of sample unit 7 is 9.
2 mm.
The calculation of SDI 2 based on Table 2 is as follows:
ycIyaya 2 = ycIyaya 1 y 2 ycIyaya 2 = 20 y 2 = 40 Then the value of SDI 2 based on Table 2 is 40.
SDI Value Calculation 3 A total of 30 holes were identified along 100 m.
Since one hundred meters is equivalent to 0.
1 kilometers, the quantity of holes can be determined by multiplying the number of holes by 10.
According to Table 3, the SDI 3 value is as follows.
Good Good Good Good Good Good Good Good Good Good Good Good 3 The PASER The PASER method closely resembles the PCI method as it considers two types of data, i.
the type and dimension of pavement distress.
Table 10 presents the outcome analysis for the PASER method.
ycIyaya 3 = ycIyaya 2 225 ycIyaya 3 = 40 225 = 265 Table 10.
Analysis results based on the PASER method Unit sample Condition Value Then the value of SDI 3 based on Table 3 is 265.
SDI Value Calculation 4 In sample unit 7 no depth of wheel marks was found so the SDI value 4 based on Table 4 is equal to SDI 3, i.
Pavement Condition The final analysis results of SDI 4 (SDI .
can determine pavement conditions.
Based on Table 5.
SDI 256 for sample 7 is categorized as Heavy Damaged or severely damaged conditions.
Very poor Poor Good Fair Very poor Poor Very poor Good Good Good Good Good 4 Comparison of Pavement Conditions The SDI analysis outcomes using primary data for the rest unit samples are presented in Table 8 while the SDI values using secondary data are shown in Table 9.
Based on pavement condition results from PCI.
SDI, and PASER methods, they have different outcomes and range INERSIA.
Vol.
No.
December 2023 Clarence Deborah Teopilus and Mukhammad Rizka Fahmi Amrozi of condition categories.
To account for variations in damage condition values across the PCI.
SDI, and PASER methodologies, equalization is carried out following the Regulation of the Minister of Public Works number 13/M.
PRT.
2011 concerning Road Maintenance and Inspection Procedures.
The deterioration categories were converted into four categories following the SDI method , good, medium, light damaged, and heavy damaged.
Thus, the pavement condition can be compared between the three methods.
Furthermore, the unit sample must be equal, thus the PCI also counted for a 100 m unit sample.
The equalization of pavement conditions for the PCI.
SDI, and PASER methods can be seen in Table 11.
value can be considered as the best relationship model .
In addition, the relationship between the X and Y variables may also be assessed using significance values or tcount and ttable values.
The significance values.
If the significance value < 0.
05, it can be claimed that the two variables have a relationship .
while if the significance value > 0.
05 indicates that the two variables have no relationship .
ot correlate.
tcount and ttable values If the value of tcount > ttable, it can be stated that variable X influences variable Y.
Meanwhile.
If the value of tcount < ttable, it can be stated that variable X does not affect variable Y.
Table 11.
Pavement condition equalization Unit Sample PCI per Heavy Light Heavy Heavy Light Heavy Heavy Good Good Good Good Medium Pavement Conditions SDI SDI PASER Primary data Secondary Data Heavy Good Heavy Heavy Good Heavy Heavy Good Medium Heavy Good Light damaged Heavy Good Heavy Heavy Good Heavy Heavy Good Heavy Medium Good Medium Good Good Medium Good Good Medium Good Good Medium Medium Good Medium .
Relationship degree The correlation coefficient intervals reveal the relationship between the X and Y variables.
In this situation, the stronger the correlation between the two variables, the higher the value .
loser to on.
The degree of relationship and coefficient interval can be seen in Table 12.
Table 12.
Relationship rates and coefficient intervals .
Correlation Coefficient (R) Relationship Level 00 Ae 0.
20 Ae 0.
40 Ae 0.
6 Ae 0.
8 Ae 1.
Very low Low Medium Strong Very Strong 1 Simple linear regression analysis .
The value of significance The analysis outputs from SPSS for the SDI-PCI relationship are presented in Figure 7.
The linear regression analyses show a sig.
value of 0.
000, it can be concluded that the SDI variable influences the PCI Table 11 indicated that the PCI.
SDI, and PASER methods give a different assessment value and distress category on the pavement condition for Daendels Road STA Road 29 775 Ae 30 975.
The Pavement conditions result in an average PCI value of 50.
ight damag.
, an SDI value of 164 .
eavy or severe damag.
, and a PASER value of 1.
ight damag.
These differences occurred as they considered different numbers and types of pavement distress were considered in the calculation procedure.
5 Correlation and Determination Figure 7.
The significance value for the SDI-PCI relationship.
Correlation and determination are concepts in statistics that quantify the relationship between variables.
In this study, regression analyses using SPSS software are used to test the relationship between the SDI.
PCI, and PASER The SDI as the standard measurement method in Indonesia was used as independent variables while the PCI and PASER methods as dependent variables.
Four types of regression analyses were carried out, i.
, simple linear regression, polynomial, logarithmic, and The regression analysis with the highest RA .
Tcount and ttable values ttable = (/.
N-K-1 or DF residua.
ttable = .
05/2.
ttable = .
ttable = 2.
As the calculated result of tcount is -7.
937 and ttable is 228 .
count < ttabl.
, it can be stated that variable the SDI variable (X) does not affect the PCI variable (Y) Clarence Deborah Teopilus and Mukhammad Rizka Fahmi Amrozi .
Correlation coefficient analysis The correlation coefficient analysis results for the SDI and PCI can be seen in Table 13.
INERSIA.
Vol.
No.
December 2023 2 Exponential analysis The exponential regression analysis for the SDI-PCI relationship can be seen in Table 15, while for SDI and PASER relationship can be seen in Table 16.
Table 13.
Model summary for the SDI-PCI relationship Adjusted RA Std.
Error of the estimate Table 15.
Model summary for the SDI-PCI relationship Adjusted RA Std.
Error of the estimate Based on Table 14, a correlation value (R) of 0.
indicates that the level of relationship between the SDI variable and the PCI variable is very strong.
The determination value (RA) is 0.
863, which means that the influence of the SDI value on the PCI variable is 86.
while the remaining 13.
7% is influenced by other variables which not considered in this study.
The analysis outputs from SPSS for the SDI-PASER relationship are presented in Figure 8.
Table 16.
Model summary for the SDI-PASER relationship Adjusted RA Std.
Error of the estimate Table 15 shows that the correlation value (R) for SDI-PCI The data suggests a high degree of correlation between the SDI variable and the PCI variable, as indicated by the determination value (RA) of 0.
Meanwhile.
Table 16 shows that the SDI-PASER has a strong correlation value (R) of 0.
785, indicating a high level of association between the SDI and the PCI variable.
The determination value (RA) is 0.
Figure 8.
The significance value for the SDI-PASER 3 Logarithmic analysis .
The value of significance The significance value Result for SDI-PASSER Simple Linear Regression is 0.
It can be concluded that the SDI variable (X) influences the PCI variable (Y).
The SPSS output of logarithmic regression analysis results for SDI and PCI method can be seen in Table 17 while logarithmic regression analysis results for SDI and PASER method can be seen in Table 18.
Tcount and ttable values ttable = (/.
n-k-1 or df residua.
ttable = .
05/2.
ttable = .
ttable = 2.
Table 17.
Model summary for the SDI-PCI Adjusted RA Std.
Error of the estimate Based on Table 17, a correlation value (R) of 0.
841 is obtained, stating that the level of relationship between the SDI variable and the PCI variable is very strong and the determination value (RA) is 0.
The calculated result of tcount is -4.
328 < ttable of 2.
count < ttabl.
, it can be concluded that the SDI variable (X) does not affect the Paser variable (Y) .
Correlation coefficient analysis The results of the RA for the SDI-Paser relationship are presented in Table 14.
The correlation value (R) for SDIPASER is 0.
807, stating that the level of relationship between the SDI and the PASER variable is very strong.
The determination value (RA) is 0.
It means that the influence of the SDI value on the PASER variable is 2%, while the remaining 34.
8% is influenced by other Table 18.
Model summary for the SDI-PASER Adjusted RA Std.
Error of the estimate Based on Table 18, a correlation value (R) of 0.
67 is obtained, stating that the level of relationship between the SDI variable and the PASER variable is strong, and the determination value (RA) is 0.
4 Polynomial analysis The SPSS output for polynomial regression analysis of the SDI-PCI method is presented in Table 19 while the output for the SDI-PASER method is presented in Table 20.
Table 14.
Model summary for the SDI-PASER relationship Adjusted RA Std.
Error of the estimate Table 19.
Model summary for the SDI-PCI Adjusted RA Std.
Error of the estimate INERSIA.
Vol.
No.
December 2023 Clarence Deborah Teopilus and Mukhammad Rizka Fahmi Amrozi Based on Table 19, a correlation value (R) of 0.
93 is obtained, stating that the level of relationship between the SDI variable and the PCI variable is very strong and the determination value (RA) is 0.
lower the PCI value but it has the same meaning that the road condition is worsened.
Meanwhile, the coefficient of determination value (RA) for PCI and PASER methods are 8631 and 0.
From these results, it can be stated that based on the simple linear regression, the SDI value is influenced by the PCI value by 86.
31%, while the 69% is influenced by variables outside the Similarly, the influence of the PASER on the SDI scores was 65.
2%, while the remaining 34.
8% was influenced by variables outside the study.
Table 20.
Model summary for the SDI-PASER Adjusted RA Std.
Error of the estimate Based on Table 20, a correlation value (R) of 0.
868 is obtained, stating that the level of relationship between the SDI variable and the PCI variable is very strong and the determination value (RA) is 0.
6 Typical Road Distress and Alternative Treatments Four regression analysis tests have been carried out and the resume can be seen in Table 21.
Based on the regression analyses, the highest RA value can be identified in the polynomial regression test.
However, in determining the analysis, several things need to be considered according to the requirements, including:
Road distress in Daendels Road varies for each unit To achieve optimum service performance, it is necessary to tailor alternative road maintenance or treatment to the specific type of road damage encountered.
The dominant types of pavement distress identified in Daendels Road STA 29 775 Ae 30 975 are presented in Table 22.
The scatterplot diagram of polynomial regression analysis does not form a parabola and the significant test does not meet the requirement as the sig.
05 which means that variable X does not affect variable Y.
The output of logarithmic and exponential regression analysis results has a lower RA value than the simple linear regression analysis and polynomial analysis.
Comparative testing of tcount and ttable values is also not satisfied as tcount < ttable, meaning variable X has also no significant effect on variable Y.
Based on those considerations, the polynomial model can be Table 22.
Dominant types of road distress Number of Damage Dominant Type of Distress Sample Units Percentage Longitudinal/Transverse Cracking 37 Patching Alligator Cracking Pothole Block Cracking Table 22 shows that the common type of road distress identified in the Daendels road includes cracking, patching, and potholes.
Longitudinal and cracking in asphalt pavement typically caused by fatigue cracking or top-down cracking, while transverse cracking is often caused by thermal cracking or top-down cracking .
The underlying factor is the displacement caused by fluctuations in temperature changes and the age hardening of the asphalt results in a higher thermal stress.
Alligator cracking which was also identified in Daendels Road indicates that the typical coastal pavement distress is not only caused by temperature load but also caused by fatigue failure under repeated traffic loading.
The key aspect of pavement condition assessment involves the identification of various types of pavement distress and linking them to the potential cause This is important in selecting an appropriate maintenance and rehabilitation technique .
Table 21.
Recapitulation of regression analysis for the SDI
with the PCI and the PASER methods Regression RA
Method
Type
Equation
PCI
PASER
Linear Exponent Logarithmic Polynomial Linear Exponent Logarithmic Polynomial Y = -15.
y = -0.
0002xA-0.
y = -0.
y = 7.
2423yce Oe0,004ycu Y = -1.
y = -0.
0001xA 0.
y = -0.
y = 87,564yce Oe0,004ycu Afterward, the most appropriate regression analysis for this case study is the simple linear regression which results in subsequent best R and R2 values.
The simple linear analysis resulted in a correlation coefficient (R) of -0.
This states that the relationship between SDI with PCI and PASER has a very strong relationship.
Since the value of R is negative, the correlation between the SDI and PCI is the opposite, i.
, the higher the SDI value the Alternative road treatments in this study were selected following the Minister of Public Works' Guidelines for the Selection of Preventive Maintenance Technology for Road Pavement, i.
, 13/PRT/M/2011 .
and 07/SE/DB/2017 .
By utilizing the outcomes of PCI.
IRI, and PASER analysis, the road condition values and Clarence Deborah Teopilus and Mukhammad Rizka Fahmi Amrozi INERSIA.
Vol.
No.
December 2023 alternative treatments along Daendels Road can be visualized in the form of a stripmap format .
ee Figure .
The strip map graphic provides a concise visualization and presentation of the road condition with alternative treatment, serving as a valuable communication tool for engineers and policymakers, particularly those without a road preservation background.
Three alternative treatments were proposed including rehabilitation and Alternative 3, rehabilitating the entire road section using a hot mix asphalt overlay, is considered the best alternative as it is more practical, efficient, and easier to implement.
S TA 29 775
PCI 25 m 3 8 3 9 16
PCI 100 m
Alt 1
Alt 2
Alt 3
S TA 30 375
46,75
27,25
S TA 30 975
71,38
78,63
72,13
67,93
Pavement Condition based on PCI Method
S TA 29 775
Primer
S TA 30 375
Alt 1
Alt 2
Alt 3
S TA 30 975
Pavement Condition based on SDI Method Ae Primary Data
S TA 29 775
Sekunder S TA 30 375
S TA 30 975
Alternatif
Pavement Condition based on SDI Method Ae Secondary Data
S TA 29 775
PASER
Alt 1
Alt 2
Alt 3
S TA 30 375
S TA 30 975
Pavement Condition based on PASER Method Description:
Good (G) R Routine Maintenance M edium (M ) B Regular Maintenance Light Damage (LD) RH Rehabilitation Heavy Damage (HD) RK Reconstruction Figure 9.
Strip map of pavement conditions and alternative treatments for Daendels Road.
Alternative 3, which involves rehabilitating the entire road section using a hot mix asphalt overlay, is considered the best alternative due to its greater practicality, efficiency, and ease of implementation.
Conclusions The main types of pavement distress identified on Daendels Road, situated in a coastal area, are cracking .
ongitudinal, transversal, crocodile, and block.
, patching, and potholes.
The PCI.
SDI, and PASER methods give a different score value and distress category for the pavement condition of Daendels Road.
The differences occurred as they considered different numbers and types of pavement distress that were considered in the The SDI method has a strong relationship with the PCI and PASER methods.
However, the SDI-PCI has a better correlation than the SDI-PASER as it has a higher coefficient of determination value (RA).
The PCI method is considered the most suitable and applicable approach as it considers a wider range of distress types .
and more accurately represents the typical distress found on road pavements on the South Coast of Java Island.
The overall condition of the pavement on the Daendels road is categorized as severely damaged.
Three alternatives are References