BENCHMARKING OF FUNCTIONAL AND STRUCTURAL …eprints.undip.ac.id/69915/2/7._Full_Paper.pdf · building failure, the state (in Indonesia’s context) would get any benefit financially,

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BENCHMARKING OF FUNCTIONAL AND STRUCTURAL

ANALYTICS FOR NATIONAL PAVEMENT CONDITION

CLASSIFICATION IN INDONESIA: A CASE STUDY

Marsinta Simamora1)

, Rizal Z Tamin2)

, and JatiUtomo Dwi Hatmoko3)

1) Lecturer, Civil Engineering Department, State Polytechnic of Kupang, Indonesia

2) Professor, Civil and Environment Engineering Department, Bandung Institute of

Technology, Indonesia 3)

Lecturer, Civil Engineering Department, Diponegoro University, Indonesia

ABSTRACT

Management of non-toll national road in Indonesia indicated is not optimal. To

improve that, the assessmet which is more objective and comprehensive needed. This

study aims to present an pavement assessment with integrating functional and

structural factors as well as considering management unit, and using terminal service

design (PSIT) as a benchmark. The results showed that the pavement which was

functionally or structurally failed, is not well-suited to classify the pavement into the

failing category. Moreover, the pavement that was functionally and structurally failed

will be able to confirm failure of pavement. In addition, the assessment of pavement

which was considering the management unit shows a very important role in

determining the failure of pavement. This valuation method is potential used to detect

the parties who are responsible for the outcome performance and to initiate the

implementation of the Indonesian Act of Construction Service, which has long been

difficult to be applied. Furthermore, the failure cause needs to be learned to ascertain

whether the failure due to natural or man-made.

Keyword: integrated assessment, functional service, structural capacity, terminal

serviceability index, present serviceability index, failed category of pavement.

INTRODUCTION

In recent years, the non-toll national road (nt-NR) management in Indonesia is

indicated not optimal (Simamora and Hatmoko, 2014). The National Transportation

Performance Evaluation and Assessment Team in 2009, a team in Indonesian

Coordinating Ministry of Economic Affairs, mentioned that governance of road

management has been suspected to be cause inefficiency and ineffectiveness of

maintenance/rehabilitation of road infrastructure. In addition, when the post-

construction and immediately after operated, several of road infrastructure newly

constructed was severely damaged, and became a legal issue in Indonesia. Although

the pavement was badly damaged category, it is very difficult to relate against the

building failure in accordance with Indonesian Act, such as the Act No. 18/1999

concerning to Construction Service. Indeed, this situation had been suspected

previously by Tumilar (2006), who mentioned that the Act No. 18/1999 is very

difficult to implement due to its definition has multy interpretations. For instance, in

the Indonesian Public Work Minister Regulation, No. 13 / PRT / 2011 is stated that

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the worst conditions on the pavement is not passable. This classification indicates that

the regulation has not been made clear to the pavement failure criteria.

Actually, when the badly damaged of pavement can be categorized into the

building failure, the state (in Indonesia’s context) would get any benefit financially,

because the Act No. 18/1999 sets the penalties against the parties who caused the

building failure. Therefore, it is important to develop an approach that can evaluate

pavement objectively and comprehensively, thus the damage occurring can be

justified according to the Act. There are several literatures that make failure category

on pavement, including: PCI less than 10, (US Army, 1982, 2001; Park et al, 2007;

Kodhuru et al, 2010), and rutt depth greater than 20 mm (Arampamoorthy and

Patrick, 2010). Unfortunately, using PCI as single variable to classify pavement into

failure category is considered to be less comprehensively, because PCI indicator is a

representation of the functional service of pavement (Bennett et al., 2007; Sukirman,

1992). Bryce et al (2013) mentioned the assessment of road pavement will be more

comprehensive if the functional and structural services are integrated.

Previously, Jimenez and Mrawira (2009) evaluated the pavement by integrating

structural and functional services. The functional service used the IRI indicator, and

the structural capacity used the deflection indicator obtained through FWD

equipment. The problem in this method is the FWD is a representation of total

strength of pavement structure (Chi et al., 2014). This makes it difficult to evaluate

the pavement layer structure units. Evaluating each layer of the pavement structure is

important, because the application of the management unit will provide a more

objective assessment. Moreover, in that valuation models, IRI does not represent a

pavement surface damage (US Army, 1982, 2001), so it is less appropriate to be used

to measure pavement damage. However, the functional failure criteria (PCI <10) (US

Army, 1982, 2001) and the method used by Jimenez and Mrawira (2009), is very

important in the effort to integrate the functional and structural services on pavement.

The purpose of this study is to present an approach that will be able to classify

the pavement into failed category, by integrating structural and functional services of

pavement, and considering management unit, and using terminal services design as a

benchmark. The specific objectives of this study were: 1) to assess the serviceability

of pavement by integrating functional and structural services, and 2) to classify the

pavement into the failing category with concerning the terminal serviceability

designed, and pavement layer units. This valuation method expected can be used to

measure whether the target is reached or not (Munehiro et al., 2011) based on each

management unit of pavement. Beside that, this valuation method can also be used to

detect the parties who are responsible for the outcome performance (Sydenham,

2004). Moreover, this method could potentially initiate to apply the Indonesian Act of

Construction Service regarding to failure building clause, which has long been very

difficult to be implemented. Thus, it is expected the NRnt management can be more

optimized.

In this study, the primary limitation or axiom of the failure is a condition did not

achieve purpose (Noon, 2009; Keminetzky, 1991 in Yates and Lockley, 2002, and

Tumilar, 2006). According to Kocks Consult GmbH and Koblenz Universinj SRL

(2009), “pavement design is a process of selection of appropriate pavement and

surfacing materials to ensure that the pavement performs adequately and requires

minimal maintenance under the anticipated traffic loading for the design period

adopted. This selection process involves adoption of material types, thicknesses and

configurations of the pavement layers to meet the design and performance

objectives”. Viewed from the aspect of pavement design, the purpose that must be

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achieved on a pavement is the serviceability, which is called a terminal serviceability

or terminal present serviceability index (PSIT). While the failure of the pavement

structure is indicated by the effectiveness of the capability under the minimum

capability (Hudson et al., 1987; Faber et al., 2004). According to the Japan Society of

Civil Engineering (JSCE, 2010), to achieve the level of the structure safety, the ratio

(R) between capacity and workload must be greater than 1, meaning that R = 1 is a

critical condition and R < 1 will result in failure.

This paper is organized into 5 sections: introduction, background review,

methods of integrated assessment of road pavement, case study and the results

discussion, and conclusions/ recommendations/future work.

BACKGROUND REVIEW

Pavement Management System(PMS)

According to Vitillo (2013), pavement management is a program to improve the

quality and performance of the pavement and minimize costs through the

implementation of good management consisting of: 1) the material management, 2)

design, 3) construction, 4) management pavement, and 5) research and development.

Meanwhile, a pavement management system is a set of procedures to collect, analyze,

maintain, and report the pavement data to help decision makers in obtaining the

optimal strategy to maintain pavement in order to provide its services during a

specified period of time, and at a low cost.

Pavement management system (PMS) is designed to provide objective

information and useful data, for analysis, so that the organizers can create a more cost

effective and appropriate decisions related to road preservation. PMS can not make a

final decision, but it can provide a basic understanding against the possible

consequences on the alternative decisions (Vitillo, 2013; Townes et al., 2004).

According to Townes et al. (2004), PMS helps provide answers to the following

questions: 1) How is the general strategy of M & R, the most effective viewed from

cost aspect ?, 2) Where (road segment) which requires M & R ?, and 3) When is the

most appropriate time to perform road maintenance ?

In addition, Townes et al. (2004) said that, although different for each organizer,

the basics of PMS is a database that includes a set of four common characteristics of

data: 1) data inventory (including, pavement structure, geometry, environmental

conditions, and others), 2) the use of the road (traffic volume and load,usually

measured in equivalent single axel load-ESAL), 3) the condition of the pavement

(driving quality, surface damage, friction, and / or capacity of the structure), and 4)

pavement construction, maintenance history, and rehabilitation.

Road Management System in Indonesia

Indonesian Public Work Minister Regulation No. 11 / PRT / 2011, contains the

procedures for road handling and classification of nt-NR conditions, as in

Tab.1.(Appendix)

Assessment of Pavement

In Technical Manual No. 5-623 (US Army, 1982) and the UFC No. 3-270-06 (US

Army, 2001), and Park et al. (2007), pavement conditions are classified as in Table 2.

According to Park et al. (2007), the PCI is a function of the IRI as in Eq. 1, while the

US Army (1982, 2001) gives a formula for PCI as in Eq. 2

PCI = f IRI (1)

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PCI = f (distress; crack, pothole, rutt, etc. ) (2)

Previously, Jimenez and Mrawira (2009) conducted an assessment of the

pavement by integrating functional and structural variables using IRI and deflection

(FWD) of pavement respectively as Eq. 3, where the PCI = Pavement condition index,

IRI = international rouhgness index, FWD = falling weight deflection, α1 and α2 are

the weight of functional IRI (0.40) and structural FWD (0.60). By considering the

valuation method according to Jimenez and Mrawira (2009), variable FWD was to

represent the condition of the structure totally, and the consequence was the condition

of the structure for each layer of pavement would not be evaluated in detail. This

condition will make difficult for decision-makers.

PCI = α1(IRI) + α2FWD (3)

Research Findings and Gap

From the previous description it can be seen that the grouping system of road

conditions is not clearly yet showing the role of pavement structure factor as in TM 5-

623 (US Army, 1982) and UFC 3-270-06 (US Army, 2001). This can be seen from

the explanatory variables of pavement conditions still tend using the surface damage

factors, such as crack, pothole, rut, and others. Therefore, it is needed an evaluation

that integrates both functional and structural factors of pavement. Indeed, integrated

assessment of the functional and structural services has been done by Jimenez and

Mrawira (2009), but that method needs to be improved with considering unit layer of

pavement as shown in Fig.1.

Figure 1. Existing conditions, gaps, and study results expected

Integrating functional and structural of pavement, considering manajement unit

and using terminal serviceability as benchmark

Assessment that more objective and comprehensive related road pavement can be

obtained by applying several concepts, including: 1) an integrated assessment of

functional ability and structural (Bryce et al., 2013; Elseife et al., 2013; Mariani et al.,

2012) , 2) the assessment is based on the management unit (Lavinson, 1999), and 3)

consider the achievements and targets (Munehiro et al., 2011). Mathematically, the

assessment method that integrates functional and structural pavement can be made as

Eq.4. The PCI is a function of the pavement surface condition obtained by Eq.2 (US

Army, 1982, 2001), and the SCI is the ratio between the effective capacity of structure

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(SNeff) and the minimum/required capacity of structure (SNmin.), as Eq.5 (Bryce et

al., 2013).

PSI = f PCI + f(SCI) (4)

where,

SCI =SNeff

SNmin . (5)

The relationship between the PSI and the SCI can be seen from the relationship

between PSI (PSIo-PSIT) and the minimum SN on flexible pavement design in the

AASHTO 1993 method (Garber and Hoel, 2002). Based on the achievement of target

that may be obtained, given the qualification of the results, namely: 1) achievement

above target, 2) achievement equal wit target, and 3) achievement under target.

Furthermore, by the semantic understanding by noted that failure, is a condition, that

did not reach the goal (Indonesian Dictionary, Cambridge Dictionary), then, the result

classification types are: 1) does not failed, 2) critical, and 3) failed. Thus, because the

goal is the PSI, and the minimum requirement, is the PSIT, then:

PSI; > 𝑃𝑆𝐼T not failed = PSIT critical < 𝑃𝑆𝐼T failed

(6)

METHOD TO CLASSIFY PAVEMENT CONDITION: AN INTEGRATED

ASSESSMENT, REFERENCE TERMINAL SERVICE, AND CONSIDER

MANAGEMENT UNIT

In this study, data collection to decision-making stage of pavement comprises of: first,

getting data which consists of primary and secondary data. Primary data consisted of

the pavement surface condition obtained manually by using the TM 5-623 sheet (US

Army, 1982), and the CBR data is obtained by using the DCP equipment. Secondary

data consitsted of the design data of pavement which is being evaluated (SN

minimum or PSIT), second, compiling the data into the PCI and SCI indicators, third,

normalizing PCI and SCI value to PSI, fourth, determining PSI of the sample, and

last, decision making against sample (not fail, critical, and fail). The stages 1-5 can be

seen in the diagram as shown in Fig.2.

Pavement condition index PCI

PCI obtained manually by using the standard of TM 5-623 (US Army, 1982) as

shown in Tab.3.

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Figure 2. Flowchart of the decision making process to classify pavement condition

into fail, critical, and not fail

Effective structural number (SN ef)

According to MassHighway (2006), the effectiveness of the SN (Snef) can be

determined using Eq.7, where: Snef(i) is the effectiveness of SN of the pavement at

the i-th layer, aef(i) is the effective relative strength of the pavement at the i-th layer,

D(i) is the thickness of the pavement at the i-th layer.

SNeff = SNef i = aefixDi ni=1

ni=1 (7)

Meanwhile, MassHighway (2006) suggested using Eq. 8 to determine the

coefficients of “aeff”, where: “adesign“ is the coefficient of the relative strength of the

design of each layer of pavement, and RF is the reduction factor against “adesign”

which depends on the pavement surface condition. Both of the “a design” and “RF”

coefficient in Eq.8 are referring to Table 2 and 3 respectively.

aeff = a desain x RF (8)

In the other, the variable of “a2 and a3” were counted using Eq.9 and 10 (DPU,

2002), where E (elasticity) of granular material or aggregate is found refer to Eq. 11

(GmbH dan SRL, 2009). CBR is california bearing ratio of aggregate found by DCP

test.

Categary:

Not failed or

Critical

PCI

preparing sampling site

pavement

design data

SNef

CBR

data

data acquisition

pavement surface

distress data

SN min.

SCI

PSI’s sample

PSIT

normalizing

PCI and SCI to PSI

PSI < PSIT

Failed category

Yes

No

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a2 = 0.249 x Log10EBS − 0.977 (9)

a3 = 0.227 x Log10ESB − 0.839 (10)

E = 5409CBR0.71 (11)

Structure condition index (SCI)

As mentioned previously, SCI is a ratio between SNef and SNmin, as Eq.5. SNef

illustrates the strength of existing structures, while SNmin illustrates the strength

required which must be met during the design service life can be obtained on the

pavement design documents being evaluated.

Normalizing of PCI and SCI to PSI

To determine the PSI, first perform the normalization process of size scale on PCI and

SCI variables, as shown in Fig.3.

Figure 3. Normalizing concept of PCI to PSIF and SCI to PSIS

Present Serviceability index (PSI)

Based on the concept of normalization parameters, there are 9 conditions that may

occur on the PCI and SCI together, which can produce PSI, involving:

PSI Eq.

PSI = ∝ 1x 2 + ∝ 2x 2 ∶ PCI = 10; SCI = 1 (12)

SI = ∝ 1x 2 + ∝ 2x SCIx2 ∶ PCI = 10; SCI < 1 (13)

PSI = ∝ 1x PCI

10x2 + ∝ 2x 2 ∶ PCI < 10; SCI = 1 (14)

PSI = ∝ 1x 2 + ∝ 2x 2 + SCI − 1

(SCImaks − 1)x3 ∶ PCI = 10; SCI > 1 (15)

PSI = ∝ 1x 2 + PCI − 10

90x3 + ∝ 2x 2 ∶ PCI > 10; SCI = 1 (16)

PSI = ∝ 1x PCI

10x2 + ∝ 2x SCIx2 ∶ PCI < 10; SCI < 1 (17)

PSI = ∝ 1x 2 + PCI − 10

90x3 + ∝ 2x SCIx2 ∶ PCI > 10; SCI < 1 (18)

PSI = ∝ 1x PCI

10x2 + ∝ 2x 2 +

SCI − 1

(SCImaks − 1)x3 ∶ PCI < 10; SCI > 1 (19)

PSI = ∝ 1x 2 + PCI − 10

90x3 + ∝ 2x 2 +

SCI − 1

(SCImaks − 1)x3 ∶ PCI > 10; SCI > 1 (20)

fail

area

not fail

area

PSI

terminal

very poor

excellent

critical

point

0

PSIT

PSIo

= 5

fail

area

not fail

area

PSIF

PCI=10

PCI=0

PCI=100

critical

point

0

PSIFT

PSIFo

= 5

not fail

area

fail

area

PSIS

SCI=1

SCI=0

SCI=3

critical

point

0

PSIST

PSISo

= 5

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Classifying of pavement condition

Once obtained the PSI, then the last step is to classify the samples into categories: fail,

critical, and not fail. In this study, the category of fail, critical, and not fail is the PSI <

PSIT, PSI = PSIT, and PSI > PSIT,respectively

CASE STUDY

Data Set Description

This study was conducted in 2013 at two locations were in Cianjur-Sukabumi and

Kupang-Soe. In this study, there are 22 samples consisting of 19 samples obtained

from Cianjur-Sukabumi (Tab.4) and 3 samples from Kupang-Soe (Tab. 5). Pavement

surface condition was consisting of crack, pothole, rutt and other distress which were

obtained using AASHTO 1993 method that was suggested by US Army (1982, 2001),

while the capacity of pavement structure was obtained using the DCP equipment. The

data of pavement structure, such as CBR and thickness of each layer was done by the

staff of Cikampek Laboratory of Indonesian Public Work Depart for the Cianjur-

Sukabumi location, and by the staff of Kupang State Polytechnic for the Kupang-Soe

location.

In this study, the primary data consists of surface condition and structure of

pavement. Pavement surface conditions consisting of surface distress such as cracks,

holes, and others stated in the PCI. Other data of this study is secondary data, which

consists of: 1) PSIT = 2 (National road qualification in Indonesia), 2) SCI max = 3

(effective strenght 3 x minimum strenght), 3) α1 = 0.30, α2 = 0.70 (Simamora and

Hatmoko, 2013) and 3) SN minimum as shown in Tab.6.

Computing of PCI, SNef, SCI, PSI

The PCI in this study are shown in Tab.7. In addition, the effective strength of

the pavement structure (SNef) can be determined using Eq.7. Meanwhile, the

effective coefficient of the relative strength of pavement layers (a) can be calculated

by Eq.8 for asphalt pavement (a11) as shown in Tab.8 and 9, and Eq.9 is used to find

the coefficient a2 (Tab.10), and Eq.10 for the coefficients a3 (Tab.11). After the

coefficients (a11, a2, and a3) are obtained, then SNef can be determined as shown in

Tab.12 and 13 for data captured from the field of Cianjur-Sukabumi, and Kupang-

Soe, respectively.

Structural condition index on pavement is a variable that describes the ratio

between the effective strength and minimum strength of pavement structure, which is

expressed in the SCI. In this study, the SCI can be determined using Eq. 5 above, and

its results can be seen in Tab.14. In Tab.14 can be seen that the SN-1 is the effective

strength of the structure from the surface layer of asphalt, and SN-1 is the effective

strength of the layer structure under the surface of the asphalt pavement structure.

Thus, SCI-1 is a structural condition index for the asphalt surface layer structure, and

SCI-2 is a structural condition index for the layer structure beneath the asphalt surface

layer structure.

Once PCI and SCI are obtained, then the PSI for each sample was computed

using Eq. 12 to 20, depending on the PCI and SCI value of sample. In this study, the

discussion focused on the sample which could potentially be a problem, such as the

sample that has the PCI and SCI was failing, or, failing PCI and SCI does not fail, or,

PCI did not fail and the SCI was failing. For example, the sample No. 1 has a PCI =

9

12, and SCI-1 = 0.77: PCI is a category not fail, and SCI-1 is a category fail. Order to

the sample No. 1 is used Eq. 18, thus, PSI is:

PSI = ∝ 1x 2 + PCI − 10

90x3 + ∝ 2x SCIx2

PSI = 0,30 x 2 + 12−10

90x3 + 0,70 x 0.77 x 2 = 1,70

In the same way, the PSI for the other sample is computed, and the results are shown

in Tab. 15.

Case Study Result: Sample Classification

Once the PSI of the sample is obtained, the next step is to classify the samples into

three categories such as not failed, critical, and failed category. Sample which has: a

PSI > PSIT is categorized into not failing, a PSI = PSIT is categorized into critical

condition, and a PSI < PSIT is categorized into failing. Based on the PSI and PSIT of

sample, the classification of whole samples can be made as shown in Tab.16.

Discussion

From 20 samples that were evaluated in this study, 9 samples could potentially face a

problem, such as: PCI is not failing and SCI is failing, PCI is critical and SCI is

failing, PCI is failing and the SCI is not failing, PCI and SCI are failing. This situation

makes the decision makers are in a difficult situation, and consequently, in the

Indonesia context, Law relating to the building failure was very difficult to be

implemented (Tumilar, 2006).

The result showed that integrated both of functional services and structural

capacity of pavement, is very important in evaluating road pavement, especially in

determining whether a pavement is failing or not. In addition, other factors play an

important role are the unit management of pavement structure and minimum limit that

must be achieved. All three of these factors can provide an objective and

comprehensive assessment of pavement as shown in Tab.16. Sample No. 1 shows that

the functional services of pavement does not fail, but the structure of the top layer of

asphalt (SCI-1) is failing. Sample No. 1 has a PSI = 1.70 if the pavement was assessed

by considering unit structure of pavement, and PSI = 2.98 if the assessment does not

consider the unit structure factor and using total capacity of structure. These

conditions make it clear that the sample No. 1 (PCI > 10) becomes failing category

when the assessment considers unit structure, and becomes not failing when the

assessment does not consider the unit structure of pavement. This assessment is very

different from the opinion which states that the PCI > 10 is not failing (US Army,

1982, 2001). In addition, consideration of the unit management of pavement structure

plays a very important role, and by doing so are expected to be obtained pavement

assessment in more detail as suggested by Lavinson (1999).

Similarly, the sample failed functionally, and not failed structurally such as

samples No.4,7,8, and 9, which have the PCI are failing category, but its SCI are not

failing. PSI for these samples are greater than PSIT = 2, both for the structure of the

top layer of pavement (SCI-1), as well as for the total pavement structure (SCItot).

These results show a clear difference to the existing failure criteria previously, which

stated that the failing category is a PCI < 10 (US Army, 1982, 2001). Due to

functional failure is not representing a pavement failure comprehensively, it is

important to consider the other factor to be more comprehensive as well as pavement

structure (Bryce et al., 2013; Elseife et al.,2013; Mariani et al., 2012).

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CONCLUSIONS, RECOMMENDATIONS AND FUTURE WORK

This study aims to present an integrated assessment by considering the unit

Management and terminal services designed. In this study, the axiom of failure is a

condition that is not reached terminal services are designed. Thus, in this study, the

failure axiom on the pavement is the road that has a serviceability (PSI) under the

design terminal serviceability (PSIT). By using that axioms of the failure can be

concluded: 1) pavement functionally, or structurally failed is not well-suited to

classify the pavement into the failing category, 2) pavement functionally and

structurally failed will be able to confirm pavement failure, and 3) pavement assessed

by considering unit management has a greater chance against failure. This valuation

method can be used to detect the parties, who are responsible for the outcome

performance, and.is potentially used to initiate the implementation of the Indonesian

Act of Construction Service, which has been very difficult to implement. Thus, it is

expected, the NRnt management can be more optimized.

Therefore, to assess the pavement, whether failed or not recommended: first, is

not enough to simply to consider condition of the road surface, but will have to

consider the ability of the pavement structure, second, the assessment should have a

clear reference, such as a minimum standard according to the design, last, an

assessment is necessary to consider the unit management. In addition, due to this

study is only limited to how to integrate between PCI and SCI, and utilize the design

in the evaluation process at the pavement operation stage, and demonstrate the role of

the unit management in road pavement evaluation system, then the failure cause needs

to be studied further to ascertain whether due to natural or human actions. Failure due

to nature is a disaster, and failure due to human is the building failure category which

is needs to be accounted for.

ACKNOWLEDGEMNT

I thank you to Indonesian Government for my Doctoral Program Scholarship in 2010

to 2015 in Diponegoro University, Indonesia, and the Sandwich Like Program by

Indonesian Education and Culture Ministry in 2014 for 4 months in Seoul National

University, South Korea.

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US Army, 1982, Technical Manual (TM) 5-623: Pavement maintenance management,

Headquarters, Department of The Army, Washington, DC.

US Army, 2001, Unified Facilities Criteria (UFC) 3-270-06: Paver Asphalt Surfaced

Airfields Pavement Condition Index (PCI), U.S. Army Corps of Engineer,

mailto:[email protected]

12

Naval Facilities Engineer Command, Air Force Civil Engineer Support

Agency.

Vitillo N., 2013, Pavement management system: pavemen management system

overeview

APPENDIX

Table 1. Road condition index (RCI)

Source: Attach. of Regulation of Public Work MinistryNo. 13/PRT/M/2011

Table 2. Relative strength coefficient of design of materials

Pavement component Coefficient (a) (per inch)

Surface course:

Hot Mix Asphalt (HMA) 0.44

Sand Asphalt 0.40

Base Course:

HMA 0.34

Asphalt treated Penetrated Stone 0.24

Crushed stone/macadam 0.14

Sandy gravel 0.07

Sub-base:

Crushed stone (dense graded) 0.14

Gravel 0.11

Sand/sandy clay 0.05 - 0.10

Source: MassHighway (2006)

Table 3. Reduction Factor (RF)

Existing condition of pavement (RF)

HMA surface exhibits appreciable cracking and crack patterns, little or no

spalling along the crack, some wheel path deformation, and is essentially stable. 0.5-0.7

HMA surface exhibits some fine cracking, small intermittent cracking patterns,

and slight deformation in the wheel path, and obviously stable. 0.7-0.9

HMA surface generally uncracked, little or no deformation in the wheel path, and

stable. 0.9-1.0

Source: MassHighway (2006)

No Description of the field, seen visually RCI

1 Impassable 0-2

2 Heavily damaged, many holes across surface area 2-3

3 Damaged and wavy, many holes 3-4

4 Rather damaged, sometimes there are holes, uneven surfaces 4-5

5 Simply no, or very few holes, somewhat uneven road surfaces 5-6

6 Good 6-7

7 Very good, pavement surface is generally flat 7-8

8 Excellent 8-10

13

Table 4. CBR of sample (Cianjur- Sukabumi)

No. Sample Layer thickness (cm) CBR (%)

Agg.Base D11 D12 D2

1 1 Exist.(L) 9.50 11.50 21.50 N/A 2 2 Exist.(L) 10.10 10.90 22.20 N/A

3 3 Exist.(L) 8.60 12.40 19.00 107

4 4 Exist.(L) 9.80 11.20 24.00 105

5 5 Exist. (L) 4.00 17.00 25.20 105

6 6 Exist.(L) 9.70 11.30 23.00 150

7 7 Exist.(L) 7.00 14.00 24.00 107

8 8 Exist.(L) 10.10 10.90 29.00 115

9 9 Exist.(L) 11.00 10.00 24.30 119

10 10 Exist.(L) 9.50 11.50 17.50 95

11 11 Exist.(L) 10.00 11.00 16.50 118

12 12 Exist.(L) 10.20 10.80 17.00 112

13 4 Exist.(R) 9.30 11.70 31.00 117

14 5 Exist.(R) 10.20 10.80 29.00 108

15 6 Exist.(R) 11.00 10.00 30.50 95

16 7 Exist.(R) 10.10 10.90 28.20 120

17 8 Exist.(R) 9.80 11.20 27.70 123

18 9 Exist.(R) 10.00 11.00 32.00 86

19 10 Exist.(R) 10.50 10.50 31.00 84

Table 5. CBR of samples (Kupang-Soe)

No. Sample Layer thickness (cm) CBR (%)

Agg.Base D1 D21 D22 D3

1 20+250 10.10 10.10 - 22.50 132

2 20+600 9.90 11.50 - 30.50 339

3 21+750 10.00 9.50 22.20 9.40 113

Tabel 6. SN minimum (SN min.)

Design data

(Cianjur-Sukabumi)

SN min.

MR-subgrade

(7,938 psi)

SN min-1

(HMA-1) used

MR-HMA-2

(300,000 psi)

SN min.2

(HMA 2 + agg.base)

= SNmin tot - SN

min-1

CESAL =

R =

So =

PSIo =

PSIT =

PSI =

PSIf =

1,696,097

kip

95%

0.45

4.2

2.0

1.7

1.5

3.69 0.87 2.83

Design data

(Kupang-Soe)

SN min.

MR-subgrade

(6,200 psi)

SN min-1

SN min.2

CESAL =

R =

So =

PSIo =

211,819 kip

95%

0.45

4.2

2.96 2.96

(not found because

road structure was

only one layer)

14

PSIT =

PSI =

PSIf =

2.0

1.7

1.5

Table 7. PCI of sampels

No. Sample PCI Location No. Sample PCI Location

1 1 Exist.(L) 9 Cianjur-

Sukabumi


Sukabumi 2 2 Exist.(L) 4 Cianjur-

Sukabumi

13 4 Exist.(R) 62 Cianjur-


Sukabumi



Sukabumi


Sukabumi 5 5 Exist. (L) 9 Cianjur-

Sukabumi



Sukabumi



Sukabumi



Sukabumi



Sukabumi

20 20+250 85 Kupang-Soe


Sukabumi

21 20+600 82 Kupang-Soe 11 11 Exist.(L) 5 Cianjur-

Sukabumi

22 21+750 87 Kupang-Soe

Table 8. Effectice coefficient of a11 for each samples

No. Sample

Surface

distress

(CDV, %)

a11

MassHighway Pt-T-01-2002-B a11

[2006] [2002] (average)

1 2 3 4 5 6= (4+5)/2

1 3 Exist.(L) 88.00 0.227 0.170 0.199

2 4 Exist.(L) 91.00 0.227 0.115 0.171

3 5 Exist. (L) 91.00 0.227 0.115 0.171

4 6 Exist.(L) 95.00 0.227 0.250 0.239

5 7 Exist.(L) 93.00 0.227 0.170 0.199

6 8 Exist.(L) 90.00 0.227 0.170 0.199

7 9 Exist.(L) 95.00 0.227 0.250 0.239

8 10 Exist.(L) 97.00 0.227 0.250 0.239

9 11 Exist.(L) 95.00 0.227 0.250 0.239

10 12 Exist.(L) 83.00 0.227 0.250 0.239

11 4 Exist.(R) 38.00 0.360 0.275 0.318

12 5 Exist.(R) 44.00 0.360 0.275 0.318

13 6 Exist.(R) 35.00 0.360 0.250 0.305

14 7 Exist.(R) 32.00 0.360 0.275 0.318

15 8 Exist.(R) 47.00 0.360 0.275 0.318

16 9 Exist.(R) 30.00 0.360 0.275 0.318

17 10 Exist.(R) 19.00 0.360 0.275 0.318

15

Table 9. Effective coefficient of a1

No. Sample

Surface

distress

(CDV, %)

a1

MassHighway Pt-T-01-2002-B a1

[2006] [2002] (average)

1 2 3 4 5 6 = (4+5)/2

1 20+250 - 0.360 0.375 0.368

2 20+600 - 0.360 0.375 0.368

3 21+750 - 0.360 0.375 0.368

Table 10. Effective coefficient of a2 (Cianjur-Sukabumi)

No Sampel CBR

(%)

(E)

(Eq.11)

a2

(Eq..9)

1 3 Exist.(L) 107 50,839 0.195

2 4 Exist.(L) 105 50,229 0.194

3 5 Exist. (L) 105 50,229 0.194

4 6 Exist.(L) 150 63,109 0.218

5 7 Exist.(L) 107 50,839 0.195

6 8 Exist.(L) 115 53,240 0.200

7 9 Exist.(L) 119 54,418 0.202

8 10 Exist.(L) 95 47,112 0.187

9 11 Exist.(L) 118 54,125 0.202

10 12 Exist.(L) 112 52,347 0.198

11 4 Exist.(R) 117 53,831 0.201

12 5 Exist.(R) 108 51,143 0.195

13 6 Exist.(R) 95 47,112 0.187

14 7 Exist.(R) 120 54,710 0.203

15 8 Exist.(R) 123 55,581 0.204

16 9 Exist.(R) 86 44,205 0.180

17 10 Exist.(R) 84 43,544 0.178

Table 11. Effective coefficient of a2 and a3 (Kupang-Soe)

No. Lokasi CBR

(%)

E a21 a2

(Eq.11) (Eq.9) (a21+a22)

23 20+250 132 58,213 0.209 0.209

24 20+600 339 106,380 0.275 0.275

25 21+750 113 52,580 0.198 0.336

E a22

23a 20+250 - -

-

24a 20+600 - -

-

25a 21+750 47 30,033 0.138

E a3

23b 20+250 79 41,752 0.174

24b 20+600 496 135,652 0.301

25b 21+750 158 65,280 0.222

16

Table 12. The SNef-1 and SNef-2 of sample (Cianjur-Sukabumi)

No Sample D11 D12 D2 a11 a12 a2 SN ef-1 SN ef SN ef SN ef-2

(cm) (cm) (cm) /inch. /inch. /inch. HMA-1 HMA-2 Pond.ag (10+11)

1 2 3 4 5 6 7 8 9=(3x6)

/2.54

10=(4x7)

/2.54

11=(5x8)

/2.54 12

1 3 Exist.(L) 8.6 12.4 19.0 0.199 0.36 0.195 0.67 1.76 1.46 3.21

2 4 Exist.(L) 9.8 11.2 24.0 0.171 0.36 0.194 0.66 1.59 1.83 3.42

3 5 Exist. (L) 4.0 17.0 25.2 0.171 0.36 0.194 0.27 2.41 1.92 4.33

4 6 Exist.(L) 9.7 11.3 23.0 0.239 0.36 0.218 0.91 1.60 1.98 3.58

5 7 Exist.(L) 7.0 14.0 24.0 0.199 0.36 0.195 0.55 1.98 1.84 3.83

6 8 Exist.(L) 10.1 10.9 29.0 0.199 0.36 0.200 0.79 1.54 2.28 3.83

7 9 Exist.(L) 11.0 10.0 24.3 0.239 0.36 0.202 1.03 1.42 1.93 3.35

8 10 Exist.(L) 9.5 11.5 17.5 0.239 0.36 0.187 0.89 1.63 1.29 2.92

9 11 Exist.(L) 10.0 11.0 16.5 0.239 0.36 0.202 0.94 1.56 1.31 2.87

10 12 Exist.(L) 10.2 10.8 17.0 0.239 0.36 0.198 0.96 1.53 1.33 2.86

11 4 Exist.(R) 9.30 11.70 31.00 0.3175 0.36 0.201 1.16 1.66 2.45 4.11

12 5 Exist.(R) 10.20 10.80 29.00 0.3175 0.36 0.195 1.28 1.53 2.23 3.76

13 6 Exist.(R) 11.00 10.00 30.50 0.305 0.36 0.187 1.32 1.42 2.24 3.66

14 7 Exist.(R) 10.10 10.90 28.20 0.3175 0.36 0.203 1.26 1.54 2.25 3.80

15 8 Exist.(R) 9.80 11.20 27.70 0.3175 0.36 0.204 1.23 1.59 2.23 3.82

16 9 Exist.(R) 10.00 11.00 32.00 0.3175 0.36 0.18 1.25 1.56 2.26 3.82

17 10 Exist.(R) 10.50 10.50 31.00 0.3175 0.36 0.178 1.31 1.49 2.17 3.66

Tablel 13. SNef-1 of samples (Kupang-Soe)

No sample D1 D21 D22 D3 a1 a21 a22 a3 SN ef SN ef SN ef SN ef-1

(cm) (cm)

(cm) /inc. /inc. /inc. /inc. HMA Pond.A Pond.B

1 2 3 4 5 6 7 8 9 10 11 12 13 14

(3x7) (4x8+5x9) (6x10) (11+12+13)

1 20+250 10.10 10.10 - 22.50 0.368 0.209 - 0.174 1.46 0.83 1.54 3.83

2 20+600 9.90 11.50 - 30.50 0.368 0.275 - 0.301 1.43 1.24 3.61 6.29

3 21+750 10.00 9.50 22.20 9.40 0.368 0.198 0.138 0.222 1.45 1.95 0.82 4.22

Table 14. SCI of samples

No Sample SN ef-1 SN ef-2 SN min-1 SN min-2 SCI-1 SCI-2

1 2 3 4 5 6 7 8

1 3 Exist.(L) 0.67 3.21 0.87 2.83 0.77 1.14

2 4 Exist.(L) 0.66 3.42 0.87 2.83 0.76 1.21

3 5 Exist. (L) 0.27 4.33 0.87 2.83 0.31 1.53

4 6 Exist.(L) 0.91 3.58 0.87 2.83 1.05 1.26

5 7 Exist.(L) 0.55 3.83 0.87 2.83 0.63 1.35

6 8 Exist.(L) 0.79 3.83 0.87 2.83 0.91 1.35

7 9 Exist.(L) 1.03 3.35 0.87 2.83 1.19 1.18

8 10 Exist.(L) 0.89 2.92 0.87 2.83 1.03 1.03

9 11 Exist.(L) 0.94 2.87 0.87 2.83 1.08 1.01

10 12 Exist.(L) 0.96 2.86 0.87 2.83 1.10 1.01

11 4 Exist.(R) 1.16 4.11 0.87 2.83 1.34 1.45

12 5 Exist.(R) 1.28 3.76 0.87 2.83 1.47 1.33

13 6 Exist.(R) 1.32 3.66 0.87 2.83 1.52 1.29

17

14 7 Exist.(R) 1.26 3.80 0.87 2.83 1.45 1.34

15 8 Exist.(R) 1.23 3.82 0.87 2.83 1.41 1.35

16 9 Exist.(R) 1.25 3.82 0.87 2.83 1.44 1.35

17 10 Exist.(R) 1.31 3.66 0.87 2.83 1.51 1.29

18 20+250 3.83 - 2.96 - 1.29 -

19 20+600 6.29 - 2.96 - 2.13 -

20 21+750 4.22 - 2.96 - 1.42 -

Table 15. PSI of samples

No Sampel PCI SCI-1 SCI-2 SCItot PSI-SCI1 PSI-SCI2 PSI-SCItot

PSI reference PSI reference PSI reference

1 3 Exist.(L) 12 0.77 1.14 1.91 1.70 Eq.18 2.33 Eq.20 2.98 Eq.20

2 4 Exist.(L) 9 0.76 1.21 1.97 1.60 Eq.17 2.16 Eq.19 2.96 Eq.19

3 5 Exist. (L) 9 0.31 1.53 1.84 0.97 Eq.17 2.50 Eq.19 2.82 Eq.19

4 6 Exist.(L) 5 1.05 1.26 2.31 1.75 Eq.19 1.97 Eq.19 3.08 Eq.19

5 7 Exist.(L) 7 0.63 1.35 1.98 1.30 Eq.17 2.19 Eq.19 2.85 Eq.19

6 8 Exist.(L) 10 0.91 1.35 2.26 1.87 Eq.13 2.37 Eq.15 3.32 Eq.15

7 9 Exist.(L) 5 1.19 1.18 2.37 1.90 Eq.19 1.89 Eq.19 3.14 Eq.19

8 10 Exist.(L) 3 1.03 1.03 2.06 1.61 Eq.19 1.61 Eq.19 2.69 Eq.19

9 11 Exist.(L) 5 1.08 1.01 2.09 1.78 Eq.19 1.71 Eq.19 2.84 Eq.19

Table 16. PSI and classification of samples (PSIT = 2)

No Sampel PCI Classif. SCI-

1 Classif. SCI-2 Classif. SCItot Classif.

PSI-SCI1 PSI-SCI2 PSI-SCItot

PSI Classif. PSI Classif. PSI Classif.

1 3 Exist.(L) 12 not failed 0.77 failed 1.14 not failed 1.91 not failed 1.70 failed 2.33 not failed 2.98 not failed

2 4 Exist.(L) 9 failed 0.76 failed 1.21 not failed 1.97 not failed 1.60 failed 2.16 not failed 2.96 not failed

3 5 Exist. (L) 9 failed 0.31 failed 1.53 not failed 1.84 not failed 0.97 failed 2.50 not failed 2.82 not failed

4 6 Exist.(L) 5 failed 1.05 not failed 1.26 not failed 2.31 not failed 1.75 failed 1.97 failed 3.08 not failed

5 7 Exist.(L) 7 failed 0.63 failed 1.35 not failed 1.98 not failed 1.30 failed 2.19 not failed 2.85 not failed

6 8 Exist.(L) 10 not failed 0.91 failed 1.35 not failed 2.26 not failed 1.87 failed 2.37 not failed 3.32 not failed




Note: not failed (PSI > PSIT ), critical (PSI = PSIT), failed (PSI < PSIT)

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