Revista Científica ‘‘INGENIAR”: Ingeniería, Tecnología e Investigación. Vol. 8 Núm. (16) 2025. ISSN: 2737-6249

Diagnosis of Urban Pavement Failures Using the PCI Method: The Case of Eloy Alfaro Street in Jipijapa.

DOI: https://doi.org/10.46296/ig.v8i16.0293

DIAGNÓSTICO DE FALLAS EN PAVIMENTOS URBANOS MEDIANTE EL

MÉTODO PCI: EL CASO DE LA CALLE ELOY ALFARO EN JIPIJAPA

DIAGNOSIS OF URBAN PAVEMENT FAILURES USING THE PCI METHOD:

THE CASE OF ELOY ALFARO STREET IN JIPIJAPA

1

2

Moreno-Ponce Luis Alfonso ; Carvajal-Rivadeneira Daniel David ;

3

4

Solórzano-Villegas Lucy Elizabeth ; Plúa Ponce Angelica Marena

1

Universidad Estatal del Sur de Manabí. Jipijapa, Ecuador.

Correo: luis.moreno@unesum.edu.ec. ORCID ID: https://orcid.org/0009-0000-9026-1009

2

Universidad Estatal del Sur de Manabí. Jipijapa, Ecuador.

Correo: daniel.carvajal@unesum.edu.ec. ORCID ID: https://orcid.org/0000-0002-5288-5483

3

Universidad Estatal del Sur de Manabí. Jipijapa, Ecuador.

Correo: lucy.solorzano@unesum.edu.ec. ORCID ID: https://orcid.org/0000-0002-9903-5304

4

Universidad Estatal del Sur de Manabí. Jipijapa, Ecuador.

Correo: angelica.plua@unesum.edu.ec. ORCID ID: https://orcid.org/0009-0007-3101-7285

Resumen

Se evaluó el estado del pavimento urbano en la calle Eloy Alfaro (Jipijapa, Ecuador), entre Febres

Cordero y Villamil, para informar la gestión de la movilidad urbana y la seguridad vial. El objetivo

fue determinar el estado de la superficie utilizando el Índice de Estado del Pavimento (PCI; ASTM

D6433-07) mediante la identificación de los tipos de deterioro, el cálculo del PCI para cada unidad

de muestreo (SU) y el establecimiento del estado general de la sección. Se realizó un estudio

observacional transversal con inspección visual estandarizada en ocho SU de 228 m². Las

densidades de deterioro se calcularon mediante métricas (área, longitud, conteo), se obtuvieron

valores de deducción (DV) y se corrigieron iterativamente (DV → CDV), y el PCI por SU se derivó

como 100 − máx. (CDV). Los deterioros registrados comprendieron agrietamiento por fatiga,

agrietamiento longitudinal/transversal, baches, desprendimiento/meteorización y ahuellamiento.

El PCI medio del tramo fue de 24,5 (muy deficiente), con una distribución heterogénea: 50 % de

los casos fueron reprobados (PCI 0-8), 25 % deficientes, 12,5 % regulares y 12,5 % buenos

(

máximo 64). Se concluyó que el tramo presentaba un deterioro avanzado, con una

concentración de problemas estructurales y funcionales en los tramos centrales, y que el PCI

proporcionaba un indicador válido y orientado a la toma de decisiones para respaldar la

priorización del mantenimiento en la gestión del pavimento urbano.

Palabras clave: pavimento urbano, PCI, deterioro de la superficie, seguridad vial; priorización

del mantenimiento.

Abstract

Urban pavement condition on Eloy Alfaro Street (Jipijapa, Ecuador), between Febres Cordero

and Villamil, was assessed to inform urban mobility and road-safety management. The objective

was to determine surface condition using the Pavement Condition Index (PCI; ASTM D6433-07)

by identifying distress types, computing PCI for each sampling unit (SU) and establishing the

section’s overall state. An observational, cross-sectional survey with standardised visual

inspection was conducted on eight SUs of 228 m². Distress densities were calculated by metric

(

area, length, count), deduct values (DV) were obtained and iteratively corrected (DV→CDV), and

PCI per SU was derived as 100 − max (CDV). Recorded distresses comprised alligator (fatigue)

cracking, longitudinal/transverse cracking, potholes, ravelling/weathering and rutting. The

section’s mean PCI was 24.5 (“very poor”), with a heterogeneous distribution: 50% failed (PCI 0–

8

), 25% poor, 12.5% fair and 12.5% good (maximum 64). It was concluded that the section

Información del manuscrito:

Fecha de recepción: 09 de junio de 2025.

Fecha de aceptación: 29 de agosto de 2025.

Fecha de publicación: 29 de septiembre de 2025.

222

Moreno-Ponce et al. (2025)

exhibited advanced deterioration with a concentration of structural and functional distresses in

the central subsections, and that PCI provided a valid, decision-oriented indicator to support

maintenance prioritisation within urban pavement management.

Keywords: urban pavement, PCI, surface distress, road safety, maintenance prioritisation.

1

. Introduction

crashes, especially under adverse

weather conditions, by reducing

tyre–pavement adhesion and vehicle

control. Low friction or high

roughness raises crash frequency in

both dry and wet conditions and

disproportionately affects vulnerable

users such as pedestrians and

cyclists (Ragnoli et al., 2018).

Adequate maintenance of urban

pavements is fundamental to

ensuring efficient mobility and road

safety in cities. The condition of road

surfaces directly influences comfort,

accessibility and the protection of all

users, including pedestrians, cyclists

and drivers.

Evidence

also

indicates

that

preventive maintenance and timely

repair can reduce serious and fatal

injuries on urban networks by up to

Pavement deterioration such as

potholes, cracking and deformation

adversely affects traffic flow and

travel comfort. Irregular surfaces

oblige drivers to reduce speed and

perform evasive manoeuvres, which

increases congestion and journey

times. Moreover, pedestrians and

cyclists tend to avoid damaged

footways or cycle lanes, shifting into

spaces not designed for them, which

aggravates mobility problems and

compromises their safety (Beketov &

Khalimova, 2023; Cafiso et al.,

60%.

In the Ecuadorian context, Eloy

Alfaro Street in the canton of Jipijapa

exemplifies

the

of

progressive

urban road

deterioration

infrastructure. Accelerated urban

growth and land-use changes driven

by commercial and service activities

have exerted pressure on traditional

streets, affecting both their heritage

value and daily functionality. The

absence of conservation and

rehabilitation has allowed physical

wear to advance, undermining urban

quality and hindering pedestrian and

2022).

Road safety largely depends on

pavement quality. Deteriorated

surfaces increase the likelihood of

Revista Científica ‘‘INGENIAR”: Ingeniería, Tecnología e Investigación. Vol. 8 Núm. (16) 2025. ISSN: 2737-6249

Diagnosis of Urban Pavement Failures Using the PCI Method: The Case of Eloy Alfaro Street in Jipijapa.

vehicular mobility on one of the

central arteries of Jipijapa

Regalado, 2023; Samaniego &

Calero, 2021).

internationally, proving reliable and

cost-effective for network monitoring,

particularly where maintenance

resources are limited (Azam et al.,

(

2

023; Karim et al., 2016; Temimi et

Despite the historical and social

relevance of Eloy Alfaro Street, there

are no conservation plans or

systematic maintenance strategies

to reverse deterioration. This lack of

al., 2021).

Comparative studies have shown

that the PCI’s ASTM-based visual

approach enables comprehensive

and repeatable assessment across

management

has

exacerbated

structural and surface problems over

time, affecting the urban image and

user safety. The situation calls for an

urgent rehabilitation masterplan that

urban

and

rural

settings.

Investigations in Yemen, Brazil and

Indonesia reported that the PCI is

suitable for identifying maintenance

needs, prioritising interventions and

optimising budgets, and that it

integrates readily with road asset

prioritises recovery of

critical

segments and integrates cultural,

social and environmental aspects in

corridor management.

management

systems

and

technologies such as GIS and

artificial intelligence (Ibragimov et al.,

Within this framework, the Pavement

Condition Index (PCI; ASTM D6433-

2

024; Loprencipe, Pantuso, Simone,

et al., 2017). The use of PCI fosters

consistent and comparable

diagnoses, which are essential for

efficient and transparent

0

7) was an appropriate standardised

visual diagnostic tool owing to its

objectivity, simplicity and

effectiveness for assessing the

surface state of urban pavements.

The PCI quantifies deterioration by

identifying and classifying distress

types, severities and extents,

generating a numerical index from 0

to 100 that supports decisions on

management of road infrastructure.

The central purpose of this study was

to evaluate the pavement condition

of Eloy Alfaro Street, between

Febres Cordero and Villamil in

Jipijapa, through application of the

PCI method (ASTM D6433-07).

Three specific objectives were set: to

maintenance

and

rehabilitation

priorities. Its visual and systematic

application has been validated

224

Moreno-Ponce et al. (2025)

identify surface distresses and

classify them by type and severity, to

quantify deterioration by computing

PCI in sampling units and to

determine the overall condition of the

section so as to inform technical

2. Materials and metods

2.1

Study area and scope

The study was conducted on Eloy

Alfaro Street in Jipijapa (Manabí,

Ecuador), along the section between

Febres Cordero and Villamil. This is

an urban corridor with commercial

criteria

for

maintenance

and

rehabilitation prioritisation.

This article presented the theoretical

and methodological framework

and

service

functions

whose

selection responded to its functional

relevance and documented evidence

of progressive surface deterioration.

The diagnosis was framed as an

underpinning the PCI application,

followed by the description of

materials, procedures and technical

criteria used for data collection. It

then reported the results of distress

diagnosis and PCI computation for

the study corridor, and finally offered

the discussion and conclusions,

where the findings were interpreted

and recommendations were outlined

observational,

cross-sectional

assessment of pavement condition

using standardised visual inspection

in accordance with the PCI method

(ASTM D6433-07), for the purposes

of maintenance management and

intervention prioritisation.

for

the

management

of urban

and

The evaluable population was

defined as the set of sampling units

conservation

road

infrastructure. In doing so, the study

contributed to the technical diagnosis

of urban pavement and to the

(SU) within the paved section; 26

SUs were identified, and

a

representative sampling design was

applied with 95 % confidence and

formulation

of

rehabilitation

guidelines applicable to the Jipijapa

context.

±

5% sampling error, assuming a

standard deviation of 8 PCI points for

a first survey. As a result, eight

georeferenced SUs were selected

and assessed. See Figure 1 for the

location of the section and the

225

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Diagnosis of Urban Pavement Failures Using the PCI Method: The Case of Eloy Alfaro Street in Jipijapa.

sequence of SUs along the

carriageway.

Figure 1. Location of the study section and sequence of sampling units.

Note. The satellite image served as the cartographic base to delineate SUs on the carriageway

and to document the visual survey; the analysis was performed with representative sampling (n

=

8, 95 % confidence, ± 5% error). Source: Google Earth; authors’ elaboration.

D6433, using the thesis version

D6433-07) and consulting the

016/2018 and 2023 editions for

The Pavement Condition Index (PCI)

(

quantifies surface condition on a 0–

2

100 scale based on the identification,

normative reference without altering

the reported computations. The

severity and extent of distresses

within sampling units. The method

has been formalised by ASTM

D6433 for roads and car parks, and

its use has been consolidated in

pavement management manuals

and in the scientific literature for

urban networks (ASTM International,

practice

comprises:

delimiting

homogeneous sections; partitioning

into sampling units (SUs) (typically

230 ± 93 m² for asphalt carriageways

with width < 7.30 m); conducting a

standardised visual survey with

distress classification by type and

severity (low, medium, high);

computing distress densities and

deduct values (VD); applying the PCI

correction to obtain the corrected

deduct value (CDV); and deriving the

2007).

2.2. PCI methodological design

and sampling scheme

The study adopted the PCI

procedure as specified in ASTM

226

Moreno-Ponce et al. (2025)

PCI per SU and the section-level PCI

as the (weighted) mean.

m

tape

(for

small-dimension

distresses), a 50 m tape (for SU

lengths and extensive distresses), a

Sample size and selection. For

project-level evaluation (not full-

network surveys), ASTM/Shahin

recommend estimating n for a target

error (±5 PCI) with an initial σ = 8 for

a first survey; if n < 5, all units should

be inspected. In this case, the paved

section comprised N = 26 SUs; a

representative design with 95%

confidence and ±5% sampling error

1

m aluminium straightedge (for level

differences), a field notebook, a

digital camera for photographic

evidence, and

a

standardised

recording form adapted from ASTM

D6433-07 to consolidate roadway

data and, for each distress, its type,

severity and measurement unit. The

use of standardised forms was

consistent with the practice defined

(σ assumed = 8 PCI points) was

by

ASTM

D6433-07,

which

adopted, and eight georeferenced

SUs were ultimately selected and

surveyed (Shahin, 2005).

formalises the Pavement Condition

Index for roads and car parks.

2

.4. Survey procedure and quality

The choice of PCI for urban roads

was supported by experience in

control

urban

pavement

management

The field and office procedure was

structured in stages in accordance

with ASTM D6433-07:

systems (PMS) and international

applications that employ PCI to

prioritise maintenance and optimise

budgets (e.g., Italy, Yemen, Iraq),

1. Segmentation and sampling

units (SUs). The carriageway

facilitating

comparability and transferability

Loprencipe et al., 2017).

methodological

was

divided

into

one

homogeneous section and

into SUs according to the

ranges indicated for asphalt

(

2.3. Instruments and material

pavements

(approximately

30 ± 93 m² when width is <

.30 m). For project-level

A basic set of instruments was used

for the standardised visual inspection

and recording of distresses in

accordance with the PCI method: a 5

2

7

evaluation, SU selection was

performed using

227

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Diagnosis of Urban Pavement Failures Using the PCI Method: The Case of Eloy Alfaro Street in Jipijapa.

representative

sampling,

These

expressions

were

applying the sampling interval

applied in accordance with the PCI

procedure.

푖 = 푁/푛as

per

the

methodology

(project

4

. Deduct values and correction

example: N = 26, n = 10, 푖 ≈

).

. Visual

(VD and CDV). For each SU,

3

distresses by type and

severity were totalled and the

Deduct Value (VD) was

obtained from the PCI

2

inspection

and

recording. In each SU,

distresses were identified and

classified by type and severity

deduction

curves.

The

(low, medium, high), noting

maximum admissible number

the corresponding

of VDs 푚was then determined

measurement unit (area,

length or number) on the

standardised PCI field form.

and,

by

iteration,

the

maximum Corrected Deduct

Value (CDV) was obtained,

accounting for the number 푞of

VD > 2, as specified by the

PCI correction procedure.

3

. Computation

of

distress

was

densities.

Density

calculated according to the

measurement unit:

5

. PCI calculation. The PCI for

each SU was computed as

PCI = 100 − max⁡(CDV). The

section PCI corresponded to

the mean of the SU PCIs

(weighted where applicable).

o Area-type distresses:

Damage area

Density =

00.

×

SU area

1

o Length-type

distresses: Density =

6. Quality control. Consistency

checks included: (i) cross-

Damage length×ꢀ.6ꢀ m

×

SU area

checking

forms

and

100.

photographs for 100% of

surveyed SUs; (ii) verification

of severities against the

ASTM catalogue prior to VD

o Count-type distresses

e.g., potholes):

(

Density =

Total pothole area

SU area

computation;

(iii)

repeat

×

100.

measurements in 10% of SUs

228

Moreno-Ponce et al. (2025)

to verify reproducibility; and

estimated

푚 = 1.00 + ₉ꢀ(100 − 퐻퐷푉 ),

as

9

(iv)

an

audit

of

the

ꢁ

8

computational

chain

where 퐻퐷푉 is the highest VD in SU 푖.

ꢁ

(densities–VD–CDV–PCI)

Iteratively, the smallest VD > 2.0 was

prior to closure. These

practices align with PCI

reduced to 2.0, VD

푞(number of VD

and

2) were

total

>

guidance

in

pavement

recalculated, and the CDV was

obtained from the correction curve.

The PCI for each SU was then

management manuals and

technical literature.

computed

as

PCI = 100 −

2.5. Data processing and analysis

max⁡(CDV)(see Figure 2).

Field records were transcribed to a

spreadsheet per SU including SU

Figure 2. Stages of the PCI method

(

calculation flow).

area,

distress

type, severity

measurement

(low/medium/high),

unit (area, length or number) and

photographic evidence. Distress

density was obtained following the

PCI method:

•

Area-type:

Density =

Damage area

×

100.

SU area

Length-type:

Density =

100.

Damage length×ꢀ.6ꢀ m

×

SU area

Count-type (e.g., potholes):

Total pothole area

Density =

00.

×

Note. Sequence applied in the study:

identification and measurement of

distresses → calculation of densities →

derivation of VD → correction (q → CDV) →

PCI per unit → section-level PCI →

SU area

1

From the densities, VDs for each

distress type/severity were derived

using the PCI tables. The maximum

admissible number of VDs was

classification

interventions.

and

prioritisation

of

229

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Diagnosis of Urban Pavement Failures Using the PCI Method: The Case of Eloy Alfaro Street in Jipijapa.

The section PCI corresponded to the

mean of the unit PCIs (weighted

where applicable). For classification,

the following ranges were used: 100–

During fieldwork, operational road-

safety practices were applied: use of

high-visibility PPE, scheduling

surveys during lower-flow periods,

continuous communication among

observers and, where necessary,

temporary warning signage in

8

5 (Excellent), 85–70 (Very good),

0–55 (Good), 55–40 (Fair), 40–25

7

(Poor), 25–10 (Very poor) and 10–0

(Failed).

accordance

guidelines

with

for

international

road safety

Data handling included automated

range and unit checks in the

spreadsheet, double verification

against field forms and photographs,

and an audit of the density → VD →

CDV → PCI processing chain,

inspections/audits and temporary

traffic control. These measures are

consistent with PIARC and FHWA

manuals and guidance to safeguard

both the survey team and road users

during evaluation activities.

ensuring

traceability

and

reproducibility.

Finally, primary records (forms,

photographs and spreadsheets)

2.6

Ethical and operational

were

safeguarded,

and

the

safety consideration

processing

workflow

was

documented to secure traceability

and reproducibility of the diagnosis,

The inspection was conducted from

public and safe areas, without

interfering with traffic and without

collecting personal data; images

following data-quality

good

management practices for PMS.

were

restricted

to

pavement

condition, avoiding the identification

of individuals or vehicle plates. As it

did not involve intervention with

human subjects or destructive

sampling, the study was considered

3

. Results and discussion

.1 Typology of identified

3

distresses

In the standardised visual survey, the

following distresses were

minimal

risk

with

negligible

environmental impact.

documented: (i) alligator (fatigue)

cracking; (ii) longitudinal and

230

Moreno-Ponce et al. (2025)

transverse cracking; (iii) potholes;

the corresponding measurement unit

(area, length or count) and the

severity classification (low, medium,

high) defined in the method

catalogue.

(iv) weathering and ravelling; and (v)

rutting. The identification and

measurement of each distress

followed ASTM D6433-07, applying

Table 1. Observed surface distresses and quantification metric (ASTM D6433-07).

Distress type

Operational

description

Measurement

unit

Severity criterion

(L/M/H)

(

summary)

Alligator (fatigue)

cracking

Interconnected crack Affected area

Crack width, opening and

extent of the mesh.

mesh in the asphalt

(m²)

surface

paths).

(wheel

Longitudinal/transverse Cracks parallel or

Length (m)

Opening, continuity and

cracking

perpendicular to the

road axis.

Localised loss of

presence

exudation/ravelling.

Diameter/depth

edges ravelled or well

defined.

Percentage of affected

surface and presence of

loose material.

of

Potholes

Count and

area (m²)

and

asphalt

mixture

forming cavities.

Binder ageing, micro-

cracking and particle

loss.

Weathering / ravelling

Rutting

Area (m²)

Permanent

Area (m²) and Measured depth and

depressions in wheel

depth

continuity

along

the

paths

due

to

wheel path.

repeated loading.

Note. These metrics were used on the field forms to compute densities, derive deduct values (VD)

and subsequently the corrected deduct value (CDV) and PCI for each sampling unit.

The observed set of distresses

indicated a mixed deterioration

pattern with a dominant structural

component: alligator cracking and

rutting evidenced fatigue and plastic

deformation under repeated loading,

whereas weathering and ravelling

indicated binder ageing and deficits

in micro-drainage. Longitudinal and

transverse cracks likely facilitated

Methods regarding the applied

metrics (area/length/count) and the

computation scheme from densities

to VD, CDV and PCI in each

sampling unit.

3.2

PCI index per sampling unit

The PCI was calculated for eight

sampling units (SUs), each 228 m²,

distributed

between

chainages

0+105 and 0+875. Table 2 reports

water

ingress,

accelerating

the exact

values and their

degradation. This pattern was

consistent with the Materials and

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Diagnosis of Urban Pavement Failures Using the PCI Method: The Case of Eloy Alfaro Street in Jipijapa.

classification according to the

adopted scale.

Table 2. PCI by sampling unit

SU

1

2

3

4

5

6

7

8

Chainage (start–end)

0+105–0+140

0+210–0+245

0+315–0+350

0+420–0+455

0+525–0+560

0+630–0+665

0+735–0+770

0+840–0+875

Area (m²)

228

PCI

38

8

54

29

0

3

64

Classification

Poor

228

Failed

Fair

Poor

Failed

Good

Note. Classification used: Excellent (100–85), Very good (85–70), Good (70–55), Fair (55–40),

Poor (40–25), Very poor (25–10), Failed (10–0). Each SU had area = 228 m².

The class distribution showed 50.0%

of the section as Failed (SU-2, SU-5,

SU-6, SU-7), 25.0% Poor (SU-1, SU-

in SU-3 (PCI 54) to prevent

downgrading to “Poor”.

3.3

Operational and road-safety

4), 12.5% Fair (SU-3) and 12.5%

implications

Good (SU-8). The mean PCI = 24.5

confirmed an overall “very poor”

condition and a compromised level of

service. The median was 18.5

Based on the exact PCI values for

each sampling unit (SU), the

operational and road-safety criticality

was characterised by subsection.

The categorisation drew on the

probability of loss of functionality, the

presence of surface irregularities

(

ordered values: 0, 0, 3, 8, 29, 38, 54,

4), evidencing skew towards

degraded states. Variability was high

minimum = 0, maximum = 64),

6

(

potholes, active cracking and

which justified prioritisation by

subsections:

rutting) and the impact on ride

comfort, in accordance with the PCI

classes. Table 3 summarises the

surface criticality level by SU and the

distress patterns observed in the

field.

▪

Reconstruction/deep recycling in

SU-5, SU-6 and SU-7 (PCI 0–3).

Major rehabilitation in SU-2 (PCI

8

) and in SU-1/SU-4 (PCI 38 and

9).

2

▪

Preservation in SU-8 (PCI 64)

and targeted major maintenance

232

Moreno-Ponce et al. (2025)

Table 3. Operational and road-safety criticality level by sampling unit

SU PCI Class

Criticality

level

Observations (dominant distresses; descriptive

only)

1

2

38

8

Poor Medium–high Longitudinal/transverse cracking and ravelling; localised

unevenness.

Failed High

Fair Medium

Severe alligator cracking and potholes; loss of surface

integrity.

Concentrated damage; moderate cracking and ravelling.

3

4

5

6

7

8

54

29

0

3

Poor Medium–high Connected cracking and ravelling; active progression.

Failed Very high

Good Low

Functional collapse; multiple concurrent distress modes.

Functional collapse; probable loss of support.

Potholes and severe cracking; high surface unevenness.

Localised deterioration; dispersed low–medium-severity

cracking.

64

Note. “Criticality level” describes the operational severity derived from the surface condition (PCI

class) and the observed distress pattern. It does not include recommended actions.

The

subsections (SU-2, SU-5, SU-6, SU-

) in the central sector implied high–

concentration

of

failed

3.4. Correction of deduct values

(VD → CDV) and computation of

PCI per unit

7

very high operational risk due to

deep potholes, connected cracking

and rutting, which forced abrupt

speed reductions and evasive

manoeuvres with potential for

vehicle–pedestrian conflict and loss

of stability for two-wheelers. The

Poor subsections (SU-1 and SU-4)

showed conditions consistent with

For each SU, deduct values (VD) by

distress type/severity were summed

and the correction curve was applied

as a function of q (number of VD > 2

in the iteration) and VD_total. In each

iteration, the smallest VD > 2 was

reduced to 2.0, VD_total was

recalculated, q was updated and the

corresponding CDV was obtained

from the correction curve. The SU

PCI was determined as:

structural

degradation

trending

towards failure, while SU-3 (Fair)

presented a state susceptible to

deterioration if not preserved. SU-8

PCI = 100 − max⁡(CDV)

The following tables present the

exact iterative values and the

resulting PCI for each SU (see Figure

(

Good) exhibited a preservable

condition with routine sealing and

monitoring needs.

3

for the effect of q on CDV).

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Diagnosis of Urban Pavement Failures Using the PCI Method: The Case of Eloy Alfaro Street in Jipijapa.

Figure 3. Correction curves (CDV vs. VD_total)

Note. The figure illustrates the effect of q (number of VD > 2) on CDV for a given VD_total: the

higher the q, the higher the CDV and, consequently, the lower the resulting PCI.

Subsections with multiple relevant

distress modes (high q) tended to

exhibit elevated CDV even when no

single VD was extreme, which

explains low PCI in subsections with

diverse deterioration.

Table 3. Correction of deduct values — SU-04 (Chainage 0+105–0+140; Area 228 m²)

Iteration

Individual VDs

33, 26, 21, 17, 16, 12

33, 26, 21, 17, 16, 2

33, 26, 21, 17, 2, 2

33, 26, 21, 2, 2, 2

33, 26, 2, 2, 2, 2

VD_total

125

115

101

86

q

6

5

4

3

2

1

CDV

62

55

58

55

1

2

3

4

5

6

67

43

49

43

33, 2, 2, 2, 2, 2

Note. Max (CDV) = 62 ⇒ PCI = 100 − 62 = 38 (Poor).

SU-04 exhibited a broad set of

distresses (initial q = 6) with

moderate–high VDs. The PCI = 38

was governed by the combination of

several distress modes (high q)

rather than a single extreme VD. The

drainage

adjustments

where

appropriate to prevent recurrence.

pattern

suggests

accumulated

deterioration consistent with the

need for structural patching, local

milling and thin overlays, with

234

Moreno-Ponce et al. (2025)

Table 4. Correction of deduct values — SU-07 (Chainage 0+210–0+245; Area 228 m²)

Iteration

Individual VDs

87, 35, 28, 21

87, 35, 28, 2

87, 35, 2, 2

VD_total

171

q

4

3

2

1

CDV

91

89

84

92

1

2

3

4

152

126

93

87, 2, 2, 2

Note. Max (CDV) = 92 ⇒ PCI = 8 (Failed).

SU-07 was dominated by a very high

principal VD (87) together with two

other relevant distresses (initial q =

the subsection. This behaviour is

typical of advanced fatigue combined

with local disintegration: a single

dominant high-severity mode can

drive the PCI to failure even when

other distresses are moderate.

4). Although the correction reduced q

and VD_total, CDV reached 92 in the

last iteration, confirming severe

deterioration with functional failure of

Table 5. Correction of deduct values — SU-10 (Chainage 0+315–0+350; Area 228 m²)

Iteration

Individual VDs

41, 12, 11

41, 12, 2

VD_total

q

3

2

1

CDV

41

40

1

2

3

64

55

45

41, 2, 2

46

Note. Max (CDV) = 46 ⇒ PCI = 54 (Fair).

SU-10 showed three distress modes

with a dominant VD = 41 and q

reduced from 3 → 1. The maximum

CDV = 46 placed the subsection in

deterioration. The state is consistent

with major maintenance needs

(crack sealing, local patching and

surface reinforcement) to prevent

downgrading to Poor.

Fair

condition,

indicating

concentrated rather than generalised

Table 6. Correction of deduct values — SU-13 (Chainage 0+420–0+455; Area 228 m²)

Iteration

Individual VDs

53, 30, 18

53, 30, 2

VD_total

101

q

3

2

1

CDV

71

61

1

2

3

85

57

53, 2, 2

57

Note. Max (CDV) = 71 ⇒ PCI = 29 (Poor).

SU-13 was governed by two principal

VDs (53 and 30) and a third relevant

distress (initial q = 3). Although the

correction reduced q and VD_total,

CDV remained high (71), placing the

subsection in Poor condition. The

pattern indicates advanced fatigue

with localised disintegration unlikely

to be resolved by superficial

treatments alone.

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Diagnosis of Urban Pavement Failures Using the PCI Method: The Case of Eloy Alfaro Street in Jipijapa.

Table 7. Correction of deduct values — SU-16 (Chainage 0+525–0+560; Area 228 m²)

Iteration

Individual VDs

65, 55, 54, 51, 20, 9

65, 55, 54, 51, 20, 2

65, 55, 54, 51, 2, 2

65, 55, 54, 2, 2, 2

65, 55, 2, 2, 2, 2

VD_total

254

q

6

5

4

3

2

1

CDV

100

86

1

2

3

4

5

6

247

229

180

128

65, 2, 2, 2, 2, 2

75

Note. Max (CDV) = 100 ⇒ PCI = 0 (Failed).

SU-16 exhibited very high VDs and

multiple distress modes (initial q = 6),

with an exceptional VD_total > 250.

CDV reached the maximum (100) in

several iterations, confirming

generalised structural failure.

Table 8. Correction of deduct values — SU-19 (Chainage 0+630–0+665; Area 228 m²)

Iteration

Individual VDs

58, 56, 39, 25, 16, 11, 7.70

58, 56, 39, 25, 16, 11, 2

58, 56, 39, 25, 16, 2, 2

58, 56, 39, 25, 2, 2, 2

58, 56, 39, 2, 2, 2, 2

58, 56, 2, 2, 2, 2, 2

VD_total

212.70

207

q

7

6

5

4

3

2

1

CDV

100

97

100

93

1

2

3

4

5

6

7

198

184

161

124

83

70

58, 2, 2, 2, 2, 2, 2

70

Note. Max (CDV) = 100 ⇒ PCI = 0 (Failed).

SU-19 displayed numerous relevant

distress modes (initial q = 7) and a

very high VD_total from the first

iteration (212.70), which drove CDV

to its maximum (100). Even as q and

in functional collapse (PCI = 0). This

pattern is consistent with generalised

structural fatigue, disintegration and

probable

loss

of

support

(base/subgrade), exacerbated by

water ingress through cracks.

VD_total decreased through

correction, the subsection remained

Table 9. Correction of deduct values — SU-22 (Chainage 0+735–0+770; Area 228 m²)

Iteration

Individual VDs

69, 68, 20, 10

69, 68, 20, 2

69, 68, 2, 2

VD_total

167

q

4

3

2

1

CDV

95

97

91

75

1

2

3

4

159

141

75

69, 2, 2, 2

Note. Max (CDV) = 97 ⇒ PCI = 3 (Failed).

SU-22 was dominated by two very

high VDs (69 and 68). Even with

relatively few distress modes (initial q

pattern suggests severe cracking

and localised disintegration that

compromise

operational

safety

=

4), CDV reached 97, placing the

(potholes, frayed edges).

subsection in Failed condition. The

236

Moreno-Ponce et al. (2025)

Table 10. Correction of deduct values — SU-25 (Chainage 0+840–0+875; Area 228 m²)

Iteration

Individual VDs

VD_total

q

CDV

1

2

3

32, 12, 11

32, 12, 2

32, 2, 2

55

46

36

3

2

1

35

34

36

Note. Max (CDV) = 36 ⇒ PCI = 64 (Good).

SU-25 presented moderate VDs and

low q, keeping CDV contained (≤ 36)

and the subsection in good condition.

Deterioration was limited and

preservable, consistent with crack

sealing, micro or ultra-thin surfacing

and routine maintenance (e.g., gutter

cleaning), prioritising prevention to

avoid downgrading to Fair/Poor.

3.5

Consolidation of PCI by unit

and overall section condition

Based on the VD → CDV iterations

presented above, the PCI values per

sampling unit (SU) and the overall

condition of the section were

consolidated. For each SU, the exact

chainage and a constant area = 228

m² were reported; condition classes

followed

Excellent, very good, Good, Fair,

Poor, very poor, Failed).

the

adopted

scale

(

Table 11. Consolidation of the surface evaluation – Eloy Alfaro Street

SU

Chainage (start–end)

0+105–0+140

0+210–0+245

0+315–0+350

0+420–0+455

0+525–0+560

0+630–0+665

0+735–0+770

0+840–0+875

Area (m²)

228

PCI

38

8

54

29

0

3

64

24.5

Classification

1

2

3

4

5

6

7

8

Poor

Failed

Fair

228

Poor

Failed

Good

Section means

Very poor

Note. Classification according to the thesis scale: Excellent (100–85); Very good (85–70); Good

70–55); Fair (55–40); Poor (40–25); Very poor (25–10); Failed (10–0).

(

3

.6

Severities and densities

densities according to the method

metric (area, length × 0.60 m, or

count). As a documented example, a

high-severity edge crack (7A) of 7.00

m in length with SU area = 228 m²

yielded a density = 1.84%, which was

(computational traceability)

Severities (L/M/H) assigned to each

distress were converted to VD using

the ASTM D6433-07 deduction

curves

after

computing

their

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Diagnosis of Urban Pavement Failures Using the PCI Method: The Case of Eloy Alfaro Street in Jipijapa.

then converted into its corresponding

VD using the deduction curves. This

procedure was applied analogously

to all distresses and SUs.

structural and operational conditions

sustain superior levels of service

(Tacuri & Ortíz, 2022). Likewise,

surveys in the Bogotá region

confirmed institutional PCI use by the

IDU and distributions with substantial

presence of failed/very poor/poor

classes in urban grids, supporting

methodological comparability and

the utility of PCI as a condition

Discussion

The mean PCI = 24.5 (Very poor)

observed on Eloy Alfaro Street was

consistent with urban scenarios of

advanced deterioration reported for

Andean and tropical cities. In Quito,

a study comparing PCI–IRI on Av.

Llira Ñan using ASTM D6433-07

visual surveys and Merlin roughness

measurements supported the validity

of the approach for urban grids;

results showed segments with

deficient condition and failed

sections, comparable to the central

portion of Jipijapa’s corridor (Freire,

descriptor

in

local

networks

(Restrepo & Cruz, 2017). From a

management and safety perspective,

the findings reinforce PCI (ASTM

D6433) as an input for prioritising

interventions,

although

greater

effectiveness is achieved when

integrated with functional and risk

indicators

(e.g.,

IRI,

friction/macrotexture).

ASTM

2

024). In Guayaquil, the assessment

practice states that PCI objectifies

surface and operational condition

without directly measuring friction or

roughness; therefore, it should be

complemented within a PMS, and

technical guidance (UCPRC/CCPIC)

recommends not relying on PCI

of Calle 47 S-E using PCI reported

marked heterogeneity, with samples

in the “very poor/poor” ranges and

others “good/very good”, a pattern

compatible with heavy loading and

irregular

maintenance,

also

observed on Eloy Alfaro (Figueroa &

Lema, 2025).

alone

to

select

conservation

International,

strategies

(ASTM

2020). Regarding underlying causes,

In contrast, the Loja–Saraguro

the literature recognises the

corridor

(km

27+000–37+000)

influence of materials, drainage and

maintenance on performance: PCI-

based studies have explored

obtained PCI = 81.82 (Very good)

and IRI = 3.83 m/km, indicating that

contexts with more favourable

238

Moreno-Ponce et al. (2025)

correlations

condition and pavement state

sometimes weak when few

between

drainage

was fair (SU-3) and one was good

(SU-8 = 64). This pattern confirmed a

significant loss of level of service and

advanced deterioration in the central

portion of the section.

(

elements are assessed), while

broader analyses show combined

effects of structure, climate and

demand on degradation (Diniz &

Melo, 2023). In terms of road safety,

Federal Highway Administration

2.

The predominant distresses

were alligator cracking, longitudinal

and transverse cracking, potholes,

weathering/ravelling and rutting. The

combination of multiple distress

modes (high q) explained the

elevated CDV and, consequently,

low PCI in several units, consistent

with structural fatigue under repeated

loading and suboptimal drainage

(2023) documents and recent

syntheses confirm statistically robust

associations

between

and crash

friction/macrotexture

rates, and indicate that rutting and

high roughness increase risk,

particularly in wet conditions;

consequently, the presence of

potholes, active cracking and rutting

observed on Eloy Alfaro is consistent

with elevated operational risk in an

urban setting.

conditions.

SU-25

(PCI

64)

evidenced preservable subsections,

whereas SU-16/SU-19/SU-22 (PCI

0–3) reflected functional collapse of

the surfacing.

3.

From

an

operational

standpoint, the highly degraded

surface condition implied a greater

likelihood of irregularities (potholes,

rutting, active cracking), affecting

ride comfort, traffic flow and urban

road safety, especially in wet

conditions. The findings supported

the use of PCI as an input for

prioritisation within a pavement

management system, distinguishing

reconstruction/rehabilitation for failed

4

. Conclusions

1. Based on the standardised

PCI visual inspection (ASTM D6433-

7) and the VD → CDV treatment,

0

Eloy Alfaro Street (Febres Cordero–

Villamil section) exhibited an overall

“very poor” condition with a mean

PCI = 24.5. Spatial response was

heterogeneous: four units were failed

(PCI 0–8: SU-2, SU-5, SU-6, SU-7),

two were poor (SU-1 and SU-4), one

239

Revista Científica ‘‘INGENIAR”: Ingeniería, Tecnología e Investigación. Vol. 8 Núm. (16) 2025. ISSN: 2737-6249

Diagnosis of Urban Pavement Failures Using the PCI Method: The Case of Eloy Alfaro Street in Jipijapa.

or poor subsections and preservation

for segments in better condition.

https://doi.org/10.1520/D6433

20

-

ASTM International. (2020). Practice

for Roads and Parking Lots

Pavement Condition Index

Surveys.

4

.

Limitations and outlook: (i) the

scope was visual (PCI) with n = 8

sampling units, without instrumental

measurements of roughness (IRI),

friction/macrotexture or structural

capacity (e.g., FWD); (ii) subsurface

drainage was not assessed with

indirect methods (e.g., GPR). Future

work should increase the sample

size, integrate functional and safety

indicators (PCI–IRI–friction), and

verify structural capacity to refine the

intervention strategy and the

maintenance programme.

https://doi.org/10.1520/D6433

-

20

Azam, A., Alshehri, A. H., Alharthai,

M., El-Banna, M. M., Yosri, A.

M., & Beshr, A. A. A. (2023).

Applications of Terrestrial

Laser Scanner in Detecting

Pavement Surface Defects.

Processes 2023, Vol. 11,

Page 1370, 11(5), 1370.

https://doi.org/10.3390/PR110

51370

Beketov, A., & Khalimova, S. (2023).

Impact of Roughness and

Friction Properties of Road

Surface of Urban Streets on

Bibliografía

Traffic

Safety.

Angelo, A. A., Sasai, K., & Kaito, K.

Http://Komunikacie.Uniza.Sk/

Doi/10.26552/Com.C.2023.05

(

2023). Safety Integrated

Network Level Pavement

Maintenance Decision

Support Framework as a

Practical Solution in

1

.Html, F51–F63.

https://doi.org/10.26552/COM

C.2023.051

25(3),

.

Developing Countries: The

Case of Addis Ababa,

Ethiopia. Sustainability 2023,

Vol. 15, Page 8884, 15(11),

Cafiso, S., Di Graziano, A.,

Marchetta, V., & Pappalardo,

G. (2022). Urban road

pavements monitoring and

assessment using bike and e-

scooter as probe vehicles.

Case Studies in Construction

8

884.

https://doi.org/10.3390/SU151

8884

1

Materials,

https://doi.org/10.1016/J.CSC

M.2022.E00889

16,

e00889.

ASTM International. (2007). Practice

for Roads and Parking Lots

Pavement Condition Index

Surveys. ASTM International.

Diniz, M., & Melo, R. (2023). Analysis

of the correlation between the

240

Moreno-Ponce et al. (2025)

condition of urban pavements

and elements of the drainage

system. Revista ALCONPAT,

with Deep Learning and

Image Analysis: An End-to-

End Approach. Sensors 2024,

Vol. 24, Page 2333, 24(7),

2333.

1

3(2), 220–234.

https://doi.org/10.21041/RA.V

3I2.604

1

https://doi.org/10.3390/S2407

2

333

Federal Highway Administration.

2023). Pavement Friction for

(

Karim, F. M. A., Rubasi, K. A. H., &

Saleh, A. A. (2016). The Road

Pavement Condition Index

Road Safety: Primer on

Friction Measurement and

Management

FHWA.

https://highways.dot.gov/safet

y/rwd/keep-vehicles-

road/pavement-

Methods

|

(PCI)

Maintenance: A Case Study of

Yemen. Organization,

Technology and Management

in Construction: An

Evaluation

and

friction/pavement-friction-

road-safety-primer-friction

International Journal, 8(1),

1446–1455.

https://doi.org/10.1515/OTMC

J-2016-0008

Figueroa, S. L., & Lema, A. J. (2025).

Evaluación del estado del

pavimento en la calle 47 S-E

mediante el método PCI,

desde la planta Fabril S.A.

hasta la Av. Domingo Comín

en el cantón Guayaquil.

http://dspace.ups.edu.ec/han

dle/123456789/30292

Loprencipe, G., Pantuso, A., &

Mascio,

P.

Di.

(2017).

Sustainable

Pavement

Management

System

in

Urban Areas Considering the

Vehicle Operating Costs.

Sustainability, 9(3), 1–16.

https://ideas.repec.org/a/gam/

jsusta/v9y2017i3p453-

Freire, B. A. (2024). Análisis

comparativo del método de

PCI e IRI en la calificación de

la condición actual del

pavimento flexible en un

tramo de la Av. Llira Ñan y

OE2 en la Ciudad de Quito

d93491.html

Loprencipe, G., Pantuso, A., Simone,

A., & Lantieri, C. (2017). A

Specified

Procedure

for

Distress Identification and

Assessment for Urban Road

Surfaces Based on PCI.

Coatings 2017, Vol. 7, Page

[

Universidad

Politécnica

Salesiana].

http://dspace.ups.edu.ec/han

dle/123456789/27921

65,

7(5),

65.

https://doi.org/10.3390/COATI

NGS7050065

Ibragimov, E., Kim, Y., Hee Lee, J.,

Cho, J., & Lee, J.-J. (2024).

Automated

Pavement

Ragnoli, A., De Blasiis, M. R., & Di

Condition Index Assessment

Benedetto,

A.

(2018).

241

Revista Científica ‘‘INGENIAR”: Ingeniería, Tecnología e Investigación. Vol. 8 Núm. (16) 2025. ISSN: 2737-6249

Diagnosis of Urban Pavement Failures Using the PCI Method: The Case of Eloy Alfaro Street in Jipijapa.

Pavement Distress Detection

Methods: Review.

Infrastructures 2018, Vol. 3,

Page 58, 3(4), 58.

https://doi.org/10.3390/INFRA

STRUCTURES3040058

Tacuri, P. J., & Ortíz, A. P. (2022).

A

Evaluación de la superficie del

pavimento de la vía Loja

Saraguro, tramo abscisas

27+000 37+000 [Universidad

Técnica Particular de Loja].

http://dspace.utpl.edu.ec/han

dle/123456789/30519

Regalado, M. L. (2023). Análisis del

estado de calle Eloy Alfaro

desde Febres Cordero hasta

Villamil de la ciudad de

Jipijapa, mediante método

Pavement Condiction Index

Temimi, F. A. R., Hadi, A., Ali, M., &

Obaidi, A. H. F. (2021). The

Pavement Condition Index

(PCI) Method for Evaluating

Pavement Distresses of The

Roads in Iraq- A Case Study

[

Universidad Esatatal del Sur

de Manabí].

http://repositorio.unesum.edu.

ec/handle/53000/5464

in

Al-

Nasiriyah

City.

University of Thi-Qar Journal

for Engineering Sciences,

Restrepo, G., & Cruz, J. P. (2017).

Evaluación Del Estado De

Pavimentos Flexibles En La

Zona Urbana De La Calera

11(2),

17–23.

https://doi.org/10.31663/TQU

JES.11.2.394(2021)

[

Universidad

Distrital

Francisco José de Caldas].

http://hdl.handle.net/11349/69

88

Samaniego, G., & Calero, L. (2021).

Guidelines for the Master Plan

of Landscape Rehabilitation in

the Heritage Center of the

Jipijapa

Notes in Networks and

Systems, 272, 273–278.

https://doi.org/10.1007/978-3-

30-80710-8_33

Canton.

Lecture

0

Shahin, M. Y. (2005). Pavement

Management for Airports,

Roads, and Parking Lots (2nd

ed.). Science.

https://download.e-

bookshelf.de/download/0000/

Springer

0

003/25/L-G-0000000325-

002340048.pdf

242