scholarly journals DEVELOPMENT OF PROGRAM IN C++ FOR ANALYSIS OF NSV SURVEY DATA BY PCI METHOD FOR FLEXIBLE PAVEMENT

2019 ◽  
Vol 31 (3) ◽  
Author(s):  
Soumadeep Bagui ◽  
Swapan Kumar Bagui ◽  
Anukul Saxena
Keyword(s):  
Road Network ◽  
Design Procedure ◽  
Condition Index ◽  
Flexible Pavement ◽  
Regression Equations ◽  
Pavement Condition ◽  
C Language ◽  
Pavement Condition Index ◽  
Future Requirement

This paper presents the development of software in C++ Language for the determination of Pavement Condition Index (PCI) based on the design procedure mentioned in ASTM D 4433 and future requirement of maintenance of existing road /road network. Presently in India, Manual Pavement Condition survey has been replaced by automated Network Survey Vehicle (NSV). PCI procedure mentioned in ASTM D 4433 which needs uses of several curves and same curves have been converted in regression equations. These equations are used to prepare a Program in C++ Language. This will be useful for Pavement Engineer to determine PCI and maintenance strategy.

VALIDITY OF GARBER MODEL IN PREDICTING PAVEMENT CONDITION INDEX OF FLEXIBLE PAVEMENT IN KERBALA CITY

2018 ◽  
Vol 09 (02) ◽  
pp. 139-151
Author(s):  
Hussein Ewadh ◽  
◽  
Raid Almuhanna ◽  
Saja Alasadi ◽  
◽  
...  
Keyword(s):  
Condition Index ◽  
Flexible Pavement ◽  
Pavement Condition ◽  
Pavement Condition Index

scholarly journals Development of Regression Models for Predicting Pavement Condition Index from the International Roughness Index

2020 ◽  
Vol 26 (12) ◽  
pp. 81-94
Author(s):  
Muataz Safaa Abed
Keyword(s):  
Regression Models ◽  
Essential Element ◽  
Condition Index ◽  
Flexible Pavement ◽  
Transportation Infrastructure ◽  
International Roughness Index ◽  
Pavement Condition ◽  
Roughness Index ◽  
Pavement Condition Index ◽  
Quadratic Models

Flexible pavements are considered an essential element of transportation infrastructure. So, evaluations of flexible pavement performance are necessary for the proper management of transportation infrastructure. Pavement condition index (PCI) and international roughness index (IRI) are common indices applied to evaluate pavement surface conditions. However, the pavement condition surveys to calculate PCI are costly and time-consuming as compared to IRI. This article focuses on developing regression models that predict PCI from IRI. Eighty-three flexible pavement sections, with section length equal to 250 m, were selected in Al-Diwaniyah, Iraq, to develop PCI-IRI relationships. In terms of the quantity and severity of each observed distress, the pavement condition surveys were conducted by actually walking through all the sections. Using these data, PCI was calculated utilizing Micro PAVER software. Dynatest Road Surface Profiler (RSP) was used to collect IRI data of all the sections. Using the SPSS software, linear and nonlinear regressions have been used for developing two models between PCI and IRI based on the collected data. These models have the coefficients of determination (R2) equal to 0.715 and 0.722 for linear and quadratic models. Finally, the results indicate the linear and quadratic models are acceptable to predict PCI from IRI directly.

scholarly journals Development of a Relationship between Pavement Condition Index and International Roughness Index in Rural Road Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Sasan Adeli ◽  
Vahid Najafi moghaddam Gilani ◽  
Mohammad Kashani Novin ◽  
Ehsan Motesharei ◽  
Reza Salehfard
Keyword(s):  
Road Network ◽  
Condition Index ◽  
Road Surface ◽  
International Roughness Index ◽  
Pavement Condition ◽  
Exponential Regression ◽  
Rural Road ◽  
Proposed Model ◽  
Pavement Condition Index ◽  
Road Surface Roughness

The main objective of this paper was to investigate the relationship between PCI and IRI values of the rural road network. To this end, 6000 pavement sections of the rural road network in Iran were selected. Road surface images and roughness linear profiles were collected using an automated car to calculate PCI and IRI, respectively. Three exponential regression models were developed and validated in three different IRI intervals. Analysis of the results indicated that exponential regression was the best model to relate IRI and PCI. In these models, R2 values were found to be acceptable, equal to 0.75, 0.76, and 0.59 for roads with good, fair, and very poor qualities, respectively, indicating a good relationship between IRI and PCI. Moreover, validation results showed that the model had a high accuracy. Also, the relation between IRI and PCI became weaker as a result of increasing the level of road surface roughness, which can be caused by the increase in the number and severity of failures. Furthermore, two failures of rail R.C. and rutting were rarely observed in the studied roads. Therefore, the proposed model is more applicable for roads without the mentioned failures and asphalt-pavement rural road network.

scholarly journals EVALUASI PENILAIAN PERKERASAN JALAN DENGAN METODE PAVEMENT CONDITION INDEX (PCI) (STUDI KASUS RUAS JALAN GAJAH MADA DAN SOROGO KEC. CEPU)

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Hariyanto Hariyanto
Keyword(s):  
Condition Index ◽  
Flexible Pavement ◽  
Pavement Condition ◽  
Pavement Condition Index

<strong>Ruas  jalan  Gajah  Mada  dan  Sorogo  merupakan  akses  menuju  dua perguruan tinggi serta akses menuju instansi pertamina di kota Cepu yang menggunakan perkerasan lentur (<em>flexible pavement</em>). Berbagai kendaraan berat dan ringan melewati ruas jalan tersebut sehingga menyebabkan terjadinya kerusakan jalan. Evaluasi kondisi kerusakan jalan sangat perlu dilakukan untuk monitoring seberapa tingkat kerusakan jalan yang terjadi pada suatu ruas jalan. Penelitian ini bertujuan untuk mengetahui tingkat kerusakan yang terjadi, serta menentukan jenis penanganan kerusakan jalan yang sesuai.Metode yang dipakai dalam penilaian kondisi kerusakan perkerasan jalan ini adalah metode PCI (Pavement Condition  Index), melakukan  survei  secara visual dengan cara melihat dan menganalisis kerusakantersebut berdasarkan jenis, tingkat kerusakaannya serta kuantitas kerusakan untukdigunakan sebagai dasar dalam melakukan kegiatan pemeliharaan dan perbaikan.Cara menganalisanya dengan membagi ruas jalan dalam sampel seluas ±50 m2,menghitung densitas, mencari <em>deduct value </em>pada grafik lalu menghitung <em>PavementConditional Index (PCI).</em>Hasil  evaluasi  penelitian  kondisi  ruas  jalan  Gajah  Mada  dan  Sorogo dengan metode PCI diperoleh kerusakan lubang (1,21%), retak kulit buaya (10,19%), retak pinggir (7,94%), retak memanjang dan melintang (7,45%), bergelombang  (8,1%),  amblas(1,7%),  bahu  jalan  turun(7,1%),  pelapukan  dan butiran lepas (3,25%), dan alur (15,93%), pengelupasan (2,25%), benjol &amp; turun (0,9%),  retak  berkelok  (3,2%),  dan  mengembang  (1,1%).  Dengan  nilai  PCI sebesar 80 untuk ruas jalan Gajah Mada dan 78 untuk ruas jalan Sorogo.</strong>

scholarly journals Study of Flexible Pavement Structure Maintenance in Runways with Pavement Condition Index (PCI) Method

2020 ◽  
Vol 27 (1) ◽  
pp. 25
Author(s):  
Meilinda Atika Rachman ◽  
Harmein Rahman ◽  
Bambang Sugeng Subagio ◽  
Sri Hendarto
Keyword(s):  
Condition Index ◽  
Flexible Pavement ◽  
Pavement Condition ◽  
Pavement Condition Index ◽  
Pavement Structure

scholarly journals Identifikasi Kerusakan Perkerasan Lentur Pada Ruas Jalan Soekarno – Hatta, Palembang

2020 ◽  
Vol 20 (01) ◽  
pp. 19-26
Author(s):  
Muhammadiya Rifqi ◽  
Heni Fitriani
Keyword(s):  
Surface Damage ◽  
Condition Index ◽  
Flexible Pavement ◽  
Road Surface ◽  
Survey Method ◽  
Pavement Condition ◽  
The Road ◽  
Road Section ◽  
Pavement Condition Index ◽  
The City

[IN] Ruas Jalan Soekarno-Hatta kota Palembang merupakan Jalan Nasional yang berkelas Jalan Arteri Primer yang dilapisi dengan perkerasan lentur (flexible pavement). Jalan yang diamati dari Simpang Empat fly over Tanjung Api-Api hingga Simpang Empat Macan Lindungan  memiliki panjang 8,45 kilometer. Saat itu kota Palembang sedang menggenjot pembangunan proyek venue dan LRT guna menyukseskan perhelatan olahraga Asian games. Ruas Jalan Soekarno-Hatta Palembang digunakan sebagai aktivitas lalulintas kendaraan proyek akibatnya terjadi peningkatan volume kendaraan dan kepadatan lalulintas yang tak terkendali, sehingga dikhawatirkan berdampak pada kualitas perkerasan jalan tersebut. Tujuan Penelitian ini adalah untuk mengidentifikasi kerusakan permukaan perkerasan lentur jalan dengan menggunakan metode PCI (Pavement Condition Index). Survei metode PCI dilakukan secara visual berdasarkan jenis dan tingkat kerusakan jalan dengan penilaian numerik antara nol (gagal) hingga seratus (sempurna). Hasil identifikasi kerusakan permukaan jalan menunjukkan bahwa kerusakan yang terjadi pada ruas jalan tersebut sebanyak tujuh jenis yaitu kegemukan, amblas, keriting, pelepasan butiran, retak kulit buaya serta tonjolan dan lengkungan. Jumlah unit sampel segmen jalan yang mengalami kerusakan sebanyak 17 unit sampel dari total yang diteliti 68 unit sampel dengan nilai rata-rata PCI didapatkan sebesar 95,655 artinya jalan tersebut dengan kondisi “Sempurna”. Meskipun ruas jalan tersebut tergolong sempurna secara kondisi, akan tetapi masih terdapat kerusakan yang terjadi pada ruas tersebut, untuk itu perlu dilakukan pemeliharaan jalan pada unit sampel yang rusak sehingga dapat menjaga kualitas serta umur layak ruas jalan tersebut. [EN] The Soekarno-Hatta Road section of the city of Palembang is a classy National Road of the Primary Arterial Road that is equipped with flexible pavement. The road chosen from Simpang Empat fly over Tanjung Api-Api to Simpang Empat Macan Lindungan has a length of 8.45 kilometers. At present the city of Palembang is being promoted by a construction site and LRT project to succeed in the sporting event Asian games. The Soekarno-Hatta Palembang Road Section is used as a project vehicle traffic activity resulting in an increase in vehicle volume and uncontrolled traffic density, so it is feared to have an impact on the quality of the pavement. The purpose of this study was to identification road surface damage using the PCI (Pavement Condition Index) method. PCI survey method is carried out  visually based on the type and severity level of road damage with a numerical rating between zero (failed) to one hundred (excellent). The results of identification of road surface damage showed that there were 7 types of damage that occurred on the road section namely bleeding, depression, corrugation, weathering and raveling, potholes, alligator cracking, and bumps and sags. The number of sample units of the road segment that suffered damage as many as 17 sample units of the total studied by 68 units samples with an average value of PCI obtained by 95,655, This means that the road with the condition "excellent". Even though the road is classified as excellent, but damage is still needed in that section, for this reason it is necessary to maintain the road on the damaged sample unit so that it can be used at a reasonable quality for the life of the road section.

scholarly journals ANALISIS KERUSAKAN LANDAS PACU BANDAR UDARA DENGAN METODE PCI DAN PERBAIKANNYA

2021 ◽  
Vol 3 (1) ◽  
pp. 57-63
Author(s):  
Lahun Wahidah ◽  
Retno Ligina Ayu ◽  
Eko Wiyono
Keyword(s):  
Total Sample ◽  
Condition Index ◽  
Flexible Pavement ◽  
Standard Test ◽  
Test Method ◽  
Milling Machine ◽  
Pavement Condition ◽  
Standard Test Method ◽  
Airport Pavement ◽  
Pavement Condition Index

One method aimed to know the condition of the pavement runway on an airport is pavement condition index (PCI). This method has three parameters, type damage, severity damage, and the number of damage or density. In this research, the assessment of PCI is done on a runway (flexible pavement) at one of the airports in Jakarta with a broad 3000 m x 45 m. PCI’s value is gained by following a method from ASTM D 5340-98 (Standard Test Method for The Airport Pavement Condition Index Surveys) from all total sample. The research obtained shows that runway airports have an average of 75,59 (very good). Consisting of excellent as many as 138 sample (38 %), very good as many as 102 sample (28 %), good 60 sample (17 %), fair 36 sample (10 %), poor 16 sample (5 %), very poor as many as 7 sample (2 %), and failed 1 sample (0.001 %). All repairs to the damaged area which are lower than excellent condition using patching with a cold milling machine.

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