Relationship between the Pavement Condition Index (PCI), Present Serviceability Index (PSI), and Surface Distress Index on Soekarno Hatta Road, Bandung

Abstract

Evaluation of the conditions of Jalan Soekarno Hatta using the PCI method shows that the most found is the condition of "Good" both on rigid and flexible pavement, with the percentage of each of them being 61.9% and 21.6%. Whereas by using the PSI method, the most commonly found are "Fair" conditions both in rigid and flexible pavement, with the percentage of each being 81% and 48,9%. Using the SDI method on the flexible pavement the most found is the condition of "Medium" with a percentage of 65,9%. The parametric analysis between PCI and PSI on rigid pavement produces R 2 = 0.0548 with a regression equation namely PCI = 96,9352 x PSI, while the PCI and PSI correlation on flexible pavement produces R 2 = 0.8163 with regression equation namely PCI = 27,673 + 16,848 x PSI., and the PCI and SDI correlations on flexible pavement yields R 2 = 0,8653 with a regression equation namely PCI = 94,663 -0,4462 x SDI The non-parametric analysis between PCI and PSI on rigid pavement produces a correlation coefficient of 0.043, while the PCI and PSI values on flexible pavements produce a correlation coefficient of 0.397, and the PCI and SDI values on flexible pavements produce a correlation coefficient of 0.492. Based on the two analysis methods both parametric and non parametric analysis the relationship between PCI and SDI was considered better than the PCI and PSI relationship because it produced a R 2 value and had greater correlation coefficient. AbstrakEvaluasi kondisi Jalan Soekarno Hatta dengan menggunakan metode PCI menunjukan bahwa yang paling banyak di temukan adalah kondisi "Good" baik pada perkerasan kaku maupun perkerasan lentur, dengan persentase masing-masing adalah 61,9 % dan 21,6 %. Sedangkan dengan menggunakan metode PSI yang paling banyak di temukan adalah kondisi "Fair" baik pada perkerasan kaku maupun perkerasan lentur, dengan persentase masingmasing adalah 81 % dan 48,9 %. Dengan menggunakan metode SDI pada perkerasan lentur yang paling sering di temukan adalah kondisi "Sedang" dengan persentase 65,9 %. Analisis parametrik antara PCI dan PSI pada perkerasan kaku menghasilkan nilai R 2 = 0,0548 dengan persamaan regresi yaitu PCI= 96,9352 x PSI, sedangkan korelasi PCI dan PSI pada perkerasan lentur menghasilkan nilai R 2 = 0,8163 dengan persamaan regresi yaitu PCI = 27,673 + 16,848 x PSI, dan korelasi PCI dan SDI pada perkerasan lentur menghasilkan nilai R 2 = 0,8653 dengan persamaan regresi yaitu PCI = 94,663 -0,4462 x SDI. Analisis non parametrik antara PCI dan PSI pada perkerasan kaku menghasilkan nilai koefisien korelasi sebesar 0,043 , sedangkan nilai PCI dan PSI pada perkerasan lentur menghasilkan nilai koefisien korelasi sebesar 0,397, dan nilai PCI dan SDI pada perkerasan lentur menghasilkan nilai koefisien korelasi sebesar 0,492. Berdasarkan kedua metode analisis baik analisis parametrik maupun non parametrik hubungan PCI dan SDI dinilai lebih baik dibandingkan hubungan PCI dan PSI karena menghasilkan nilai R 2 dan koefisien korelasi yang lebih besar.Kata Kunci: Pavement condition index, pres...

Relationship of present serviceability index for flexible and rigid pavement in urban road damage assessment using pavement condition index and international roughness index

“…The value is determined by visually assessing road conditions and detecting different forms of road damage [25]. In this study, the following steps were taken to determine the PCI (Pavement Condition Index) value: measurement of the quantity of the type of damage, the level of damage, the level of road damage, the deduction value, the total deduct value, the corrected subtract value, the PCI value, which is then used to calculate the value of road conditions [13].…”

“…Road pavement conditions must be assessed to determine road maintenance and repair activities. There are numerous approaches to evaluate pavement conditions, including the Bina Marga, Asphalt Institute, and PCI methods [11,13]. Direct visual observation in the field utilizing the Surface Distress Index (SDI), Pavement Condition Index (PCI), and International Roughness Index (IRI) approaches [2,[14][15] is one of several road maintenance assessment procedures.…”

Analysis of Road Damage Level Using the Pavement Condition Index (PCI) Method on the Surabaya-Gresik Toll Road, East Java

“…This research analyzes the road damage on the Surabaya-Gresik toll road and the solution for addressing this damage using the Pavement Condition Index (PCI) method. The Pavement Condition Index (PCI) method is a system for evaluating pavement condition based on type, extent, and severity of damage, which can be used as a guide in maintenance efforts [4] [5]. PCI aims to provide an objective overview of the level of damage or wear on the road surface based on visual evaluation and specific measurements [6].…”

Estimation of the pavement condition index via tyre cavity noise measurements and AI signal processing

“…Given the large variety of general conditions of the pavement's location, global pavement condition indicators are based on distinct aspects of the pavement surface and ride quality. The most well-known performance indicators are Pavement Condition Index (PCI), International Roughness Index (IRI), Present Serviceability Index (PSI), and Surface Distress Index (SDI) [3][4][5]. In this context, the SURFace Analysis for Classification and Evaluation (SURFACE) project developed by iPOOL s.r.l.…”