Validation of Pavement Condition Index – PCI: A Portuguese Case Study

Road is an infrastructure that built to support the movement of the vehicle from one place to another for different purposes. Today, it is often found damage to road infrastructure, both local roads, and arterial roads. Therefore, to keep the pavement condition to remain reliable, in Indonesia has a periodic program by conducting an objective functional inspection of roads regulated by Bina Marga using the International Roughness Index (IRI). However, the IRI examination is not sufficient to represent the actual field condition; it is necessary to perform subjective functional examination by appraising the road one of them is Pavement Condition Index (PCI, ASTM D 6433). This method has been widely applied in some countries because it has many advantages. However, to do this inspection requires considerable cost, then there needs to be a model to get the relationship between these two parameters of the road. The selected case study was arterial road segment in Medan City, that is in Medan inner ring road. Based on the results of the analysis, there is a difference between the functional conditions of PCI and IRI. The equation derived from these two parameters is by exponential regression equation, with equation IRI = 16.07exp-0.26PCI. with R2 of 59% with correlation coefficient value (r) of -0.768. The value of R2 indicates that PCI gives a strong influence on IRI value.

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Assessment of Al-Amarah street within the Al-kut city using pavement condition index (PCI) and GIS technique

MATEC Web of Conferences ◽

10.1051/matecconf/201816201033 ◽

2018 ◽

Vol 162 ◽

pp. 01033

Author(s):

Mohammed Al-Neami ◽

Rasha Al-Rubaee ◽

Zainab Kareem

Keyword(s):

Condition Index ◽

Pavement Management ◽

Pavement Condition ◽

The Road ◽

Gis Technique ◽

Gis Data ◽

Pavement Condition Index ◽

Specific Sign ◽

Maintenance Process

The capabilities of geographical system and their spatial analysis is considered the most appropriate tools to enhance pavement management operations, with features such as graphical display of pavement condition. In Iraq, most of transportation agencies do not have a tool that is used as a database for road deteriorations, so there is a need for road surveying and storing the collected information in GIS to know the condition of every road with details. Furthermore, these data can be used for maintenance process and estimation of prior cost. This research has been carried out to estimate of flexible pavement condition through visual surveys using the Pavement Condition Index (PCI) method; so it can provide an easy way to calculate the PCI based on GIS data with Micro PAVER software 5.2. Al-Amarah Street, which is internal road in Al-Kut city in the eastern part of Iraq, is used as a case study. The average pavement condition index of the selected case study is found to be “64” using Micro PAVER 5.2 software which mean “Fair” pavement condition. Arc Map 9.3 has been applied in this study to make an integrated maintenance system for each road in the region demonstrating the annual road deteriorations and the resulting change in the PCI values which occurs every year. The study provides an easy and simplified way of presentation the details of deteriorations on the satellite or the geographical map of the road in which each type of distress has been symbolized with specific sign and each PCI value has been represented with specific color.

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Use of Deep Learning to Study Modeling Deterioration of Pavements a Case Study in Iowa

Infrastructures ◽

10.3390/infrastructures5110095 ◽

2020 ◽

Vol 5 (11) ◽

pp. 95

Author(s):

Seyed Amirhossein Hosseini ◽

Ahmad Alhasan ◽

Omar Smadi

Keyword(s):

Short Term Memory ◽

Weighted Average ◽

Condition Index ◽

The State ◽

Pavement Management ◽

Department Of Transportation ◽

Pavement Condition ◽

Process And Outcome ◽

Pavement Condition Index

This paper describes the process and outcome of deterioration modeling for three different pavement types (asphalt, concrete, and composite) in the state of Iowa. Pavement condition data is collected by the Iowa Department of Transportation (DOT) and stored in a Pavement-Management Information System (PMIS). In the state of Iowa, the overall pavement condition is quantified using the Pavement Condition Index (PCI), which is a weighted average of indices representing different types of distress, roughness, and deflection. Deterioration models of PCI as a function of time were developed for the different pavement types using two modeling approaches. The first approach is the long/short-term memory (LSTM), a subset of a recurrent neural network. The second approach, used by the Iowa DOT, is developing individual regression models for each section of the different pavement types. A comparison is made between the two approaches to assess the accuracy of each model. The results show that the LSTM model achieved a higher prediction accuracy over time for all different pavement types.

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