scholarly journals The Pavement Condition Index (PCI) Method for Evaluating Pavement Distresses of The Roads in Iraq- A Case Study in Al- Nasiriyah City

2021 ◽  
Author(s):  
Feras A.R. Temimi ◽  
Ameer Hadi M. Ali ◽  
Amenah H. F. Obaidi
Keyword(s):  
Condition Index ◽  
Pavement Condition ◽  
Pavement Condition Index ◽  
Pavement Distresses

scholarly journals Study of Pavement Condition Index (PCI) relationship with International Roughness Index (IRI) on Flexible Pavement.

2019 ◽  
Vol 258 ◽  
pp. 03019 ◽  
Author(s):  
Rijal Psalmen Hasibuan ◽  
Medis Sejahtera Surbakti
Keyword(s):  
Condition Index ◽  
International Roughness Index ◽  
Pavement Condition ◽  
The Road ◽  
Roughness Index ◽  
Pavement Condition Index ◽  
Local Roads ◽  
Actual Field ◽  
Two Parameters

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.

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

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.

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

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.

Use of Time–Temperature Superposition Principle to Create Pavement Performance Master Curves and Relate Pavement Condition Index and International Roughness Index

2021 ◽  
pp. 036119812110049
Author(s):  
Jose R. Medina ◽  
Ali Zalghout ◽  
Akshay Gundla ◽  
Samuel Castro ◽  
Kamil Kaloush
Keyword(s):  
Superposition Principle ◽  
Condition Index ◽  
Pavement Performance ◽  
International Roughness Index ◽  
Pavement Condition ◽  
Roughness Index ◽  
Temperature Superposition ◽  
Pavement Condition Index ◽  
Pavement Distresses ◽  
Time Temperature Superposition Principle

The international roughness index (IRI) is one of the most popular indices to measure pavement roughness. State agencies and cities with plenty of resources often collect IRI and pavement distresses every year or every other year, but some others with fewer resources will collect this information every 3 to 5 years. Collecting IRI is much more affordable than collecting pavement distresses. With this in mind, the objective of this paper was to establish a relationship between IRI and pavement condition index (PCI) using pavement deterioration models for both PCI and IRI based on the concept of time–deterioration superposition similar to the time–temperature superposition principle, and then combine both models to establish this relationship. Additionally, this study was used to establish threshold limits for IRI measurements that can be used as a general reference for pavement condition. Data from the Long-Term Pavement Performance InfoPave was used to perform the analysis for three network samples from Arizona, California, and Wisconsin. This analysis only included flexible pavements. The results from Arizona, California, and Wisconsin showed a good relationship between IRI and PCI using the proposed approach with a coefficient of determination ranging from 0.71 to 0.85. Furthermore, the analysis showed that the change in IRI over time can be related to the change in PCI over time. The general thresholds developed in this study apply to the sections evaluated but the approach can be used to set limits for other networks.

scholarly journals Geopathic Stress: A Possible Cause for Pavement Distresses and Road Accidents

2020 ◽  
Vol 9 (6) ◽  
pp. 850-854
Keyword(s):  
Magnetic Field ◽  
Black Spot ◽  
Causative Factor ◽  
Condition Index ◽  
Road Accident ◽  
Magnetic Characteristics ◽  
Road Accidents ◽  
Pavement Condition ◽  
Pavement Condition Index ◽  
Pavement Distresses

India loses 3% of its GDP due to road accidents. Significance of Geopathic stress as a causative factor of road accidents has been studied by few researchers; however its effect on Pavement distresses and hence road accident is yet unexplored. The aim of this research is to determine the correlation between average number of accidents, Pavement Condition Index (PCI) values and Geopathic stress. Accident data was collected from Pune traffic department for 3 years period from 2015-16 to 2017-18. Based on the number of accidents during this period accident blackspots were found. On each black spot pavement distresses survey was carried out and its condition was analyzed by Indian Road Congress (IRC) 82:2015 code method. At these accident blackspots detection of geopathic stress was done by using 2 copper L-rods, lecher antenna. Intensity was measured in terms of electrical and magnetic field. Electrical field reading was measured using Esmog-spion and magnetic field reading was measured by magnetometer. Data was analyzed using Karl Pearson’s correlation coefficient and a linear regression model is developed for average number of road accidents (Ā) with Pavement Condition Index (PCI). Utility of the equation is for forecasting the number of fatal accidents at similar black spots based on their pavement distress condition. A further attempt is to investigate the effect of electric and magnetic characteristics of geopathic stress on road accidents.

scholarly journals Evaluation of pavement condition index by different methods: Case study of Maringá, Brazil

2020 ◽  
Vol 4 ◽  
pp. 100100 ◽  
Author(s):  
Jéssica Marcomini Pinatt ◽  
Marcelo Luiz Chicati ◽  
Jesner Sereni Ildefonso ◽  
Cláudia Regina Grégio D'arce Filetti
Keyword(s):  
Condition Index ◽  
Pavement Condition ◽  
Pavement Condition Index
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