Pavement Condition Rating Determination Method using Tire-surface Friction Noise

2019 ◽  
Vol 21 (4) ◽  
pp. 11-18
Author(s):  
Daeseok HAN
Keyword(s):  
Surface Friction ◽  
Determination Method ◽  
Pavement Condition ◽  
Friction Noise ◽  
Condition Rating

A modified pavement condition rating index for flexible pavement evaluation in Egypt

2020 ◽  
Vol 5 (2) ◽  
Author(s):  
E. M. Ibrahim ◽  
S. M. El-Badawy ◽  
M. H. Ibrahim ◽  
Emad Elbeltagi
Keyword(s):  
Flexible Pavement ◽  
Pavement Condition ◽  
Condition Rating ◽  
Pavement Evaluation

scholarly journals Utility-Based Road Maintenance Prioritization Method Using Pavement Overall Condition Rating

2020 ◽  
Vol 15 (1) ◽  
pp. 126-146 ◽  
Author(s):  
Saleh Abu Dabous ◽  
Ghadeer Al-Khayyat ◽  
Sainab Feroz
Keyword(s):  
Resource Constraints ◽  
Road Maintenance ◽  
Service Levels ◽  
Pavement Condition ◽  
Pavement Maintenance ◽  
Condition Rating ◽  
Prioritization Method ◽  
Maintenance And Rehabilitation ◽  
The Given ◽  
Utility Approach

Pavement maintenance and rehabilitation are expensive activities and the available budget to manage the existing pavement infrastructure is limited. Managers require a prioritization method to assist them in selecting the most appropriate maintenance options. Maintenance prioritization is necessary to maintain pavement sections at acceptable service levels within the given budget and resource constraints. In this paper, a utility approach is proposed for maintenance prioritization purposes based on the condition assessment results of the pavement sections. A pavement network of five sections is considered in this study, and a numerical example is illustrated considering one section to show the implementation of the utility approach for section ranking. The overall assessment of various pavement sections was provided by the inspector as degrees of belief in seven assessment grades, which are: A (Good), B (Satisfactory), C (Fair), D (Poor), E (Very poor), and F (Serious). The assessment of pavement condition and the estimated grade utilities are used to calculate maximum, minimum, and average utilities for each of the five pavement sections. Based on the results, the pavement sections are ranked for maintenance and rehabilitation actions.

Improving IDOT's Pavement Condition Rating Process

1998 ◽  
Vol 4 (2) ◽  
pp. 79-85 ◽  
Author(s):  
Kathryn A. Zimmerman ◽  
Ronald Knox
Keyword(s):  
Pavement Condition ◽  
Condition Rating ◽  
Rating Process

Statistical Analysis of Automated versus Manual Pavement Condition Surveys

2005 ◽  
Vol 1940 (1) ◽  
pp. 54-62 ◽  
Author(s):  
Jason M. McQueen ◽  
David H. Timm
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Regression Analysis ◽  
Condition Index ◽  
Pavement Management ◽  
Severity Level ◽  
Pavement Condition ◽  
Condition Rating ◽  
Pavement Condition Index ◽  
Level 3

The Alabama Department of Transportation (ALDOT) has used a vendor to perform automated pavement condition surveys for the Alabama pavement network since 1997. In 2002, ALDOT established a quality assurance (QA) program to check the accuracy of the automated pavement condition data. The QA program revealed significant discrepancies between manual and automatically collected data. ALDOT uses a composite pavement condition index called pavement condition rating (PCR) in its pavement management system. The equation for PCR was developed in 1985 for use with manual pavement condition surveys; however, ALDOT continues to use it with data from automated condition surveys. Since the PCR equation was developed for manual surveys, the discrepancies between the manual and automated data led ALDOT to question the continuity between its manual and automated pavement condition survey programs. A regression analysis was completed to look for any systematic error or general trends in the error between automated and manual data. Also, Monte Carlo simulation was used to determine which distress parameters most influence the PCR and whether they require more accuracy. The regression analysis showed the following general trends: automated data overreport outside wheelpath rut depth, under-report alligator severity Level 1 cracking, and overreport alligator severity Level 3 cracking. Through Monte Carlo simulation, it was determined that all severity levels of transverse cracking, block cracking, and alligator cracking data require greater accuracy.

North Carolina’s Experience in Development of Pavement Performance Prediction and Modeling

1997 ◽  
Vol 1592 (1) ◽  
pp. 80-88 ◽  
Author(s):  
Paul K. Chan ◽  
Mary C. Oppermann ◽  
Shie-Shin Wu
Keyword(s):  
North Carolina ◽  
Performance Prediction ◽  
Pavement Performance ◽  
Department Of Transportation ◽  
Pavement Condition ◽  
North Carolina Department ◽  
Condition Rating ◽  
The North ◽  
Initial Idea ◽  
Pavement Performance Prediction

Development efforts in pavement performance prediction by the North Carolina Department of Transportation are described. Research into other states’ approaches was also conducted. The initial idea was to use family curves. However, because of a lack of data in key areas, it was decided to use an individual section’s pavement condition rating (PCR) data for performance prediction. The process of selection and justification of a functional form for curve fitting is detailed. An adaptive scheme to accommodate a realistic PCR history containing cycles of decline and improvement in the ratings is detailed. Abnormal sections that did not fit the models developed for individual sections were identified. These were either ( a) section with too few datum points for modeling or ( b) sections in which the last few ratings leveled out, resulting in a prediction of an unreasonably long life span. The development of family curves and their application in the processing of abnormal sections are also discussed. The developed models were then evaluated by comparing the predicted rating with the actual rating.

Development of Pavement Condition Rating Model and Pavement Roughness Model for Saudi Highways

2013 ◽  
Vol 723 ◽  
pp. 820-828 ◽  
Author(s):  
Muhammad Mubaraki
Keyword(s):  
Iri Model ◽  
International Roughness Index ◽  
Pavement Condition ◽  
Safety Issues ◽  
Condition Rating ◽  
Pavement Roughness ◽  
Roughness Model ◽  
Pavement Deterioration ◽  
Total Observation ◽  
Rating Model

The Pavement Condition Rating (PCR) has been used by the Ministry of Transport (MOT) in Saudi Arabia to report pavement condition. The World Bank developed the PCR in 1986. PCR is based on International Roughness Index (IRI), Rutting (RUT), Cracking (CRA), and Raveling (RAV). The MOT collects pavement condition data using a digital inspection vehicle called Road Surface Tester (RST) vehicle. On some expressways, the MOT measures the Skid Number (SN) using a Skid Test Unit as complimentary measurement for safety issues. The objective of this paper is to develop PCR model and pavement roughness model using survey data for overlaid sections on some expressways in the network with total observation number is 3469. The PCR model is a function of pavement age (T), Traffic Volume (TV), and IRI. The IRI model is a function of RUT, RAV, and CRA. Overlaid sections across the entire network have been selected to study the mechanisms of pavement deterioration, to develop the model and to draw conclusions.

Network Pavement Evaluation with Falling-Weight Deflectometer and Ground-Penetrating Radar

2003 ◽  
Vol 1860 (1) ◽  
pp. 90-99 ◽  
Author(s):  
A. Samy Noureldin ◽  
Karen Zhu ◽  
Shuo Li ◽  
Dwayne Harris
Keyword(s):  
Data Collection ◽  
Ground Penetrating Radar ◽  
Pavement Management ◽  
Falling Weight Deflectometer ◽  
Pavement Condition ◽  
Condition Rating ◽  
Structural Capacity ◽  
Pavement Evaluation ◽  
Ground Penetrating ◽  
Falling Weight

Nondestructive testing has become an integral part of pavement evaluation and rehabilitation strategies in recent years. Pavement evaluation employing the falling-weight deflectometer (FWD) and ground-penetrating radar (GPR) can provide valuable information about pavement performance characteristics and be a very useful tool for project prioritization purposes and estimation of a construction budget at the network level. Traditional obstacles to the use of the FWD and GPR in pavement evaluation at the network level used to be expenses involved in data collection, limited resources, and lack of simplified analysis procedures. Indiana experience in pavement evaluation with the FWD and the GPR at the network level is presented. A network-level FWD and GPR testing program was implemented as a part of a study to overcome those traditional obstacles. Periodic generation of necessary data will be useful in determining how best to quantify structural capacity and estimate annual construction budgets. Three FWD tests per mile on 2,200 lane-mi of the network is recommended annually for network-level pavement evaluation. The information collected will allow the equivalent of 100% coverage of the whole network in 5 years. GPR data are recommended to be collected once every 5 years (if another thickness inventory is needed) after the successful network thickness inventory conducted in this study. GPR data collection is also recommended at the project level and for special projects. Both FWD and GPR data are recommended to be used as part of the pavement management system, together with automated collection of data such as international roughness index, pavement condition rating, rut depth, pavement quality index, and skid resistance.

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