Safety effects of maintenance treatments to improve pavement condition on two-lane rural roads – Insights for pavement management

2020 ◽  
Author(s):  
Alireza Jafari Anarkooli ◽  
Iliya Nemtsov ◽  
Bhagwant Persaud
Keyword(s):  
Empirical Bayes ◽  
State Of The Art ◽  
Pavement Management ◽  
Property Damage ◽  
International Roughness Index ◽  
Rural Roads ◽  
Pavement Condition ◽  
Pavement Maintenance ◽  
Roughness Index ◽  
A Site

The research used data from two-lane rural roads in Ontario, Canada to evaluate the change in safety following maintenance treatments to improve pavement condition as measured by International Roughness Index (IRI). The state-of-the-art empirical Bayes (EB) before-after methodology was applied to estimate the effects on crashes, separately for arterial and collector roads. The results indicate statistically significant reductions (P<0.05) in all crashes and property damage only (PDO) crashes of about 5% and 7%, respectively, for arterial roads and about 11% and 13% for collector roads. For fatal plus injury (FI) crashes, there were small, statistically insignificant changes for the two road types. The results provide interesting, and sometimes counterintuitive insights for those planning maintenance treatments to improve IRI. In sum, the results suggest that consideration should be given to designing and planning pavement maintenance treatments on a site-by-site basis, and, in so doing, to optimize the IRI levels and safety effects that may be accomplished with specific treatments.

scholarly journals Analysis of Vehicle Vibrations – New Approach to Rating Pavement Condition of Urban Roads

2012 ◽  
Vol 23 (6) ◽  
pp. 485-494 ◽  
Author(s):  
Stjepan Lakušić ◽  
Davor Brčić ◽  
Višnja Tkalčević Lakušić
Keyword(s):  
Surface Roughness ◽  
Ride Comfort ◽  
Road Traffic ◽  
Heavy Traffic ◽  
Pavement Management ◽  
International Roughness Index ◽  
New Approach ◽  
Pavement Condition ◽  
Roughness Index ◽  
Road Surface Roughness

Urban road infrastructure is daily burdened by heavy traffic volume. Pavement structure roughness observations are significantly more difficult in urban agglomerations than on roads in unpopulated areas. Roughness, expressed by IRI (International Roughness Index), directly affects the quality and safety of road traffic. Within the framework of the pavement management in relation to safety and the achievement of the best possible ride comfort, it is very important to foresee when a road should be reconstructed. The method for quality evaluations of safety and ride comfort on urban roads presented in this paper is based on vehicle vibrations measurements. In the article, measuring of vehicle vibrations was performed on the main urban roads in Zagreb (Croatia). Measurements covered roads with different pavement surface roughness. This method can be simply and very easily used in pavement management aimed at achieving road safety and better ride comfort. The results of measurements according to this method could be used by traffic and civil engineering experts as an indication for the roads that require reconstruction or maintenance. KEY WORDS: urban roads, traffic flow, safety, vehicle vibrations, road surface roughness (IRI)

Optimization of data collection needs for manual and automated network-level pavement condition ratings based on transverse variability and neural networks

2007 ◽  
Vol 34 (2) ◽  
pp. 139-146 ◽  
Author(s):  
Ahmed Shalaby ◽  
Alan Reggin
Keyword(s):  
Back Propagation ◽  
Predictive Ability ◽  
Kappa Coefficient ◽  
Pavement Management ◽  
Back Propagation Algorithm ◽  
International Roughness Index ◽  
Pavement Condition ◽  
Roughness Index ◽  
Transverse Variability ◽  
Data Elements

The paper deals with two approaches to optimizing pavement condition surveys for the urban pavement network of the City of Winnipeg, Manitoba. First, a nonparametric statistical test was applied to assess the transverse variability of the data. The test compared the ratings for one lane with those of all lanes of each segment. The test concluded that the medians of the two groups are equal at a 92% confidence interval and that there are observed biases in the data. The bias can be eliminated if the surveyed lane is selected randomly. The second approach was to predict visual survey scores from automated (laser-based) measurement of rut depth and international roughness index (IRI). A resilient back-propagation algorithm was selected, and the Kappa coefficient was used to examine the strength of the agreement. The results showed that only moderate agreement was achieved and that additional data elements are required to improve the predictive ability of the model.Key words: international roughness index (IRI), rutting, cracking, spalling, pavement management system (PMS), Kappa coefficient, distress surveys.

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 the road based on PCI and IRI roadroid measurement

2018 ◽  
Vol 195 ◽  
pp. 04006 ◽  
Author(s):  
Donny A. Putra ◽  
Mamok Suprapto
Keyword(s):  
Good Condition ◽  
Visual Assessment ◽  
Condition Index ◽  
Assessment Method ◽  
Visual Methods ◽  
International Roughness Index ◽  
Pavement Condition ◽  
The Road ◽  
Roughness Index ◽  
Pavement Condition Index

There are two methods of road assessment, ie, visually and using tools. Visual assessment makes use of the PCI (Pavement Condition Index), while assessment with the Roadroid app can be used to obtain the value of IRI (International Roughness Index) with less cost. Functional assessment of roads in the field more use of visual methods. This method is influenced by the subjectivity of surveyors. Therefore, the assessment using the visual method should be correlated with the assessment method using tools, in order to reduce the subjectivity of road assessment. The research location used is Magetan District Road consisting of 5 road segments. The result shows that the r road assessment using the PCI method has a very good condition, and using IRI Roadroid has a Medium condition. There is a negative (r) correlation between PCI and IRI Roadroid, valued at -0.23. The negative correlation shows that both judgments reversed. Comparison of PCI assessment with IRI Roadroid has a low correlation value and with ttest, yields no comparison of correlation. This result is because the PCI and IRI equally assess the pavement, using different methods.

Full-Car Roughness Index as Summary Roughness Statistic

2005 ◽  
Vol 1905 (1) ◽  
pp. 148-156 ◽  
Author(s):  
Renato A. C. Capuruço ◽  
Tarek Hegazy ◽  
Susan L. Tighe ◽  
Sameh Zaghloul
Keyword(s):  
Mechanical Response ◽  
Pavement Management ◽  
Ride Quality ◽  
Management Decision ◽  
International Roughness Index ◽  
Front Suspension ◽  
Car Model ◽  
Roughness Index ◽  
Wheel Model ◽  
Management Decision Making

The international roughness index (IRI) and the half-car roughness index (HRI) are the two commonly used roughness indices for pavement management, decision making, prioritization, budgeting, and planning. This work presents a new statistic, termed the full-car roughness index (FRI), for calculation of roughness from longitudinal pavement profiles. FRI is calculated from a single, equivalent profile that is a composite of four corner profiles based on both civil and mechanical engineering principles. More specifically, the full-car (four-wheel) model combines the rear and front suspension systems through an interdependent relation of motion with the longitudinal axle. To validate this model, the FRI values for different pavement sections are determined for sampling roughness measurements from several states and provinces. Then, the behavior of FRI is compared with that of IRI and HRI. The methodology of assessment uses a Monte Carlo simulation for calibration and validation of the index. Correlations derived from this sensitivity analysis on the basis of regression analysis arrive at a conversion chart to propose conversion values from these indices to FRIs. Overall, this paper suggests that the mechanical response of the proposed full-car model is more representative of the characteristics of a real vehicle than the response of a quarter- or half-car model. The results also indicate that FRI is less sensitive to the governing factors that account for the quarter-car simulation and thus provides an index that is unique, insightful, and more effective in the characterization of ride quality.

International Roughness Index Prediction Model for Thin Hot Mix Asphalt Overlay Treatment of Flexible Pavements

2018 ◽  
Vol 2672 (40) ◽  
pp. 7-13 ◽  
Author(s):  
Jinsong Qian ◽  
Chen Jin ◽  
Jiake Zhang ◽  
Jianming Ling ◽  
Chao Sun
Keyword(s):  
Low Temperature ◽  
Prediction Model ◽  
Temperature Region ◽  
Hot Mix Asphalt ◽  
Pavement Management ◽  
Pavement Performance ◽  
International Roughness Index ◽  
Medium Temperature ◽  
Asphalt Overlay ◽  
Roughness Index

Pavement performance prediction after maintenance and rehabilitation is important to pavement management. A two-parameter exponential international roughness index (IRI) regression model for thin hot mix asphalt overlay was developed based on information from the U.S. Long Term Pavement Performance (LTPP) database. The model influence parameters α and β, which represent the initial IRI as the thin overlay completion and shape factor of IRI deterioration curve, were statistically analyzed. The results suggested that the IRI deterioration trends in high-temperature and low-temperature regions are different. This is because β was mainly affected by the structural strength and equivalent single axle loads in the high and medium temperature region and mainly affected by the average annual precipitation in low temperature region. In-situ data from LTPP database was used to verify the IRI prediction model, and it was found that the predicted IRI and measured IRI exhibited similar trends.

scholarly journals Study of Site-Specific Roughness Progression for a Bitumen-Sealed Unbound Granular Pavement Network

2003 ◽  
Vol 1819 (1) ◽  
pp. 273-281 ◽  
Author(s):  
P. D. Hunt ◽  
J. M. Bunker
Keyword(s):  
Road Network ◽  
Network Performance ◽  
Comprehensive Evaluation ◽  
Average Rate ◽  
Pavement Management ◽  
Pavement Performance ◽  
Pavement Condition ◽  
Pavement Maintenance ◽  
Pavement Management Systems ◽  
Economic Analyses

Pavement management systems assist engineers in the analysis of road network pavement condition data and subsequently provide input to the planning and prioritization of road infrastructure works programs. The data also provide input to a variety of engineering and economic analyses that assist in determining the future road network condition for a range of infrastructure-funding scenarios. The fundamental calculation of future pavement condition is commonly based on a pavement age versus pavement roughness relationship. However, roughness–age relationships commonly do not take into account the pavement’s historical performance; rather, an “average” rate of roughness progression is assigned to each pavement based on its current age or current roughness measurement. Results of a research project are documented; the project involved a comprehensive evaluation of pavement performance by examining roughness progression over time with other related variables. A method of calculating and effectively displaying roughness progression and the effects of pavement maintenance was developed. The method provides a better understanding of pavement performance, which in turn led to a methodology of calculating and reporting road network performance for application to the pavement design and delivery system in Queensland, Australia. Means of using this information to improve the accuracy of roughness progression prediction were also investigated.

Evaluation of low-cost consumer-level mobile phone technology for measuring international roughness index (IRI) values

2014 ◽  
Vol 41 (9) ◽  
pp. 819-827 ◽  
Author(s):  
Trevor Hanson ◽  
Coady Cameron ◽  
Eric Hildebrand
Keyword(s):  
Low Cost ◽  
Pavement Management ◽  
Vehicle Speed ◽  
Accelerometer Data ◽  
International Roughness Index ◽  
Mobile Phone Technology ◽  
Vehicle Type ◽  
Roughness Index ◽  
Class 1 ◽  
Test Scenarios

International roughness index (IRI) values were calculated from multi-step processing of accelerometer data collected using three smartphone devices in three consumer vehicles under 11 test scenarios on a 1000 m stretch of secondary highway in New Brunswick. These data were compared to IRI data from a Class 1 inertial profiler averaged over 1000 m (2.60 m/km, std. dev. = 0.029). The combinations of factors producing average IRI values closest to Class 1 inertial profiler were the compact car, Galaxy SIII, windshield mount, at 80 km/h (2.58 m/km, std. dev. = 0.075) and the SUV, iPhone 5, windshield mount, at 50 km/h (2.63 m/km, std. dev. = 0.054). Changes in device type, vehicle type, and mounting arrangement significantly impacted IRI variance, while vehicle speed (50 km/h and 80 km/h) did not. The development of correction factors and analysis automation could make these devices a low-cost option for real-time network-level pavement management.

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