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 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)

scholarly journals International Roughness Index Modeling For Jointed Plain Concrete Pavement Using Artificial Neural Network

2021 ◽  
Vol 1203 (3) ◽  
pp. 032034
Author(s):  
Salma Sultana ◽  
Hakan Yasarer ◽  
Waheed Uddin ◽  
Rulian Barros
Keyword(s):  
Prediction Model ◽  
Extreme Temperature ◽  
Plain Concrete ◽  
Concrete Pavement ◽  
Ann Model ◽  
International Roughness Index ◽  
Statistical Measures ◽  
Maintenance And Rehabilitation ◽  
Roughness Index ◽  
Artificial Neural

Abstract Climate attributes such as precipitation, extreme temperature, and freeze-thaw cycles along with traffic loads cause pavement distresses. The maintenance need for pavements is decided based on the pavement condition rating such as International Roughness Index (IRI). Generally, an IRI rating less than 2.68 m/km is acceptable, and a rating greater than 2.68 m/km is considered unacceptable and classified as “very poor” condition of the pavement. It is imperative to be able to accurately predict pavement conditions to prepare proper Maintenance and Rehabilitation (M&R) programs for the pavements. This study aims to develop IRI models that can successfully estimate the IRI values for Jointed Plain Concrete Pavement (JPCP) considering the M&R history of the pavements using Artificial Neural Networks (ANNs) approach. The study was carried out with the database collected from Long Term Pavement Performance (LTPP) program. The variables used for the ANN model development are initial IRI, pavement age, concrete pavement thickness, equivalent single axle load (ESAL), climatic region (wet-freeze, wet non-freeze, dry-freeze, dry non-freeze), construction number (CN), and several climatological data. After utilizing various ANN model structures, the best performing ANN model resulted in promising statistical measures (i.e. R2 = 0.87). The IRI prediction model can successfully estimate the increase of IRI values with the increase of ESAL value over time. The IRI prediction model can also estimate the decrease of IRI value after maintenance and rehabilitation. The predicted IRI values with good accuracy will help the local and state agencies to prepare for M&R programs for JPCP pavements and allocate a projected budget accordingly.

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.

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.

Research on Relationship between Riding Quality and Pavement Distress of Asphalt Pavement

2012 ◽  
Vol 178-181 ◽  
pp. 1306-1313 ◽  
Author(s):  
Bo Peng ◽  
Lu Hu ◽  
Yang Sheng Jiang ◽  
Liang Yun
Keyword(s):  
Performance Evaluation ◽  
Nonlinear Regression ◽  
Asphalt Pavement ◽  
Theoretical Research ◽  
Pavement Performance ◽  
International Roughness Index ◽  
Pavement Distress ◽  
Pavement Roughness ◽  
Roughness Index ◽  
Linear And Nonlinear

For asphalt pavement performance evaluation, pavement roughness, which is subject to cracks, potholes, road repairs and so on, is a major factor to influence riding quality. Therefore, riding quality is partly correlated with pavement distress, and the relationship can be transformed to that between pavement roughness and distress rate. However, this relationship is not clear, and not reflected in existing evaluation models. Thus, correlation analysis and non-parametric test of independent samples were applied in this paper to find that, international roughness index and pavement distress rate are significantly different due to different grades of roads, then, linear and nonlinear regression were used to analyze the relationships between international roughness index and pavement distress rate for different road grades. Furthermore, original data were processed by logarithmic transformation, radical transformation, exponential transformation and so on, based on which, corresponding relationships were analyzed by linear and nonlinear regression. Finally, best models to describe relationships between international roughness index and pavement distress rate for different road grades were solved out, and corresponding 90% confidence intervals were computed. Research in this paper offers a reference for improving asphalt pavement performance evaluation system and models, which is conducive to further theoretical research and practice.

Pavement Performance Evaluation and Analysis on Different Cooling Ways of Asphalt Mixture

2013 ◽  
Vol 361-363 ◽  
pp. 1689-1692
Author(s):  
Yan Hai Yang ◽  
Xiao Xi Gao ◽  
Zhuo Liu ◽  
Yang Shen ◽  
Jing Xiong Gao
Keyword(s):  
Low Temperature ◽  
Energy Saving ◽  
Hot Mix Asphalt ◽  
Asphalt Mixture ◽  
Temperature Stability ◽  
Economic Benefits ◽  
Pavement Performance ◽  
High Temperature Stability ◽  
Low Temperature Cracking ◽  
Road Materials

By adding Sasobit, DAT and BLT additives to reach the purpose of hot mix asphalt mixture construction temperature decreasing, achieve energy conservation and environmental protection. With an AC-13 SBS modified hot mix asphalt as reference, the high and low temperature performance and water stability of three energy-saving and environment-protecting road materials are evaluated through laboratory tests. The results show that compared with HMA, all the three kinds of energy-saving and environment-protecting road materials exhibited a significant increase by 8%-15% in high temperature stability. While the low temperature cracking resistance and anti-water damage performance of Sasobit mixture were decreased somewhat, little changes in these two aspects of the performance of the other two materials. Finally, analyses the economic benefits, and provide the basis for the choice of asphalt mixture cooling additives.

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.

scholarly journals Measurement of International Roughness Index by UsingZ-Axis Accelerometers and GPS

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Yuchuan Du ◽  
Chenglong Liu ◽  
Difei Wu ◽  
Shengchuan Jiang
Keyword(s):  
Measurement System ◽  
Low Cost ◽  
Pavement Management ◽  
Small Scale ◽  
International Roughness Index ◽  
The Road ◽  
Quarter Car Model ◽  
Pavement Management Systems ◽  
Roughness Index ◽  
Wheel Model

The International Roughness Index (IRI) is a well-recognized standard in the field of pavement management. Many different types of devices can be used to measure the IRI, but these devices are mainly mounted on a full-size automobile and are complicated to operate. In addition, these devices are expensive. The development of methods for IRI measurement is a prerequisite for pavement management systems and other parts of the road management industry. Based on the quarter-car model and the vehicle vibration caused by road roughness, there is a strong correlation between the in-carZ-axis acceleration and the IRI. The variation of speed of the car during the measurement process has a large influence on IRI estimation. A measurement system equipped withZ-axis accelerometers and a GPS device was developed. Using the self-designing measurement system based on the methodology proposed in this study, we performed a small-scale field test. We used a one-wheel linear model and two-wheel model to fit the variation of theZ-axis acceleration. The test results demonstrated that the low-cost measurement system has good accuracy and could enhance the efficiency of IRI measurement.

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