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.

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)

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.

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.

Smartphone-Based Pavement Roughness Estimation Using Deep Learning with Entity Embedding

2020 ◽  
pp. 2050007
Author(s):  
Armstrong Aboah ◽  
Yaw Adu-Gyamfi
Keyword(s):  
Deep Learning ◽  
Traditional Method ◽  
Road Surface ◽  
Accelerometer Data ◽  
International Roughness Index ◽  
Surface Information ◽  
Pavement Roughness ◽  
Roughness Index ◽  
Accuracy Of Prediction ◽  
Deep Learning Model

The commonly used index for measuring pavement roughness is the International Roughness index (IRI). Traditional method for collecting road surface information is expensive and as such researchers over the years have resorted to other cheaper ways of collecting data. This study focuses on developing a deep learning model to quickly and accurately determine the IRI values of road sections at a cheaper cost. The study proposed a model that uses accelerometer data and previous year’s IRI values to predict current year IRI values. The study concludes that addition of accelerometer readings to previous year’s IRIs increased the accuracy of prediction.

International Roughness Index Thresholds Based on Whole-Body Vibration in Passenger Cars

2020 ◽  
pp. 036119812096047
Author(s):  
Peter Múčka
Keyword(s):  
Whole Body Vibration ◽  
Vertical Vibration ◽  
Whole Body ◽  
Vehicle Speed ◽  
Passenger Cars ◽  
International Roughness Index ◽  
Body Vibration ◽  
Car Seat ◽  
Roughness Index ◽  
Iso 2631

This study analyzed whole-body vibration (WBV) on a car seat (seat surface and feet) in passenger cars as a function of longitudinal road roughness. Measurements were provided on nine different cars in six categories and included a total travel distance of 1,860 km. The root mean square (RMS) of the frequency-weighted acceleration was used to quantify WBV. The relationship between seat acceleration response and comfort reactions according to the ISO 2631-1 and the International Roughness Index (IRI) was estimated. IRI thresholds were proposed as a function of vehicle speed and road category. Proposed IRI thresholds decreased with vehicle velocity and were similar with published IRI threshold proposals based on simulation. IRI thresholds as a function of speed limit should decrease with power by approximately –0.75. Substantially lower (by ~ 40%) IRI thresholds were calculated for the total vibration value (six signals) in comparison with vertical vibration on the seat surface.

Effects of the International Roughness Index and Rut Depth on Crash Rates

2018 ◽  
Vol 2672 (40) ◽  
pp. 418-429 ◽  
Author(s):  
Michael Mamlouk ◽  
Mounica Vinayakamurthy ◽  
B. Shane Underwood ◽  
Kamil E. Kaloush
Keyword(s):  
Pavement Management ◽  
Ride Quality ◽  
International Roughness Index ◽  
Accident Rate ◽  
Crash Data ◽  
Crash Rates ◽  
Vehicle Operation ◽  
Roughness Index ◽  
Pavement Distresses ◽  
The Relationship

Pavement distresses directly affect ride quality, and indirectly contribute to driver distraction, vehicle operation, and accidents. In this study, analysis was performed on highways in the states of Arizona, North Carolina, and Maryland to investigate the relationship between accident rate and pavement ride quality (roughness) and rut depth. Two main types of data were collected: crash data from the accident records and International Roughness Index (IRI) and rut depth data from the pavement management system database in each state. Crash rates were calculated using the U.S. Department of Transportation method, which is the number of accidents per 100 million vehicle-miles of travel. Sigmoidal function regression analysis was performed to study the relationship between crash rate and both IRI and rut depth. In all cases, the crash rate did not show substantial increases until an IRI value of 210 inches/mile or a critical rut depth of 0.4 inches. When the IRI or rut depth increased above these values the crash rate increased. This is a key conclusion that provides empirically derived thresholds for IRI and rut depth to reducing the accident rate.

scholarly journals Evaluation of the methodologies used to generate random pavement profiles based on the power spectral density: An approach based on the International Roughness Index

2017 ◽  
Vol 37 (1) ◽  
pp. 49 ◽  
Author(s):  
Boris Jesús Goenaga ◽  
Luis Guillermo Fuentes Pumarejo ◽  
Otto Andrés Mora Lerma
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Pavement Management ◽  
Dynamic Loads ◽  
International Roughness Index ◽  
Vertical Excitation ◽  
Shaping Filter ◽  
Pavement Roughness ◽  
Power Spectral ◽  
Roughness Index

The pavement roughness is the main variable that produces the vertical excitation in vehicles. Pavement profiles are the main determinant of (i) discomfort perception on users and (ii) dynamic loads generated at the tire-pavement interface, hence its evaluation constitutes an essential step on a Pavement Management System. The present document evaluates two specific techniques used to simulate pavement profiles; these are the shaping filter and the sinusoidal approach, both based on the Power Spectral Density. Pavement roughness was evaluated using the International Roughness Index (IRI), which represents the most used index to characterize longitudinal road profiles. Appropriate parameters were defined in the simulation process to obtain pavement profiles with specific ranges of IRI values using both simulation techniques. The results suggest that using a sinusoidal approach one can generate random profiles with IRI values that are representative of different road types, therefore, one could generate a profile for a paved or an unpaved road, representing all the proposed categories defined by ISO 8608 standard. On the other hand, to obtain similar results using the shaping filter approximation a modification in the simulation parameters is necessary. The new proposed values allow one to generate pavement profiles with high levels of roughness, covering a wider range of surface types. Finally, the results of the current investigation could be used to further improve our understanding on the effect of pavement roughness on tire pavement interaction. The evaluated methodologies could be used to generate random profiles with specific levels of roughness to assess its effect on dynamic loads generated at the tire-pavement interface and user’s perception of road condition.

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