International Roughness Index Model Research Articles

Urban road pavements monitoring and assessment using bike and e-scooter as probe vehicles

Comfort and safe-mobility are two aspects that should be provided to non-motorized users that are an increasing component of the urban micro-mobility (e.g. bikes and e-scooters). For these road users, road pavement unevenness, cracks, potholes and other surface defects can make riding uncomfortable and potentially hazardous. Traditionally, road managers use standardized surveys and Key Performance Indicators (KPIs) of pavement to make decision for maintenance programs and pavement management of road urban network poses challenges in the survey of pavement surface for International Roughness Index (IRI) and distress assessment both for operational and cost constrains. Moreover, there are theoretically limitations of IRI model for low speed urban roads and low-damped vehicles like bikes and e-scooters. In such framework aims of the paper is to investigate the use of smartphone sensors to collect data for the assessment of pavement conditions and definition of KPIs for bike and e-scooter users’ ride comfort and safety. A controlled experiment was performed with repeated runs of a bicycle and e-scooter equipped with a smartphone and an android application was used to collect acceleration and position data. Detailed pavement conditions have been identified with an advanced survey equipment. After data treatments for removing signal noise, adjustment for speed variability and outlier detection, root mean square (RMS) of vertical acceleration signal and weighted frequency content of the vibrations according to ISO 2631–1, have been confirmed suitable KPIs of pavement conditions and comfort rating that can be collected by bikes and e-scooter. Results confirmed the lack of correlation of vibrations in bikes and e-scooter with analogous parameters collected with car as probe vehicle and with IRI standard values, as well. Instead, pavement monitoring by bikes and e-scooter can provide effective detection of typologies and severities of distresses not detectable by similar approaches in damped vehicles. Good correlations have been identified between RMS and medium severity alligator, longitudinal and transversal cracks. High severity pavement distress and potholes have been identified by outliers in RMS.

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

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.

Local calibration and Implementation of AASHTOWARE Pavement ME performance models for Oklahoma pavement systems

ABSTRACT AASHTOWare Pavement ME Design software was developed as an advanced pavement design tool based on the mechanistic-empirical pavement design guide. Existing performance prediction models utilised in this design guide are nationally calibrated, and it is essential to calibrate these models based on the specific local materials and environmental conditions, and pavement performance information. In this study, Pavement ME Design prediction models, including IRI, rutting, top-down and bottom-up fatigue cracking, and transverse cracking models were calibrated for the state of Oklahoma. The predictions from distress and international roughness index models were evaluated and compared with the measured distress values, and the accuracy and bias terms of each model were determined. Nationally calibrated models showed large errors and significant bias values, which asserts the need for local calibration. Locally calibrated coefficients of distresses and international roughness index models for the Oklahoma pavement system were determined. Predictions from calibrated models show that the use of calibrated coefficients improves Pavement ME predictions and the design of flexible pavements in the state. The impact of the local calibration of Pavement ME on flexible pavement designs was analysed. Locally calibrated models lead to lower IRI prediction and higher fatigue cracking and rutting in the designs.

Optimization of International Roughness Index Model Parameters for Sustainable Runway

Pavement roughness is a critical airport pavement characteristic that has been linked to impacts such as safety and service life. A properly defined roughness evaluation method would reduce airport operational risk, prolong the life of aircraft landing gear, and optimize the decision-making process for pavement preservation, which together positively contribute to overall airport sustainability. In this study, we optimized the parameters of the International Roughness Index (IRI) model to resolve the current poor correlation between the IRI and aircraft vibration responses in order to adapt and extend the IRI’s use for airport runway roughness evaluation. We developed and validated a virtual prototype model based on ADAMS/Aircraft software for the Boeing 737–800 and then employed the model to predict the aircraft’s dynamic responses to runway pavement roughness. By developing a frequency response function for the standard 1/4 vehicle model, we obtained frequency response distribution curves for the IRI. Based on runway roughness data, we used fast Fourier transform to implement the frequency response distribution of the aircraft. We then utilized Particle Swarm Optimization to determine more appropriate IRI model parameters rather than modifying the model itself. Our case study results indicate that the correlation coefficient for the optimized IRI model and aircraft vibration response shows a qualitative leap from that of the original IRI model.

Local Calibration of the MEPDG Distress and Performance Models for Ontario’s Flexible Roads: Overview, Impacts, and Reflection

Built upon a seven-year local calibration study of Ontario’s flexible pavements, this paper provides a summary of the calibration results and design impact and, more importantly, shares the experience and lessons learned from the process. The best results have been achieved on the local calibration of the rutting, bottom-up fatigue cracking, and international roughness index (IRI) distress models minimizing the residual sum of squares (RSS) while maintaining the average bias at zero. Significant efforts have been made to calibrate the other distress models with limited success. A design impact study found that local calibration of the rutting models was very important, whereas the alligator fatigue cracking did not usually govern the design in Ontario, although the global model was found to under-predict the cracking damage. The performance of the calibrated IRI model in the design of heavy traffic freeways for both reconstructed and rehabilitated sections was unsatisfactory and needs further study. The paper also presents several open questions for future research. These include the handling of section-length effects of observed cracking data, the determination of initial IRI, the updating of standard deviation functions and the overall reliability models, and the prioritization of pavement research under the new paradigm of the Mechanistic–Empirical Pavement Design Guide (MEPDG).

Local calibration of the fatigue cracking models in the Mechanistic-Empirical Pavement Design Guide for Tennessee

This study summarises the local calibration of AASHTO Mechanistic-Empirical Pavement Design Guide (MEPDG) for flexible pavements in Tennessee. Data retrieved from the Highway Pavement Management Application of Tennessee were utilised. The alligator cracking, wheel-path longitudinal cracking, and roughness transfer functions were calibrated. The curve fitting procedure in MATLAB was used to determine the coefficients for those transfer functions. The Jackknife method was used to validate the calibrated models. Generally, MEPDG underestimated the alligator cracking for Tennessee, but overestimated the longitudinal cracking. After the calibration, both the bias and the standard error of estimate were reduced, and the predictability of those models increased appreciably. With all distress models like alligator cracking, longitudinal cracking, and rutting models being calibrated, the accuracy of the prediction from the international roughness index model increased noticeably. Jackknife is not only an unbiased way to validate the calibrated model, but also an efficient tool to detect outliers. Due to the limits in availability of data for calibrating the alligator cracking and longitudinal cracking, it is recommended that more data from different functional classes of highway, more sections with higher level of distresses and detailed traffic information be included in the future for calibration and implementation activities.

Travel Quality Assessment of Urban Roads Based on International Roughness Index: Case Study in Colombia

Roughness is the main feature of the pavement surface that defines user comfort. Pavement roughness is generally defined as irregularities in the pavement surface that adversely affect ride quality, specifically user perception of the road condition. This paper highlights the limitations associated with the evaluation and implementation of the international roughness index (IRI) on urban roads. The paper focuses on ( a) roughness evaluation with full-scale profilers and ( b) conditions particular to urban roads—namely, traffic, intersections, and operating speeds. Given that the speed of urban networks is typically less than the 80 km/h used in the IRI quarter-car model, the implementation of the IRI model on urban roads was evaluated. Even though a given pavement surface reported a unique IRI value, user experience of the profile depended on the travel speed. This result was evidence that user perceptions of road condition are highly influenced by travel speed. The results suggested the need to develop a roughness index that captures the unique characteristics of urban roads and can estimate the road condition as perceived by users. For that reason, this research study focused on establishing thresholds for IRI on the basis of the weighted vertical acceleration parameter as an aid to assessing user perception. The proposed method allows the maximum allowable IRI value for a given road to be established on the basis of the road’s operational speed. The results indicated that IRI thresholds agreed with international and local Colombian standards.

International roughness index models for HMA overlay treatment of flexible and composite pavements

Timely rehabilitation and preservation of pavement systems are imperative to minimising agency's costs and maximising benefits. Reasonable estimates of treatment life and pavement life extension can be made possible by developing reliable treatment performance models. Louisiana Department of Transportation and Development initiated a three-phased study to develop pavement treatment performance models in support of cost-effective selection of pavement treatment type and the time of treatment. As a result of the study, international roughness index (IRI) models for overlay treatment of composite and flexible pavements were developed. Various factors affecting the IRI of overlay treatment were identified. Climatic indices pertaining to Louisiana were developed which exhibited strong statistical significance along with the other variables as used in the IRI models. The developed IRI models provided good agreement between the measured and predicted IRI values with the majority of data within 5% of prediction error. The models could be used as a good pavement management tool for pavement maintenance and rehabilitation actions.

Evaluation of MEPDG flexible pavement design using pavement management system data: Louisiana experience

This paper presents a recent study on using the mechanistic–empirical pavement design guide (MEPDG) design software (version 1.1) to evaluate the performance of typical Louisiana flexible pavement structures and compared it to the existing pavement performance data available in the pavement management system. The pavement performance indicators evaluated in this study include fatigue cracking, rutting and International Roughness Index (IRI). In addition to comparing the measured and predicted mean performance, the predicted pavement distresses at certain reliability design levels were also compared with the field variation of distresses extracted from a number of selected projects. The overall comparison results between the measured and predicted pavement distresses showed strong dependency on the pavement structure type considered in this study. Generally speaking, the MEPDG rutting models tended to over-predict the total rutting for Louisiana flexible pavements, whereas both fatigue cracking and IRI models in the MEPDG seemed to be adequate for most projects selected. Finally, a special optimisation approach was introduced to determine a set of preliminary local calibration factors for the MEPDG rutting models for two types of flexible pavements in Louisiana.

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

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.

Mechanistic-Empirical IRI Model Accounting for Potential Bias

The roughness prediction models in the International Roughness Index (IRI) currently incorporated into the Mechanistic-Empirical Pavement Design Guide (M-E PDG) have been developed by means of ordinary least squares (OLS). However, some of the variables used in predicting future IRI were previously estimated by means of separate performance models. This could cause bias because of the correlation between the previously estimated distress types and unobserved components on the IRI model. The bias can be corrected by considering additional variables that are correlated with the distress types causing the bias, thus eliminating the correlation to the unobserved terms in the model. Bias in the IRI model can also be generated by unobserved factors not included in the model. If these factors are section-specific, the bias can be removed considering variations in the performance history of different pavement sections. The writers have used updated Long-Term Pavement Performance (LTPP) data consistent with the data set originally used to fit the M-E IRI model for flexible pavements over thick granular bases contained in the M-E PDG. The data were then used in modeling IRI by means of OLS and instrumental variable (IV) regressions analyzing the data as pooled and as a panel data set (by random-effects, fixed-effects, and joint random-effects approach) to check for possible bias in the model. It was found that the current IRI model, as estimated by OLS, exhibits several types of biases attributed to heterogeneity and incorrect assumptions in the modeling process. It was identified that the preferred IRI model was the joint random-effects approach and, therefore, the model parameters were estimated by correcting the omitted-variable bias and simultaneous-equation bias. Estimating the model by accounting for possible bias in the data indicated considerable changes in the effects of different parameters affecting IRI through time, mainly rutting of the pavement structure.

Investigating effects of asphalt pavement conditions on traffic accidents in Tennessee based on the pavement management system (PMS)

AbstractPavement maintenance is essential for ensuring good riding quality and avoiding traffic congestion, air pollution, and accidents. Improving road safety is one of the most important objectives for pavement management systems. This study utilized the Tennessee Pavement Management System (PMS) and Accident History Database (AHD) to investigate the relationship between accident frequency and pavement distress variables. Focusing on four urban interstates with asphalt pavements, divided median types, and 55 mph speed limits, 21 Negative Binomial Regression models were developed for predicting various types of traffic accident frequencies based on different pavement condition variables, including rut depth (RD), International Roughness Index (IRI), and Present Serviceability Index (PSI). The modeling results indicated that the RD models did not perform well, except for predicting accidents at night and accidents under rain weather conditions; whereas, IRI and PSI were always significant prediction variables in all types of accident models. Comparing the models goodness‐of‐fit results, it was found that the PSI models had a better performance in crash frequency prediction than the RD models and IRI models. This study suggests that the PSI accident prediction models should be considered as a comprehensive approach to integrate the highway safety factors into the pavement management system. Copyright © 2010 John Wiley & Sons, Ltd.

Relationship Between Highway Pavement Condition, Crash Frequency, and Crash Type

Research has been conducted to relate highway pavement conditions with vehicle accidents. Utilizing the Tennessee Pavement Management System (PMS) and Accident History Database (AHD), this study developed 20 negative binomial regression models to examine the relationships between pavement condition parameters, crash frequency, and crash types. The modeling results indicated that either Present Serviceability Index (PSI) or International Roughness Index (IRI) was a significant pavement condition parameter for predicting the crash frequency of highway segment; whereas, the Rut Depth (RD) was not statistically significant in the crash prediction models. Due to the collinearity between PSI and IRI, it was found that when the two parameters were applied together into the crash regression models, the statistical regression results could not be well explained. Comparing to the IRI and RD models, the models’ goodness-of-fit results indicated that PSI models had the best performance consistently in predicting the frequency for each type of crashes. Additionally, regression results indicated that either right shoulder width or left clearance to median up to 8 feet wide would significantly reduce the crash frequency.

Effects of Variation in Quarter-Car Simulation Speed on International Roughness Index Algorithm

The international roughness index (IRI) is widely used throughout the world as a measure of road roughness. A quarter-car simulation at 80 km/h is performed on the longitudinal profile to compute IRI. Questions have been raised regarding the applicability of IRI for roads that are used at speeds above or below this simulation speed. To gain more insight into the effects of simulation speed, an investigation was carried out to determine how the roughness computed from the IRI model changes for different simulation speeds of the quarter car. This investigation was performed on an asphalt concrete data set and a jointed portland cement concrete data set. For simulation speeds between 60 and 110 km/h, the response from the IRI model was within ±0.20 m/km of the IRI for 80% of the asphalt sections and 61 % of the concrete sections used in the study. Although the output from the quarter-car model for the different simulation speeds was different from the IRI (simulation speed of 80 km/h), it is unclear to what extent a user's perception of the roughness of a roadway changes with the speed of travel. If examples of roadways are found where the subjective opinion of roadway users of the roughness seems inconsistent with the IRI, it is recommended that the IRI model be used with the current speed limit of the roadway to examine whether the obtained output provides a better match with the opinion expressed by roadway users.