Modeling Pavement Roughness Using Simulated Vertical Accelerations Across Multiple Vehicles and Speeds

Riyadh City established and implemented a Pavement Maintenance Management System (PMMS) through the General Directorate of Maintenance and operation. The system was created to address the difficulties that come with maintaining and reserving the pavement network. To evaluate pavement conditions, Riyadh (PMMS) uses visual checks, structural capacity roughness, and skid resistance. An Urban Distress Index (UDI) is calculated during the visual assessment process. Distressed pavement types, severity, and quantity are taken into account when calculating UDI values. As a result, the procedure gathers extensive data on the pavement’s condition. However, the procedure is time-consuming and very costly. The Automatic Road Analyzer car provides data on road roughness in accordance with the International Roughness Index (IRI). The IRI data are often generated quite quickly and at a cheap cost as compared to the distress survey. This study’s aim is to examine whether a sample of Riyadh city pavement sections can be connected to the IRI depending on the distress type. The research develops statistical models that correlate IRI values with several distress-types associated with roadway classes. Correlating the International Roughness Index values to distress type will eliminate the necessity to implement the manual inspection at a network- level. This saves money and time for PMMS employees when preparing annual maintenance requirements and setting priorities. The finding of the study, of the relationship between the pavement distresses and the International Roughness Index showed a statistically significant relationship between pavement roughness and some ride-quality distresses, like depression and patching, as well as some non-ride quality distresses like potholes and rutting. In addition, for both main and secondary streets, an analysis of variance shows the existence of a correlation between the two variables.

This paper analyses some of the most widely used indices for the characterization of longitudinal roughness. Measured longitudinal profiles available in the Long-Term Pavement Performance (LTPP) program database were used. A total of 207 pavement sections, in 42 States of the USA, were used in these analyses. Using a suitable software, the International Roughness Index (IRI), the Standard Deviation of longitudinal roughness (σ) and the Root Mean Square of Vertical Acceleration (RMSVA) with different base lengths were computed for every measured longitudinal pavement profile. Linear regression analyses between the IRI and σ statistics produced a very high correlation (R2=0.93). Multivariate regression between IRI and the RMSVA also showed a high correlation (R2=0.96) for base lengths of 1.0 m and 3.5 m. On the other hand, when the RMSVA and σ statistics were analyzed, it was observed that all regressions including a RMSVA with the base length of 1.5m presented a nearly perfect correlation (R2≅1.00). Probabilistic analyses were also carried out using the First Order Second Moment (FOSM) method applied to the regression models obtained between the IRI × RMSVA and σ × RMSVA. Thus, it was possible to determine the relative contribution of different base lengths used in the RMSVA statistic on each roughness index. It was concluded that the base length of 1.5 m contributed in average with 96% of roughness values computed for σ statistic, whereas the base length of 1.0 m was responsible for 74.5% of computed IRI values.

Summary roughness statistics are commonly used by highway agencies to characterize road roughness profiles to provide convenient numerical indices for pavement performance monitoring and management planning. Many different roughness indices have been used by different highway agencies worldwide. Unfortunately, since different indices are computed with different considerations and mathematical procedures, they often do not correlate with one another well. This presents a practical problem for exchange of information and experience among practitioners or highway agencies. A proposal is presented for the use of wavelet transform to overcome this problem. Wavelet transform can represent detailed pavement roughness features of different wavelengths quantitatively in terms of wavelet energy. The usefulness of wavelet transform representation of pavement roughness profiles was studied by analyzing the roughness data of 200 flexible pavement and 200 rigid pavement sections. Comparisons were made with four common roughness indices, namely, the international roughness index (IRI), rootmean-square vertical acceleration (RMSVA), mean absolute vertical acceleration (MAVA), and slope variance (SV). It was found that IRI, RMSVA, MAVA, and SV had pairwise coefficients of multiple determination ( R2) ranging from 0.18 to 0.75. But wavelet energy statistics had an R2 of at least 0.857 with each of the roughness indices. Therefore, wavelet energy statistics can be a useful common basis to relate different forms of pavement roughness measures.

Artificial Neural Network-based model to predict the International Roughness Index of national highways in Nepal

The vibration-based pavement roughness measurement method has garnered significant attention in recent years due to the development of low-cost sensors. The vehicle calibration process is necessary before field measurement due to the effect of changeable vehicle dynamics, restricting the large-scale implementation of pavement roughness measurement. This paper proposes a novel calibration method for vibration-based pavement roughness measurement. It can quickly calibrate vehicle dynamic parameters through a few controlled impact tests. The model updating technique is employed to estimate vehicle dynamic parameters, resulting in the development of a full-car numerical model that accurately simulates the vibration of the test vehicle. A road-vehicle coupled simulation system is introduced, incorporating randomly generated road profiles and the calibrated full-car model. This system can simulate vibration responses under various conditions. A linear relationship between IRI (International Roughness Index) and vehicle vibration indices (e.g., RMS – Root Mean Square of vertical acceleration) is estimated based on simulated results. The efficiency and accuracy of the calibration method are validated on actual roads in Shanghai, China. The average IRI difference between the ground truth and test data reached 0.5 m/km in a 100 m road section, demonstrating the reliability and practicality of this fast calibration method.

Predicting the International Roughness Index (IRI) is crucial for maintaining road quality and ensuring the safety and comfort of road users. Accurate IRI predictions help in the timely identification of road sections that require maintenance, thus preventing further deterioration and reducing overall maintenance costs. This study aims to develop robust predictive models for the IRI using advanced machine learning techniques across different climatic conditions. Data were sourced from the Ministry of Energy and Infrastructure in the UAE for localized conditions coupled with the Long-Term Pavement Performance (LTPP) database for comparison and validation purposes. This study evaluates several machine learning models, including regression trees, support vector machines (SVMs), ensemble trees, Gaussian process regression (GPR), artificial neural networks (ANNs), and kernel-based methods. Among the models tested, GPR, particularly with rational quadratic specifications, consistently demonstrated superior performance with the lowest Root Mean Square Error (RMSE) and highest R-squared values across all datasets. Sensitivity analysis identified age, total pavement thickness, precipitation, temperature, and Annual Average Daily Truck Traffic (AADTT) as key factors influencing the IRI. The results indicate that pavement age and higher traffic loads significantly increase roughness, while thicker pavements contribute to smoother surfaces. Climatic factors such as temperature and precipitation showed varying impacts depending on the regional conditions. The developed models provide a powerful tool for predicting pavement roughness, enabling more accurate maintenance planning and resource allocation. The findings highlight the necessity of tailoring pavement management practices to specific environmental and traffic conditions to enhance road quality and longevity. This research offers a comprehensive framework for understanding and predicting pavement performance, with implications for infrastructure management both locally and worldwide.

The international roughness index (IRI) is an important indicator reflecting the roughness of the road surface. Pavement roughness performance is affected by many factors such as pavement surface disease, pavement service life and natural environment. In view of this, this paper aims to study an IRI prediction model based on the random forest algorithm of multi-feature analysis. The mechanism of influencing factors of pavement roughness was analyzed to extract IRI and its influencing factors data in the seven major states of the United States for nearly 26 years from the long term pavement performance (LTPP) database, and then an asphalt pavement roughness dataset was established. Furthermore, through the feature selection based on the linear Pearson correlation coefficient, feature heat map and the nonlinear random forest, XGBoost (extreme Gradient Boosting) algorithm, the factors that affect the road surface roughness were analyzed. Finally, in order to effectively fit the IRI through the pavement surface roughness influencing factors, a random forest model based on grid search optimization was constructed. Optimized random forest was compared with the traditional method KNN, decision tree and random forest to verify their performance on the pavement roughness data set. Experiments show that the random forest prediction model based on grid search optimization has higher prediction accuracy, and the fitting goodness R² is as high as 98.6%.

IRI Estimation by the Frequency Domain Analysis of Vehicle Dynamic Responses

Highway performance prediction model of International Roughness Index based on panel data analysis in subtropical monsoon climate

Simulation of pavement roughness and IRI based on power spectral density

Flexible pavements are considered an essential element of transportation infrastructure. So, evaluations of flexible pavement performance are necessary for the proper management of transportation infrastructure. Pavement condition index (PCI) and international roughness index (IRI) are common indices applied to evaluate pavement surface conditions. However, the pavement condition surveys to calculate PCI are costly and time-consuming as compared to IRI. This article focuses on developing regression models that predict PCI from IRI. Eighty-three flexible pavement sections, with section length equal to 250 m, were selected in Al-Diwaniyah, Iraq, to develop PCI-IRI relationships. In terms of the quantity and severity of each observed distress, the pavement condition surveys were conducted by actually walking through all the sections. Using these data, PCI was calculated utilizing Micro PAVER software. Dynatest Road Surface Profiler (RSP) was used to collect IRI data of all the sections. Using the SPSS software, linear and nonlinear regressions have been used for developing two models between PCI and IRI based on the collected data. These models have the coefficients of determination (R2) equal to 0.715 and 0.722 for linear and quadratic models. Finally, the results indicate the linear and quadratic models are acceptable to predict PCI from IRI directly.

This study investigates the possibility for augmenting infrequent standard pavement survey data with spatiotemporally continuous sensor data crowdsourced from connected vehicles. A framework is proposed to leverage physics-integrated machine learning (ML) models to reconcile the two disparate data sources. The proposed approach is demonstrated for the International Roughness Index (IRI). Using the quarter-car simulation, vertical acceleration data records were generated based on random stratified sampling from probability distributions of vehicle suspension properties at different speed levels on elevation profiles collected from various pavement sections. An ML model was pretrained to approximate the quarter-car simulation. Using transfer learning, the parameters of the outer layers of the model were finetuned to adapt model output to the measured values, while the parameters of the inner layers were fixed to preserve the embedded knowledge of the physical behavior of the suspension. The resulting physics-integrated ML model can predict the standard IRI value of a pavement section using speed, suspension properties, and features of the acceleration response from vehicles traversing that road section. The evaluation results on independent training and test datasets showed high accuracy, precision, and generalization capability, especially on smoother road profiles. This is attributed to the limited training data for rougher roads. After averaging the predicted IRI from all vehicles passing a certain road segment, a correction function is used to rectify the model’s under-predicted output and estimate the actual IRI value for that segment. High-frequency data resulting from the proposed approach can enhance pavement design, maintenance management, and asset management practices.

The effect of pavement roughness on the roadbed and retaining structure underground was studied. Three different groups of International Roughness Index (IRI) were analyzed based on 6221 data collection instrument in this paper. Results show that different pavement roughness has different effect on retaining structure. Additionally, the vibration RMS increases with IRI when it is in the range of normal driving for the car, but amplitude of the IRI is larger. Finally, the main factors which influence the stability of structure and some corresponding improving measures are presented.

The importance of the road network in Indonesia as a vital infrastructure that connects various regions has made road maintenance a top priority in development planning. However, various challenges such as ineffective handling methods, limited experts, and minimal equipment have caused road management to not be optimal. Therefore, innovations are needed in road condition measurement, one of which is through the development of an ultrasonic sensor-based surface roughness measuring instrument as a prototype of International Roughness Index (IRI) measurement to support more accurate road maintenance evaluation and planning. The purpose of this research is to measure road roughness through IRI and pavement modulus values to improve road condition assessment.This study employs the International Roughness Index (IRI) to assess the functional condition of roads and the Pavement Modulus to evaluate the structural strength of the pavement. The IRI is measured through road surface roughness surveys using a roughness meter, with the results used to classify the severity of road damage. The IRI calculation is based on a quarter-car simulation model that utilizes vehicle dynamic parameters in response to road surface profiles, following the mathematical approach developed by Sayers, Gillespie, and Paterson (1986). The research results show that the prototype Ultrasonic Surface Roughness Meter was able to measure IRI values ranging from 4 to 8 at three different locations. These measurements fall within the "Good–Fair" classification, indicating relatively mild surface roughness. Based on these findings, the Directorate General of Highways recommends light rehabilitation and periodic maintenance, and the prototype device has the potential to serve as an effective, low-cost alternative for road condition monitoring, especially in areas with limited access to conventional IRI measurement tools.

An Artificial Neural Network approach to assess road roughness using smartphone-based crowdsourcing data