Correlations and Analyses of Longitudinal Roughness Indices

The International Roughness Index (IRI) is calculated from elevation profiles acquired by high-speed profilers or laser scanners, but these raw data often contain measurement noise and extraneous wavelength components that can degrade the accuracy of IRI calculations. Existing filtering methods expose a limitation in removing IRI-insensitive wavelength components. Thus, this paper proposes a Gaussian filtering algorithm based on the Nyquist sampling theorem to remove IRI-insensitive components of the longitudinal profile. The proposed approach first adaptively determines Gaussian template lengths according to sampling intervals, and then incorporates a boundary padding strategy to ensure processing stability. The proposed method enables precise wavelength selection within the IRI-sensitive band of 1.3–29.4 m while maintaining computational efficiency. The method was validated using the Paris–Lille dataset and the U.S. Long-Term Pavement Performance (LTPP) program dataset. The filtered profiles were evaluated by Power Spectral Density (PSD), and IRI values were calculated and compared with those obtained by conventional profile filtering methods. The results show that the proposed method is effective in removing the non-sensitive components of IRI and obtaining highly accurate IRI values. Compared with the standard IRI provided by the LTPP dataset, mean absolute error of the IRI values from the proposed method reaches 0.051 m/km, and mean relative error is less than 4%. These findings indicate that the proposed method improves the reliability of IRI calculation.

Vibration data provided by vehicle fleets can offer a continuous measurement system for pavement roughness, complementing profilometer surveys for network-level management. However, its heterogeneous nature—varying suspensions and speeds—poses significant application challenges. This study examines whether vertical accelerations measured above the suspension can predict the International Roughness Index (IRI) without requiring hard-to-obtain vehicle parameters or complex techniques, thus facilitating practical adoption by road agencies. Quarter-car simulations were performed on 36 measured pavement profiles for 18 representative vehicles spanning realistic parameter ranges and travel speeds (30–120 km/h). The root mean square (RMS) of vertical acceleration was computed for each run and related to IRI through six linear regression forms, including logarithmic variants accounting for speed. Monte Carlo analysis showed that aggregating RMS across nine vehicles reduced data dispersion and yielded the lowest prediction error. Achieving the best trade-off in accuracy, the logarithm model using separate RMS acceleration and speed terms yielded a root mean square error of 0.21 m/km and an adjusted R² of 0.84 on the test phase. This work also shows that a smoothed curve of mean RMS acceleration and box plot graphs can highlight rough segments when no IRI data is available, providing a rapid screening tool for maintenance prioritization. These findings indicate that collecting data from roughly 8 to 10 vehicles per segment and applying a simple logarithm calibration can provide stable and interpretable IRI estimates. The approach can potentially scale with connected-vehicle or smartphone fleets and guide agencies in continuous condition monitoring and targeted inspections.

The surface roughness in terms of International Roughness Index (IRI) captures variation in vertical surface elevations along the pavement length. Diamond grinding is a rigid pavement preservation treatment which is typically utilized to increase pavement smoothness by eliminating the surface undulations. The focus of this study is to evaluate the impact of diamond grinding on rigid pavement performance by using the mechanistic-empirical procedure. The effectiveness of a diamond grinding was assessed by evaluating the IRI before and after the treatment. Generally, smoother pavements will have lower IRI, which will result in less vehicle body and axle bounce. Consequently, the pavement will be subjected to reduced dynamic loads which may cause less pavement damage or vice versa. The longitudinal profile and axle load data from the Long-Term Pavement Performance (LTPP) in the SPS-6 experiment were utilized to evaluate the impact of diamond grinding on pavement performance. These experiments were designed to evaluate the effectiveness of rigid pavement rehabilitation treatments. The data for pavement sections from two climatic zones were evaluated before and after diamond grinding. The axle distributions for each pavement section were utilized to generate truck gross-vehicle weight (GVW) distributions. Subsequently, the truck GVW distributions were used to generate truck response on different pavement surface profiles by using TruckSim software. The impact of surface roughness on dynamic loads was evaluated by comparing axle load spectra before and after diamond grinding. The variations in the axle load spectra are used to explain the differences in the predicted pavement performance in terms of cracking, faulting and IRI.

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.

Practitioners have often wondered whether, during ride measurement with inertial devices, the motion of the laser through pavement texture introduces nonrepresentative values of international roughness index (IRI), particularly in certain textures. In response to this problem, a special texture study created a nontextured strip by a recession of the middle 4 ft of a texturing broom dragged longitudinally behind the paver. The study measured IRI and other surface properties in adjacent textured and nontextured strips by using a lightweight profiler outfitted with a line laser and a triple laser arranged in tandem. IRI measurements were performed after sufficient concrete strength gain and repeated as soon as the joints were sawn. The same measurements were repeated after the joints were deployed. Results showed a significant difference between the IRI of a textured strip and that of an nontextured strip. Further analysis indicated that, although texture appears to affect IRI, this effect was amplified by the type of laser used, as the triple laser appeared to indicate higher IRIs in comparison with the RoLine laser. Although the RoLine is not a reference profiler for IRI values unaffected by texture, the prevalence of the RoLine and the triple laser in construction acceptance testing is sufficient reason to be concerned about the difference inherent in the obtained results. Chi-square and t-test statistical analysis showed that laser type induced comparable and even higher IRI anomalies than did the experimental drag texture. In addition, the texture-induced IRI anomaly can be minimized by measuring smoothness for acceptance at least 2 weeks after paving.

The accurate prediction of pavement performance is essential for transportation administration or management to appropriately allocate resources road maintenance and upkeep. The international roughness index (IRI) is one of the most commonly used pavement performance indicators to reflect the surface roughness. However, the existing research on IRI prediction mainly focuses on using linear regression or traditional machine learning, which cannot take into account the historical effects of IRI caused by climate, traffic, pavement construction and intermittent maintenance. In this work, a long short-term memory (LSTM)-based model, LSTM+MA, is proposed to predict the IRI of pavements using the time-series data extracted from the long-term pavement performance (LTPP) dataset. Effective preprocessing methods and hyperparameter fine-tuning are selected to improve the accuracy of the model. The performance of the LSTM+MA is compared with other state-of-the-art models, including logistic regressor (LR), support vector regressor (SVR), random forest (RF), K-nearest-neighbor regressor (KNR), fully connected neural network (FNN), XGBoost (XGB), recurrent neural network (RNN) and LSTM. The results show that selected preprocessing methods can help the model learn quickly from the data and reach high accuracy in small epochs. Also, it shows that the proposed LSTM+MA model significantly outperforms other models, with an R2 of 0.965 and a mean square error (MSE) of 0.030 in the test datasets. Moreover, an overfitting score is proposed in this work to represent the severity degree of the overfitting problem, and it shows that the proposed model does not suffer severely from overfitting.

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.

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%.

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

A Roadware ARAN inertial profiler was used to calculate the International Roughness Index (IRI) for the Auburn University National Center for Asphalt Technology (NCAT) test track tangent sections as well as three paving projects in Michigan. IRI values were calculated for short intervals [4.6 to 7.6 meters (15 to 25 feet)] rather than for the traditional 160 meters (0.1 mile). These short intervals were able to identify easily localized anomalies in the pavement smoothness due to changes in the construction activities. They were also useful in evaluating relationships between IRI and mix variables in the short NCAT test track test sections [60 meters (200 feet)]. IRI repeatability (standard deviation for three replicates) immediately after onstruction was found to be 0.063 m/km (3.99 inches/mile) in the right wheel path for mixes at the test track. The left wheel path tended to have higher and more variable IRI values. This was attributed to the left laser path being close to potentially segregated mix at the edges of the screed extension. The IRI variability increased with traffic. Using this nitial estimate of variability, some statistically significant differences due to mix variables such as aggregate source and/or binder grade can be seen. Results from the test track indicate that the finer mixes tended to have IRI values that ncrease slightly as density increases. This suggests that working the fine mixes to obtain higher densities results in a higher initial roughness.This observation was also generally seen in the data from the Michigan field projects. IRI values decreased significantly within 6 months of trafficking for the majority of the mixes at the test track. This suggests that traffic can have a smoothing affect on the pavement for up to 6 months after the start of trafficking. All results from this research were based on the construction of test track sections. These results should be verified on actual paving projects.

One aspect of pavement management systems (PMS) that has begun to receive increased attention is that of data quality and variability. This paper provides recommendations for quantifying and controlling the variability of longitudinal profile data, and the resulting International Roughness Index (IRI) values. The recommendations were determined from an analysis of the profile data collected by the Long-Term Pavement Performance Program (LTPP). In particular, the effort was focused on developing quantifications of variability that could be useful and economical for network-level pavement management.

ABSTRACTThe performance of asphalt pavement is highly affected by climate factors, such as temperature and precipitation. Different temperature and moisture conditions change the paving material properties, which consequently influence pavement performance. Considerable research has been conducted to study the effect of climate factors on pavement performance; however, there are no focused studies that have investigated the performance of asphalt pavements in cold regions. In this research, the effect of different design factors on asphalt pavement performance in cold regions is investigated utilising data extracted from the Long-Term Pavement Performance database. Only control sections with no historical maintenance or rehabilitation records were considered. The International Roughness Index (IRI) was adopted as the pavement performance measure. The IRI value is expected to increase gradually over time due to pavement deterioration. Initial screening of the data showed that most of the pavement sections experienced remarkable IRI increase followed by an unexpected decrease. An in-depth investigation revealed that frost heave was the main reason of this unexpected IRI trend as it was correlated in most of the cases to sudden monthly freezing index increases, especially for sections with no subsurface drainage and less layers thicknesses. An Artificial Neural Network with a Forward Sequential Feature Selection algorithm and regression analysis were employed to model pavement performance in cold regions and determine the most significant design factors prevailing in cold climate conditions. Moreover, a sensitivity analysis was performed to investigate the interrelation between the considered features and the IRI. As a result, several climate-related factors were found to have a significant impact on the performance of asphalt pavements in cold regions such as average temperature, freezing index, freeze/thaw, wind velocity and relative humidity.

The International Roughness Index (IRI) is the one of the most important roughness indexes to quantify road surface roughness. In this paper, we propose a new hybrid approach between adaptive network based fuzzy inference system (ANFIS) and various meta-heuristic optimizations such as the genetic algorithm (GA), particle swarm optimization (PSO), and the firefly algorithm (FA) to develop several hybrid models namely GA based ANGIS (GANFIS), PSO based ANFIS (PSOANFIS), FA based ANFIS (FAANFIS), respectively, for the prediction of the IRI. A benchmark model named artificial neural networks (ANN) was also used to compare with those hybrid models. To do this, a total of 2811 samples in the case study of the north of Vietnam (Northwest region, Northeast region, and the Red River Delta Area) within the scope of management of the DRM-I Department were used to validate the models in terms of various criteria like coefficient of determination (R) and the root mean square error (RMSE). Experimental results affirmed the potentiality and effectiveness of the proposed prediction models whereas the PSOANFIS (RMSE = 0.145 and R = 0.888) is better than the other models named GANFIS (RMSE = 0.155 and R = 0.872), FAANFIS (RMSE = 0.170 and R = 0.849), and ANN (RMSE = 0.186 and R = 0.804). The results of this study are helpful for accurate prediction of the IRI for evaluation of quality of road surface roughness.

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.