A Comparative Study of Pavement Roughness Prediction Models under Different Climatic Conditions

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%.

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.

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

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

Imporved model for pavement performance prediction based on recurrent neural network using LTPP database

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 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.

It is hypothesised that maintenance treatments should be applied in the preventive mode before pavements display significant amounts of distress in order to be more cost-effective. The objective of this study was to verify the concept of preventive maintenance by examining the long-term effectiveness of chip seal treatment in four climatic zones in the USA using the long-term pavement performance database. Pavement sections were categorised into smooth, medium and rough pavements, based upon initial condition (IC) as indicated by the international roughness index. Pavement performance of treated and untreated sections was collectively modelled using exponential regression analysis. Effectiveness was evaluated in terms of life extension, relative benefit and benefit–cost ratio. The results showed that preventive maintenance is cost-effective. The life extension, relative benefit and benefit cost ratio were highest for sections whose IC was smooth at the time of treatment. Chip seal treatment effectiveness showed no correlation to climatic conditions or to traffic levels.

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.

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.

This study focuses on the critical aspect of skid resistance in asphalt pavements, an essential factor for ensuring road safety. Several factors, including pavement texture, aggregate properties, and environmental conditions, influence skid resistance. Utilizing data from the Long-Term Pavement Performance (LTPP) database, this research develops an Artificial Neural Network (ANN) model to predict skid resistance based on a comprehensive array of inputs such as traffic volume, pavement age, asphalt content, and environmental factors. A key outcome of this study is the derivation of an explicit algebraic equation that fully represents the trained ANN model, allowing practitioners to compute Friction Number (FN) without running the whole model. The model was trained with data involving 135 GPS pavement sections across various climate zones in the United States, achieving a robust Coefficient of Determination (R²) of 0.87, a Root Mean Square Error (RMSE) of 2.52, a Mean Absolute Error (MAE) of 2.04, and a Mean Absolute Percentage Error (MAPE) of 4.4%. The findings underscore the significant impact of traffic loads, pavement age, and environmental conditions on skid resistance. The study’s methodology and findings contribute to the broader understanding of pavement management and safety strategies, providing a valuable tool for predicting and maintaining optimal skid resistance in asphalt pavements. Recommendations for future research include expanding the dataset and refining the model with additional influential factors to enhance prediction accuracy and reliability.

Long-term average solar radiation prediction via seasonal Global Horizontal Irradiance (GHI) forecasting is increasingly leveraging Machine Learning (ML) to uncover complex relationships in historical GHI and climatic parameters surpassing traditional methods. This study investigates seasonal GHI forecasting across four climatically distinct Indian locations namely Chennai, Jaisalmer, Leh and Mawsynram using data from the National Solar Radiation Database. Following rigorous data quality assessment and feature selection based on Spearman's correlation and Mutual Information, eight key features are identified for training three ML models: Efficient Linear Regression (ELR), Regression Trees (RT) and Gaussian Process Regression (GPR). The GPR model demonstrates superior accuracy in seasonal GHI prediction across diverse Indian climatic conditions making it ideal for reliable and cost-efficient Stand-Alone Photovoltaic (SAPV) systems. It is observed that the GPR model achieves the lowest Root Mean Square Error (RMSE) of 0.0030, Mean Absolute Error (MAE) of 0.0022 and Coefficient of Determination (R²) of 0.9999 achieving a reduction of 189.1% in RMSE, 190.09% in MAE and 20.56% improvement in R² compared to ELR. Furthermore, the GPR model surpasses the RT model with a reduction of 124.05% in RMSE and 111.1% in MAE, together with an improvement of 0.2604% in R². While RT provides reasonably accurate forecasts, ELR shows the least reliability. These results confirm GPR's exceptional precision and ability to capture nearly all dataset variability.

Simulation of pavement roughness and IRI based on power spectral density

Maintaining and rehabilitating pavement in a timely manner is essential for preserving or improving its condition, with roughness being a critical factor. Accurate prediction of road roughness is a vital component of sustainable transportation because it helps transportation planners to develop cost-effective and sustainable pavement maintenance and rehabilitation strategies. Traditional statistical methods can be less effective for this purpose due to their inherent assumptions, rendering them inaccurate. Therefore, this study employed explainable and supervised machine learning algorithms to predict the International Roughness Index (IRI) of asphalt concrete pavement in Sri Lankan arterial roads from 2013 to 2018. Two predictor variables, pavement age and cumulative traffic volume, were used in this study. Five machine learning models, namely Random Forest (RF), Decision Tree (DT), XGBoost (XGB), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), were utilized and compared with the statistical model. The study findings revealed that the machine learning algorithms’ predictions were superior to those of the regression model, with a coefficient of determination (R2) of more than 0.75, except for SVM. Moreover, RF provided the best prediction among the five machine learning algorithms due to its extrapolation and global optimization capabilities. Further, SHapley Additive exPlanations (SHAP) analysis showed that both explanatory variables had positive impacts on IRI progression, with pavement age having the most significant effect. Providing accurate explanations for the decision-making processes in black box models using SHAP analysis increases the trust of road users and domain experts in the predictions generated by machine learning models. Furthermore, this study demonstrates that the use of explainable AI-based methods was more effective than traditional regression analysis in IRI prediction. Overall, using this approach, road authorities can plan for timely maintenance to avoid costly and extensive rehabilitation. Therefore, sustainable transportation can be promoted by extending pavement life and reducing frequent reconstruction.