Aug 2026· Transportation Research Record· 0 citations· 41 references
TL;DR
A multioutput machine learning regression framework for the simultaneous prediction of the International Roughness Index (IRI) and pavement condition rating (PCR) for major district roads (MDRs) in Punjab, India demonstrates that reliable pavement condition assessment can be achieved using routinely collected survey data.
Abstract
This study develops and validates a multioutput machine learning regression framework for the simultaneous prediction of the International Roughness Index (IRI) and pavement condition rating (PCR) for major district roads (MDRs) in Punjab, India. The dataset comprises 1,859 pavement segments characterized by six predictor variables: bituminous thickness, granular sub-base thickness, rut depth, crack severity, air temperature, and pavement surface temperature. Six regression models, namely linear regression (LR), support vector regression (SVR), K-nearest neighbors (KNN), random forest (RF), gradient boosting (GB), and neural networks (NN), were evaluated within a multioutput regression framework. Among these, GB achieved the best performance, with an
R
2
of 0.639 (root mean square error [RMSE] = 0.951; mean absolute error [MAE] = 0.625) for the International Roughness Index (IRI) and 0.998 (RMSE = 0.032; MAE = 0.007) for the pavement condition rating (PCR). The framework combines simultaneous prediction using a common set of predictor variables with a dual-layer validation strategy based on Taylor diagrams and scaled mutual information (IRI MI ≈ 0.71; PCR MI > 2.1), providing complementary statistical and information-theoretic evaluation. The predicted IRI and PCR values were interpreted using maintenance thresholds derived from Indian Roads Congress (IRC) guidelines to support pavement management decisions. The results demonstrate that reliable pavement condition assessment can be achieved using routinely collected survey data, providing a practical and scalable decision-support tool for maintenance planning in data-constrained road networks.
The potential effectiveness of the proposed robustness-oriented evaluation framework for ML-assisted Ra prediction under limited-data machining conditions is supported, and ELM achieved the highest prediction accuracy.
Phong Thanh Huynh, T. Nguyen, H. Thuong· Engineering Research Express· 0 citations
This study applies several machine learning models to predict the Marshall stability of Stone Mastic Asphalt mixtures incorporating steel slag as a partial replacement for conventional coarse aggregates, with CatBoost providing the best performance.
T. Nguyen, Hoang-Long Nguyen, N. Trần et al.· Journal of Science and Trans...· 0 citations
The soaked California bearing ratio (CBR) is an essential parameter of transportation engineering to design flexible pavement. The laboratory determination of CBRs is a lengthy, time-consuming process. To assess the CBRs of pavement materials, this investigation introduces a robust artificial intelligence model by comparing the multilinear regression (MLR), linear (LSSVM_L), and polynomial (LSSVM_P) kernel-based least squares support vector machine (LSSVM) models. Moreover, the six training datasets were developed to determine the impact of the quality and quantity of the training database on the MLR and LSSVM models. The performance analysis reveals that the linear kernel-based LSSVM (referred to as LSSVM_L50) model is simple yet highly accurate, with reduced complexity, and achieves higher performance (0.9992 and 0.9602 in the training and testing phases, respectively) using a 50% training database. Still, the LSSVM_P70 model achieved CBRs performance of 0.9999 (in training) and 0.9791 (in testing), outperforming the LSSVM_L50 model, as the polynomial kernel effectively handles the database’s nonlinearity and complexity. In addition, the generalizability analysis, uncertainty analysis, and validation using external and laboratory-tested samples confirmed the robustness of the LSSVM_P70 model. Conversely, the Shapley additive explanations analysis revealed that the optimum moisture content dominates model predictions, with a mean absolute impact of 7.14, followed by gravel and fine content as secondary drivers, while demonstrating clear nonlinear relationships, threshold effects, and instance-level interpretability.
Jitendra Khatti· Journal of Structural Design...· 0 citations
Accurate prediction of pavement surface deterioration is crucial for effective pavement management and efficient allocation of limited maintenance resources. In Nepal, pavement condition evaluation and maintenance prioritization mainly depend on the Surface Distress Index (SDI); however, robust SDI-based deterioration models aligned with national practices are still limited. This study develops and validates a multiple linear regression (MLR) model to predict SDI across Nepal’s national highway network using nationally available pavement, traffic, and climatic data. The model links SDI to six explanatory variables: initial SDI, initial International Roughness Index (IRI), pavement age, total annual rainfall, temperature variation, and equivalent single axle loads (ESAL). Data from 157 highway sections, totaling 790 observations collected between 2012 and 2022, were used. Model development and validation involved an 80:20 data split, followed by a thorough out-of-sample forecast assessment with independent 2023 data from 125 highway sections. The MLR model shows strong explanatory and predictive performance, with coefficients of determination (R²) of 0.724, 0.730, and 0.727 for the development, validation, and overall datasets, respectively. Error metrics reflect satisfactory accuracy, with mean absolute error (MAE) values between 0.292 and 0.358 and mean squared error (MSE) between 0.145 and 0.197. Sensitivity analysis highlights initial SDI, initial IRI, pavement age, and rainfall as the most influential factors in surface distress progression. Under true out-of-sample conditions, the model achieves an R² of 0.723, MAE of 0.359, RMSE of 0.460, and mean absolute percentage error (MAPE) of 16.93%, confirming its robustness and generalization ability. These results demonstrate that a well-specified deterministic regression model can reliably forecast SDI at the network level and serve as a practical, data-driven tool integrated into Nepal’s Pavement Management System, assisting maintenance prioritization, budgeting, and long-term asset managemen
Keywords: Surface Distress Index, International Roughness Index, Multiple Linear Regression, National Highway Network
K. Basnet, J. Shrestha, Rabindranath Shrestha· Journal on Transportation Sy...· 0 citations
With the growing complexity and variability of the operational environment of asphalt pavements and the continuous increase in traffic loads, traditional pavement performance prediction models cannot accurately depict the nonlinear degradation process of pavement performance with the passage of time. Therefore, a novel approach is proposed in this paper to forecast the service performance of asphalt pavements accurately. This method optimises a Backpropagation (BP) neural network using the Levenberg–Marquardt (LM) algorithm. Seven main influencing factors were selected as the input parameters to construct the prediction model, and the performance of the prediction model was evaluated. The parameters considered in this analysis are: road age, average daily traffic volume for one year, annual temperature range, annual precipitation, relative humidity, pavement thickness and pavement surface compressive strength. Through these factors cumulatively, the model is able to predict and evaluate the road condition and Pavement Quality Index (PQI) accurately. The results indicate that the proposed model is better than the baseline model of traditional BP neural networks in predicting the Road Condition Index (RCI), with a Mean Absolute Error (MAE) of 0.395. This is an important reference to predict the service performance of asphalt pavements and validate the effectiveness of the model.
Xinyu Zuo, Yufan Du, Guangsheng Zeng et al.· Materials· 0 citations
Effective pavement condition assessment is critical for maintaining durable, safe, and well-performing road networks, particularly in arid regions such as Saudi Arabia, where heavy traffic loading, high temperatures, and surface distress accelerate pavement deterioration. Traditional mechanistic or deterministic approaches often struggle to capture the nonlinear and complex patterns of pavement deterioration. This study aims to develop and validate an integrated, explainable machine-learning (XML) framework for predicting the International Roughness Index (IRI) using 4 years of traffic, pavement distress, and weather data from a 190 km-long rural highway in Al-Qassim Province, Saudi Arabia. IRI is classified into three distinct classes (low, medium, and high) based on predefined thresholds. The dataset included ~ 27,700 observations on cracking, rutting, texture, pavement serviceability, pavement condition rating, traffic volume, speed, temperature, humidity, wind speed, and precipitation. The Synthetic Minority Over-sampling Technique (SMOTE) trained the data and addressed data imbalance. Seven machine learning models, namely Decision Tree (DT), Support Vector Machine (SVM), Gradient Boosting (GB), Adaptive Boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Deep Neural Network (DNN), were evaluated using stratified k-fold cross-validation and an independent testing set. To enhance the model’s transparency and interpretability, feature importance and SHAP (Shapley Additive Explanations) analyses are also performed. The study also developed a pavement deterioration curve to quantify IRI progression over time. Model assessment across multiple performance indicators (accuracy, precision, recall, F-1 score) showed that XGBoost and CatBoost outperformed other models, achieving the highest classification accuracy (0.87). Feature importance estimates from CatBoost indicated traffic loading as the most influential predictor of IRI, followed by rutting and cracking. SHAP yielded an interpretable visualization of the collective influence of significant predictors to shape IRI outcomes. The pavement deterioration curve indicated IRI approaching a critical threshold of 4.0 in 6.6–6.7 years, under prevailing conditions without major rehabilitation. The proposed framework provides an efficient data-driven tool for identifying high-risk pavement sections, prioritizing maintenance interventions, and supporting proactive pavement asset management in arid and heavy-traffic environments.
Fawaz Alharbi, Malik Parras Hussan Abbas, Arshad Jamal et al.· Scientific Reports· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
