Over time, roads undergo deterioration caused by various factors such as traffic loads, climate conditions, and material properties. Considering the substantial global investments in road construction, it is crucial to periodically assess and implement maintenance and rehabilitation (M and R) plans to ensure the network’s acceptable level of service. An integral component of the M and R plan involves utilizing performance prediction models, especially for rutting distress, a significant issue in asphalt pavement. This study aimed to develop rutting prediction models using data from the Long-Term Pavement Performance (LTPP) database, employing several machine learning techniques such as regression tree (RT), support vector machine (SVM), ensembles, Gaussian process regression (GPR), and Artificial Neural Network (ANN). These techniques are well-known for effectively handling extensive and complex datasets. To achieve the highest modeling accuracy, the parameters of each model were meticulously fine-tuned. Upon evaluation, the results revealed that the GPR models outperformed other techniques in various metrics, including Root Mean Square Error (RMSE), R-squared, Mean Absolute Error (MAE), and Mean Square Error (MSE). The best GPR model achieved an RMSE of 1.96, R-squared of 0.70, MAE of 1.32, and MSE of 109.33, indicating its superior predictive capabilities compared with the other machine learning methods tested in this study. Comparison Analysis was made for 10 randomly selected sections on our novel machine learning model that outperforms existing models, with an R² of 0.989 compared with 0.303 and 0.3095 for other models. This demonstrates the potential of advanced machine learning in accurate rut depth prediction across diverse climates, aiding pavement management decisions.

Deteriorating road infrastructure is a global concern, especially in low-income countries where financial and technological constraints hinder effective monitoring and maintenance. Traditional methods, like inertial profilers, are expensive and complex, making them unsuitable for large-scale use. This paper explores the integration of cost-effective, scalable smartphone technologies for road surface monitoring. Smartphone sensors, such as accelerometers and gyroscopes, combined with data preprocessing techniques like filtering and reorientation, improve the quality of collected data. Machine learning algorithms, particularly CNNs, are utilized to classify road anomalies, enhancing detection accuracy and system efficiency. The results demonstrate that smartphone-based systems, paired with advanced data processing and machine learning, significantly reduce the cost and complexity of traditional road surveys. Future work could focus on improving sensor calibration, data synchronization, and machine learning models to handle diverse real-world conditions. These advancements will increase the accuracy and scalability of smartphone-based monitoring systems, particularly for urban areas requiring real-time data for rapid maintenance.

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.

Continuously reinforced concrete pavement (CRCP), crucial for the resilience of transportation infrastructure owing to its continuous steel reinforcement, confronts a critical challenge in the form of spalling—a distress phenomenon posing a threat to pavement durability and overall structural integrity. The detachment or breakage of concrete from the surface compromises CRCP’s functionality and raises safety concerns and escalating maintenance costs. To address this pressing issue, our study investigates the multifaceted factors influencing spalling, employing a comprehensive approach that integrates statistical and machine learning techniques for predictive modeling. Descriptive statistics meticulously profile the dataset, emphasizing age, thickness, precipitation, temperature, and traffic parameters. Regression analysis unveils key relationships, emphasizing the significance of age, annual temperature, annual precipitation, maximum humidity, and the initial International Roughness Index (IRI) as influential factors. The correlation matrix heatmap guides feature selection, elucidating intricate interdependencies. Simultaneously, feature importance analysis identifies age, Average Annual Daily Traffic (AADT), and total pavement thickness as crucial contributors to spalling. In machine learning, adopting models, including Gaussian Process Regression and ensemble tree models, is grounded in their diverse capabilities and suitability for the complex task at hand. Their varying predictive accuracies underscore the importance of judicious model selection. This research advances pavement engineering practices by offering nuanced insights into factors influencing spalling in CRCP, refining our understanding of spalling influences. Consequently, the study not only opens avenues for developing improved predictive methodologies but also enhances the durability of CRCP infrastructure, addressing broader implications for informed decision-making in transportation infrastructure management. The selection of Gaussian Process Regression and ensemble tree models stems from their adaptability to capture intricate relationships within the dataset, and their comparative performance provides valuable insights into the diverse predictive capabilities of these models in the context of CRCP spalling.

For efficient maintenance planning and long-term performance assessment of continuously reinforced concrete pavement (CRCP), an accurate estimate of the International Roughness Index (IRI) is necessary. In order to forecast IRI using data taken from the Long-Term Pavement Performance (LTPP) database, this study proposes a hybrid machine learning model that combines Support Vector Regression (SVR) and Particle Swarm Optimization (PSO). Incorporating structural, climatic, and traffic-related variables, 395 observations from 33 CRCP sections were used. The PSO algorithm was employed to optimize SVR hyperparameters, resulting in enhanced model accuracy and stability. The proposed PSO-SVR model achieved outstanding predictive performance with an average RMSE of 0.04116 and an R² of 0.99058 across fivefold cross-validation, outperforming benchmark models including Decision Tree, Random Forest, and XGBoost. By highlighting important input characteristics affecting IRI, variable importance analysis and 3D interaction plots improved the interpretability of the model even more. The outcomes show the PSO-SVR framework's superiority and dependability, underscoring its potential as a strong decision-support tool for pavement management and performance forecasting in rigid pavement systems.