Search Results (80)

Traditional Pavement Condition Index (PCI) assessments are highly resource-intensive, demanding substantial time and labor while generating significant carbon emissions through extensive field operations. To address these sustainability challenges, this research presents an innovative methodology utilizing Gene Expression Programming (GEP) to determine PCI values based on International Roughness Index (IRI) measurements from Iraqi road networks, offering an environmentally conscious and resource-efficient approach to pavement management. The study incorporated 401 samples of IRI and PCI data through comprehensive visual inspection procedures. The developed GEP model exhibited exceptional predictive performance, with coefficient of determination (R²) values achieving 0.821 for training, 0.858 for validation, and 0.8233 overall, successfully accounting for approximately 82–85% of PCI variance. Prediction accuracy remained robust with Mean Absolute Error (MAE) values of 12–13 units and Root Mean Square Error (RMSE) values of 11.209 and 11.00 for training and validation sets, respectively. The lower validation RMSE suggests effective generalization without overfitting. Strong correlations between predicted and measured values exceeded 0.90, with acceptable relative absolute error values ranging from 0.403 to 0.387, confirming model effectiveness. Comparative analysis reveals GEP outperforms alternative regression methods in generalization capacity, particularly in real-world applications. This sustainable methodology represents a cost-effective alternative to conventional PCI evaluation, significantly reducing environmental impact through decreased field operations, lower fuel consumption, and minimized traffic disruption. By streamlining pavement management while maintaining assessment reliability and accuracy, this approach supports environmentally responsible transportation systems and aligns contemporary sustainability goals in infrastructure management. Full article

Pavement performance models are considered a key element in pavement management systems since they can predict the future condition of pavements using historical data. Several indicators are used to evaluate the condition of pavements (such as the pavement condition index, rutting depth, and cracking severity), and the international roughness index (IRI), which is the most widely employed worldwide. This study aimed to develop an accurate IRI prediction model. Ten prediction methods were trained on a dataset of 35 independent variables. The performance of the methods was compared, and the light gradient boosting machine was identified as the best-performing method for IRI prediction. Then, the SHAP was synchronized with the best-performing method to prioritize variables based on their relative influence on IRI. The results suggested that initial IRI, mean annual temperature, and the duration between data collections had the strongest relative influence on IRI prediction. Another objective of this study was to determine the optimal uncertainty model for IRI prediction. In this regard, 12 uncertainty models were developed based on different conformal prediction methods. Gray relational analysis was performed to identify the optimal uncertainty model. The results showed that Minmax/80 was the optimal uncertainty model for IRI prediction, with an effective coverage of 93.4% and an average interval width of 0.256 m/km. Finally, a further analysis was performed on the outcomes of the optimal uncertainty model, and initial IRI, duration, annual precipitation, and a few distress parameters were identified as uncertain. The results of the framework indicate in which situations the predicted IRI may be unreliable. Full article

In recent decades, numerous studies have investigated the application of Artificial Intelligence (AI), and more precisely of Artificial Neural Networks (ANNs), in the prediction of complex technical parameters, particularly in the field of road infrastructure management. Among them, prediction of the widely used International Roughness Index (IRI) has attracted much attention due to its importance in pavement maintenance planning. This study focuses on predicting future IRI values using traditional regression models and neural networks, specifically Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) networks, on two highway sections, each analyzed in two experimental setups. The models consider only traffic and structural road characteristics as variables. The results show that the LSTM method provides significantly lower prediction errors for both highway sections, indicating better performance in capturing roughness trends over time. These results confirm that ANNs are a useful tool for engineers by predicting future IRI values, as they help to extend pavement life and reduce overall maintenance costs. The integration of machine learning into pavement evaluation is a promising step forward in ongoing efforts to optimize pavement management. Full article

The roughness of haul roads significantly impacts fuel consumption in open-pit coal mine trucks, yet there is currently a lack of quantitative road classification methods in this regard. This study proposes a fuel-efficient road classification methodology for open-pit coal mines. Using UAV-captured point cloud data of mine roads as the basis for roughness analysis and the International Roughness Index (IRI) as the evaluation metric, the research establishes linear relationships between IRI and fuel consumption for both loaded and unloaded trucks. The K-means clustering algorithm is employed to classify road quality into “good”, “moderate”, and “poor” categories, with the Haerwusu Open-pit Coal Mine serving as a case study. Results demonstrate that 150 m represents an appropriate IRI segmentation interval for Haerwusu, with IRI thresholds of 12 (15) and 20 (21) serving as critical segmentation points for loaded (unloaded) trucks. From analyzing two end-slope roads in the case study mine we found that upgrading “poor” roads to “moderate” quality could reduce fuel costs by 3% for loaded trucks and 2% for unloaded trucks. This study provides a quantitative road classification method for open-pit coal mines, offering a theoretical foundation for reducing transportation costs and promoting sustainable mining development. Full article

This work aims to investigate the effectiveness of road maintenance interventions by analyzing changes in the International Roughness Index (IRI) by means of crowdsourced connected vehicle data. For this purpose, 136 pavement maintenance interventions on a single lane were considered over a period between 2021 and 2024. A multiple linear regression model (R² = 0.780) has been employed as statistical tool to assess the relationship between pre/post-intervention IRI scores and various factors. Despite the fact that results showed a general improvement in pavement condition, the effectiveness of the interventions was found to be influenced by several factors. In particular, intervention on the middle lane appears to be the most effective for enhancing section characteristics, and the effectiveness of maintenance on the overall condition of the section tends to be reduced as the number of lanes increases. Additionally, maintenance appears to be less effective in improving post-maintenance performance as the initial IRI value increases; this suggests that severely deteriorated sections may require more extensive rehabilitation strategies. The ultimate aim of study is to provide evidence to support the inclusion of crowdsource vehicle data in Pavement Management Systems (PMSs) to optimize maintenance planning and resource allocation. Full article

Flooding can damage pavement infrastructure significantly, causing both immediate and long-term structural and functional issues. This research investigates how flooding events affect pavement deterioration, specifically focusing on measuring pavement roughness by the International Roughness Index (IRI). To quantify these effects, we utilized 20 years of pavement condition data from TxDOT’s PMIS database, which is integrated with flood event data, including duration and spatial extent. Statistical analyses were performed to compare IRI values before and after flooding and to calculate the deterioration rates influenced by flood exposure. Moreover, we applied explainable artificial intelligence (XAI) techniques, such as Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), to assess the impact of flooding on pavement performance. The results demonstrate that flood-affected pavements experience a more rapid increase in roughness compared to non-flooded sections. These findings emphasize the need for proactive flood mitigation strategies, including improved drainage systems, flood-resistant materials, and preventative maintenance, to enhance pavement resilience in vulnerable regions. Full article

The increasing demand for sustainable road infrastructure necessitates alternative materials that enhance soil stabilization while reducing environmental impact. This study investigated the application of organosilane-based nanotechnology to improve the structural performance and durability of road corridors in Peru, offering a viable alternative to conventional stabilization methods. A comparative experimental approach was employed, where modified soil and asphalt mixtures were evaluated against control samples without nanotechnology. Laboratory tests showed that organosilane-treated soil achieved up to a 100% increase in the California Bearing Ratio (CBR), while maintaining expansion below 0.5%, significantly reducing moisture susceptibility compared to untreated soil. Asphalt mixtures incorporating nanotechnology-based adhesion enhancers exhibited a Tensile Strength Ratio (TSR) exceeding 80%, ensuring a superior resistance to moisture-induced damage relative to conventional mixtures. Non-destructive evaluations, including Dynamic Cone Penetrometer (DCP) and Pavement Condition Index (PCI) tests, confirmed the improved long-term durability and load-bearing capacity. Furthermore, statistical analysis of the International Roughness Index (IRI) revealed a mean value of 2.449 m/km, which is well below the Peruvian regulatory threshold of 3.5 m/km, demonstrating a significant improvement over untreated pavements. Furthermore, a comparative reference to IRI standards from other countries contextualized these results. This research underscores the potential of nanotechnology to enhance pavement resilience, optimize resource utilization, and advance sustainable construction practices. Full article

This study proposes an explainable extreme gradient boosting (XGBoost) model for predicting the international roughness index (IRI) and identifying the key influencing factors. A comprehensive dataset integrating multiple data sources, such as structure, climate and traffic load, is constructed. A voting-based feature selection strategy is adopted to identify the key influencing factors, which are used as inputs for the prediction model. Multiple machine learning (ML) models are trained to predict the IRI with the constructed dataset, and the XGBoost model performs the best with the coefficient of determination ($R^2$) reaching 0.778. Finally, interpretable techniques including feature importance, Shapley additive explanations (SHAP) and partial dependency plots (PDPs) are employed to reveal the mechanism of influencing factors on IRI. The results demonstrate that climate conditions and traffic load play a critical role in the deterioration of IRI. This study provides a relatively universal perspective for IRI prediction and key factor identification, and the outputs of the proposed method contribute to making scientific maintenance strategies of roads to some extent. Full article

Existing models for predicting the international roughness index (IRI) of a road surface often lack adaptability, struggling to accurately reflect variations in climate, traffic, and pavement distresses—factors critical for effective and sustainable maintenance. This study presents a novel dual-model approach that integrates pavement condition index (PCI), pavement distress types, climatic, and traffic data to improve IRI prediction. Using data from the Long-Term Pavement Performance database, a dual-model approach was developed: pavements were classified into groups based on key factors, and tailored regression models were subsequently applied within each group. The model exhibits good predictive accuracy, with R² values of 0.62, 0.72, and 0.82 for the individual groups. Furthermore, the validation results (R² = 0.89) confirm that the combination of logistic regression and linear regression enhances the precision of IRI value predictions. This approach enhances adaptability and practicality, offering a versatile tool for estimating IRI under diverse conditions. The proposed methodology has the potential to support more effective, data-driven decisions in pavement maintenance, fostering sustainability and cost efficiency. Full article

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. Full article

Roads are essential for economic development, facilitating the circulation of services and resources. This research seeks to provide local governments with a comprehensive framework to enhance road maintenance, focusing on the surface and functional evaluation of pavements. It compares the conventional methods International Roughness Index (IRI) and the Pavement Condition Index (PCI) with novel methodologies that employ smart technologies. The efficiency of such technologies in the maintenance of local roads in Peru is analyzed, taking as a case study a 2 km section of the AR-780 highway in the city of Arequipa. The International Roughness Index (IRI) obtained through the Merlin Roughness Meter and the Roadroid application were compared, finding a minimum variation of 4.0% in the left lane and 8.7% in the right lane. Roadroid turned out to be 60 times faster than the conventional method, with a cost difference of 220.11 soles/km (USD $57.92/km). Both methods classified the Present Serviceability Index (PSI) as good, validating the accuracy of Roadroid. In addition, the Pavement Condition Index (PCI) was evaluated with conventional methods and a DJI Mavic 2 Pro drone, finding a variation of 6.9%. The cost difference between the methodologies was 1047.73 soles/km (USD $275.72/km), and the use of the drone proved to be 10 times faster than visual inspection. This study contributes to closing the knowledge gap regarding the use of smart technologies for better pavement management on local roads, so the actors in charge of such infrastructure make decisions based on science, contributing to the well-being of the population. Full article

Road maintenance is essential for supporting road safety and user comfort. Developing predictive models for road surface conditions enables highway agencies to optimize maintenance planning and strategies. The international roughness index (IRI) is widely used as a standard for evaluating road surface quality. This study compares the performance of deep neural networks (DNNs) and graph convolutional networks (GCNs) in predicting IRI values. A unique aspect of this research is the inclusion of additional predictor features, such as the type and timing of recent roadwork, hypothesized to affect IRI values. Findings indicate that, overall, the DNN model performs similarly to the GCN model across the entire highway network. Given the predominantly linear structure of national highways and their limited connectivity, the dataset exhibits a low beta index, ranging from 0.5 to 0.75. Additionally, gaps in IRI data collection and discontinuities in certain highway segments present challenges for modeling spatial dependencies. The performance of DNN and GCN models was assessed across the network, with results indicating that DNN outperforms GCN when highway networks are sparsely connected. This research underscores the suitability of DNN for low-connectivity networks like highways, while also highlighting the potential of GCNs in more densely connected settings. Full article

Road networks are crucial infrastructures that play a significant role in the progress and advancement of societies. However, roads deteriorate over time due to regular use and external environmental factors. This deterioration leads to discomfort for road users as well as the generation of noise and vibrations, which negatively impact nearby structures. Therefore, it is essential to regularly maintain and monitor road networks. The International Roughness Index (IRI) is commonly used to quantify road roughness and serves as a key indicator for assessing road condition. Traditionally, obtaining the IRI involves manual or automated methods that can be time-consuming and expensive. This study explores the potential of using artificial neural networks (ANNs) and dynamic vehicle accelerations from two simulated car models to reconstruct road roughness profiles. These models include a simplified quarter-car (QC) model with two degrees of freedom, valued for its computational efficiency, and a more intricate full-car (FC) model with seven degrees of freedom, which replicates real-life vehicle behavior. This study also examines the ability of ANNs to predict the mechanical properties of the FC model from dynamic vehicle responses to obstacles. We compare the accuracy and computational efficiency of the two models and find that the QC model is almost 10 times faster than the FC model in reconstructing the road roughness profile whilst achieving higher accuracy. Full article

Proper road network maintenance is essential for ensuring safety, reducing transportation costs, and improving fuel efficiency. Traditional pavement condition assessments rely on specialized equipment, limiting the frequency and scope of inspections due to technical and financial constraints. In response, crowdsourcing data from connected and autonomous vehicles (CAVs) offers an innovative alternative. CAVs, equipped with sensors and accelerometers by Original Equipment Manufacturers (OEMs), continuously gather real-time data on road conditions. This study evaluates the feasibility of using CAV data to assess pavement condition through the International Roughness Index (IRI). By comparing CAV-derived data with traditional pavement auscultation results, various thresholds were established to quantitatively and qualitatively define pavement conditions. The results indicate a moderate positive correlation between the two datasets, particularly in segments with good-to-satisfactory surface conditions (IRI 1 to 2.5 dm/km). Although the IRI values from CAVs tended to be slightly lower than those from auscultation surveys, this difference can be attributed to driving behavior. Nonetheless, our analysis shows that CAV data can be used to reliably identify pavement conditions, offering a scalable, non-destructive, and continuous monitoring solution. This approach could enhance the efficiency and effectiveness of traditional road inspection campaigns. Full article

Trail pavement roughness significantly impacts user experience and safety. Measuring roughness over large areas using traditional equipment is challenging and expensive. The utilization of smartphones and bicycles offers a more feasible approach to measuring trail roughness, but the current methods to capture data using these have accuracy limitations. While machine learning has the potential to improve accuracy, there have been few applications of real-time roughness evaluation. This study proposes a hybrid ensemble machine learning model that combines sequence-based modeling with support vector regression (SVR) to estimate trail roughness using smartphone sensor data mounted on bicycles. The hybrid model outperformed traditional methods like double integration and whole-body vibration in roughness estimation. For the 0.031 mi (50 m) segments, it reduced RMSE by 54–74% for asphalt concrete (AC) trails and 50–59% for Portland cement concrete (PCC) trails. For the 0.31 mi (499 m) segments, RMSE reductions of 37–60% and 49–56% for AC and PCC trails were achieved, respectively. Additionally, the hybrid model outperformed the base random forest model by 17%, highlighting the effectiveness of combining ensemble learning with sequence modeling and SVR. These results demonstrate that the hybrid model provides a cost-effective, scalable, and highly accurate alternative for large-scale trail roughness monitoring and assessment. Full article