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Pavement performance assessment and prediction are crucial for efficient infrastructure management and strategic planning of maintenance activities. Conventional techniques are insufficient and lack the efficiency and flexibility required for modern transportation networks. This study proposes a groundbreaking integrated approach that merges machine learning (ML) classification techniques with Geographical Information Systems (GIS) to evaluate road conditions using the Pavement Condition Index (PCI) and the International Roughness Index (IRI). Given that IRI data collection is more straightforward and cost-effective than gathering pavement distress data, this study aims to classify the IRI of flexible pavements to estimate PCI models using advanced ML algorithms (Artificial Neural Network (ANN), Adaptive Boosting (AdaBoost), Support Vector Machine (SVM), Decision Trees (DT), and Random Forest (RF)) and accurately determine pavement conditions. This research gathered 1042 data points using a smartphone application, TotalPave, to measure the IRI values for the (Nizwa–Muscat) and (Muscat–Nizwa) routes in the Sultanate of Oman. It meticulously applied feature selection techniques to identify the pavement parameters significantly impacting pavement performance. The research then spatially visualized and analyzed the results to determine the critical pavement sections. Among the ML models, RF demonstrated outstanding performance with an accuracy rate of 99.9% and an F1-score of 99. SVM has the lowest accuracy of 85.8% and an F1-score of 40.3. A comprehensive assessment comprising a confusion matrix, uncertainty analysis, box and whisker plot, and noise sensitivity provides in-depth insights into the reliability and consistency of predictions. The ML and GIS methods revolutionized the way transportation agencies interpret and implement their findings. The proposed framework is not merely a tool, but a transformative solution that facilitates the formulation of proactive maintenance strategies and optimizes resource utilization by providing a scalable and intelligent decision-support tool designed specifically for pavement management systems (PMS).

Traffic accidents represent a serious problem in Jordan. The major objective of this study was to investigate the effect of pavement conditions on urban road accidents. To achieve the objective of the study, 25 urban pavement sections were selected. A comprehensive data base, including pavement conditions in terms of pavement condition index (PCI), traffic volume, and traffic accidents, for the selected sections, was developed. Regression analyses were carried out to explore possible relationships between PCI and the number of accidents and accident rates. The results of the statistical analysis indicated that urban pavement conditions had a significant effect on accident occurrences and accident rates. The relationship between accidents and PCI had a parabolic shape. The regression models explained 69% and 61% of the variation in accident frequency and rate, respectively. It was found that urban pavement sections with PCI lower than 50 were consistently associated with higher accident frequency or accident rate. Also, a notable increase in accidents was observed for all sections having a PCI greater than 85. The results of the study are comparable with the results found for rural pavement sections. To provide a safe urban environment; therefore, municipal engineers should keep urban pavement in fair conditions and implement appropriate calming measures for good pavement conditions. Keywords: Accident occurrence, Accident rate, Road safety, Pavement condition index (PCI), PAVER system, Pavement management system (PMS), Pavement distresses.

Pavements are the main assets of highway infrastructure. Pavement Condition Index (PCI) is a numerical index used to describe the general condition of a pavement. This paper is another trial in monitoring and find pavement condition index (PCI) by applying PAVER software version 5.2. This work aims to evaluate (PCI) of a flexible pavement of some miner collector highways in the north sector in Najaf city divaricate from both sides of Najaf- Karbala suburban main collector highway in its part away from Al- Askariin intersection towards Karbala. These highway sections are Al-Rahma, Al Hizam Al Akhdar, and Al Muearid, Al Shamalii garage highways which cover a total length approximately 11.54 km in both direction of traffic movement (diverging from Najaf- Karbala highway and return to it). Field survey data such as highway section geometric design and distresses type, dimension, and severity, were collected depending on sample size and number of samples extracted, and then entered into the PAVER program to calculate PCI. The result of PAVER shows Results approved that Al-Rahma and Al Shamalii garage highways sections are in satisfactory level, while Al Muearid highway section in (fair) and Al Hizam Al Akhdar in worst case (poor). In addition to that, reasons of these defects had been figuring out according to results obtained.

Assessing the condition of airport pavements is essential to ensure operational safety and efficiency. This study presents an innovative, fully automated approach to calculate the Pavement Condition Index (PCI) by combining UAV-based aerial photogrammetry with advanced Artificial Intelligence (AI) techniques. The method follows three key steps: first, analyzing orthophotos of individual pavement sections using a custom-trained AI model designed for precise crack detection and classification; second, utilizing skeletonization and semantic mask analysis to measure crack characteristics; and third, automating the PCI calculation for faster and more consistent evaluations. By leveraging high-resolution Unmanned Aerial Vehicle (UAV) imagery and advanced segmentation models, this approach achieves superior accuracy in detecting transverse and longitudinal cracks. The automated PCI calculation minimizes the need for human intervention, reduces errors, and supports more efficient, data-driven decision-making for airport pavement management. This study demonstrates the transformative potential of integrating UAV and AI technologies to facilitate infrastructure maintenance and enhance safety protocols.

Roads and transportation infrastructure are critical assets for supporting political stability as well as economic and sustainable expansion in developing countries. Yet, pavement maintenance backlogs and the high capital expenditures of road rehabilitation need the adoption of pavement evaluation methods to ensure the best value for the investment. road maintenance is considered one of the main factors for preserving the road structurally and functionally, and before carrying out maintenance, pavement defects must be identified by evaluating the condition of the pavement to perform the appropriate maintenance. In this study, the Pavement Condition Index (PCI) method was relied upon to evaluate roads in Al-Diwaniyah City. Where several data were collected for the study, including the number of crashes for each region, the number of lanes, street width, the presence of a median, and the pavement condition evaluated for the regions with the highest crash rate, which are Al-Askari District with 707 crashes, Al-Urouba District with 379 crashes, Al-Wahda District with 271 crashes, Al-Jamhouri District with 235 crashes, and Al-Jazaer District with 212 crashes. This study is considered the first study to evaluate roads in Al-Diwaniyah city. The results of the study showed that the condition of the roads ranged from poor to satisfactory and there is no road reach to serious and failed. The results showed that the worst road is Al-Askari District, which lacks a lot of maintenance, at a rate of PCI 52.1. It is followed by Al-Wahda District with a PCI rate of 57.6, Al-Jamhouri District with a PCI rate of 67.2, Al-Urouba District with a PCI rate of 70.7, and Al-Jazaer District with a PCI rate of 73.7. The deterioration of the pavement surface condition affects the rash rate, and it is more dangerous when it is correlated to other road factors. Additionally, it was noted that the most prominent defects in the pavement surface of the five evaluated roads are: potholes, rutting, longitudinal and transverse cracks, corrugation, patching, raveling, depressions, and alligator cracks. The study recommended conducting more research on evaluating more roads in Al-Diwaniyah City on an annual basis and conducting periodic maintenance in order to improve the status of this city, which suffers from neglect in all respects, measures must be taken to enhance the environment and economy, sustain the streets, reduce maintenance costs, ensure traffic safety, and protect the lives of citizens.

The fuzzy logic technique is one of the effective approaches for evaluating flexible and rigid pavement distress. The process of classifying pavement distress is usually performed by visual inspection of the pavement surface or using data collected by automated distress measurement equipment. Fuzzy mathematics provides a convenient tool for incorporating subjective analysis, uncertainty in pavement condition index, and maintenance-needs assessment, and can greatly improve consistency and reduce subjectivity in this process. This paper aims to develop a fuzzy logic-based system of pavement condition index and maintenance-needs evaluation for a pavement road network by utilizing pavement distress data from the U.S. and Canada. Considering rutting, fatigue cracking, block cracking, longitudinal cracking, transverse cracking, potholes, patching, bleeding, and raveling as input variables, the variables were fuzzified into fuzzy subsets. The fuzzy subsets of the variables were considered to have triangular membership functions. The relationships between nine pavement distress parameters and PCI were represented by a set of fuzzy rules. The fuzzy rules relating input variables to the output variable of sediment discharge were laid out in the IF–THEN format. The commonly used weighted average method was employed for the defuzzification procedure. The coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE) were used as the performance indicator metrics to evaluate the performance of analytical models.

The pavement is a complex structure that is influenced by various environmental and loading conditions. The regular assessment of pavement performance is essential for road network maintenance. International roughness index (IRI) and pavement condition index (PCI) are well-known indices used for smoothness and surface condition assessment, respectively. Machine learning techniques have recently made significant advancements in pavement engineering. This paper presents a novel roughness-distress study using random forest (RF). After determining the PCI and IRI values for the sample units, the PCI prediction process is advanced using RF and random forest trained with a genetic algorithm (RF-GA). The models are validated using correlation coefficient (CC), scatter index (SI), and Willmott’s index of agreement (WI) criteria. For the RF method, the values of the three parameters mentioned were −0.177, 0.296, and 0.281, respectively, whereas in the RF-GA method, −0.031, 0.238, and 0.297 values were obtained for these parameters. This paper aims to fulfill the literature’s identified gaps and help pavement engineers overcome the challenges with the conventional pavement maintenance systems.

This paper describes the process and outcome of deterioration modeling for three different pavement types (asphalt, concrete, and composite) in the state of Iowa. Pavement condition data is collected by the Iowa Department of Transportation (DOT) and stored in a Pavement-Management Information System (PMIS). In the state of Iowa, the overall pavement condition is quantified using the Pavement Condition Index (PCI), which is a weighted average of indices representing different types of distress, roughness, and deflection. Deterioration models of PCI as a function of time were developed for the different pavement types using two modeling approaches. The first approach is the long/short-term memory (LSTM), a subset of a recurrent neural network. The second approach, used by the Iowa DOT, is developing individual regression models for each section of the different pavement types. A comparison is made between the two approaches to assess the accuracy of each model. The results show that the LSTM model achieved a higher prediction accuracy over time for all different pavement types.

This study proposes a novel framework for determining variables’ weights in transportation assets condition indices calculations using statistical and machine learning techniques. The methodology leverages subjective ratings alongside objective measurements to derive data-driven weights. The motivation for this study lies in addressing the limitations of existing expert-based weighting methods for condition indices, which often lack transparency and consistency; this research aims to provide a data-driven framework that enhances accuracy and reliability in infrastructure asset management. A case study was performed as a proof of concept of the proposed framework by applying the framework to obtain data-driven weights for pavement condition index (PCI) calculations using data for the city of West Des Moines, Iowa. Random forest models performed effectively in modeling the relationship between the overall condition index (OCI) and the objective measures and provided feature importance scores that were converted into weights. The data-driven weights showed strong correlation with existing expert-based weights, validating their accuracy while capturing contextual variations between pavement types. The results indicate that the proposed framework achieved high model accuracy, demonstrated by R-squared values of 0.83 and 0.91 for rigid and composite pavements, respectively. Additionally, the data-driven weights showed strong correlations (R-squared values of 0.85 and 0.98) with existing expert-based weights, validating their effectiveness. This advanceIRIment offers transportation agencies an enhanced tool for prioritizing maintenance and resource allocation, ultimately leading to improved infrastructure longevity. Additionally, this approach shows promise for application across various transportation assets based on the yielded results.

Pavement condition assessment is a fundamental aspect of airport pavement management systems (APMS) for ensuring safe and efficient airport operations. However, conventional methods, which rely on extensive on-site inspections and complex calculations, are often time-consuming and resource-intensive. In response, Industry 4.0 has introduced machine learning (ML) as a powerful tool to streamline these processes. This study explores five ML algorithms (Linear Regression (LR), Decision Tree (DT), Random Forest (RF), Artificial Neural Network (ANN), and Support Vector Machine (SVM)) for predicting the Pavement Condition Index (PCI). Using basic alphanumeric distress data from three international airports, this study predicts both numerical PCI values (on a 0–100 scale) and categorical PCI values (3 and 7 condition classes). To address data imbalance, random oversampling (SMOTE—Synthetic Minority Oversampling Technique) and undersampling (RUS) were used. This study fills a critical knowledge gap by identifying the most effective algorithms for both numerical and categorical PCI determination, with a particular focus on validating class-based predictions using relatively small data samples. The results demonstrate that ML algorithms, particularly Random Forest, are highly effective at predicting both the numerical and the three-class PCI for the original database. However, accurate prediction of the seven-class PCI required the application of oversampling techniques, indicating that a larger, more balanced database is necessary for this detailed classification. Using 10-fold cross-validation, the successful models achieved excellent performance, yielding Kappa statistics between 0.88 and 0.93, an error rate of less than 7.17%, and an area under the ROC curve greater than 0.93. The approach not only significantly reduces the complexity and time required for PCI calculation, but it also makes the technology accessible, enabling resource-limited airports and smaller management entities to adopt advanced pavement management practices.