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Airports strive to prevent safety issues, such as foreign object debris (FOD), by pavement management using the pavement condition index (PCI). The index is used in decision-making processes for overall pavement maintenance and repair, such as the prevention of additional damage due to cracks and the like. However, considering the current situation in Korea where mostly mid-sized and large commercial airports exist, problems regarding direct applications of the existing PCI deduct value have been consistently pointed out. In addition, as the relationship between the PCI and whether maintenance and repair are required is unrealistic, there have been difficulties in communication between maintenance and repair staff and decision makers. Therefore, to resolve these problems, this study first analyzed the calculation procedure of the existing PCI and then redefined the main distress type of Korean airport pavements. In addition, a deduct value curve (DVC) in terms of the severity level for six main distress factors of asphalt pavements and eight main distress factors of concrete pavements and a corrected deduct value curve (CDVC) for multiple distresses in terms of the pavement form were developed using panel rating, which is an engineering approach, by forming an airport pavement expert panel. Finally, a Korea airport pavement condition index (KPCI) was proposed using the curves, and the field application results were compared against the existing PCI to examine the adequacy of the KPCI. As a result, the developed criteria showed an overall trend lower than existing PCI. Moreover, it was verified that this trend increases with worsening pavement condition. It appears that a more discriminating evaluation may be possible when determining pavement conditions by PCI results of the developed criteria.

The degradation of road pavements due to environmental factors is a pressing issue in infrastructure maintenance, necessitating precise identification of pavement distresses. The pavement condition index (PCI) serves as a critical metric for evaluating pavement conditions, essential for effective budget allocation and performance tracking. Traditional manual PCI assessment methods are limited by labor intensity, subjectivity, and susceptibility to human error. Addressing these challenges, this paper presents a novel, end-to-end automated method for PCI calculation, integrating deep learning and image processing technologies. The first stage employs a deep learning algorithm for accurate detection of pavement cracks, followed by the application of a segmentation-based skeleton algorithm in image processing to estimate crack width precisely. This integrated approach enhances the assessment process, providing a more comprehensive evaluation of pavement integrity. The validation results demonstrate a 95% accuracy in crack detection and 90% accuracy in crack width estimation. Leveraging these results, the automated PCI rating is achieved, aligned with standards, showcasing significant improvements in the efficiency and reliability of PCI evaluations. This method offers advancements in pavement maintenance strategies and potential applications in broader road infrastructure management.

Stone pavements are present in many cities and their historical and cultural importance is well recognized. However, there are no standard monitoring methods for this type of pavement that allow road managers to define appropriate maintenance strategies. In this study, a novel method is proposed in order to monitor the road surface conditions of stone pavements in a quick and easy way. Field tests were carried out in an Italian historic center using accelerometer sensors mounted on both a car and a bicycle. A post-processing phase of that data defined the comfort perception of the road users in terms of the awz index, as described in the ISO 2631 standard. The results derived from the dynamic surveys were also compared with the corresponding values of typical pavement indicators such as the International Roughness Index (IRI) and the Pavement Condition Index (PCI), measured only on a limited portion of the urban road network. The network’s implementation in a Geographic Information System (GIS) represents the surveys’ results in a graphical database. The specifications of the adopted method require that the network is divided into homogeneous sections, useful for measurement campaign planning, and adopted for the GIS’ outputs representation. The comparisons between IRI-awz (R2 = 0.74) and PCI-awz (R2 = 0.96) confirmed that the proposed method can be used reliably to assess the stone pavement conditions on the whole urban road network.

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.

A comprehensive representation of the road pavement state of health is of great interest. In recent years, automated data collection and processing technology has been used for pavement inspection. In this paper, a new signal on graph (SoG) model of road pavement distresses is presented with the aim of improving automatic pavement distress detection systems. A novel nonlinear Bayesian estimator in recovering distress metrics is also derived. The performance of the methodology was evaluated on a large dataset of pavement distress values collected in field tests conducted in Kazakhstan. The application of the proposed methodology is effective in recovering acquisition errors, improving road failure detection. Moreover, the output of the Bayesian estimator can be used to identify sections where the measurement acquired by the 3D laser technology is unreliable. Therefore, the presented model could be used to schedule road section maintenance in a better way.

Prediction models in mobility and transportation maintenance systems have been dramatically improved by using machine learning methods. This paper proposes novel machine learning models for an intelligent road inspection. The traditional road inspection systems based on the pavement condition index (PCI) are often associated with the critical safety, energy and cost issues. Alternatively, the proposed models utilize surface deflection data from falling weight deflectometer (FWD) tests to predict the PCI. Machine learning methods are the single multi-layer perceptron (MLP) and radial basis function (RBF) neural networks as well as their hybrids, i.e., Levenberg–Marquardt (MLP-LM), scaled conjugate gradient (MLP-SCG), imperialist competitive (RBF-ICA), and genetic algorithms (RBF-GA). Furthermore, the committee machine intelligent systems (CMIS) method was adopted to combine the results and improve the accuracy of the modeling. The results of the analysis have been verified through using four criteria of average percent relative error (APRE), average absolute percent relative error (AAPRE), root mean square error (RMSE) and standard error (SE). The CMIS model outperforms other models with the promising results of APRE = 2.3303, AAPRE = 11.6768, RMSE = 12.0056 and SD = 0.0210.

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).

Evaluation of surface distress is an important aspect of pavement management. The most common practice to assess surface distress is to develop a pavement condition index (PCI), with ASTM-PCI being the most widely used in evaluating flexible pavements. Traditional PCI evaluation methods rely on labour-intensive, manual inspections, leading to significant time consumption. In recent years, real-time visualization and crowdsourcing have been explored, but their potential has yet to be fully realized. Integrating real-time visualization through GIS technology offers immediate insights into pavement conditions, aiding prompt decision-making. Crowdsourcing allows a broader community to contribute to condition reporting, enhancing data accuracy and coverage. This study aims to develop an artificial intelligence (AI)-based method for road condition assessment from crowd-sourced images. A deep-learning object detection model is utilized for precise crack detection and classification. The model is trained to recognize various distress types accurately and quantify attributes crucial for determining the PCI. The developed model is then integrated into a web-based platform accessible through mobile phones and dash cameras, allowing real-time capture and classification of cracks. The study demonstrates that the automated methodology significantly enhances PCI estimation efficiency, with a high correlation between semi-automated and automated methods. Stakeholders can benefit from deep learning and automation in pavement distress detection, aiding informed decision-making through crowdsourcing data. Future work includes the detection of subclasses within crack types based on severity and the creation of digital twins for public assets. Overall, this study highlights the transformative potential of AI and crowdsourcing in improving pavement management.

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.

This study identifies a critical knowledge gap, revealing how the deterioration of roads, compounded by extensive usage and additional factors, poses significant risks to the road networks’ functionality. Without a robust fund allocation and prioritization strategy, the extent of this risk may be overlooked, adversely affecting the performance of essential infrastructure elements. Our research introduces an integrated decision-making model for existing road infrastructures to address this gap. This innovative approach combines a Geographic Information System (GIS)-based road management model with a fund allocation prioritization strategy, enhanced by an optimization engine via a genetic algorithm. The primary aim is to precisely determine Maintenance and Repair (M&R) interventions tailored to the condition states, thereby improving the Pavement Condition Index (PCI) of the road segments. The research is structured around three key objectives: (1) develop a detailed GIS-based road management database incorporating inspection data and attributes of road infrastructure for proactive M&R decision-making; (2) efficiently allocate funds to maintain service delivery on deteriorated roads; and (3) pinpoint the optimal type and timing of M&R interventions to boost the condition and performance of the road segments. Anticipated results will provide asset managers with a comprehensive decision support system for executing effective M&R practices.