Pavement Condition Research Articles

Automated instance segmentation of asphalt pavement patches based on deep learning

The location and pixel-level information of the patch are all critical data for the quantitative evaluation of pavement conditions. However, obtaining both parch location and pixel-level information simultaneously is a challenge in intelligent pavement patch surveys. This paper proposes a deep-learning-based patch instance segmentation network (PISNet) that employs you only look once (YOLO)v5 as the baseline and adds a semantic segmentation branch to provide an effective solution for this challenge. The proposed PISNet replaces the original backbone CSPDarknet53 and neck of YOLOv5 with a novel feature extractor named symmetrical pyramid network (SPN). The proposed SPN aims at repeating fusion and transfer of shallow semantic features and deep spatial localization features in the order of “FPN-PAN-FPN” such that the multi-scale semantic expression and localization ability of the feature map could be enhanced. Moreover, a modified feature selection module is also incorporated into the SPN as a skip connection to aggregate more spatial details of the feature map while suppressing redundant features. Experimental results show that compared with Mask region convolutional neural network (R-CNN), You only look at coefficients (YOLACT), YOLACT++, EfficientDet, Fully convolutional one stage object detector (FCOS), You only look once version 5m (YOLOv5m), U-Net, DeepLabv3+, and High resolution network-object contextual representations (HRNet-OCR), the proposed PISNet has the best detection performance. Meanwhile, the proposed PISNet achieves superior accuracy/frames per second trade-offs compared to Mask R-CNN, YOLACT, and YOLACT++. Particularly, the proposed PISNet has certain promising potential in supporting pavement patch detection in real-time scenarios and potentially degraded pavement patch detection. Moreover, the proposed PISNet can yield superior segmentation results compared with Mask R-CNN, YOLACT, YOLACT++, U-Net, HRNet-OCR, and DeepLabv3+ on public CRACK500 datasets. Code has been made available at: https://github.com/716HAZ/PISNet .

Impact Assessment and Damage Fee Development for Pavement Utility Cuts Using Functional and Structural Field Data

The objectives of this study were: 1) to provide transparency of a recent pavement utility cut impact study which informed the adoption of a pavement utility cut fee in Davis, California; and 2) to present an additional methodology for assessing pavement damage using field data referenced to pavement condition. A total of 24 test sites were selected to represent all functional classifications across the Pavement Condition Index (PCI) spectrum. Functional and structural field data were collected and analyzed. Of the 24 test sites, 67% exhibited both functional and structural damage with 96% exhibiting at least one form of damage. The average PCI reduction resulting from utility cuts was 5.1 points and the corresponding percent reduction in pavement service life was 6%. Of the test sites, 63% exhibited damage 2 ft from the edge of the patch, thus indicating that the zone of influence extends past the current restoration area. The statistical analyses indicated that the pavement utility cuts caused significant damage and supported a tiered utility cut fee structure based on functional classification and PCI. The resulting fee schedule is based on 2022 material and construction cost estimates; consequently, implementation including an annual adjustment for inflation is recommended. Since the negative impact of utility cuts is amplified when there are multiple or large cuts on the same street, and treatments are typically carried out across entire management sections, a full-recovery fee at a threshold of 10% of the management section area is appropriate. Example fee calculations were provided.

Presentation of artificial neural network models based on optimum theories for predicting accident severity on rural roads in Iran

This study focuses on predicting the number and severity of rural accidents using nine accident-related variables by the multi-layer perceptron (MLP) approach as a type of artificial neural network modeling method. The models were developed using the input parameters of shoulder width, roadway width, roadside hazard, access density, passing zone ratio, speed limit, pavement condition index, shoulder rumble strips, and centerline rumble strips. MLP models were created based on accident data that occurred from 2019 to 2020 on the Tehran–Qom and Tehran-Saveh rural roads in Iran. In order to achieve the highest accuracy, twenty MLP models have been built with various structures. The MLP model, consisting of a multilayer feedforward network with hidden sigmoid and softmax output, has been used in this study. In this regard, an attempt was made to present an optimum theory to select the best model. The most accurate model was chosen based on the R-value and root mean square error (RMSE), mean absolute error (MAE), f, SD, and R2. Results indicated that the R-value obtained from the optimum model was 0.912, representing the accurate performance of the selected model. In addition, access density, roadside hazard, and roadway width were identified as the most significant variables.

Long-Term Performance of Flexible Pavements Containing Crumb Rubber Modified Asphalt in Louisiana

The use of crumb rubber modifiers (CRMs) in asphalt mixtures plays a significant role in creating a sustainable circular economy. This study aims to evaluate the field performance of CRM asphalt sections with service years greater than eight. Five field projects were evaluated. Each project had one or two CRM sections constructed alongside a companion control section. Five types of CRM products were considered. A 16-mesh proprietary CRM, a Rouse-80 powder (R-80), and a Neste Wright powder were used in the wet blending process. Further, a 16-mesh generic crumb rubber, a PlusRideTM shredded rubber, and R-80 were used in a dry blend. Field performance data, including rutting, roughness, and cracking, were collected and analyzed. Moreover, economic analysis was performed to ascertain the cost-effectiveness (CE) of constructing CRM sections. Furthermore, laboratory performance indicators measured during construction were analyzed to ascertain their ability to rank field performance. Generally, CRM sections exhibited significantly similar or better ride quality, cracking and rutting resistance, and pavement condition index than their corresponding control sections. In some cases, the styrene-butadiene-styrene polymer modifier showed better cracking resistance in the control sections than the CRMs. CRM asphalt sections generally exhibited higher CE than their corresponding control sections because of their high initial unit costs. The Rouse wet CRM asphalt section in field project 4, which had a relatively lower unit cost, was found to be cost-effective compared to its corresponding control section. The limited laboratory mechanical performance parameters considered were ineffective in consistently ranking field performance indicators.

Enhancing Pavement Sustainability: Prediction of the Pavement Condition Index in Arid Urban Climates Using the International Roughness Index

Municipalities and transportation departments worldwide are striving to keep road pavement conditions acceptable, thus enhancing pavement sustainability. Although the pavement condition index (PCI) is widely used to assess distress conditions, traditional visual surveys used for PCI estimation can be laborious, expensive, and time-consuming. The international roughness index (IRI) can be measured more economically and conveniently than PCI; however, it does not directly indicate the surface condition of the pavement. In this study, a PCI–IRI correlation is proposed for urban roads located in the New Beni-Suef region, Egypt. For this purpose, a total of 44 km of urban roads was divided into homogenous sections. A visual distress survey was conducted to measure PCI considering typical distress patterns. The IRI values for the same sections were measured using an ultrasonic distance sensor mounted on an automobile. An exponential model was proposed to capture the relationship between IRI and PCI. With a coefficient of determination of 0.82, the exponential model seems to outperform reported IRI-PCI correlations. Model validation, along with a comparison to the existing models, supports its applicability to a wide range of roads. The proposed model provides a cost-effective means for accurately predicting PCI based on IRI, which is particularly useful for pavement maintenance management programs on limited budgets.

Automated Pavement Condition Index Assessment with Deep Learning and Image Analysis: An End-to-End Approach.

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.

Adaptive Terminal Sliding Mode Trajectory Tracking Control for Autonomous Vehicles Considering Completely Unknown Parameters and Unknown Perturbation Conditions

In the actual implementation of autonomous vehicle controller and related applications, it is difficult to obtain all the actual parameters of the vehicle. Considering factors such as uneven pavement and different pavement conditions, it is difficult to accurately establish the vehicle dynamic system model. Based on the non-singular terminal sliding mode and adaptive control theory, this paper establishes a trajectory tracking control strategy for an autonomous vehicle with unknown parameters and unknown disturbances. Firstly, the complex trajectory tracking problem is decoupled from the position and heading angle tracking problem, and the preview error equation is established. Secondly, a non-singular terminal sliding mode (NTSM) controller is established to stabilize the trajectory tracking error to the origin in a finite time, and adaptive laws are proposed to estimate the unknown vehicle parameters to adapt to environmental changes. Through the CarSim–Matlab platform, typical working conditions are implemented to verify the proposed controller. Our experimental outcomes affirm that the NTSM controller effectively guarantees the autonomous vehicle’s accurate following of the reference path, ensuring smooth control inputs throughout the entire process.

Design and Construction Aspects of Concrete Block Paved Vertical Traffic-Calming Devices Located in Home Zone Areas

In recent years, speed reduction measures have been increasingly used, especially in heavily urbanised areas. If local conditions allow, traffic-calming schemes are implemented, which include a variety of traffic-calming measures (TCM). Some of the most common traffic-calming measures are concrete block paved vertical traffic-calming devices (VTCDs), including speed tables, speed humps, speed bumps and raised pedestrian crossings. Different design and construction recommendations apply to the respective VTCD types. The aim of this article is to examine the effectiveness of VTCDs in speed reduction and the impact of horizontal forces on pavement conditions, both within and beyond VTCDs, after over a dozen years of exposure to traffic. For this purpose, speed surveys were carried out on selected two-way streets running through home zones in Poland. The pavements on selected VTCDs were identified and visually assessed for damage, and subsidence areas were estimated using the terrestrial laser scanning (TSL) technique. The analysis resulted in the development of the Deviation Model 3D (DM3D), showing local deviations from the theoretical surface, obtained by superimposing the two models, the Real Surface Digital Terrain Model (RS DTM) and the Theoretical Surface Digital Terrain Model (TS DTM). A comparative analysis of the pavement surface condition and the magnitudes of horizontal forces allowed us to identify the locations of critical spots in VTCDs. The results were used as the basis for developing recommended pavement structures and deriving engineering recommendations for concrete block paving in VTCDs.

Identification of Road Pavement Conditions in the Tanjung Balai City Road Section

Each region will try to increase its original income and the quality of existing infrastructure in that area. In order to realize this goal, quality infrastructure is needed to support economic development activities and good quality infrastructure. One of the infrastructure needed in this case is the road, where the intended road must have good conditions and quality so that it can function as it should. Where the condition of a good road is a road with good condition is a road with a completely flat pavement surface, no waves and no surface damage. For this reason, it is necessary to identify the condition of the pavement on existing roads in an area, so that the main goal of increasing income and infrastructure can be realized. One of the cities that can be used as a reference for identifying road pavement conditions is the City of Tanjung Balai, where the City of Tanjung Balai is located between 2°58′00″ North Latitude and 99°48′00″ East Longitude. Its area is 60.52 km2. The city of Tanjung Balai also has the longest bridge in the province of North Sumatra along ± 600 m which connects the city of Tanjung Balai with the villages of Sei Kepayang Kiri, Sei Kepayang Tengah to the upstream area in Sei Kepayang Kanan, Asahan district. Identification of road pavement conditions can be done using several methods, one of several methods that can be used is the PCI (Pavement Condition Index) method. Where this method is carried out by measuring the area of road surface damage, determining the level of damage to the road surface by looking at the Deduct Value chart, calculating level of damage occurs on the road surface (density), calculating Total Deduct Value (TDV), calculating Corrected Deduct Value (CDV), calculating PCI values and determining the level of the road, whether it is still feasible or not feasible. From the identification results it was found that on A. Rahman Road, there are several types of damage with varying levels of damage and the deduct value can be determined by looking at the graph, such as holes/potholes (low) damage, shoving with high damage, ravelling with high damage, basin cracks with high damage. Then on SMA 3 Road, there are several types of damage such as high crocodile skin cracks, patch damage with high damage levels, medium damage (medium) damage to basins. Next on Prof. Dr. Ir. Sutami / Education road there are types of holes / potholes (low) damage, grade depression with medium damage, patches with medium (medium) damage levels, ravelling with medium damage levels. And on Husni Thamrin Road, there are several types of damage such as holes/potholes (low) damage, basin cracks with high damage, ravelling with high damage levels. The results of this identification can be used as a reference for maintaining or improving the quality of existing roads in urban areas.

Analysis of Pavement Conditions and Handling of Road Damage at the Surface Layer

The Cukul - Buni Kasih road section at the Garut Regency Boundary in Pangalengan District, West Bandung Regency, West Java Province, is pivotal in facilitating economic growth and regional development. Spanning 3.493 km with a width of 3.12 m, this study aims to assess its condition and identify road damage types. Key indices, including SDI (Surface Distress Index), RCI (Road Condition Index), IRI (International Roughness Index), and PCI (Pavement Condition Index), were employed to evaluate road health. Visual analysis revealed an average SDI value of 112, indicating minor damage. The RCI method yielded an average value of 5, denoting slightly damaged conditions. IRI assessment resulted in an average value of 9, reflecting slight damage. The PCI method yielded an average of 46, signifying poor (bad) conditions. Predominant damage types include potholes, alongside alligator cracking, patching, edge cracking, weathering/raveling, and rutting. In summary, the Cukul – Buni Kasih road section (Garut Regency Boundary) is predominantly damaged, necessitating urgent attention. The assessment utilizing SDI, RCI, IRI, and PCI methods provides valuable insights for planning and executing necessary road maintenance measures.

Pavement Condition Management Coupled with Skid Management at the Network Level: A Multi-Choice Goal Programming Approach

Highways promote economic growth and social development. Traditionally, pavement management considers pavement conditions for maintenance and rehabilitation (M&R) decision-making along with system constraints. One of the major tasks of highway agencies is to promote safety. Highway safety involves a complicated interaction of multiple factors. However, it is well established that there is a link between pavement surface friction and safety. Pavement surface characteristics affect the safety and comfort of passengers. Since the pavement management process considers only pavement physical conditions in M&R decision-making, there is a need to include safety indicators in the process to promote both riding comfort and safety. This study aims to integrate the pavement surface friction, that is, the skid resistance, into traditional pavement management. An optimization model has been proposed based on multi-choice goal programming by systematically integrating skid resistance into pavement M&R decision-making along with pavement conditions. Multi-choice goal programming allows setting interval aspirations rather than a fixed target. A case study has been conducted to demonstrate the usefulness of the proposed model in managing pavement conditions and pavement skids together. The case study considered five goals related to pavement conditions, skid states, and M&R costs. Interaction among the goals is discussed in different scenarios. The case study also uses friction treatment with traditional pavement management treatments. The model can capture the interaction between pavement conditions and pavement skids and determine the program that best achieves the goals. The proposed method provides a simple approach to selecting a network-level M&R program and obtaining insights into the management of conditions and skid together.

Maintenance Strategy for the Muliama-Wa2e-Makki Road Section in Jayawijaya Regency, Papua Province, Highlands

In Jayawijaya Regency, there are several prioritized road sections, including national, provincial, and district roads. The Muliama-Wame-Makki road section is classified as a provincial road serving as an alternative route, with a total length of 22 kilometers connecting Jayawijaya Regency and Lanni Jaya Regency. The traffic on this road is quite heavy with a diverse composition (Mixed Traffic). An analysis using the Pavement Condition Index (PCI) was conducted based on field survey data, including measurements of the extent, types, and quantities of surface damage per 200 meters along each road segment. The types of damage observed on the Muliama-Wame-Makki road section include patching, longitudinal cracks, alligator cracking, and pavement subsidence, with a total damage percentage of 1.684%. The maintenance strategy is determined based on the PCI calculation and Bina Marga method, suggesting that routine maintenance is sufficient for addressing the observed issues.

Network for robust and high-accuracy pavement crack segmentation

Timely segmentation and repair of pavement cracks significantly impacts both traffic safety and the service life of the roadway. However, current crack segmentation algorithms are limited in terms of imprecise segmentation and poor robustness. To overcome current limitations, this study proposes a pavement crack segmentation algorithm called MixCrackNet. MixCrackNet leverages deformable convolution, weighted loss functions, an efficient multi-scale attention module, and the Mix Structure to identify pavement cracks. Three datasets were used to train and validate the effectiveness of MixCrackNet. By comparing with classical semantic segmentation networks, the results demonstrate that MixCrackNet outperforms all the other models in crack segmentation. Furthermore, MixCrackNet not only exhibits exceptional performance across all three datasets, but also achieves decent results in untrained dataset. These results indicate that MixCrackNet is not only highly accurate but also robust, thereby promoting the application of semantic crack segmentation technology in pavement condition detection.

CORRELATION ROAD GEOMETRIC ON ROAD DAMAGE

The implementation of safe road infrastructure can be defined by the minimum number of traffic accidents that occur on these roads. Traffic accidents can caused by several factors, encompassing the geometric attributes of the road, characteristics of vehicles, human elements, and environmental conditions. Previous studies have identified specific geometric features, including superelevation, degree of curvature, road slope, and shoulder type, as influential factors in traffic accidents. Simultaneously, the state of the pavement, characterized by issues such as rutting, potholes, IRI value, and skid resistance, also plays a crucial role in traffic accident. The interplay between road geometric and pavement conditions underscores the importance of effective road management for ensuring safety. Existing research highlights a correlation between road geometric and road damage, with the Pavement Condition Index (PCI) directly correlating with road and shoulder width, and inversely correlating with transverse and longitudinal road slope. This phenomenon is, in part, attributed to heightened shear strain on narrow road shoulders and a reduction in the resilience modulus value of the subgrade on sloped roads.

A new FCM-XGBoost system for predicting Pavement Condition Index

Predicting Pavement Condition Index (PCI) is crucial for identifying potential pavement distresses and developing effective pavement maintenance strategies. Here, a new Fuzzy C-Means-eXtreme Gradient Boosting (FCM-XGBoost) PCI prediction system is developed. First, asphalt pavement sections and concrete pavement sections are distinguished, and the Fuzzy C-Means (FCM) clustering algorithm is employed to cluster the asphalt and concrete expressway sections respectively based on multiple factors influencing PCI. Next, an eXtreme Gradient Boosting (XGBoost) model is trained for each cluster of expressway sections. Finally, in the system application stage, the PCI of an expressway section for the next year is predicted using the XGBoost model trained for the specific cluster that the expressway section belongs to. Validation results based on actual pavement condition data indicate that the proposed FCM-XGBoost PCI prediction system can achieve slightly higher accuracy compared with several high performance benchmark models, which include the Random Forest model, the Gradient Boosting Decision Tree model, the Light Gradient Boosting Machine model and the XGBoost model. The developed PCI prediction system contributes to the improvement of PCI prediction approaches and can be potentially applied in deploying effective and cost-efficient pavement maintenance strategies.

Unmanned Aircraft Systems in Road Assessment: A Novel Approach to the Pavement Condition Index and VIZIR Methodologies

This paper presents an innovative approach to road assessment, focusing on enhancing the Pavement Condition Index (PCI) and Visión Inspection de Zones et Itinéraires Á Risque (VIZIR) methodologies by integrating Unmanned Aircraft System (UAS) technology. The research was conducted in an urban setting, utilizing a UAS to capture high-resolution imagery, which was subsequently processed to generate detailed orthomosaics of road surfaces. This study critically analyzed the discrepancies between traditional field measurements and UAS-derived data in pavement condition assessment. The study findings demonstrate that photogrammetry-derived data from UAS offer at least similar or, in some cases, improved information on the collection of a comprehensive state of roadways, particularly in local and collector roads. Furthermore, this study proposed key modifications to the existing methodologies, including dividing the road network into segments for more precise and relevant data collection. These enhancements aim to address the limitations of current practices in capturing the diverse and dynamic conditions of urban infrastructure. Integrating UAS technology improves the measurement of pavement condition assessments and offers a more efficient, cost-effective, and scalable approach to urban infrastructure management. The implications of this study are significant for urban planners and policymakers, providing a robust framework for future infrastructure assessment and maintenance strategies.

Road conditions and driving patterns on fuel usage: Lessons from an emerging economy

Given its usage in freight and urban transportation, diesel supply is a major global issue. Diesel dependence and the urgency of the energy transition are normally addressed on the supply side by either expanding oil refineries or evaluating alternative fuels. Here we focus on the demand-side by examining how road pavement conditions and eco-driving practices affect diesel fuel consumption on highways. This can be particularly important in emerging countries, where road pavement is normally not well-managed and conserved. This analysis encompasses six scenarios for Brazil that vary in road conditions, driving cycles, and tire inflation states. Findings show a significant potential for annual diesel savings, reaching 4.52 billion liters, or 16% of fuel efficiency. A financial analysis indicates a positive return by the year 2037. Hence, this study emphasizes the need for a multidimensional approach to reduce fuel consumption and enhance environmental sustainability within the transportation sector of an emerging economy. While infrastructural improvements are crucial, their effectiveness is greatly amplified when paired with efforts in driver education.

A novel technique for multi-objective sustainable decisions for pavement maintenance and rehabilitation

To maintain pavement in good condition while considering financial costs and sustainability, it is necessary to develop a comprehensive pavement management plan. Pavement Maintenance and Rehabilitation (M&R) consists of two essential components: firstly, predicting the pavement condition within a specified timeframe, and secondly, employing an appropriate optimization algorithm. This study utilized three ensemble learning techniques including extreme gradient boosting, categorical boosting, and light gradient boosting machine to develop accurate predictions about the pavement condition. Subsequently, the most accurate prediction technique, which was extreme gradient boosting, was combined with non-dominated sorting genetic algorithm III which is a multi-objective metaheuristic optimization algorithm, resulting in a hybrid technique that offers highly accurate multi-objective maintenance and rehabilitation planning. Although previous studies neglected important criteria such as road closure in the optimization process, this study takes into account four objective functions including greenhouse gas emission, M&R cost, pavement condition, and road closure to be minimized over a 5-year program. This process generated 52 non-dominated optimal solutions known as the Pareto front. To compare and rank various optimal maintenance and rehabilitation plans, grey relational analysis was employed. The results suggested that there is a direct correlation between M&R costs and GHG emissions. Minimizing only pavement conditions in the planning can significantly increase GHG emissions, M&R costs, and road closure. Implementing preventive M&R actions can reduce M&R costs and overall road closure while light and medium rehabilitation actions are recommended to optimize the condition of pavements.

Machine Learning Modeling of Wheel and Non-Wheel Path Longitudinal Cracking

Roads degrade over time due to various factors such as traffic loads, environmental conditions, and the quality of materials used. Significant investments have been poured into road construction globally, necessitating regular evaluations and the implementation of maintenance and rehabilitation (M&R) strategies to keep the infrastructure performing at a satisfactory level. The development and refinement of performance prediction models are essential for forecasting the condition of pavements, especially to address longitudinal cracking distress, a major issue in thick asphalt pavements. This research leverages multiple machine learning methods to create models predicting non-wheel path (NWP) and wheel path (WP) longitudinal cracking using data from the Long-Term Pavement Performance (LTPP) program. This study highlights the marked differences in distress conditions between WP and NWP, underscoring the importance of precise models that cater to their unique features. Aging trends for both types of cracking were identified through correlation analysis, showing an increase in WP cracking with age and a higher initial International Roughness Index (IRI) linked to NWP cracking. Factors such as material characteristics, kinematic viscosity, pavement thickness, air voids, particle size distribution, temperature, KESAL, and asphalt properties were found to significantly influence both WP and NWP cracking. The Exponential Gaussian Process Regression (GPR) emerged as the best model for NWP cracking, showcasing exceptional accuracy with the lowest RMSE of 89.11, MSE of 7940.72, and an impressive R-Squared of 0.63. For WP cracking, the Squared Exponential GPR model was most effective, with the lowest RMSE of 12.00, MSE of 143.93, and a high R-Squared of 0.62. The GPR models, with specific kernels for each cracking type, proved their adaptability and efficiency in various pavement scenarios. A comparative analysis highlighted the superiority of our new machine learning model, which achieved an R2 of 0.767, outperforming previous empirical models, demonstrating the strength and precision of our machine learning approach in predicting longitudinal cracking.

Pavement Inspection in Transport Infrastructures Using Unmanned Aerial Vehicles (UAVs)

The growing demand for the transportation of goods and people has led to an increasing reliance on transportation infrastructure, which, in turn, subjects the pavements to high traffic volumes. In order to maintain adequate service and safety standards for users, it is essential to establish effective maintenance strategies that ensure the preservation of pavement conditions. As a result, emerging innovations in pavement surface inspection methods, surpassing traditional techniques in terms of inspection and data processing speed and accuracy, have garnered significant attention. One such groundbreaking innovation in inspection systems that has been tested and used in recent years to assess infrastructure condition is the use of unmanned aerial vehicles (UAVs). This study aims to present a critical open-access literature review on the use of UAVs in the inspection of transportation infrastructure pavement in order to assess the type of equipment used, the technology involved, applicability conditions, data processing, and future evolution. The analysis of relevant literature suggests that the integration of intelligent technologies substantially enhances the accuracy of data collection and the detection of pavement distress. Furthermore, it is evident that most applications and research efforts are oriented towards exploring image processing techniques for the creation of 3D pavement models and distress detection and classification.