Pavement Condition Assessment Research Articles

Reimagining unbound road pavement technology: Integrating testing, design, construction and performance in the post-digital era

The Industry 4.0 revolution signifies a pivotal transition in pavement technology, emphasising the integration of digital and physical systems to revamp traditional, empirical-based pavement design, construction, and maintenance practices. This shift promises enhanced efficiency, sustainability, and a move towards integrated practices, highlighted by adopting “Digital Twin” technology. Unlike traditional Building Information Modeling (BIM), Digital Twin technology offers a real-time, dynamic representation of physical infrastructure, enabling improved asset management through distributed sensing and predictive performance analytics. Leveraging findings from the Australian Research Council (ARC)’s Industry Transformation Research Hub for Smart Next Generation Transport Pavements (SPARC Hub), this paper focuses on the integration of pavement testing, design, construction, condition assessment and asset management into the lifecycle of unbound road pavements. The paper outlines how such technological integration streamlines decision-making processes and significantly boosts the functionality and longevity of pavement infrastructures. Despite facing challenges like cost, data security, and the need for specialised skills, the potential for digital technologies to improve pavement durability, efficiency, and sustainability is significant. Future research directions are identified to overcome implementation barriers, explore untapped technological potentials, and assess the sustainability benefits of digital approaches in pavement engineering, aiming to forge more resilient, efficient, and sustainable road networks for future generations.

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.

An Evaluation of ASTM Standards for Implementation of Ground Penetrating Radar for Pavement and Bridge Deck Evaluations

Abstract Ground penetrating radar (GPR) is a common nondestructive evaluation technique that operates with electromagnetic energy (EM) to detect objects and anomalies in a material’s subsurface. GPR is an effective method for civil engineering applications for the assessment of structures and roadways specifically for pavement and bridge deck condition assessment. While surveying with GPR is straightforward, requirements differ depending on the testing objective, and interpretation of the results can pose some challenges. Fortunately, the American Society for Testing Materials (ASTM) provides a standard set of suggestions for the utilization of GPR, which include general information and recommendations for operating GPR systems and analyzing the output for different purposes. However, reaching a full comprehensive understanding of each standard may require some additional knowledge. This paper focuses on three separate ASTM standards that are related to GPR implementations. These standards include the following: ASTM D6432-19, Standard Guide for Using the Surface Ground Penetrating Radar Method for Subsurface Investigation; ASTM D4748-10(2020), Standard Test Method for Determining the Thickness of Bound Pavement Layers Using Short-Pulse Radar; and ASTM D6087-22, Standard Test Method for Evaluating Asphalt-Covered Concrete Bridge Decks Using Ground Penetrating Radar. After a review and assessment of these three standards, an analysis is presented to discuss the applications, contemplations, and limitations of the standards.

Postflooding Asphalt Pavement Condition Assessment for Roadway Operation Strategy

Postflooding Asphalt Pavement Condition Assessment for Roadway Operation Strategy

Toward Real-World Implementation of Deep Learning for Smartphone-Crowdsourced Pavement Condition Assessment

Toward Real-World Implementation of Deep Learning for Smartphone-Crowdsourced Pavement Condition Assessment

Positioning and detection of rigid pavement cracks using GNSS data and image processing

Modern pavement management systems depend mainly on pavement condition assessment to plan rehabilitation strategies. Manual inspection is performed by trained inspectors to assess pavement damages conventionally. This can be cost-intensive, time-consuming, and a source of risk for inspectors. An image-based inspection using a smartphone is adopted to overcome such problems. This paper proposes an automatic crack detection and mapping program for rigid pavement, which can automate the visual inspection process. The program uses Global Navigation Satellite System (GNSS) data recorded by smartphones and various image processing techniques to detect crack lengths and areas in images. The performance of the program was evaluated by a field study. A crack quantification process was performed to compare the manually measured values and crack lengths obtained from the program. The results show that the program can detect other types of distress, such as pop-outs and punch-outs. This method can achieve satisfactory performance compared to the effort and costs spent.

Assessment of road pavement conditions using Surface Distress Index (SDI) and Pavement Condition Index (PCI) methods

Road pavement is an important element in transportation infrastructure that affects the comfort and safety of road users. It is important to carry out regular assessments of the condition of the road pavement. This study aims to evaluate the condition of the road pavement using the Surface Distress Index (SDI) and Pavement Condition Index (PCI) methods. It is collecting field data regarding the condition of the road pavement, including the type and extent of existing damage. This data is then analyzed using the formula that has been determined in the SDI and PCI methods. In the research that has been done, the average SDI value for flexible pavement for Jalan Gunung Agung is 18 (Good) and the average PCI value is 63.8 (Good). The technical measures that can be applied to the flexible pavement of Jalan Gunung Agung are P2 (Asphalt), P4 (Filling cracks), P5 (Filling holes), and P6 (Gravelling).

The Evaluation of Pavement Condition Assessment Methods for Road Assets in Coastal Areas

The Daendels road is a vital provincial road asset that facilitates the distribution of goods and services, enhances tourism access, and promotes socio-economic development in the southern region of Java Island. The deteriorated condition of the Daendels road pavement has the potential to escalate both the likelihood of accidents and vehicle operating costs. In Indonesia, road distress is measured using the Surface Distress Index (SDI), but certain types of distress are not yet incorporated into the calculation. Therefore, this study aims to identify the typical road distress in the coastal region and then to evaluate and compare several visual methods for evaluating the functional condition of road pavements, i.e., the SDI, Pavement Condition Index (PCI), and Pavement Surface Evaluation and Rating (PASER). Pavement conditions for Daendels Road have different analysis results depending on the method used. The average value of PCI is 50.5 (slightly damaged), the SDI is 164 (severely damaged), and the PASER is 4 (slightly damaged). The statistical analyses indicate that both the SDI-PCI and SDI-PASER methods have a very strong relationship. The SDI-PCI method has a higher correlation and coefficient of determination value (R= -0,929, R²= 0,8631) than SDI-PASER (R= -0,807, R²= 0,652). The PCI method is more applicable than the SDI dan PASER as it considers a wider range of pavement distress (19 categories) and more accurately represents the typical distress encountered on the South Coast of Java Island. The pavement condition of Daendels Road is classified as severely damaged with typical distress involving cracking (longitudinal, transversal, alligator, and blocks), patching, and pothole. Hence, a comprehensive plan for road maintenance was suggested, encompassing major rehabilitation using a hot mix asphalt overlay.

A transformer-based deep learning method for automatic pixel-level crack detection and feature quantification

Purpose Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement performance.Design/methodology/approach This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient productivity.Findings In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 × 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher reliability.Originality/value The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and evaluation metrics, which can provide solutions with potentially serving as the basis for an automated, cost-effective pavement condition assessment scheme.

Temporal Convolutional Network-Based Axle Load Estimation from Pavement Vibration Data

Measuring the axle loads of vehicles with more accuracy is a crucial step in weight enforcement and pavement condition assessment. This paper proposed a vibration-based method, which has an extended sensing range, high temporal sampling rate, and dense spatial sampling rate, to estimate axle loads in concrete pavement using distributed optical vibration sensing (DOVS) technology. Temporal convolutional networks (TCN), which consist of non-causal convolutional layers and a concatenate layer, were proposed and trained by over 6000 samples of vibration data and ground truth of axle loads. Moreover, the TCN could learn the complex inverse mapping between pavement structure inputs and outputs. The performance of the proposed method was calibrated in two field tests with various conditions. The results demonstrate that the proposed method obtained estimated axle loads within 11.5% error, under diverse circumstances that consisted of different pavement types and loads moving at speeds ranging from 0~35 m/s. The proposed method demonstrates significant promise in the field of axle load reconstruction and estimation. Its error, closely approaching the 10% threshold specified by LTPP, underscores its efficacy. Additionally, the method aligns with the standards set by Cost-323, with an error level-up to category C. This indicates its capability to provide valuable support in the assessment and decision-making processes related to pavement structure conditions.

Modified pavement condition assessment model for asphalt concrete pavements

Modified pavement condition assessment model for asphalt concrete pavements

Prediction of Pavement Overall Condition Index Based on Wrapper Feature-Selection Techniques Using Municipal Pavement Data

Municipalities make significant efforts with limited resources to collect pavement condition data. Overall Condition Index (OCI), which uses the pavement surface evaluation and rating manual to identify roadways needing repair, is a convenient and common way of pavement condition assessment. Many data used in assessing the OCI are collected from fieldwork. Some data features give little insight into road conditions, and one feature may provide similar information to another; thus, effective data-collection resources can be optimized by selecting which data feature to keep and which to discard. In addition, the OCI reflects how local agencies highlight the important variables driving their pavement-management investments. It is also a reflection of the triggers that they use to propose various treatment strategies. This research aimed to evaluate pavement distresses in West Des Moines, Iowa, using machine-learning methods, and determine which combination of distresses and their distress proportions can accurately predict the OCI class of a particular pavement type. The wrapper feature-selection methods were used, fitting their results to classification-tree models. Automatic feature selection, using Featurewiz, was also considered to select the desired number of variables. Feature parameters were screened for OCI prediction using the mean decrease impurity, and the results could be used to model classification that may be used for year-long predictions. Results showed that an effective OCI estimate methodology could be determined with significant accuracy with fewer features.

Detection and classification of pavement damages using wavelet scattering transform, fractal dimension by box-counting method and machine learning algorithms

Pavement condition assessment is essential for roadway maintenance and rehabilitation processes. Image-based inspection methods provide information regarding the surface of the pavement and allow quantitative analyses of pavement conditions. A methodology for the detection of damages in the pavement, by applying pattern recognition and image analysis and machine learning algorithms, is presented in the paper. This methodology consists of image acquisition, image processing using the wavelet scattering transform (WST), feature extraction employing the fractal dimension by box-counting method, and finally classification. The methodology was applied for the detection of three common types of damages: potholes, longitudinal and alligator cracks. Two different supervised learning algorithms, Artificial Neural Networks (ANN) and Support Vector Machine (SVM), were used for classification and results are compared training Convolutional Neural Networks (CNN). The multilayer ANN an overall accuracy of 98.36% and an F-score of 98.33%, while the SVM an accuracy of 97.22% and an F-score of 97.22%.

Unmanned aerial vehicle implementation for pavement condition survey

The Utilization of Unmanned Aerial Vehicle (UAV) as a vehicle for comprehensive and fast road condition data acquisition is expected to minimize or even replace conventional road condition surveys in the field. In addition, the final UAV product in the form of a 2D/3D model of road conditions has been proven to be used as a medium for interpreting road damage and measuring the dimensions of road damage without having to do it in the field. The significant difference between this study and other studies is found in the use of the Surface Distress Index and Pavement Condition Index methods for assessing road conditions on the 2D/3D model of the UAV results. No research compares the use of the two methods in 2D/3D models. This research took photos of pavement conditions using drones to produce 2D and 3D models as visual media for assessing pavement conditions using the Pavement Condition Index and Surface Distress Index methods. The results of the assessment on the model compared with the assessment in the field. In comparison, the dimension level of accuracy of pavement distress in the model against the results of manual measurements in the field, edge cracking has the lowest accuracy value of 75.72%, and joint reflective crack has the highest accuracy value of 97.86%. The linear regression equation for the Pavement Condition Index method in the model against manual in the field is y = 0.931x + 3.6003 with a coefficient of determination r2 = 0.86 and an MSE of 121.333, while the linear regression equation for the Surface Distress Index method in the model against manual in the field is y = 0.5831x + 28.867 with a coefficient of determination that is r2 = 0.653 and MSE = 2115. Based on the comparison above, in this study, the application of UAV for pavement condition assessment is more precise using the Pavement Condition Index method.

Implementation of UAV for Pavement Functional Performance Assesment

Pavement conditions could be degraded throughout its service life. Hence, a pavement management system is needed to ensure pavement performance according to its design life. To support a reliable pavement, a pavement condition survey needs to be conducted most effective and practical way. One of the technologies used in pavement condition surveys is the Unmanned Aerial Vehicle (UAV) or known as drone. The use of UAVs for road maintenance will reduce the cost and time and with the 3D model, the accuracy level is at the centimeter level which indicates UAVs are an excellent and promising tool for road work.In this study, a PCI method of pavement condition evaluationwill be used to asses pavement through both manually surveyed and 3D model calculation with the use of Agisoft application. Later on, statistical test will be carried out such as ANOVA and correlation to determine the relationship between pavement condition obtained from two different survey method as well as the comparison in identifying pavement distresss, PCI value and pavement condition. In result, those two methods can identify identical types of damage, with a fairly high percentage of > 60%. While, manual method could identify a higher percentage of degree of severity than 3D model with the help of application. Themanual of pavement condition assessment can identify in more detail than the Agisoft Metashape application. Based on statistical tests,the two methods used show a close and unidirectional relationship.

A review of advanced pavement distress evaluation techniques using unmanned aerial vehicles

ABSTRACT The economy of the nation is significantly influenced by pavement management systems. The use of cutting-edge techniques and technology in various pavement management system applications, as well as research into these topics, are particularly important today. To reduce human errors in data collection, advances in technology and automated surveillance should be used. The most recent methods used in modern equipment for the automatic evaluation of pavement deterioration are reviewed in this paper. To identify trending tools, research gaps, emerging technologies, challenges and limitations of using computer vision for pavement distress and condition assessment, papers collected using UAV (Unmanned Ariel Vehicle) data collection and the application of machine learning methods are investigated. The review comes to the conclusion that the application of machine learning techniques is the general trend in evaluating the condition of pavements, despite some limitations not only in the detection of a small number of pavement distresses with complex patterns but also in the indication of the severity and density of distresses, opening up possibilities for further study.

Evaluating the impact of factors in vehicle based pavement sensing implementation: sensor placement, pavement temperature, speed, and threshold

The purpose of the paper is to improve the efficiency of vehicle based sensing technology in highway pavement condition assessment by evaluating the effect of four factors (sensor placement, pavement temperature, drive speed, and threshold for pavement distress classification) and providing suggestions to better improve the accuracy of pavement condition detection and minimize the interruption of pavement sensing operation. Two I-10 corridors in the Phoenix region were selected for vibration data collection and data analysis. A series of statistical analyses were performed to determine if each one of the factors has a significant impact on the pavement distress detection. The results of Analysis of Variance (ANOVA) tests and Analysis of Covariance (ANCOVA) tests show that the placement of sensors have a significant effect in the pavement condition assessments. The significant differences occurred in the group of sensors that were placed on the same side of the vehicle, as well as, in either front wheels or rear wheels of the vehicle. The effect of pavement temperature on the vehicle based sensing implementation is significant while the mean drive speed is not seen as a significant factor in the pavement condition survey. The two thresholds were determined to select points of interest (POI; cracks, potholes) for the pavement distress classification and these POIs are in good agreement with international roughness index (IRI) data in an ArcGIS map. The findings of the paper can be used to better improve the computing algorithms of vehicle based sensing techniques.

Relationship of present serviceability index for flexible and rigid pavement in urban road damage assessment using pavement condition index and international roughness index

Functional and structural failures in the pavement structure can be distinguished. The Road state Value measures the functional state of road pavements in managing flexible and stiff pavements for urban roadways in Indonesia. The pavement condition assessment is based on the unevenness of the pavement surface, rutting, and direct surface damage. This research aims to assess the worth of road damage on flexible and rigid pavements and establish a special relationship between Present Serviceability, International Roughness, and Pavement Condition Index. Direct observation in the field is utilized to obtain data on the different types of damage. According to the field survey data, the most common types of damage for flexible pavements were filled (56), followed by longitudinal cracks (18) for rigid pavements. PSI, IRI, and PCI models have a strong association, with the R2 value found to be acceptable. It shows a high correlation with scores above 90% for good, medium, and inferior roads. The resulting equations establish a strong link between IRI and PCI. Furthermore, the validation findings demonstrate that the model is entirely accurate.

A comprehensive study & outlook on integrated structural health monitoring and C-intelligent transport system for pavement condition assessment

Intelligent Transport Systems (ITS) and Structural Health Monitoring (SHM) projects have been a trending topic of research in the last decade. However, even with positive results, ITS and SHM projects struggle to compete with conventional roads and transport infrastructure projects. The standalone systems encounter many limitations which motivate to have integrated ITS and SHM systems. Nevertheless, SHM combined with ITS could be an interesting approach to expand single-minded systems into systems with multiple functions. Both ITS and SHM are vast fields involving complex technology and data management systems, but using an integrated approach could increase the efficiency of a standalone system. This research focuses on doing a literature survey and identifying the shared aims and limitations of standalone ITS and SHM systems. This would help in developing an integrated framework to mitigate the limitations of individual systems and help expand the projects beyond their initial used cases. It would also improve the applicability of these projects.

Intelligent Assessment of Pavement Condition Indices Using Artificial Neural Networks

The traditional manual approach of pavement condition evaluation is being replaced by more sophisticated automated vehicle systems. Although these automated systems have eased and hastened pavement management processes, research is ongoing to further improve their performances. An average state road agency handles thousands of kilometers of the road network, most of which have multiple lanes. Yet, for practical reasons, these automated systems are designed to evaluate road networks one lane at a time. This requires time, energy, and possibly more equipment and manpower. Multiple Linear Regression (MLR) analysis and Artificial Neural Network (ANN) were employed to examine the feasibility of modeling and predicting pavement distresses of multiple lanes as functions of pavement distresses of a single adjacent lane. The successful implementation of this technique has the potential to cut the energy and time requirement at the condition evaluation stage by at least half, for a uniform multi-lane highway. Results showed promising model performances that indicate the possibility of evaluating a multi-lane highway pavement condition (PC) by single lane inspection. Traffic direction parameters, location, and lane matching parameters contributed significantly to the performance of the ANN PC prediction models.