Pavement Condition Research Articles

Development of the algorithm for determining the thickness of the structural layers of the non-rigid pavement and searching for local inclusions using sub-surface georadiolocation methods

Problem. The article is part of the authors' research in the field of road construction using GPR technologies. Ensuring a certain level of pavement reliability on highways requires the development of methods and means for continuous information acquisition and processing in real time, taking into account the parameters of structural heterogeneity, heterogeneity of physical and mechanical parameters of structures, as well as their changes over time. The solution of this problem is limited by the complexity of tasks of obtaining and interpreting the data of subsurface sensing and the associated errors in determining the parameters of structures, imperfection of methods of flaw detection of structures with subsurface inhomogeneities, multifactorial tasks of estimation of physical and mechanical parameters of multicomponent materials. Goal. Improving the accuracy and reliability of determining the boundaries of layers of non-rigid pavement structure, which have a low level of electrodynamic contrast due to interpenetration of layer materials or changes in their physical and mechanical properties. The solution of this problem is connected with the development of a method of obtaining reflected GPR signals and the improvement of algorithms for their processing. Methodology. The development of methods for the analysis of the electromagnetic field with a variable state of polarization makes it possible to assess the localization of violations of the integrity of the pavement structure at a qualitatively new level and allows the positioning of small inclusions even with a slight contrast in the dielectric constant. The proposed method of recording the reflection of an ultra-wideband GPR pulse signal at different polarizations is implemented by measuring the reflected signal from a metal sheet at oblique incidence and parallel polarization, recording the reflected pulse at perpendicular polarization, followed by measuring the reflection coefficient from the structure of the nonrigid pavement under study at parallel and perpendicular polarization. The electrophysical parameter recovery procedure is based on the solution of inverse scattering problems and ensures the accuracy of determining the low-contrast boundaries of the pavement structure and increases the accuracy of positioning subsurface heterogeneities. GPR signal processing algorithms are based on procedures for analyzing their polarization state, which allows determining the geometric parameters of subsurface heterogeneities. Practical value. The combination of the obtained results increases the accuracy and reliability of the results of GPR diagnosis and assessment of the condition of non-rigid pavement based on the most complete and reliable information about the subsu

Deep Learning for Pavement Condition Evaluation Using Satellite Imagery

Civil infrastructure systems cover large land areas and need frequent inspections to maintain their public service capabilities. Conventional approaches of manual surveys or vehicle-based automated surveys to assess infrastructure conditions are often labor-intensive and time-consuming. For this reason, it is worthwhile to explore more cost-effective methods for monitoring and maintaining these infrastructures. Fortunately, recent advancements in satellite systems and image processing algorithms have opened up new possibilities. Numerous satellite systems have been employed to monitor infrastructure conditions and identify damages. Due to the improvement in the ground sample distance (GSD), the level of detail that can be captured has significantly increased. Taking advantage of these technological advancements, this research evaluated pavement conditions using deep learning models for analyzing satellite images. We gathered over 3000 satellite images of pavement sections, together with pavement evaluation ratings from the TxDOT’s PMIS database. The results of our study show an accuracy rate exceeding 90%. This research paves the way for a rapid and cost-effective approach for evaluating the pavement network in the future.

Development of Pavement Deterioration Models Using Markov Chain Process

A common phenomenon in developing countries is that the function of the pavement in the road network will experience structural damage before the completion of life is reached, and the uncertainty of pavement damage is difficult to predict. Planning for maintenance treatment depends on the accuracy of predicting future pavement performance and observing current conditions. This study aims to apply the Markovian probability operational research process to develop a decision support system predicting future pavement conditions. Furthermore, it determines policies and effectiveness in managing and maintaining roads. A standard approach that can be used by observing the history of pavement damage from year to year is to estimate the transition probability as a Markovian-based performance prediction model. The results show that the application of the model is quite optimal, changes in pavement conditions after repair can be easily compared with an increase in good condition, reaching 92.8%. Routinely and consistently handling road deterioration will give favorable results regarding pavement condition value. This will ease in the management of the road network and the accomplishment of the optimal maintenance and repair policies. Doi: 10.28991/CEJ-2024-010-09-012 Full Text: PDF

Predicting pavement surface conditions through artificial neural networks

In this study, six distinct types of pavement distress were evaluated using the pavement condition index (PCI) rating: ravelling, rutting, cracking (including alligator, transverse and longitudinal cracks), shoving, patching and potholes. The severity levels of these distress types are utilised for classification, and these levels are subsequently converted into PCI values. The proposed methodology incorporates a thorough analysis, considering various variables such as average daily traffic, land-use classification, number of lanes, road width and length, pavement age, road alignment, vehicle type and weather conditions in Berzhite, Tirana, Albania. To predict PCI values, an artificial neural network model was employed due to its versatility in handling diverse inputs. Key performance metrics, including R2, mean squared error and mean absolute error, are used for model assessment. Notably, model 2, featuring two hidden layers, exhibits superior performance over model 1. However, due to size constraints in testing and validation datasets in this study, the accuracy of the model is somewhat limited. Future directions for research involve expanding the dataset to enhance model accuracy and incorporating advanced features to capture a more complex comprehension of pavement conditions.

The use of geodetic measurements in the assessment of the technical condition of road pavement – case study

Abstract Technical infrastructure is exposed to environmental impacts throughout its entire life cycle. For linear objects, such as roads, additional impacts resulting from their continuous use – vehicle traffic – are distinguished. Due to their communication function, these facilities are subject to ongoing condition monitoring. This procedure allows to identify any damage and indicate appropriate protective measures. Observations of the terrain as well as the road surface can be carried out using commonly used geodetic measurements. Cyclic monitoring applies especially to facilities in mining areas, where impacts in the form of continuous and discontinuous deformations significantly contribute to their damage. The research presents the results of selected geodetic measurements made on a deformed road surface affected by underground mining. The aim of the observations was to use both classic and modern measurement techniques to determine, among others, the shape of the surface or identify damage. The applied measurements included commonly used total station measurements and GNSS satellite positioning. Additionally, low-ceiling photogrammetry using an unmanned aerial vehicle and laser scanning were used to illustrate the shape of the road surface. Finally, a comparison was made of selected methods; their advantages and disadvantages allowed us to determine their usefulness in monitoring the condition of the pavement.

Nonlinear attitude tracking control for four-wheel-leg-vehicle with series active suspension

This study proposes the active disturbance rejection control (ADRC) algorithm to decouple the attitudes of the nonlinear active suspension system of a four-wheel-leg-vehicle (FWLV) system and solve its attitude-tracking problem. Current technologies control the attitude state variables of the vehicle typically at 0° to maintain the car body at a horizontal inclination. However, in complex terrains, the centre of gravity needs to be elevated to improve passability or reduce the centre of gravity to increase driving stability. During steering or braking, the pitch or roll angle of the body needs to be actively changed to improve the gripping force and reduce the possibility of rollover. This study considered the non-linear characteristics of shock absorbers and exploited the advantages of the active disturbance rejection technology, such as high tracking efficiency and small overshoot, to achieve stable tracking. The stability of the tracking attitude angle is analysed within the Lyapunov framework. Simultaneously, a linear suspension attitude-tracking control algorithm is designed as a linear quadratic regulator (LQR) for verifying the tracking control performance of the FWLV. The accuracies of the two algorithms were compared under stationary state, single-side sinusoidal, and random pavement conditions through simulations and vehicular tests. The results indicate that the stability of the attitude-tracking values by ADRC is better than that by the LQR. In addition, comparisons between the simulations and tests illustrated the validity of the active attitude-tracking system.

APPLICATION OF COIR GEOTEXTILE IN SUBGRADE FOR IMPROVED STRENGTH AND DRAINAGE CONDITION OF FLEXIBLE PAVEMENT

APPLICATION OF COIR GEOTEXTILE IN SUBGRADE FOR IMPROVED STRENGTH AND DRAINAGE CONDITION OF FLEXIBLE PAVEMENT

Enhancing pavement crack segmentation via semantic diffusion synthesis model for strategic road assessment

Computer-aided deep learning has significantly advanced road crack segmentation. However, supervised models face challenges due to limited annotated images. There is also a lack of emphasis on deriving pavement condition indices from predicted masks. This article introduces a novel semantic diffusion synthesis model that creates synthetic crack images from segmentation masks. The model is optimized in terms of architectural complexity, noise schedules, and condition scaling. The optimal architecture outperforms state-of-the-art semantic synthesis models across multiple benchmark datasets, demonstrating superior image quality assessment metrics. The synthetic frames augment these datasets, resulting in segmentation models with significantly improved efficiency. This approach enhances results without extensive data collection or annotation, addressing a key challenge in engineering. Finally, a refined pavement condition index has been developed for automated end-to-end defect detection systems, promoting more effective maintenance planning.

Cost-Effective Pavement Condition Survey for Municipal Road Networks

One of the most challenging issues for municipalities in the U.S. is to secure federal funding, state funding, or both, for local roadway improvement. Existing frameworks such as manual data collection, light detection and ranging have proven to be expensive and cumbersome. In this paper, a low-cost pavement management framework is proposed using artificial intelligence (AI). AI has solidified itself across industries as a revolutionary advancement that can automate many tasks that were performed by humans. AI has the potential to make roadway assessment easier and more cost-effective than ever, but this application has been hindered by dataset quality and quantity. Roadway datasets are often imbalanced, containing many more images of certain deformations than others. This decreases the performance of AI models. In this paper, different methods of pavement dataset labeling are tested to gain an understanding of which is best for pavement distress detection using a classification neural network. An AI-friendly pavement condition index is designed to give a clear indicator of the current pavement condition and provide a metric by which to rank the roads based on the need to repair them. The best-performing AI model is incorporated into the low-cost pavement management framework.

Monitoring data motivated health condition assessment of cement concrete pavements in field based on FBG sensing technology

Health condition of cement concrete pavements in field is difficult to characterize due to the random action of traffic loads and environmental variations. It is also difficult to accurately get the deformation merely based on theoretical or simulation analysis, for the heterogeneous material and complicated interfacial bonding properties of multilayered concrete pavements. Therefore, in situ health monitoring is particularly significant for structural condition assessment. To contribute this topic, improved fiber Bragg grating sensors have been designed and embedded in the two-layered pavement to measure the strain, deformation, and temperature. Deformation information of the cement concrete pavement under environmental action in about 3 years has been analyzed. Data analysis indicates that the longitudinal and transverse deformations of the pavement have behaved in nonuniform distribution, and local points suffered from the large tensile strains. The vertical measurement data declared that local interfacial micro void defects existed in the soil base and kept stable during the following 3 years. The temperature variation inside the pavement was highly correlated with the environmental temperature, and the variation amplitude was relatively smaller than the environmental temperatures, with the maximum amplitude smaller than 4°C. The load and temperature transfer principles in different layers have also been checked. Generally, the monitoring data in field from the year 2020 to year 2023 have declared that the concrete pavement has good performance to resist the combined action of traffic loads and environmental temperatures. The study can help to understand the actual structural performance of cement concrete pavements in field based on the optical fiber sensing system.

Terrestrial LiDAR derived 3D point cloud model, digital elevation model (DEM) and hillshade map for identification and evaluation of pavement distresses

The periodic assessment of pavement condition is one of the important requirements in ensuring not only the service life of the pavement but also for the safety of motorists as pavement distresses like potholes led to many fatal accidents in recent years in India. The traditional approach of manual evaluation is a cumbersome and time-consuming process which can be replaced by latest technologies like LiDAR. Literature on use of Terrestrial LiDAR for distress detection are very limited and even the reported studies have used mostly the raw point cloud data from LiDAR for detecting only selected distresses. However, there are many outputs which can be derived from Terrestrial LiDAR data like Digital Elevation Model (DEM), Hillshade, etc. which are not explored much. Hence in the present study, an attempt has been made to utilize all the possible outputs of Terrestrial LiDAR data, i.e., raw 3D model from point cloud data and LiDAR derived DEM and Hillshade to detect 15 pavement distresses in both bituminous and concrete pavement for a case study area of Vellore in Tamilnadu, India. Their suitability also was compared in order to find which output would be more suitable in identifying a particular distress on the road. The results revealed that Hillshade maps can detect all the pavement distresses except cracks when compared to 3D model and DEM. For the detection of pavement cracks, an image processing based solution using LiDAR data was tried out and was found to perform well in detecting the alligator cracks. The distress parameters such as length, width, depth, etc. were also calculated using LiDAR data and compared with the field observations. The error was found to be only few cms and thus indicate that the Terrestrial LiDAR can be employed to detect the pavement distresses accurately than the conventional manual methods.

Environmental impacts from traffic on highway construction work zones: Framework and simulations

Emissions from internal combustion engine vehicles on highways are the major contributors to global warming in the United States. Transportation sector pavement-related emissions come from gasolines and diesel use in vehicles from pavement-vehicle interaction, which is affected by pavement conditions, and by the trucking of new pavement materials and demolition. The objective of this study was to develop a framework for determining the fuel use resulting in environmental impacts caused by construction work zones (CWZs) on a range of vehicles and to produce initial calculations of these impacts by modeling traffic closure conditions for highway maintenance and rehabilitation activities. The study included two common highway categories—freeways/multi-lane highways and two-lane highways. The framework was demonstrated using three CWZ operations under different traffic congestion levels. In the simulation results for a freeway with a CWZ and heavy congestion, fuel consumption increased by 85% and the carbon-dioxide equivalent emissions increased by 86%. Changing CWZ traffic congestion from heavy (average speed 5 mph) to medium (average speed 25 mph for a freeway section) reduced fuel consumption by 40% on a freeway. This study also included use of a pilot car in a CWZ on a two-lane road typical of lower traffic volume state highways and county roads to compare with the drive cycles in MOVES used for the scenarios. The pilot-car operation scenario results indicate that a one-lane closure with pilot-car operation on a two-lane road might consume 13% excess fuel because of idling time and the slow movement of vehicles following the pilot car.

Research Progress of Optical Satellite Remote Sensing Monitoring Asphalt Pavement Aging

The aging condition of asphalt pavement is an invaluable basis for traffic infrastructure evaluation. Due to the amount of time and high cost of monitoring and identifying asphalt pavement aging, many current studies focus on satellite remote sensing methods. In this paper, some methods and technologies for monitoring asphalt pavement degradation by optical satellite remote sensing are introduced as a literature review. Many researchers have developed spectrum libraries based on the actual aging of asphalt pavements, and it is possible to construct pavement health indices based on spectrum changes. Some indexes can extract different aging degrees of asphalt pavement from optical satellite images. Of course, current research can only preliminarily reflect the aging phenomenon of asphalt pavement and cannot accurately describe the distress characteristics of asphalt pavement. Future research needs to further strengthen mechanism research, develop higher resolution images, improve image processing technology, and adopt multi-means fusion analysis methods.

Imporved model for pavement performance prediction based on recurrent neural network using LTPP database

Accurate pavement performance prediction plays a critical role in formulating maintenance and repair strategies for transportation departments, enabling the achievement of better pavement performance with limited financial resources. However, due to the intricate influence of numerous factors on pavement performance deterioration, improving the accuracy of pavement performance prediction poses a challenge for conventional models. Therefore, the aim of this study is to establish a machine learning-based model pavement performance prediction model. First, this study considers five factors that affect pavement performance, including pavement initial performance indicators, traffic loads, weather, pavement structure, and maintenance measures, and identifies 15 specific indicators that affect pavement performance based on these five factors. Then, based on the Long-Term Pavement Performance (LTPP) database, the study screens and summarizes these indicators, obtaining 2464 high-quality pavement performance data for Pavement Conditions Index (PCI) prediction and 3238 high-quality pavement performance data for IRI prediction. Finally, three distinct prediction models are established, namely, the Fully Connected Neural Network (FCNN) model, the Long Short-Term Memory (LSTM) model, and the combined LSTM-Attention model. The study shows that the LSTM-Attention model performs significantly better than the FCNN and LSTM models, with an R2 coefficient of determination of 0.81 for PCI and 0.79 for IRI. The innovation of this paper is that the authors have introduced the Attention mechanism on the basic of LSTM model, which makes the fitting accuracy of the prediction model further improved.

Operational Dynamic Load Testing for Repair Recommendations Case Study of Access Road to Vehicle Weight in Motion

The indication of inaccuracies in vehicle weight measurement using Weight in Motion (WIM) technology on mining roads has been identified due to the non-fulfillment of the design criteria required by the WIM manufacturer. This study aims to identify the gap between the actual pavement conditions and the design criteria and to design improvements to eliminate this gap. The method employed involves dynamic load testing, followed by processing the test data with operational modal analysis (OMA) to obtain dynamic parameters that will serve as a reference for calculating the pavement capacity. From OMA, a natural structural frequency of 26.342 Hz was obtained, while the theoretical calculation based on the design criteria was 29.481 Hz. This frequency decrease indicates structural damage of 10.65% and a capacity reduction of 26.8% from the design criteria. This finding is reinforced by a critical damping ratio of 5.25%, which is higher than the damping for intact concrete, ranging between 2%-5%. This indicates energy dissipation through concrete cracks. It is recommended to inject the cracks in the existing pavement with a cement base and perform a 130 mm overlay with mortar grout (fc’= 50 MPa) with one layer of M12 wire mesh. The connection with the old pavement will use concrete bonding and D13-600/600 chemical anchors. With this improvement, the structural capacity will become 104% of the design criteria.

Customized Approaches for Introducing Road Maintenance Management in I-BIM Environments

Road maintenance management aims to satisfy quality, comfort, and safety requirements for the various assets. To overcome delays and barriers in the widespread adoption of road management systems, the Building Information Modeling (BIM) approach may offer significant advantages as a convenient alternative for road maintenance management. Although existing BIM platforms are not fully equipped for this purpose, defining original modules and scripts can extend their capabilities, allowing for the handling of road condition information and maintenance management. In this context, this paper presents an operative framework designed to leverage BIM benefits for road maintenance management, particularly in terms of virtual inspection, asset condition assessment, and maintenance design. To achieve this, specific original and customized smart objects and routines were coded in I-BIM platforms, tailored to different scales, aims, and detail levels. These smart objects incorporate user-defined extended attributes related to pavement condition and maintenance planning (such as roughness, rutting, structural capacity). In particular, the authors have developed original virtual smart objects in different platforms, serving as “containers” for the survey information. These objects are adapted to display quality levels of the pavement segments in a realistic and user-friendly environment. Additionally, original routines were coded to automatically import survey data from external datasets and associate this information with the appropriate objects. This customized and extended approach, not available in commercial platforms, can effectively support maintenance operators.

Machine learning-based technology for asphalt concrete pavement performance decision-making in hot and humid climates

Highway state agencies incur significant budget savings through optimal allocation of pavement Maintenance, Rehabilitation, and Reconstruction (MR&R) activities. These activities require robust prediction models that can handle large-scale, real-world data and can forecast pavement performance in the long run. Unfortunately, the traditional performance prediction models have been questionable in terms of efficiency and accuracy, are based on a limited number of explanatory variables, and are designated to predict short-term (up to five years) pavement conditions. Therefore, the goal of this study was to propose a machine learning-based technology that can predict the field performance by up to 11 years of Asphalt Concrete (AC) overlays placed on asphalt pavements in Southern states in the US based on key project conditions. The proposed technology resulted from assessing the prediction accuracy of machine learning algorithms, including Decision-Tree (DT), eXtreme Gradient Boosting (XGBoost), Artificial Neural Network (ANN), and ensemble-learning method, in forecasting the Pavement Condition Index (PCI) as the pavement performance indicator. For each algorithm, six models were developed sequentially based on historical pavement condition data collected from the Louisiana Department of Transportation and Development (LaDOTD) Pavement Management System (PMS) database. The six models learned from 892 log miles of randomly placed AC overlay sections in Louisiana. The output of these models was the future PCI of AC overlays at a biannual rate from one to 11 years. The findings showed that XGBoost and ensemble learning showed similar performance during model training and were further evaluated using the testing dataset. During model testing, the ensemble learning method yielded higher prediction accuracy than other algorithms, with R2 values decreasing from 0.77 at age 1 to 0.67 at age 11, Root Mean Square Error (RMSE) values increasing from ±1.65 to ±4.74, and Mean Absolute Error (MAE) increasing from 1.24 to 4.59. The resulting models can be used by state agencies to replace traditional prediction techniques.

Quantifying the effectiveness of an active treatment in improving highway-railway grade crossing safety in Canada: an empirical Bayes observational before–after study

Transportation engineers face challenging safety investment decisions, particularly for highway-railway grade crossings (HRGCs), where rare collision occurrences and incomplete historical records complicate the assessment of countermeasure cost-effectiveness. This study introduces an empirical Bayes (EB) observational before–after approach to address these challenges, specifically examining the impact of a widely used safety countermeasure: flashing lights, bells, and gates (FLBG). The research covers a total 171 FLBG crossings with pre-implementation conditions varying between standard reflective crossing signs and flashing lights and bells, utilizing collision data from 2000 to 2012 for analysis. This study is the first to substantiate quantitatively the effectiveness of FLBG treatment in improvising the safety of HRGCs and show that the effect is dependent of crossing characteristics, such as track angle, pavement conditions, and train speed limits. These findings can provide valuable support to decision-makers, enabling them to make more effective decisions for grade crossing safety improvement projects.

Novel approach for forest road maintenance using smartphone sensor data and deep learning methods

ABSTRACT High costs primarily pose challenges to forest management in planning and executing the repair of forest roads. With budget limitations and inadequate oversight, it has become critically essential to monitor the state of these roads. Monitoring the condition of forest roads has become imperative, driven by budget constraints and a lack of effective supervision. While smartphones have proven effective in detecting road defects on public roads, their application on forest roads is hindered by the absence of suitable indices and software infrastructure. Addressing this gap, this research focuses on the development of the Forest Road Pavement Condition Index (FRPCI) to facilitate smartphone-based monitoring. We collected and compared data from 4 kilometers of forest roads, employing two traditional harvesting methods alongside smartphone sensor data. Utilizing deep learning methods, including Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), and CNN-LSTM, we processed the collected data. Signal processing using GPS data, coupled with wavelet transformation, demonstrated promising results with an accuracy and recall exceeding 80%. The proposed system functions as a distributed information system, transitioning data from organizational mode to field mode. It measures damage, assesses forest road conditions, and leverages image processing and GPS technologies. This monitoring system technology offers capabilities for preparing, storing, updating, maintaining, and analyzing diverse information. Importantly, adopting this method can significantly reduce operating costs, making forest road monitoring for maintenance purposes more feasible.

Characteristics of life-cycle carbon dioxide emissions of arterial highway maintenance and the influencing factors

With the focus of highway development transitioning from construction to maintenance, a comprehensive understanding of the characteristics and influencing factors of carbon dioxide (CO2) emissions from highway maintenance activities is crucial for formulating effective strategies to promote the low-carbon development of road infrastructure. However, the quantitative relationships between CO2 emissions from highway maintenance schemes and factors such as pavement deterioration, traffic volume, and road grade remain unclear owing to a lack of comprehensive, multi-category, and real data. Using real maintenance data from 340 arterial highway segments in China, this study conducts the life cycle assessment (LCA) to estimate CO2 emissions from maintenance activities and examines the primary emission sources among various structural layers and materials. Furthermore, multiple linear regression (MLR) analysis is conducted to investigate the impact of traffic volume, road grade, and pavement deterioration on CO2 emissions from maintenance projects, and factors influencing the early-stage degradation of pavement performance. The results demonstrate that average CO2 emissions from heavy rehabilitation projects are 6.97 times higher than those from medium rehabilitation projects. Emissions from heavy rehabilitation projects exhibit a significantly negative linear relationship with the riding quality index (RQI) before maintenance (p < 0.05), and emissions from medium rehabilitation projects show a significant negative linear relationship with the pavement condition index (PCI) before maintenance (p < 0.05). Emissions from heavy and medium rehabilitation projects are significantly positively correlated with heavy vehicle traffic volume before maintenance (p < 0.05). Moreover, the early-stage degradation of PCI after heavy rehabilitation and RQI after medium rehabilitation exhibit significantly negative linear relationships with their respective indicators before maintenance (p < 0.05). The early-stage degradation of RQI after heavy rehabilitation is significantly positively correlated with CO2 emissions from the base course and cushion layers (p < 0.05). The findings emphasize that timely maintenance and reduction of CO2 emissions from asphalt mixing equipment are essential for mitigating emissions from road maintenance. This study offers valuable insights for advancing the low-carbon development of highways in temperate regions.