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Asphalt pavement is the most widely used type of pavement in the world and is mainly utilised in the construction of infrastructures such as highways, urban roads, parking lots and airstrips. Pavement maintenance technology and materials are gradually developing towards systematisation and diversification with the extensive use of asphalt pavement. Choosing more economical technologies and fast and sustainable materials is the future of asphalt pavement maintenance work. This paper provides an overview of asphalt pavement repair technology and asphalt pavement repair materials. It categorises and summarises pavement repair technologies, which include cost-effective technologies such as crack sealing, overlay, seal coats and hot in-place recycling. Further, it summarises the repair materials applied in repair technologies and compares the performance, cost-effectiveness and sustainability of these materials. The study shows that asphalt and cement are the most commonly used repair materials. The higher-potential, more economical and more sustainable materials for asphalt pavement repair include epoxy resin, polyurethane and hydrogel. The future of asphalt pavement repair requires advancing towards rapid, sustainable, environmentally friendly and economical repairs. A survey indicates that in addition to improving the performance of existing repair materials (e.g. modified asphalt, binder processes, material composition or ratios), attention can be given to new materials such as polyurethanes and hydrogels that have the potential for rapid repair and are low cost. More research can be done to perfect the application of new materials in road engineering. [ABSTRACT FROM AUTHOR]

Affected by climatic factors (e.g., low temperature and intense ultraviolet radiation), high-altitude regions experience numerous pavement diseases, which compromise driving safety and negatively impact user travel experience. Timely planning and execution of pavement maintenance are particularly critical. In this paper, considering the characteristics of pavement maintenance in high-altitude regions (e.g., volatility of traffic volume, seasonality of maintenance timing, and fragility of the ecological environment), we aim to derive optimal monthly maintenance plans. We develop a multi-objective nonlinear optimization model that comprehensively accounts for minimizing maintenance costs, affected traffic volume and carbon emissions, and maximizing pavement maintenance effectiveness. Utilizing linearization methods, the model is reconstructed into a typical mixed-integer linear programming (MILP) model, enabling it to be solved directly using conventional solvers. We consider five types of decision strategies to reflect the preferences of different decision-makers. Given the uncertainty of maintenance costs, we also utilize the robust optimization method based on the acceptable objective variation range (AOVR) to construct a robust optimization model and discuss the characteristics of optimistic, robust, and pessimistic solutions. The results suggest that different decision strategies show differences in the indicators of maintenance costs, affected traffic volume, carbon emissions, and pavement performance. When multiple decision objectives are comprehensively considered, the indicators are between the maximum and minimum values, which can effectively balance the decision needs of maintenance effectiveness, maintenance timing, and environmental protection. The number of maintenance workers, the requirement of the minimum pavement condition index (PCI), and the annual budget influence the maintenance planning. The obtained robust solution can somewhat overcome the conservative nature of the pessimistic solution. The method proposed in this paper helps address the complexities of pavement maintenance decisions in high-altitude regions and provides guidance for pavement maintenance decisions in such areas. [ABSTRACT FROM AUTHOR]

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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. [ABSTRACT FROM AUTHOR]

Automated crack sealing could significantly benefit the maintenance of road pavements, but there is difficulty in depositing the correct volume of sealant material into the hidden crack space. A simulated model of the material flow within a crack space would allow the development of a predictive control scheme, such that the repair robot can apply suitable trajectories and operational parameters to accomplish neatly sealed surfaces. For the first time, the position‐based fluid (PBF) method, a computationally cheap and fast but approximate model of fluid flows, is studied for its feasibility for sealant flow simulation in the robotic injection crack sealing scenario. A Real‐to‐Sim experiment is performed, in which a PBF simulation of sealant in a virtual robotic crack sealing environment is mirrored from the physical lab setup. The fluid simulation is tuned to match the real‐world dynamics through comparison with 132 simulation runs, varying the artificial viscosity parameters U (fluid–fluid viscous interaction) and D (fluid–wall viscous interaction). It was found that U had a varied three‐stage influence on the simulation error while D 's negative influence on the simulation error only effectively applied to fluids satisfying 11\unicode{x0003C}U\unicode{x0003C}100. Through comparing the physical and virtual crack sealing results, the simulation was validated with an average fluid level error of 1.26 mm along a 3.1 mm wide, 16 mm deep and 80 mm long artificial crack, which shows the usefulness of the PBF method for robotic injection sealing. The accuracy and computational requirements of the PBF method are also discussed. [ABSTRACT FROM AUTHOR]

Cold mix patching materials (CMPMs) present an environmentally friendly and sustainable solution for addressing road potholes, particularly during cold and wet weather conditions. While they offer cost-effectiveness and reduced ecological impact, CMPMs encounter challenges related to their mechanical and functional performance in practical applications. There is an increasing demand for higher-quality CMPMs, yet their durability tends to be relatively short-lived. This study conducted a comprehensive assessment of various CMPM variants available on the European market, such as solvent-containing mixtures (SC) and membrane-containing mixtures (MC). The investigation placed specific emphasis on critical aspects, including stability, workability, raveling, and bonding, employing both conventional and innovative testing methodologies such as Marshall stability, indirect tensile strength, Hubbard-Field stability, indentation stability, Leutner shear bond, locking point, and brush tests conducted across a spectrum of temperatures and compaction pressures. The findings indicated that mixtures with locking points ranging from 40 to 60 exhibited good to excellent workability. MC mixtures generally outperformed SC mixtures, except in terms of bonding properties. Despite achieving air voids of up to 31.7 %, their stabilities exceeded recommended values in the literature. Optimal abrasion resistance was observed with higher compaction pressure (600 kPa) and longer cure time (30 days) working in conjunction. Furthermore, increasing the compaction pressure from 200 kPa to 600 kPa resulted in a two to threefold increase in interlayer shear strength for all products.

This study focuses on investigating the significant impacts of speed breakers on various parameters, including travel time delays, vehicle speeds, fuel consumption, pavement maintenance costs, and vehicular exhaust emissions. Field data was collected and analyzed to assess the effects of different types of traffic calming measures on these parameters. The findings provide valuable insights into the implications of speed breakers on road safety, environmental pollution, and overall road infrastructure management. The results reveal that the implementation of speed humps, speed bumps, and triple bumps effectively slows down vehicles, as evidenced by considerable reductions in the 85th percentile speeds. The reduction percentages were 41.65% for speed humps, 73.52% for speed bumps, and 86.27% for triple bumps. This indicates the effectiveness of these traffic calming measures in improving road safety by reducing vehicle speeds. However, the presence of speed breakers also leads to increased travel time delays. On average, traversing stretches with speed humps, speed bumps, and triple bumps resulted in delays of 9.31, 16.42, and 29.51 seconds, respectively. While the individual delay times may appear relatively short, the cumulative effect of multiple speed obstacles along a road needs to be considered. Another significant impact observed is the increased fuel consumption associated with speed breakers. The study found that for every 100 km of travel, motorcycles and passenger cars consumed approximately 12.07 km and 27.37 km of additional fuel, respectively, when the density of speed breakers was 1.33/km. This translates to a fuel consumption increase of 13.73% for motorcycles and 37.74% for passenger cars. Furthermore, the presence of speed humps was found to contribute to pavement deterioration, as indicated by decreased Pavement Condition Index (PCI) values. The study also revealed that sections with speed humps incurred significantly higher maintenance costs compared to sections without speed humps. The increase in maintenance cost ranged from 100 to 264% across different road sections, with higher traffic volumes leading to greater cost escalation. Additionally, the study confirms that lower vehicle speeds, particularly between 0-15 km/hr, are associated with higher emissions of pollutants, including carbon monoxide (CO) and other pollutants. This highlights the environmental implications of speed breakers and their contribution to urban air pollution. [ABSTRACT FROM AUTHOR]

Traditional asphalt concrete (AC) and stone matrix asphalt (SMA), which are used as thin asphalt overlays, are common maintenance strategies to enhancing ride quality, skid resistance, and durability. Recently, several studies have used a novel asphalt mixture known as a high-friction thin overlay (HFTO) to improve surface characteristics. However, it remains uncertain whether the laboratory properties of HFTO differ significantly from those of conventional mixtures. This study aims to evaluate the laboratory properties of HFTO mixtures and compare them with those of AC and SMA. Those mixtures with nominal maximum size of 9.5 mm were produced in the laboratory, and performance tests were conducted, including wheel tracking test, low temperature flexural creep test, moisture susceptibility test, Cantabro Abrasion Test, Marshall Test, sand patch test, British pendulum test, and indoor tire-rolling-down test. The results showed that the HFTO exhibited a lower tire/pavement noise than the AC and SMA. Additionally, HFTO had superior high-temperature stability, larger macro texture, and higher skid resistance in comparison to those of AC, but lower than those of SMA. Consequently, HFTO mixtures may be considered a suitable replacement for traditional AC mixtures in regions where skid resistance and noise reduction are concerns. [ABSTRACT FROM AUTHOR]

Survey of road cracks in a timely, complete, and accurate way is pivotal to pavement maintenance planning. Motivated by the increasingly heavy task of identifying cracks, researchers have developed extensive crack segmentation models based on Deep learning (DL) methods with significantly different levels of accuracy, efficiency, and generalizing capacity. Although many of the models provide satisfying detection performance, why these models work still needs to be determined. The objective of this study is to survey recent advances in automated DL crack recognition and provide evidence for their underlying working mechanism. We first reviewed 54 DL crack recognition methods to summarize critical factors in these models. Then, we conducted a performance evaluation of fourteen famous semantic segmentation models using the quantitative metrics: F-1 score and mIoU. Then, the effective receptive field and class activation map of the included models are visualized to demonstrate the training results as qualitative evaluation. Based on the literature review and comparison results, larger kernel size, feature fusion, and attention module all contribute to the improvement of model performance. Striking a balance between increasing the effective receptive field and computational/memory efficiency is the key to designing DL crack segmentation models. Finally, some potential directions and suggestions for future development are provided, such as developing semi-supervised or unsupervised learning for the high cost of pixel-level labeling.

Sidewalk infrastructure presence is a key indicator of pedestrian safety and walkability for neighborhoods in cities throughout the United States. The existence and condition of sidewalk infrastructure, however, is not prioritized as much as motor vehicle infrastructure. Many cities lack sustained maintenance and operations programs for sidewalk infrastructure and comprehensive datasets covering the locations and distributions of sidewalk infrastructure, limiting the ability to develop such programs. This work refines prior sidewalk infrastructure network generation techniques, contributing new methods to identify sidewalk infrastructure presence.QA/QC efforts were conducted for in Atlanta’s sidewalk network by correcting errors identified in input data. Error identification and correction times were comprehensively tracked and used to estimate future labor costs. A Custom application with online access to Bing Maps Streetside and aerial imagery was developed to allow technicians to verify sidewalk presence data, which were joined to the structural sidewalk network and associated with adjacent parcels. Cost of ownership of Atlanta’s sidewalk infrastructure over an 80-year management period is then broken down by asset type and allocated in part to property owners directly adjacent to the applicable infrastructure, while remaining costs are recovered through a proportional increase in property tax millage rates. Sidewalk network estimates developed in previous Atlanta research efforts decreased sidewalk network mileage by 12% (386 miles), post-QA/QC. Regression analysis of error correction activity and labor data indicates gaps between tax parcels and misplacement of intersection centroids significantly increased QA/QC labor costs. Overall, 46% of Atlanta’s potential sidewalk links were present(i.e., along property superblock boundaries), with significant clustering in the city’s oldest neighborhoods. Hence, sidewalk repair and maintenance costs accrue disproportionately to these areas. Sidewalk infrastructure costs across neighborhoods also differ considerably, depending on whether estimates account for existing sidewalk infrastructure. The annual cost burden on property owners to implement a program to fund sustainable sidewalks (lifecycle assessment) by increasing property tax millage rates varies significantly across household income and ethnicity. The research suggests that sustainable sidewalk infrastructure assessments should consider spatial and demographic disparities in cost allocation (i.e., equity) for any proposed pedestrian infrastructure asset management program.View the NCST Project Webpage

The use of sustainable materials in road construction has been gaining popularity to provide a better future for the next generations. This study investigates the possible use of two common sustainable road construction materials, recycled asphalt pavement (RAP) and recycled tire rubber (RTR), for pavement maintenance applications. Mixtures were produced either with combined RAP and RTRs or 100% RAP with petroleum-based (RAP-A) and bio-based (RAP-B) rejuvenating oils. Because pavement maintenance applications such as pothole patching have traditionally been performed with cold mixtures (CM), a typical cold patching mixture was also investigated along with six hot asphalt mixtures. RTR mixtures were produced using different rubber modification technologies. They were manufactured using crumb rubber wet (WET), crumb rubber dry (DRY), devulcanized rubber (DEV), and crumb rubber terminal blend (TB) technologies. Each RTR-modified mixture included 40% RAP and approximately 12% rubber content by weight of the binder. The performance of the mixtures was compared using dynamic modus (DM) testing for linear viscoelastic characterization, flow number (FN) testing for rutting potential, and tensile strength ratio (TSR) testing for moisture susceptibility. The cost-effectiveness of different mixtures was performed using the life cycle cost analyses (LCCA) approach for bituminous materials. The results showed that any recycled hot-mix asphalt outperformed the CM. The 100% hot RAP mixture with petroleum-based rejuvenator performed as well as any combined RTR-RAP mixtures, and it became the most cost-effective alternative for pavement maintenance applications. [ABSTRACT FROM AUTHOR]

With the extension of road service life, cracks are the most significant type of pavement distress. To monitor road conditions and avoid excessive damage, pavement crack detection is absolutely necessary and an indispensable part of road periodic maintenance and performance assessment. The development and application of computer vision have provided modern methods for crack detection, which are low in cost, less labor-intensive, continuous, and timely. In this paper, an intelligent model based on a target detection algorithm in computer vision was proposed to accurately detect and classify four classes of cracks. Firstly, by vehicle-mounted camera capture, a dataset of pavement cracks with complicated backgrounds that are the most similar to actual scenarios was built, containing 4007 images and 7882 crack samples. Secondly, the YOLOv5 framework was improved from the four aspects of the detection layer, anchor box, neck structure, and cross-layer connection, and thereby the network's feature extraction capability and small-sized-target detection performance were enhanced. Finally, the experimental results indicated that the proposed model attained an AP of the four classes of 81.75%, 83.81%, 98.20%, and 92.83%, respectively, and a mAP of 89.15%. In addition, the proposed model achieved a 2.20% missed detection rate, representing a 6.75% decrease over the original YOLOv5. These results demonstrated the effectiveness and practicality of our proposed model in addressing the issues of low accuracy and missed detection for small targets in the original network. Overall, the implementation of computer vision-based models in crack detection can promote the intellectualization of road maintenance. [ABSTRACT FROM AUTHOR]

Maintenance planning of low volume rural road network is a challenging task due to its large length and importance to the concerned habitation. The proposed methodology is focused on developing a cost-effective maintenance plan for low volume rural Himalayan hill roads by considering the predominant distresses and factors. Maximizing overall network pavement condition rating while minimizing the budget is taken as the objective function in the optimization problem. The maintenance plan is optimized using Knapsack modified Genetic Algorithm technique developed in MATLAB. Validation of the proposed model was done by applying it to a case study of 42 low volume rural hill roads of Hamirpur district of Himachal Pradesh state in India, located in the Himalayan region. Knapsack modified GA makes maintenance planning easier by considering different scenarios of budgetary limitations varying from 0 to 100% of total need. The present model can be very helpful for pavement maintenance planners in developing an optimized cost-effective maintenance program in developing countries where budget limitation is a big concern. [ABSTRACT FROM AUTHOR]

A pavement maintenance treatment that has increased in popularity in recent years is ultrathin overlay (UTO). While the selection and construction processes for UTO has been well researched in previous studies, few studies assessed the field performance and cost-effectiveness of UTO especially in hot and wet climates. Therefore, the objective of this study was to evaluate the field performance and cost-effectiveness of UTO projects in Louisiana. To achieve this objective, asphalt pavements in Louisiana that received UTO were monitored for up to 14 years. The performance and cost-effectiveness of these sections were evaluated in terms of pavement condition Index (PCI), alligator cracking index (ALCR), rutting index (RUT), random cracking index (RNDM), and roughness index (RUFF). Results showed that UTO extended the pavement service life (PSL) by 8.7, 9.1, 7.0, 9.7, and 9.5 years, on average, for PCI, RUFF, RNDM, ALCR, and RUT, respectively. This extension varied based on the pretreatment pavement conditions and traffic level. As such, a predictive model was developed, with reasonable accuracy, to predict the extension in PSL of UTO based on project conditions. Results of the cost-benefit analysis indicated that UTO was cost-effective in most of the cases. The findings of this study were incorporated into a decision-making tool to assist transportation agencies in hot and humid climates determine the expected immediate and long-term field performance as well as the cost-effectiveness of UTO based on their project conditions. This will help these agencies make effective decisions for the maintenance and rehabilitation of their pavements. [ABSTRACT FROM AUTHOR]

Road defect inspection is a crucial task in maintaining a good transportation infrastructure as road surface distress can impact user's comfortability, reduce the lifetime of vehicles' parts, and cause road casualties. In recent years, machine learning has been adapted widely in various fields, including object detection, thanks to its superior performance and the availability of high computing power which is generally needed for its model training. Many works have reported using machine-learning-based object detection algorithms to detect defects, such as cracks in buildings and roads. In this work, YOLOv5, YOLOv6 and YOLOv7 models have been implemented and trained using a custom dataset of road cracks and potholes and their performances have been evaluated and compared. Experiments on the dataset show that YOLOv7 has the highest performance with mAP@0.5 score of 79.0% and an inference speed of 0.47 m for 255 test images. [ABSTRACT FROM AUTHOR]

To address the issue of detecting complex-shaped cracks that rely on manual, which may result in high costs and low efficiency, this paper proposed a lightweight ground crack rapid detection method based on semantic enhancement. Firstly, the introduction of the Context Guided Block module enhanced the YOLOv8 backbone network, improving its feature extraction capability. Next, the incorporation of GSConv and VoV-GSCSP was introduced to construct a lightweight yet efficient neck network, facilitating the effective fusion of information from multiple feature maps. Finally, the detection head achieved more precise target localization by optimizing the probability around the labels. The proposed method was validated through experiments on the public dataset RDD-2022. The experimental results demonstrate that our method effectively detects cracks. Compared to YOLOv8, the model parameters have been reduced by 73.5 %, while accuracy, F1 score, and FPS have improved by 6.6 %, 4.3 %, and 116, respectively. Therefore, our proposed method is more lightweight and holds significant application value.

Detection and estimation of pothole dimensions is an essential step in road maintenance. Aging, heavy rainfall, traffic, and weak underlying layers may cause pavement potholes. Potholes can cause accidents when drivers lose control after hitting or swerving to avoid them, which may lead to injuries or fatal crashes. Also, potholes may result in property damages, such as flat tires, scrapes, dents, and leaks. Additionally, potholes are costly; for example, in the United States, potholes cost drivers about $3 Billion annually. Traditional ways of attending to potholes involve field surveys carried out by skilled personnel to determine their sizes for quantity and cost estimates. This process is expensive, prone to errors, subjectivity, unsafe, and time-consuming. Some authorities use sensor vehicles to carry out the surveys, a method that is accurate, safer, and faster than the traditional approach but much more expensive; therefore, not all authorities can afford them. To avoid these challenges, a modern, real-time, cost-effective approach is proposed to ensure the efficient and fast process of pothole maintenance. This paper presents a Deep Learning model trained using the You Only Look Once (YOLO) algorithm to capture potholes and estimate their dimensions and locations using only built-in vehicle technologies. The model attained 93.0 % precision, 91.6 % recall, 87.0 % F1-score, and 96.3 % mAP. A statistical analysis of the on-site test results indicates that the results are significant at a 5 % level, with a p-value of 0.037. This approach provides an economical and faster way of monitoring road surface conditions.

Reliable predictions of pavement performance are crucial for road maintenance, rehabilitation, and reconstruction planning. To facilitate predictions of the International Roughness Index (IRI) changes over time on national highways in Nepal, this study develops a comprehensive overall model, along with regional models that consider climatic and traffic variations among the highways. The study models IRI over time using the Artificial Neural Network (ANN) approach and compares the results with those obtained from a multiple linear regression-based model. The models are developed using pavement IRI, traffic, and climatic (rainfall and temperature) data specific to national highways of Nepal, encompassing 1745 sections and 3710 total observations. The ANN-based overall model has a coefficient of determination (R2) value of 0.82 and outperforms the regression-based model, which has an R2 value of 0.76. The regional models developed for the Terai, Hill, high volume Terai and low volume Terai highways have R2 values of 0.87, 0.91, 0.85 and 0.88, respectively, indicating a good fit. Analysis of the IRI trend over time, as observed from the performance curves generated from the ANN-based model, revealed an S-shaped pattern and lower Root Mean Square Error (RMSE) compared to the regression-based model. Sensitivity analysis highlighted the initial pavement IRI as the most significant parameter in all cases. High temperature days emerged as the second most influential parameter in most models, except for the high volume Terai model, where the number of commercial vehicles serves as the second most sensitive parameter after the initial IRI.

Affected by climatic factors (e.g., low temperature and intense ultraviolet radiation), high-altitude regions experience numerous pavement diseases, which compromise driving safety and negatively impact user travel experience. Timely planning and execution of pavement maintenance are particularly critical. In this paper, considering the characteristics of pavement maintenance in high-altitude regions (e.g., volatility of traffic volume, seasonality of maintenance timing, and fragility of the ecological environment), we aim to derive optimal monthly maintenance plans. We develop a multi-objective nonlinear optimization model that comprehensively accounts for minimizing maintenance costs, affected traffic volume and carbon emissions, and maximizing pavement maintenance effectiveness. Utilizing linearization methods, the model is reconstructed into a typical mixed-integer linear programming (MILP) model, enabling it to be solved directly using conventional solvers. We consider five types of decision strategies to reflect the preferences of different decision-makers. Given the uncertainty of maintenance costs, we also utilize the robust optimization method based on the acceptable objective variation range (AOVR) to construct a robust optimization model and discuss the characteristics of optimistic, robust, and pessimistic solutions. The results suggest that different decision strategies show differences in the indicators of maintenance costs, affected traffic volume, carbon emissions, and pavement performance. When multiple decision objectives are comprehensively considered, the indicators are between the maximum and minimum values, which can effectively balance the decision needs of maintenance effectiveness, maintenance timing, and environmental protection. The number of maintenance workers, the requirement of the minimum pavement condition index (PCI), and the annual budget influence the maintenance planning. The obtained robust solution can somewhat overcome the conservative nature of the pessimistic solution. The method proposed in this paper helps address the complexities of pavement maintenance decisions in high-altitude regions and provides guidance for pavement maintenance decisions in such areas.

This study aimed to develop a framework for the assessment of the effect of subsidy policy on pavement maintenance programming using the HDM-4 tool. In this study, the financial and economic analyses were performed for four maintenance activities including routine maintenance, 50 mm overlay, microsurfacing, and crack sealing + patching +50 mm overlay. The results showed that maintenance activity type was more effective in comparison with the intervention level in optimum maintenance programming based on user cost. In other words, in terms of economic analysis, the amount of subsidy cost of each maintenance activity had an impact on their priority on the maintenance program. Also, results indicated that the subsidy policy decreases the role of the maintenance program and intervention level in the minimisation of user cost. [ABSTRACT FROM AUTHOR]

Adequate monitoring and timely treatment of partial-depth, surface-initiated cracks are essential in order to prolong the life cycle of pavement structures. Therefore, methodologies to precisely identify and assess the progression of top-down cracking (TDC) depth are fundamental. For this purpose, a calibrated model for ultrasound inspection of various types of bituminous mixtures for paving (semi-dense asphalt concrete AC-S, stone mastic asphalt SMA, and porous asphalt PA) is proposed to assess the depth and extent of top-down propagated macro-cracks in asphalt pavements. Extensive laboratory measurements were recorded on specimens of the different materials, thicknesses, temperatures and distances between sensors, with varying crack depths. The laboratory results were compared with in situ tests and with the theoretical approach. The application of the calibrated model allowed an appraisal of the depth of TDC with sufficient precision for a routine practical application regarding pavement maintenance. This non-destructive, inexpensive and easy-to-implement methodology for on-site identification and evaluation of TDC depth in asphalt pavements ensures straightforward implementation on non-prepared surfaces and provides sufficient reliability in the laboratory and on in-service pavements. [ABSTRACT FROM AUTHOR]

Accurate identification of factors that primarily affect the number of low-temperature cracks is crucial for selection of road materials and planning of pavement maintenance. Field investigations of low-temperature cracks were performed in the years 2014 and 2020 on the same 68 road sections being in service in typical traffic conditions. The collected data were statistically analysed using the ordered logistic regression model. Comparison of the odds ratios which were calculated on the basis of the model enabled ordering of the selected factors from those having the greatest effect on low-temperature cracking of pavements to those with the least influence: (1) pavement age, (2) type of asphalt concrete, (3) modification of bitumen, (4) climatic zone (on the basis of low performance grade temperature). The odds of a section belonging to the group of cracked sections decreased by half when a polymer-modified bitumen was used in its binder course and asphalt base. Regardless of the considered factors, the odds of a pavement section being classified into the group of heavily cracked sections are comparable. It means that some external factors, including quality of paving works and bitumen chemistry, may prove the most crucial. [ABSTRACT FROM AUTHOR]

Decision makers have different attitudes towards risks and opportunities of maintenance and rehabilitation (M&R) strategies. However, most existing pavement management studies simply assumed the neutral attitudes of decision makers. The available risk-based network-level M&R optimisation research equated risk with uncertainty which is actually different. Hence, this study aims to develop a method to quantitatively incorporate decision makers' attitudes towards risk and opportunity into network-level pavement maintenance planning. Quantitative criteria were developed and incorporated into the maintenance optimisation model. A multi-objective optimisation (MOO) model was established to explore the trade-offs between expected returns, risks, and opportunities. The proposed methods were applied to a real-world highway network as a demonstration. The results show that budget increases can simultaneously reduce expected total costs and downside risks and increase upside potential by up to 0.41%, 5.26%, and 0.92%, respectively, for each 1% increase in current year's budget, but their marginal effects are diminishing. Risk reduction requires compromising the expected performance and upside potential of the M&R strategy. The solutions derived from the mean-semivariance model dominate those from the mean-variance model. The outcomes of this study provide decision-makers with ways to incorporate their attitudes into maintenance optimisation, thereby reducing risk exposure and exploiting potential opportunities. [ABSTRACT FROM AUTHOR]

The processes in the pavement life cycle can be defined as: material extraction and production; construction; transport of materials and demolition; the use stage, where the pavement interacts with other systems; the materials, construction, and transport associated with maintenance and rehabilitation; and end-of-life. Local governments are increasingly being asked to quantify greenhouse gas emissions from their operations and identify changes to reduce emissions. There are many possible strategies that local governments can choose to reduce their emissions, however, prioritization and selection of which to implement can be difficult if emissions cannot be quantified. Pavement life cycle assessment (LCA) can be used by local governments to achieve the same goals as state government. The web-based software environmental Life Cycle Assessment for Pavements, also known as eLCAP has been developed a project-level LCA tool. The goal of eLCAP is to permit local governments to perform project-level pavement LCA using California specific data, including consideration of their own designs, materials, and traffic. eLCAP allows modeling of materials, transport, construction, maintenance, rehabilitation, and end-of-life recycling for all impacts; and in the use stage it considers the effects of combustion of fuel in vehicles as well as the additional fuel consumed due to pavement-vehicle interaction (global warming potential only). This report documents eLCAP and a project that created an interface for eLCAP that is usable by local governments.

Cold mix patching materials (CMPMs) are commonly used as an alternative to hot mix asphalt (HMA) for repairing road pavement potholes, especially in cold and wet seasons. Currently, the acceptance criteria and expectations for the mechanical strength and durability of CMPMs are often compared or related to those adopted for traditional HMA pavements without considering the specific performance characteristics of CMPMs required to ensure their effectiveness. In response to this, the authors proposed innovative solutions derived from pre-existing methodologies to evaluate CMPMs. Several parameters, indicative of CMPMs' structural and functional performance, were measured to optimize the mix design and systematize the quality assurance/quality control (QA/QC) process at various service life stages and under different boundary conditions. Besides the standard Marshall stability and indirect tensile strength (ITS), which were chosen as references, Hubbard-Field and indentation stability tests were reintroduced, suggesting modified procedures specifically tailored to CMPM stability evaluation. Similarly, brush, Leutner, and locking point test methods were customized for analyzing the raveling potential, bonding properties, and workability of the patching materials. The research outcome showed that Hubbard-Field and indentation stability tests provided more accurate stability assessments, even for materials unsuitable for testing using Marshall stability and ITS methods. Modified brush and Leutner tests effectively replicated real-world conditions, enhancing the relevance of laboratory findings to practical applications. Furthermore, the locking point method enabled mixture workability classification. These findings contribute to a better understanding of CMPM performance and can inform more targeted and effective road repair strategies.

The demand for preserving existing roadway infrastructure has been increasing to regulate expensive reconstruction activities. The maintenance of homogeneous road sections is one of the approaches to economize the overall management of pavement systems. The existing homogeneous delineation methods consider one or two parameters for segmenting the pavements based on similar characteristics, which are found to be a repetitive process. Also, there is a need to consider multiple parameters that represent the functional, structural, and traffic characteristics in segmentation process. Therefore, the objective of this study was to develop a multi-parameter-based delineation approach (MPDA) to segment the pavements into subsections with similar features considering functional, structural, and traffic characteristics. Deflection bowl parameters, unified pavement health index (functional performance metric), surface layer modulus, and traffic reported in terms of AADT were employed for developing a multi-parametric delineation index (MPDI). A total of 1781 datapoints covering 26 road sections in the State of Andhra Pradesh, India, were used. The C-charts method-based segmentation for MPDI was applied to obtain the homogeneous sections. The devised approach was found to be efficient in segmenting the pavements as well as robust in selecting suitable maintenance strategies for each group of the homogeneous sections. Further, the segmentation processes were automated for easier implementation by the agencies. [ABSTRACT FROM AUTHOR]

When choosing the pavement maintenance scheme, the actual pavement condition cannot be truthfully reflected by traditional fixed indexes and weights, making accurately evaluating the pavement condition particularly important. This study aims to analyze the relationship between international roughness index (IRI) and pavement condition index (PCI), and propose the IRI-based pavement condition rating to improve the correctness of the maintenance decision. Based on the inspection data and maintenance data of large and medium-sized repair and maintenance projects in Shaanxi Province, representative road sections in northern Shaanxi, The Central Shaanxi Plain, and southern Shaanxi were selected to establish a simple model to examine the relationship between PCI and IRI. The model development data were selected from 312 different road sections, including 1665 data points. The model validation data were selected from 140 road sections, including 333 data points. The road sections selected for validation data and those for development data had the same data range. Results demonstrate that, the S-function could best express the relationship between PCI and IRI. The function established by data fitting had a high coefficient of determination (R2=0.959), and the deviation of the predicted IRI value was low. The model validation of different datasets all yielded relatively more accurate predictions (R2=0.973). Finally, the IRI-based pavement condition rating was proposed, and the pavement condition was divided into five grades, namely, excellent, good, fair, poor, and very poor, by using IRI detection data. This proposed rating provides double verification of the pavement condition with PCI rating and improves the correctness of the maintenance decision. [ABSTRACT FROM AUTHOR]

With the extension of road service life, cracks are the most significant type of pavement distress. To monitor road conditions and avoid excessive damage, pavement crack detection is absolutely necessary and an indispensable part of road periodic maintenance and performance assessment. The development and application of computer vision have provided modern methods for crack detection, which are low in cost, less labor-intensive, continuous, and timely. In this paper, an intelligent model based on a target detection algorithm in computer vision was proposed to accurately detect and classify four classes of cracks. Firstly, by vehicle-mounted camera capture, a dataset of pavement cracks with complicated backgrounds that are the most similar to actual scenarios was built, containing 4007 images and 7882 crack samples. Secondly, the YOLOv5 framework was improved from the four aspects of the detection layer, anchor box, neck structure, and cross-layer connection, and thereby the network’s feature extraction capability and small-sized-target detection performance were enhanced. Finally, the experimental results indicated that the proposed model attained an AP of the four classes of 81.75%, 83.81%, 98.20%, and 92.83%, respectively, and a mAP of 89.15%. In addition, the proposed model achieved a 2.20% missed detection rate, representing a 6.75% decrease over the original YOLOv5. These results demonstrated the effectiveness and practicality of our proposed model in addressing the issues of low accuracy and missed detection for small targets in the original network. Overall, the implementation of computer vision-based models in crack detection can promote the intellectualization of road maintenance.

Cold-in-place recycling with foamed asphalt binders (CIR-F) has been considered as an environmentally sustainable option for asphalt pavement maintenance due to the low mixing temperature and the reuse of up to 100% of existing asphalt materials. However, the quantitative assessment of its economic and environmental performance from a life cycle perspective has rarely been addressed in the literature. To fill in the gap, this study conducted cradle-to-laid life cycle assessment (LCA) and life cycle cost analysis (LCCA) to estimate the annual energy consumption, carbon emissions, and equivalent uniform annual cost (EUAC) of CIR-F and compared it with the traditional mill & fill (M&F). The considered life cycle stages include raw material production, construction, transportation, and work zone traffic management. The heat map visualization technique was employed to explore the influence of service life on the relative environmental and economic advantages of CIR-F. The results show that both economic and environmental impacts of CIR-F were significantly reduced compared to conventional M&F, with 66.45% less cost, 59.62% less energy consumption and 45.73% less carbon emissions. Reducing the service life of CIR-F will reduce, or even completely offset, its benefits over M&F. Assuming a service life of 15 years for conventional M&F, the service life of CIR-F needs to be greater than 8 years to maintain its environmental advantage. The results of this study provide a quantitative assessment of the economic and environmental benefits of CIR-F, providing evidence for its broader application.

Pavement maintenance is widely thought to be critical for promoting sustainability, playing a pivotal role in sustainable and resilient transportation infrastructure for growth in economic development and improvements in social inclusion. It has attracted increasing attention from both academia and industry over the past 20 years. Although several literature reviews have been conducted, there is still a lack of systematic quantitative and visual investigation of the structure and evolution of knowledge in this field. To address this lack, reported here is a comprehensive and objective scientometric analysis to visualize the status quo of research areas regarding pavement maintenance. Focusing on 614 journal articles collected from the Web of Science for 2001–2020, key researchers within the field are identified, as are the key research institutions, key countries, and their interconnections, as well as keywords, evolution trends, key publications, and citation patterns, along with the extent to which these interact with each other in research networks. Based on the in-depth analysis, a knowledge roadmap is provided to inscribe how pavement maintenance-related research evolves over time, greatly contributing to the understanding of the underlying structure of pavement maintenance, and to highlight the identified current research challenges and future research trends, thus potentially benefiting the academic community and practice field on multiple themes of pavement maintenance. The results of this research are instructive, providing a broad overview and holistic thinking for researchers and practitioners with respect to pavement maintenance research, as well as facilitating further research and applications for both academia and industry in improving pavement maintenance for sustainability. [ABSTRACT FROM AUTHOR]

Pavement maintenance and repair is a crucial part of pavement management systems. Accurate and reliable pavement performance prediction is the prerequisite for making reasonable maintenance decisions and selecting suitable repair schemes. Rutting deformation, as one of the most common forms of asphalt pavement failures, is a key index for evaluating the pavement performance. To ensure the accuracy of the commonly used prediction models, the input parameters of the models need to be understood, and the coefficients of the models should be locally calibrated. This paper investigates the prediction of the rutting development of pavements with flexible overlays based on the data of the Canadian Long-Term Pavement Performance (C-LTPP) program. Pavement performance data that may be related to rutting were extracted from the survey of Dipstick for data analysis. Then, an artificial neural network (ANN) was adopted to analyze the factors affecting the rut depth, and to establish a model for the rutting development of pavements with flexible overlays. The results of the sensitivity analysis indicate that rutting is not only affected by traffic and climatic conditions, but it is also greatly affected by the thickness of the surface layer and voids in the mixture. Finally, a rutting evaluation index was provided to describe the rutting severity, and the threshold of the pavement maintenance time was proposed based on the prediction results. These results provide a basis for predicting rut development and pavement maintenance. [ABSTRACT FROM AUTHOR]

Construction and demolition waste (CDW) materials account for a considerable part of waste materials throughout the world. As these materials are not usually recycled, reusing them in construction projects is of major significance. In this study, recycled concrete, bricks, and glass were used as 100% aggregates of chip seal, which is a corrective or preventive pavement maintenance method. A cationic rapid setting (CRS-2) bitumen emulsion was also used to prepare the chip seal. Different tests, including the sand patch test, sweep test, British pendulum tester (BPT), interface bond, and Vialit test, were conducted. The results of these tests revealed that all these materials had sufficient aggregate embedment for vehicle speeds of more than 70 km/h, and the number of chips was less than 10%, indicating their good performance. All developed chip seals ranked as high skid resistance pavement at ambient temperature. The chip seals developed with concrete and glass showed the best adhesion with an asphalt pavement surface and an aggregate–bitumen adhesion at very cold and ambient temperatures due to the fact of their chemical compositions. Overall, using concrete aggregates to develop chip seals under different traffic loads is recommended. Finally, these findings can provide a novel approach for recycling CDW materials with low costs. [ABSTRACT FROM AUTHOR]

Introduction. One of the most significant tasks facing road experts is to maintain the transport network in good condition. The process of selecting an appropriate approach to providing such condition is quite complex since it requires considering many parameters, such as the existing condition of the pavement, road category, weather conditions, traffic volume, etc. Recently, the rising trend of digitization in the industry has contributed to the use of artificial intelligence to address problems in several fields, including the bodies in charge of operational control over the status of roadways. Within the context of any control system, the main task of the control system is to carry out reliable forecasting of the operational state of the road in the medium and long term.Materials and Methods. This study investigated the possibility of using artificial neural networks to assess existing pavement characteristics and their potential application in developing road maintenance strategies. A back-propagation neural network was implemented, trained using data from 1,614 investigated sections of the M4 «DON» highway in the road network of the Russian Federation in the period from 2014 to 2018. Several models were developed and trained using the MATLAB application, each with a different number of neurons in the hidden layers.Results. The results of the models showed a convergence between the inferred paving state values and the actual values, as the multiple correlation coefficient (R2) values exceeded 92 % for most of the models during all learning stages.Discussion and Conclusions. The findings suggest that public road authorities may utilize the established models to choose the best road maintenance strategy and assign the most efficient steps to restore road bearing capacity and operation.

Recent technological developments have attracted the use of machine learning technologies and sensors in various pavement maintenance and rehabilitation studies. To avoid excessive road damages, which cause high road maintenance costs, reduced mobility, vehicle damages, and safety concerns, the periodic maintenance of roads is necessary. As part of maintenance works, road pavement conditions should be monitored continuously. This monitoring is possible using modern distress detection methods that are simple to use, comparatively cheap, less labor-intensive, faster, safer, and able to provide data on a real-time basis. This paper proposed and developed two models: computer vision and sensor-based. The computer vision model was developed using the You Only Look Once (YOLOv5) algorithm for detecting and classifying pavement distresses into nine classes. The sensor-based model combined eight Controller Area Network (CAN) bus sensors available in most new vehicles to predict pavement distress. This research employed an extreme gradient boosting model (XGBoost) to train the sensor-based model. The results showed that the model achieved 98.42% and 97.99% area under the curve (AUC) metrics for training and validation datasets, respectively. The computer vision model attained an accuracy of 81.28% and an F1-score of 76.40%, which agree with past studies. The results indicated that both computer vision and sensor-based models proved highly efficient in predicting pavement distress and can be used to complement each other. Overall, computer vision and sensor-based tools provide cheap and practical road condition monitoring compared to traditional manual instruments. [ABSTRACT FROM AUTHOR]

Traffic loads and environmental factors cause various forms of distress on road pavements (cracks, depressions, potholes, ruts, etc.). Depressions and ruts produce localized variations of longitudinal and cross slopes, which are very hazardous for drivers, especially during rain. In such conditions, these defects alter the surface water path, creating abnormal water accumulations and significant water film depths to induce aquaplaning risk. In current practice, in preliminary analysis phases and at the network scale, the control of road surfaces is carried out with expeditious techniques and with synthetic indicators, e.g., pavement condition index (PCI), through which a quality judgment related to the detected distresses on the pavement surface, is given. In truth, the detection of specific defects (ruts and depressions) should also include further analyses to evaluate the hydraulic efficiency of the carriageway related to their severity. Therefore, in this paper, a synthetic indicator called Hydraulic Condition Index (HCI) is proposed for evaluating the hydraulic quality of road pavement surfaces. This index is related to the hydrologic conditions of the site, the pavement characteristics, and the defects that can alter the flow of water on the carriageway, determining and increasing the risk of aquaplaning. The methodological framework is discussed by means of some numerical applications developed for different road typologies according to their functional classification. The final aim is to provide road agencies with another solution to evaluate road quality and ensure safer roads for users. The methodological framework for evaluating the HCI may be adopted by the road agencies for the network-scale priority ranking of road segments maintenance needs also involving safety traffic conditions. [ABSTRACT FROM AUTHOR]

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In recent years, the growing survey capability for continuous and frequent inspections of road pavement conditions has raised questions regarding the optimal solutions for data management. Considering traditional approaches, classical Pavement Management Systems seem inadequate for exploiting the potential of similar data, especially for visualization and infrastructure modelling. The development of BIM procedures may be helpful although the existing solutions are not ready for handling road condition and maintenance management. Furthermore, the recent international regulations gradually adopt BIM in all infrastructure projects, as reference model. Then, as a preliminary approach, in this paper, a solution for presenting and elaborating pavement condition information in an I-BIM environment is proposed. This procedure simplifies representation and elaboration of survey information regarding structural and functional conditions of pavements in a realistic 3D environment, exploiting smart object potential. Specific routines have been coded for upgrading and elaborating specific extended attributes related to pavement condition. This approach represents an important evolution compared to traditional methodologies, as a modern and 'user-friendly' decision support tool for road agencies, combining in a single environment all the life cycle phases of the infrastructure. The proposed approach has been tested, in a preliminary form, on an existing motorway, with positive results. [ABSTRACT FROM AUTHOR]

The demand for preserving existing roadway infrastructure has been increasing to regulate expensive reconstruction activities. The maintenance of homogeneous road sections is one of the approaches to economize the overall management of pavement systems. The existing homogeneous delineation methods consider one or two parameters for segmenting the pavements based on similar characteristics, which are found to be a repetitive process. Also, there is a need to consider multiple parameters that represent the functional, structural, and traffic characteristics in segmentation process. Therefore, the objective of this study was to develop a multi-parameter-based delineation approach (MPDA) to segment the pavements into subsections with similar features considering functional, structural, and traffic characteristics. Deflection bowl parameters, unified pavement health index (functional performance metric), surface layer modulus, and traffic reported in terms of AADT were employed for developing a multi-parametric delineation index (MPDI). A total of 1781 datapoints covering 26 road sections in the State of Andhra Pradesh, India, were used. The C-charts method-based segmentation for MPDI was applied to obtain the homogeneous sections. The devised approach was found to be efficient in segmenting the pavements as well as robust in selecting suitable maintenance strategies for each group of the homogeneous sections. Further, the segmentation processes were automated for easier implementation by the agencies.