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In the field of computer vision, the detection of road potholes at night represents a critical challenge in enhancing the safety of intelligent transportation systems. Ensuring road safety is of paramount importance, particularly in promptly repairing pothole issues. These abrupt road depressions can easily lead to vehicle skidding, loss of control, and even traffic accidents, especially when water has pooled in or submerged the potholes. Therefore, the detection and recognition of road potholes can significantly reduce vehicle damage and the incidence of safety incidents. However, research on road pothole detection lacks high-quality annotated datasets, particularly under low-light conditions at night. To address this issue, this study introduces a novel Nighttime Pothole Dataset (NPD), independently collected and comprising 3831 images that capture diverse scene variations. The construction of this dataset aims to counteract the insufficiency of existing data resources and strives to provide a richer and more realistic benchmark. Additionally, we develop a baseline detector, termed WT-YOLOv8, for the proposed dataset, based on YOLOv8. We also evaluate the performance of the improved WT-YOLOv8 method and eight state-of-the-art object detection methods on the NPD and the COCO dataset. The experimental results on the NPD demonstrate that WT-YOLOv8 achieves a 2.3% improvement in mean Average Precision (mAP) over YOLOv8. In terms of the key metrics—AP@0.5 and AP@0.75—it shows enhancements of 1.5% and 2.8%, respectively, compared to YOLOv8. The experimental results provide valuable insights into each method's strengths and weaknesses under low-light conditions. This analysis highlights the importance of a specialized dataset for nighttime pothole detection and shows variations in accuracy and robustness among methods, emphasizing the need for improved nighttime pothole detection techniques. The introduction of the NPD is expected to stimulate further research, encouraging the development of advanced algorithms for nighttime pothole detection, ultimately leading to more flexible and reliable road maintenance and road safety. [ABSTRACT FROM AUTHOR]

Ultra-thin overlays (UTOL) are a standard highway pre-maintenance method used to improve the road surface performance of asphalt pavements and to repair minor rutting and cracking. However, the thin thickness makes it very sensitive to external changes, which increases its wear and shortens its life. So, this paper aims to prepare a durable and skid-resistance asphalt ultra-thin overlay using epoxy asphalt (EA) and steel slag. First, the physical properties of EA were characterized by penetration, softening point, flexibility, and kinematic viscosity tests. The dynamic shear rheometer (DSR) test characterizes EA's rheological properties. Differential Scanning Calorimetry (DSC), kinematic viscosity, and Fourier transform infrared spectroscopy (FTIR) characterized the EA's curing process. Finally, the pavement performance of an epoxy ultra-thin overlay (EUTOL) prepared with EA and steel slag was tested. The results show that the epoxy resin particles increase with the increase in epoxy resin dosage, and at 40%, its epoxy particles are uniformly distributed with the most significant area share. With the addition of epoxy resin, the needle penetration of EA decreases and then increases, the flexibility decreases at a slower rate, and the softening point rises significantly. Moreover, the growth of the elastic component in EA significantly improved the high-temperature viscoelastic properties. Considering its physical and rheological properties, the optimal doping amount of 40% was selected. By analyzing the curing behavior of EA (optimum dosage), the combination temperature of EA is 150 °C, which meets the needs of mixing and paving asphalt mixtures. After 12 h of maintenance at 120 °C, its reaction is sufficient. The skid-resistance durability, high-temperature, low-temperature, water stability, and fatigue resistance of UTOL can be effectively improved using steel slag coarse aggregate. [ABSTRACT FROM AUTHOR]

Global trade depends on long-haul transportation, yet comfort for drivers on lengthy trips is sometimes neglected. Rough roads have a major negative influence on driver comfort and increase the risk of weariness, distracted driving, and accidents. Using Random Forest regression, a machine learning technique well-suited to examining big datasets and nonlinear relationships, this study examines the relationship between road roughness and driver comfort. Using the MIRANDA mobile application, data were gathered from 1,048,576 rows, including vehicle acceleration and values for the International Roughness Index (IRI). The Support Vector Regression (SVR) and XGBoost models were used for comparative analysis. Random Forest was preferred because of its ability to be deployed in real time and use less memory, even if XGBoost performed better in terms of training time and prediction accuracy. The findings showed a significant relationship between driver discomfort and road roughness, with rougher roads resulting in increased vertical acceleration and lower comfort levels (Road Roughness: SD—0.73; Driver's Comfort: Mean—10.01, SD—0.64). This study highlights how crucial it is to provide smooth surfaces and road maintenance in order to increase road safety, lessen driver weariness, and promote long-haul driver welfare. These results offer information to transportation authorities and policymakers to help them make data-driven decisions that enhance the efficiency of transportation and road conditions. [ABSTRACT FROM AUTHOR]

We aimed to analyze the natural population characteristics and habitat growth conditions of the valuable seaweed Silvetia siliquosa. Its population characteristics and habitat conditions were assessed monthly from May 2022 to April 2023 and April to August 2022, respectively, on selected habitats. The average population density, coverage, and frequency of S. siliquosa were 579 ± 94.18 ind./m2, 27.82 ± 6.92%/m2, and 78.37 ± 5.98/m2, respectively. The average thallus length and width were 47.53 ± 4.35 and 46.33 ± 4.17 mm, respectively, while the branch width, thickness, and frequency were 2.35 ± 0.03 mm, 0.59 ± 0.12 mm, and 2.8 ± 0.2 times, with a receptacle length and width of 24.13 ± 2.07 and 2.81 ± 0.19 mm, respectively. Among the 40 previously known natural habitats of S. siliquosa, growth was confirmed only in Sepo, Sebang, and Bangpo. The causes for the declining S. siliquosa populations could be attributed to habitat changes due to construction, coastal road maintenance projects, habitat disturbances, and increased pollutants. Habitat substrate disturbances and changes were the main causes of the decrease in S. siliquosa growth. Studies on environmental factors and habitat degradation, growth related to environmental factors, mass cultivation, and the marine ecosystem restoration of S. siliquosa are needed. [ABSTRACT FROM AUTHOR]

Engineering vehicles play a vital role in supporting construction projects. However, due to their substantial size, heavy tonnage, and significant blind spots while in motion, they present a potential threat to road maintenance, pedestrian safety, and the well-being of other vehicles. Hence, monitoring engineering vehicles holds considerable importance. This paper introduces an engineering vehicle detection model based on improved YOLOv6. First, a Swin Transformer is employed for feature extraction, capturing comprehensive image features to improve the detection capability of incomplete objects. Subsequently, the SimMIM self-supervised training paradigm is implemented to address challenges related to insufficient data and high labeling costs. Experimental results demonstrate the model's superior performance, with a m A P 50 : 95 value of 88.5% and m A P 50 value of 95.9% on the dataset of four types of engineering vehicles, surpassing existing mainstream models and proving its effectiveness in engineering vehicle detection. [ABSTRACT FROM AUTHOR]

During road construction, the accuracy of compaction work is critical for the structural stability and maintenance of the road. Although the plate load test (PLT) is commonly used for quality inspections, it is impractical to test every section due to time and cost constraints. Therefore, simpler testing methods are being extensively developed. This study compared quality inspection results using the commonly used PLT, the relatively simple dynamic cone penetrometer test (DCPT), the lightweight deflectometer (LWD) test, and an intelligent quality control system equipped with accelerometer and global positioning system (GPS) sensors in intelligent compaction (IC) rollers. The results showed a strong correlation between the conventional tests (PLT, DCPT, and LWD) and the values obtained from the intelligent quality control system. The correlation analysis between the intelligent quality control system and PLT, LWDT, and DCPT yielded R-square values of 0.69, 0.91, and 0.95, respectively, indicating significantly high correlations. The implementation of intelligent quality management systems in earthwork construction projects will facilitate a thorough verification of the compaction quality throughout all construction segments, ensuring consistent compaction across the project. By enabling real-time data acquisition and analysis, these systems differ markedly from traditional methods, reducing the frequency and necessity of manual inspections. This approach not only streamlines construction processes, but also enhances operational efficiency. As a result, integrating these intelligent systems is anticipated to significantly increase productivity by optimizing the workflow and resource utilization in earthwork construction. [ABSTRACT FROM AUTHOR]

In view of the climate crisis, green technologies should be used to ensure sustainable structures in the construction industry. Road construction could also contribute to the creation of a circular economy, as it is partly responsible for several current environmental phenomena, such as greenhouse gas (GHG) emissions and depletion of natural aggregates and landfills. The use of Reclaimed Asphalt Pavement (RAP) is considered one of these recycling solutions, as it can be reused in road construction projects. Implementing it in practice is already a topic that should be included in the technical guidelines for road construction and maintenance. Therefore, this study is a critical overview of the worldwide experiences with the installation of RAP in road pavements published by different road authorities worldwide, aiming to prove the inconsistency in using these materials in pavement courses. The results of this review are analyzed to identify possible knowledge gaps regarding RAP content. It was shown that the use of RAP is different on all five continents. The main findings were that the RAP content in asphalt layers is still at a low level of about 30% and that the use of RAP materials in unbound layers in road pavement construction is not yet fully accepted. It is expected that the results of this study will help to improve further research on the performance of RAP and motivate more countries to develop appropriate guidelines for the use of RAP materials in road pavement construction. [ABSTRACT FROM AUTHOR]

Traditional cement epoxy pavements suffer from inherent limitations such as terrible tribological properties, poor wear resistance, and weak impact resistance, presenting significant challenges to ensure the safety and continuous operation of urban roads. As a solution, high-performance cement epoxy composite grouting materials have emerged as the preferred option for engineering construction and road maintenance. In this study, CSP/epoxy cement (CSEC) composite materials were prepared by emulsion polymerization. The thermal properties of the materials were characterized, revealing that CSP enhances the thermal properties of epoxy cement (EC) to a certain extent. Furthermore, the frictional properties of CSEC composite materials and pure epoxy cement under different normal loads were investigated. The results indicated that the CSEC composite material exhibited a slight increase in friction coefficient and a notable decrease in wear rate compared to pure epoxy cement (EC). Specifically, the wear rate of CSEC decreased by 14.4% at a load of 20 N, highlighting the enhanced frictional performance facilitated by CSP. Mechanistic analysis attributed the improvement to the unique core-shell structure of CSP, which imparted higher impact resistance and eliminated alleviate residual stresses at the friction interface. This structural advantage further enhanced the wear resistance of materials, making it a promising choice for improving the durability and safety of urban road surfaces. [ABSTRACT FROM AUTHOR]

The construction and maintenance of road infrastructure is required for the sustained economic growth of communities and societies. Nonetheless, these activities imply the tangible risk of boosting the depletion of non-renewable resources (e.g., aggregates and binders). A widely used strategy for preserving as much of these natural resources as possible is the design of high-performance composite materials. For instance, antistripping agents (ASAs) are employed to mitigate the loss of adhesive bonding between asphalt binders and aggregates, enhancing the mechanical behaviour of hot-mix asphalts (HMAs). There is still no consensus on the effectiveness of ASAs. In this regard, the present research aims to contribute to the literature by conducting a case study on the influence of three different ASAs (hydrated lime, an amines-based liquid additive, and a silanes-based liquid additive) on the moisture susceptibility, stiffness, and rutting resistance of HMA. For these purposes, indirect tensile strength, indirect tensile stiffness modulus, and uniaxial cyclic compression tests were carried out. Overall, the involved experimental protocol drew the main conclusion that the incorporation of hydrated lime as a mineral filler (at a content of 1.68% by dry weight of aggregates) is capable of improving the mechanical performance of HMAs through decreases in humidity sensitivity and permanent deformation, together with a slight increase in rigidity. [ABSTRACT FROM AUTHOR]

Detection of pavement diseases is crucial for road maintenance. Traditional methods are costly, time-consuming, and less accurate. This paper introduces an enhanced pavement disease recognition algorithm, MS-YOLOv8, which modifies the YOLOv8 model by incorporating three novel mechanisms to improve detection accuracy and adaptability to varied pavement conditions. The Deformable Large Kernel Attention (DLKA) mechanism adjusts convolution kernels dynamically, adapting to multi-scale targets. The Large Separable Kernel Attention (LSKA) enhances the SPPF feature extractor, boosting multi-scale feature extraction capabilities. Additionally, Multi-Scale Dilated Attention in the network's neck performs Spatially Weighted Dilated Convolution (SWDA) across different dilatation rates, enhancing background distinction and detection precision. Experimental results show that MS-YOLOv8 increases background classification accuracy by 6%, overall precision by 1.9%, and mAP by 1.4%, with specific disease detection mAP up by 2.9%. Our model maintains comparable detection speeds. This method offers a significant reference for automatic road defect detection. [ABSTRACT FROM AUTHOR]

Road extraction from high-resolution remote sensing images (HRSIs) is one of the tasks in image analysis. Deep convolutional neural networks have become the primary method for road extraction due to their powerful feature representation capability. However, roads are often obscured by vegetation, buildings, and shadows in HRSIs, resulting in incomplete and discontinuous road extraction results. To address this issue, we propose a lightweight post-processing network called RIRNet in this study, which include an information inference module and a road direction inference task branch. The information inference module can infer spatial information relationships between different rows or columns of feature images from different directions, effectively inferring and repairing road fractures. The road direction inference task branch performs the road direction prediction task, which can constrain and promote the road extraction task, thereby indirectly enhancing the inference ability of the post-processing model and realizing the optimization of the initial road extraction results. Experimental results demonstrate that the RIRNet model can achieve an excellent post-processing effect, which is manifested in the effective repair of broken road segments, as well as the handling of errors such as omission, misclassification, and noise, proving the effectiveness and generalization of the model in post-processing optimization. [ABSTRACT FROM AUTHOR]

Winter continues to witness numerous automobile accidents attributed to graupel and hail precipitation in Japan. Detecting these weather phenomena using radar technology holds promise for reducing the impact of such accidents and improving road maintenance operations. Weather radars operating at different frequencies, such as C- and X-band, prove effective in graupel detection by analyzing variations in backscattered signals within the same radar volume. When particle diameters exceed 5 mm, the study of Mie scattering characteristics across different melting ratios reveals insights. The dual frequency ratio (DFR) shows potential for graupel detection. The DFR presents wider variations with ten-times difference in melting ratios with increased density, offering opportunities for precise detection. Additionally, the DFR amplitude rises with temperature changes. However, for hydrometeor diameters below approximately 3 mm, and within the Rayleigh region, the DFR exhibits minimal fluctuations. Hence, this technique is best suited for diameters exceeding 3 mm for optimal efficacy. Additionally, a "detection alert" for graupel/hail has been proposed. Based on this alert, and with realistic rain/graupel size distributions, graupel/hail can be detected with an approximate probability of 70%. [ABSTRACT FROM AUTHOR]

Roads play a crucial role in urban transportation by facilitating the movement of materials within a city. The condition of road surfaces, such as damage and road facilities, directly affects traffic flow and influences decisions related to urban transportation maintenance and planning. To gather this information, we propose the Detecting and Clustering Framework for sensing road surface conditions based on crowd-sourced trajectories, utilizing various sensors (GPS, orientation sensors, and accelerometers) found in smartphones. Initially, smartphones are placed randomly during users' travels on the road to record the road surface conditions. Then, spatial transformations are applied to the accelerometer data based on attitude readings, and heading angles are computed to store movement information. Next, the feature encoding process operates on spatially adjusted accelerations using the wavelet scattering transformation. The resulting encoding results are then input into the designed LSTM neural network to extract bump features of the road surface (BFRSs). Finally, the BFRSs are represented and integrated using the proposed two-stage clustering method, considering distances and directions. Additionally, this procedure is also applied to crowd-sourced trajectories, and the road surface condition is computed and visualized on a map. Moreover, this method can provide valuable insights for urban road maintenance and planning, with significant practical applications. [ABSTRACT FROM AUTHOR]

Monitoring pavement conditions is critical to pavement management and maintenance. Traditionally, pavement distress is mainly identified via accelerometers, videos, and laser scanning. However, the geographical coverage and temporal frequency are constrained by the limited amount of equipment and labor, which sometimes may delay road maintenance. By contrast, crowdsourced data, in a manner of crowdsensing, can provide real-time and valuable roadway information for extensive coverage. This study exploited crowdsourced Waze pothole and weather reports for pavement condition evaluation. Two surrogate measures are proposed, namely, the Pothole Report Density (PRD) and the Weather Report Density (WRD). They are compared with the Pavement Quality Index (PQI), which is calculated using laser truck data from the Tennessee Department of Transportation (TDOT). A geographically weighted random forest (GWRF) model was developed to capture the complicated relationships between the proposed measures and PQI. The results show that the PRD is highly correlated with the PQI, and the correlation also varies across the routes. It is also found to be the second most important factor (i.e., followed by pavement age) affecting the PQI values. Although Waze weather reports contribute to PQI values, their impact is significantly smaller compared to that of pothole reports. This paper demonstrates that surrogate pavement condition measures aggregated by crowdsourced data could be integrated into the state decision-making process by establishing nuanced relationships between the surrogated performance measures and the state pavement condition indices. The endeavor of this study also has the potential to enhance the granularity of pavement condition evaluation. [ABSTRACT FROM AUTHOR]

In maintaining roads and ensuring safety, promptly detecting and repairing pavement defects is crucial. However, conventional detection methods demand substantial manpower, incur high costs, and suffer from low efficiency. To enhance road maintenance efficiency and reduce costs, we propose an improved algorithm based on YOLOv8. Our method incorporates several key enhancements. First, we replace conventional convolutions with a module composed of spatial-to-depth layers and nonstrided convolution layers (SPD-Conv) in the network backbone, enhancing the capability of recognizing small-sized defects. Second, we replace the neck of YOLOv8 with the neck of the ASF-YOLO network to fully integrate spatial and scale features, improving multiscale feature extraction capability. Additionally, we introduce the FasterNet block from the FasterNet network into C2f to minimize redundant computations. Furthermore, we utilize Wise-IoU (WIoU) to optimize the model's loss function, which accounts for the quality factors of objects more effectively, enabling adaptive learning adjustments based on samples of varying qualities. Our model was evaluated on the RDD2022 road damage dataset, demonstrating significant improvements over the baseline model. Specifically, with a 2.8% improvement in m A P and a detection speed reaching 43 FPS, our method proves to be highly effective in real-time road damage detection tasks. [ABSTRACT FROM AUTHOR]

To ensure the safety of vehicle travel, the maintenance of road infrastructure has become increasingly critical, with efficient and accurate detection techniques for road cracks emerging as a key research focus in the industry. The development of deep learning technologies has shown tremendous potential in improving the efficiency of road crack detection. While convolutional neural networks have proven effective in most semantic segmentation tasks, overcoming their limitations in road crack segmentation remains a challenge. To address this, this paper proposes a novel road crack segmentation network that leverages the powerful spatial feature modeling capabilities of Swin Transformer and the Encoder–Decoder architecture of DeepLabv3+. Additionally, the incorporation of a multi-scale coding module and attention mechanism enhances the network's ability to densely fuse multi-scale features and expand the receptive field, thereby improving the integration of information from feature maps. Performance comparisons with current mainstream semantic segmentation models on crack datasets demonstrate that the proposed model achieves the best results, with an MIoU of 81.06%, Precision of 79.95%, and F1-score of 77.56%. The experimental results further highlight the model's superior ability in identifying complex and irregular cracks and extracting contours, providing guidance for future applications in this field. [ABSTRACT FROM AUTHOR]

The major highway in Sri Lanka that links the capital, Colombo, with the second capital, Kandy, passes through Kadugannawa, characterized by steep hills. The geological and geomorphological setting of the terrain often leads to slope failures. The objective of this study is to interpret the key factors influencing the slope failures that occurred in close proximity at two separate locations with two different slope conditions. Typical local and regional brittle and ductile structures include fault scarps, deep-seated detachments, and variable folding. According to our results, one of the studied locations experienced translational landslides because of weakened basement rock surfaces, hydrophilic clay minerals, and anthropogenic influences, whereas the other location experienced multiple stages of mass movement influenced by inhomogeneous colluvial soil and regional, geological, and hydrogeological conditions. Based on the present study, it can be concluded that geological studies must be carried out within the local area rather than at the regional scale. Otherwise, the constructions for the prevention of landslides in complicated geological settings will fail or may not be used for a long period. Moreover, consideration of future climate change is essential when undertaking construction in challenging terrains. [ABSTRACT FROM AUTHOR]

Accurate and comprehensive detection of pavement cracks is important for maintaining road quality and ensuring traffic safety. However, the complexity of road surfaces and the diversity of cracks make it difficult for existing methods to accomplish this challenging task. This paper proposes a novel network named the global graph multiscale network (GGMNet) for automated pixel-level detection of pavement cracks. The GGMNet network has several innovations compared with the mainstream road crack detection network: (1) a global contextual Res-block (GC-Resblock) is proposed to guide the network to emphasize the identities of cracks while suppressing background noises; (2) a graph pyramid pooling module (GPPM) is designed to aggregate the multi-scale features and capture the long-range dependencies of cracks; (3) a multi-scale features fusion module (MFF) is established to efficiently represent and deeply fuse multi-scale features. We carried out extensive experiments on three pavement crack datasets. These were DeepCrack dataset, with complex background noises; the CrackTree260 dataset, with various crack structures; and the Aerial Track Detection dataset, with a drone's perspective. The experimental results demonstrate that GGMNet has excellent performance, high accuracy, and strong robustness. In conclusion, this paper provides support for accurate and timely road maintenance and has important reference values and enlightening implications for further linear feature extraction research. [ABSTRACT FROM AUTHOR]

The attributes of diversity and concealment pose formidable challenges in the accurate detection and efficacious management of distresses within subgrade structures. The onset of subgrade distresses may precipitate structural degradation, thereby amplifying the frequency of traffic incidents and instigating economic ramifications. Accurate and timely detection of subgrade distresses is essential for maintaining and repairing road sections with existing distresses. This helps to prolong the service life of road infrastructure and reduce financial burden. In recent years, the advent of numerous novel technologies and methodologies has propelled significant advancements in subgrade distress detection. Therefore, this review delineates a concentrated examination of subgrade distress detection, methodically consolidating and presenting various techniques while dissecting their respective merits and constraints. By furnishing comprehensive guidance on subgrade distress detection, this review facilitates the expedient identification and targeted treatment of subgrade distresses, thereby fortifying safety and enhancing durability. The pivotal role of this review in bolstering the construction and operational facets of transportation infrastructure is underscored. [ABSTRACT FROM AUTHOR]

The operational efficacy of lane departure warning systems (LDWS) in autonomous vehicles is critically influenced by the retro-reflectivity of road markings, which varies with environmental wear and weather conditions. This study investigated how changes in road marking retro-reflectivity, due to factors such as weather and physical wear, impact the performance of LDWS. The study was conducted at the Yeoncheon SOC Demonstration Research Center, where various weather scenarios, including rainfall and transitions between day and night lighting, were simulated. We applied controlled wear to white, yellow, and blue road markings and measured their retro-reflectivity at multiple stages of degradation. Our methods included rigorous testing of the LDWS's recognition rates under these diverse environmental conditions. Our results showed that higher retro-reflectivity levels significantly improve the detection capability of LDWS, particularly in adverse weather conditions. Additionally, the study led to the development of a simulation framework for analyzing the cost-effectiveness of road marking maintenance strategies. This framework aims to align maintenance costs with the safety requirements of autonomous vehicles. The findings highlight the need for revising current road marking guidelines to accommodate the advanced sensor-based needs of autonomous driving systems. By enhancing retro-reflectivity standards, the study suggests a path towards optimizing road safety in the age of autonomous vehicles. [ABSTRACT FROM AUTHOR]

In order to reduce the accident risk in road construction and maintenance, this paper proposes a novel solution for road-worker safety based on an untethered real-time locating system (RTLS). This system tracks the location of workers in real time using ultra-wideband (UWB) technology and indicates if they are in a predefined danger zone or not, where the predefined safe zone is delimited by safety cones. Unlike previous works that focus on road-worker safety by detecting vehicles that enter into the working zone, our proposal solves the problem of distracted workers leaving the safe zone. This paper presents a simple-to-deploy safety system. Our UWB anchors do not need any cables for powering, synchronisation, or data transfer. The anchors are placed inside safety cones, which are already available in construction sites. Finally, there is no need to manually measure the positions of anchors and introduce them to the system thanks to a novel self-positioning approach. Our proposal, apart from automatically estimating the anchors' positions, also defines the limits of safe and danger zones. These features notably reduce the deployment time of the proposed safety system. Moreover, measurements show that all the proposed simplifications are obtained with an accuracy of 97%. [ABSTRACT FROM AUTHOR]

Microwave heating is an emerging alternative pretreatment method for road maintenance in cold seasons. The thermal behavior of asphalt pavement under microwave heating is mainly determined by the complex permittivity of the asphalt mixture. In this study, an innovative approach for calculating the complex permittivity of an asphalt mixture based on a three-dimensional meso-scale heterogeneous structure was proposed. A series of experiments was conducted to verify the accuracy of this approach. The effect of porosity, void size, moisture content and aggregate gradation on the complex permittivity for an asphalt mixture were computationally analyzed based on the validated approach. Moreover, the applicability of commonly used classical dielectric models was analyzed. The classical Lichtenecker–Rother (LR) dielectric model was modified on the basis of simulation data for various conditions. The results showed that the real part of the complex permittivity decreased with the increase in porosity. Some sudden change in the imaginary part of the complex permittivity was observed within the frequency range from 2.6 GHz to 3.9 GHz. A larger air void size would lead to a larger frequency at which sudden change occurs. The real part and imaginary part of the complex permittivity tend to be smaller when more coarse aggregates are replaced with fine aggregates. Both the real part and the imaginary part of the complex permittivity increase with higher moisture content due to the stronger dielectric property of water. Each 1% increase in moisture content would lead to about a 3~4% increase in the real part of the complex permittivity. The determination coefficients R2 for the real part and the imaginary part of the complex permittivity fitted by the modified Brown model were the maximum values, which were 0.922 and 0.980, respectively. The method presented in this study is useful for transportation agencies to optimize microwave heating during winter maintenance. [ABSTRACT FROM AUTHOR]

The detection of road damage is highly important for traffic safety and road maintenance. Conventional detection approaches frequently require significant time and expenditure, the accuracy of detection cannot be guaranteed, and they are prone to misdetection or omission problems. Therefore, this paper introduces an enhanced version of the You Only Look Once version 8 (YOLOv8) road damage detection algorithm called RDD-YOLO. First, the simple attention mechanism (SimAM) is integrated into the backbone, which successfully improves the model's focus on crucial details within the input image, enabling the model to capture features of road damage more accurately, thus enhancing the model's precision. Second, the neck structure is optimized by replacing traditional convolution modules with GhostConv. This reduces redundant information, lowers the number of parameters, and decreases computational complexity while maintaining the model's excellent performance in damage recognition. Last, the upsampling algorithm in the neck is improved by replacing the nearest interpolation with more accurate bilinear interpolation. This enhances the model's capacity to maintain visual details, providing clearer and more accurate outputs for road damage detection tasks. Experimental findings on the RDD2022 dataset show that the proposed RDD-YOLO model achieves an mAP50 and mAP50-95 of 62.5% and 36.4% on the validation set, respectively. Compared to baseline, this represents an improvement of 2.5% and 5.2%. The F1 score on the test set reaches 69.6%, a 2.8% improvement over the baseline. The proposed method can accurately locate and detect road damage, save labor and material resources, and offer guidance for the assessment and upkeep of road damage. [ABSTRACT FROM AUTHOR]

Grass collected as part of roadside maintenance is conventionally subjected to composting, which has the disadvantage of generating significant CO2 emissions. Thus, it is crucial to find an alternative method for the utilisation of grass waste. The aim of this study was to determine the specific biogas yield (SBY) from the anaerobic mono-digestion of grass from road verges and to assess the content of Fatty Acid Methyl Esters (FAMEs) in grass in relation to the time of cutting and the preservation method of the studied material. The biochemical biogas potential (BBP) test and the FAMEs content were performed on fresh and ensiled grass collected in spring, summer, and autumn. The highest biogas production was obtained from fresh grass cut in spring (715.05 ± 26.43 NL kgVS−1), while the minimum SBY was observed for fresh grass cut in summer (540.19 ± 24.32 NL kgVS−1). The methane (CH4) content in the biogas ranged between 55.0 ± 2.0% and 60.0 ± 1.0%. The contents of ammonia (NH3) and hydrogen sulphide (H2S) in biogas remained below the threshold values for these inhibitors. The highest level of total FAMEs was determined in fresh grass cut in autumn (98.08 ± 19.25 mg gDM−1), while the lowest level was detected in fresh grass cut in spring (56.37 ± 7.03 mg gDM−1). C16:0 and C18:0, which are ideal for biofuel production, were present in the largest amount (66.87 ± 15.56 mg gDM−1) in fresh grass cut in autumn. The ensiling process significantly impacted the content of total FAMEs in spring grass, leading to a reduction in total saturated fatty acids (SFAs) and an increase in total unsaturated fatty acids (USFAs). We conclude that grass biomass collected during the maintenance of road verges is a valuable feedstock for the production of both liquid and gaseous biofuels; however, generating energy from biogas appears to be more efficient than producing biodiesel. [ABSTRACT FROM AUTHOR]

This study introduced an innovative approach for detecting structural anomalies in road manhole covers using structured light cameras. Efforts have been dedicated to enhancing data quality by commencing with the acquisition and preprocessing of point cloud data from real-world manhole cover scenes. The RANSAC algorithm is subsequently employed to extract the road plane and determine the height of the point cloud structure. In the presence of non-planar point cloud exhibiting abnormal heights, the DBSCAN algorithm is harnessed for cluster segmentation, aiding in the identification of individual objects. The method culminates with the introduction of a sector fitting detection model, adept at effectively discerning manhole cover features within the point cloud and delivering comprehensive height and structural information. Experimental findings underscore the method's efficacy in accurately gauging the degree of subsidence in manhole cover structures, with data errors consistently maintained within an acceptable range of 8 percent. Notably, the measurement speed surpasses that of traditional methods, presenting a notably efficient and dependable technical solution for road maintenance. [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]

Conventional human-involved maintenance methods for asphalt pavement pose significant challenges when applied to high-traffic road sections, often leading to congestion and safety risks, as well as reduced maintenance efficiency. In recent years, explorations into unmanned construction technology for newly constructed expressways have yielded beneficial and encouraging results. However, its application in road maintenance in more complex environments still needs to be expanded. In this study, an intelligent unmanned maintenance technology for asphalt pavement was applied to the Lilong Highway in Zhejiang Province, China, and the compactability, thickness, surface smoothness, permeability coefficient, and constructure depth of maintenance road sections were measured. Then, based on fuzzy comprehensive mathematics and the analytic hierarchy process, a comprehensive evaluation was performed on the intelligent unmanned maintenance technology, considering the aspects of road quality, safety, application, and socio-economic benefits. The results show that the road quality of intelligent unmanned maintenance technology can meet the road specification requirements. In addition, the membership degree of unmanned maintenance technology in the excellent grade is the highest, reaching 0.805, and the quantified value for the overall evaluation of the application effectiveness of unmanned maintenance technology is 92.10. This means that the final comprehensive evaluation result of unmanned maintenance technology is rated as excellent. The research findings provide decision-makers with valuable insights into the unmanned automation maintenance challenges faced by asphalt pavement, enabling them to implement appropriate measures to elevate the maintenance standards of road transportation. [ABSTRACT FROM AUTHOR]

Magnesium potassium phosphate cement (MKPC) is formed on the basis of acid–base reaction between dead burnt MgO and KH2PO4 in aqueous solution with K-struvite as the main cementitious phase. Due to the unique characteristics of these cements, they are suitable for special applications, especially the immobilization of radioactive metal cations and road repair projects at low temperature. However, there are few articles about the hydration mechanism of MKPC. In this study, the types, proportions and formation mechanism of MKPC crystalline phases under different magnesium to phosphorus (Mg/P) ratios were studied by means of AAS, ICP-OES, SEM, EDS and XRD refinement methods. Corresponding MD simulation works were used to explain the hydration mechanism. This study highlights the fact that crystalline phases distribution of MKPC could be adjusted and controlled by different Mg/P ratios for the design of the MKPC, and the key factor is the kinetic of K+. [ABSTRACT FROM AUTHOR]

Multi-purpose agricultural tractors are vehicles that are usually involved in different operations, including road maintenance in small villages and cultivation of small agricultural plots, particularly in mountainous areas. These vehicles typically feature narrow tracks to enhance manoeuvrability, making rollover stability a critical consideration in operational settings. This characteristic arises from the interplay between the suspension and vehicle chassis. This paper introduces a numerical multi-body model designed to replicate the dynamics of a multi-purpose tractor with a torsional chassis. Model parameters were derived through experimental measurements conducted on an actual vehicle. Static measurements were performed to assess tire and suspension stiffness while tilting tests were performed to establish the static rollover limit of the vehicle. Dynamic tests conducted on a four-post test rig characterised the vehicle's dynamics. The validated model was utilised to explore the vehicle's stability by reproducing the static rollover tests and simulating the vehicle's performance under working conditions on a banked road. The vehicle rollover stability was studied by performing a sensitivity analysis that considered both chassis torsional stiffness and suspension stiffness under different loading conditions. The results indicate a trade-off between frame and suspension stiffnesses that enhances overall vehicle stability. [ABSTRACT FROM AUTHOR]

Crack detection plays a critical role in ensuring road safety and maintenance. Traditional, manual, and semi-automatic detection methods have proven inefficient. Nowadays, the emergence of deep learning techniques has opened up new possibilities for automatic crack detection. However, there are few methods with both localization and segmentation abilities, and most perform poorly. The consistent nature of pavement over a small mileage range gives us the opportunity to make improvements. A novel data-augmentation strategy called CrackMover, specifically tailored for crack detection methods, is proposed. Experiments demonstrate the effectiveness of CrackMover for various methods. Moreover, this paper presents a new instance segmentation method for crack detection. It adopts a redesigned backbone network and incorporates a cascade structure for the region-based convolutional network (R-CNN) part. The experimental evaluation showcases significant performance improvements achieved by these approaches in crack detection. The proposed method achieves an average precision of 33.3%, surpassing Mask R-CNN with a Residual Network 50 backbone by 8.6%, proving its effectiveness in detecting crack distress. [ABSTRACT FROM AUTHOR]

Road infrastructure is essential to national security and growth. Potholes on the road surface cause accidents and costly automotive damage. Novel technology that detects potholes and alerts drivers in real-time may address this challenge. These approaches can improve road safety and lower vehicle maintenance cost in resource-constrained developing nations. This study reviews deep learning and sensor-based pothole detection approaches. An analysis shows that deep learning computer-vision-based algorithms are the most accurate, but computational and economic constraints limit their use in developing nations like Nigeria. Meanwhile, the sensor-based solutions are cost-effective and can be utilized in developing nations for pothole detection. [ABSTRACT FROM AUTHOR]

The need for clear and updated information is pivotal when authorities plan and perform routinary, periodic and emergency maintenance of both road network and their roadside assets, e.g., curbs, signals, and barriers. With particular regard to road barriers, the development of remote sensing technologies, such as Laser Imaging Detection and Ranging (LiDAR), has played a disruptive role in acquiring information, so the surveys today are predominantly automatic, faster, and less biased than the traditional (i.e., visual and manual) inventorying methodologies. However, even though they are accurate, these emerging procedures usually focus only on the surveyed elements and do not provide any other information about the surrounding environment or about the qualitative degradation of the elements. The primary objective of this research effort was to present a ranking methodology for enhancing road safety in urban contexts. Due to an innovative synthetic index which takes into account both the deterioration and the location of the surveyed elements, maintenance priority of road barriers was outlined in Bologna, Italy. All the collected information was georeferenced in a Geographic Information System (GIS) environment and hence plotted in thematic maps for an easier analysis. In addition, compliance to the norm was verified. The research was tested to provide public authorities with an effective tool in the evaluation of maintenance activities and road safety policies. [ABSTRACT FROM AUTHOR]

Timely maintenance of road pavements is crucial to ensure optimal performance. The accurate prediction of trends in pavement defects enables more efficient allocation of funds, leading to a safer, higher-quality road network. This article systematically reviews machine learning (ML) models for predicting the international roughness index (IRI), specifically focusing on flexible pavements, offering a comprehensive synthesis of the state-of-the-art. The study's objective was to assess the effectiveness of various ML techniques in predicting IRI for flexible pavements. Among the evaluated ML models, tree ensembles and boosted trees are identified as the most effective, particularly in managing data related to traffic, pavement structure, and climatic conditions, which are essential for training these models. Our analysis reveals that traffic data are present in 89% of the studies, while pavement structure and climatic factors are featured in 78%. However, maintenance and rehabilitation history appears less frequently, included in 33% of the studies. This research underscores the need for high-quality, standardized datasets, and highlights the importance of model interpretability and computational efficiency. Addressing data consistency, model interpretability, and replicability across studies are crucial for leveraging ML's full potential in fine-tuning IRI predictions. Future research directions include developing more interpretable, computationally efficient, and less complex models to maximize the impact of this research field in road infrastructure management. [ABSTRACT FROM AUTHOR]

At present, the task of maintaining and managing rural roads in China is becoming increasingly severe. To solve the problems of insufficient scheme benefits, complex feasible solutions, and low optimization efficiency in long-term maintenance planning of rural road networks under a limited budget, it is urgent to develop maintenance decision-making models and optimization methods suitable for rural roads in China. This paper focuses on the critical aspects of performance evaluation, prediction, and decision-making. Firstly, this paper proposes evaluation indicators and maintenance countermeasures suitable for rural roads, combining them with the characteristics of rural road performance degradation. Based on different treatment measure levels, RPCI and RRQI performance prediction models are established. On this basis, an improved heuristic optimization method is proposed, which realizes rapid optimization of the most cost-effective solution. Finally, the model and method proposed in this paper are applied to the case analysis of 10 rural roads in Haimen City, generating 171 optimal maintenance sections, further verifying the feasibility and effectiveness of the model. The study provides a theoretical basis for the scientific management of rural road maintenance. [ABSTRACT FROM AUTHOR]

Artificial intelligence models are currently being proposed for application in improving performance in addressing contemporary management and production issues. With the goal of automating the detection of road surface defects in transportation infrastructure management to make it more convenient, this research harnesses the advancements of the latest artificial intelligence models. Notably, new technology is used in this study to develop software that can automatically detect road surface damage, which shall lead to better results compared to previous models. This study evaluates and compares machine learning models using the same dataset for model training and performance assessment consisting of 9053 images from previous research. Furthermore, to demonstrate practicality and superior performance over previous image recognition models, mAP (mean average precision) and processing speed, which are recognized as a measure of effectiveness, are employed to assess the performance of the machine learning object recognition software models. The results of this research reveal the potential of the new technology, YOLO V5 (2023), as a high-performance model for object detection in technical transportation infrastructure images. Another significant outcome of the research is the development of an improved software named RTI-IMS, which can apply automation features and accurately detect road surface damages, thereby aiding more effective management and monitoring of sustainable road infrastructure. [ABSTRACT FROM AUTHOR]

Road defect detection is a crucial task for promptly repairing road damage and ensuring road safety. Traditional manual detection methods are inefficient and costly. To overcome this issue, we propose an enhanced road defect detection algorithm called BL-YOLOv8, which is based on YOLOv8s. In this study, we optimized the YOLOv8s model by reconstructing its neck structure through the integration of the BiFPN concept. This optimization reduces the model's parameters, computational load, and overall size. Furthermore, to enhance the model's operation, we optimized the feature pyramid layer by introducing the SimSPPF module, which improves its speed. Moreover, we introduced LSK-attention, a dynamic large convolutional kernel attention mechanism, to expand the model's receptive field and enhance the accuracy of object detection. Finally, we compared the enhanced YOLOv8 model with other existing models to validate the effectiveness of our proposed improvements. The experimental results confirmed the effective recognition of road defects by the improved YOLOv8 algorithm. In comparison to the original model, an improvement of 3.3% in average precision mAP@0.5 was observed. Moreover, a reduction of 29.92% in parameter volume and a decrease of 11.45% in computational load were achieved. This proposed approach can serve as a valuable reference for the development of automatic road defect detection methods. [ABSTRACT FROM AUTHOR]

Road defect detection is a crucial aspect of road maintenance projects, but traditional manual methods are time-consuming, labor-intensive, and lack accuracy. Leveraging deep learning frameworks for object detection offers a promising solution to these challenges. However, the complexity of backgrounds, low resolution, and similarity of cracks make detecting road cracks with high accuracy challenging. To address these issues, a novel road crack detection algorithm, termed Road Defect Detection YOLOv5 (RDD-YOLOv5), was proposed. Firstly, a model was proposed to integrate the transformer structure and explicit vision center to capture the long-distance dependency and aggregate key characteristics. Additionally, the Sigmoid-weighted linear activations in YOLOv5 were replaced with the Gaussian Error Linear Units to enhance the model's nonlinear fitting capability. To evaluate the algorithm's performance, a UAV flight platform was constructed, and experimental freebies were provided to boost inspection efficiency. The experimental results demonstrate the effectiveness of RDD-YOLOv5, achieving a mean average precision of 91.48%, surpassing the original YOLOv5 by 2.5%. The proposed model proves its ability to accurately identify road cracks, even under challenging and complex traffic backgrounds. This advancement in road crack detection technology has significant implications for improving road maintenance and safety. [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. [ABSTRACT FROM AUTHOR]

In recent decades, the international community has recognized the detrimental impact of the construction industry on the environment. In recent years, the use of recycled aggregates has attracted increasing interest as a sustainable and cost-effective solution for the construction and maintenance of road pavements. The life cycle assessment (LCA) represents a valuable methodology for evaluating the environmental sustainability of technologies involving the use of such materials. This study deals with the LCA of alternative solutions for the construction and maintenance of unpaved rural roads. Different scenarios using recycled materials, such as reclaimed asphalt and mineral sludge, are analyzed and compared to a reference solution that employs only virgin aggregates. The environmental sustainability of the proposed alternatives is assessed by considering the global warming potential (GWP), energy requirements, and water consumption. The LCA analysis is performed using SimaPro software (version 9.1.1.7). The obtained results demonstrate that solutions involving the use of recycled materials represent a more sustainable and environmentally friendly option. In particular, a significant reduction in water depletion was found for the alternative scenarios, with savings between 56% and 99%. For GWP and energy, the total savings ranged from approximately 20% to 40%. [ABSTRACT FROM AUTHOR]

Deep learning has enabled a straightforward, convenient method of road pavement infrastructure management that facilitates a secure, cost-effective, and efficient transportation network. Manual road pavement inspection is time-consuming and dangerous, making timely road repair difficult. This research showcases You Only Look Once version 5 (YOLOv5), the most commonly employed object detection model trained on the latest benchmark Road Damage Dataset, Road Damage Detection 2022 (RDD 2022). The RDD 2022 dataset includes four common types of road pavement damage, namely vertical cracks, horizontal cracks, alligator cracks, and potholes. This paper presents an improved deep neural network model based on YOLOv5 for real-time road pavement damage detection in photographic representations of outdoor road surfaces, making it an indispensable tool for efficient, real-time, and cost-effective road infrastructure management. The YOLOv5 model has been modified to incorporate several techniques that improve its accuracy and generalization performance. These techniques include the Efficient Channel Attention module (ECA-Net), label smoothing, the K-means++ algorithm, Focal Loss, and an additional prediction layer. In addition, a 1.9% improvement in mean average precision (mAP) and a 1.29% increase in F1-Score were attained by the model in comparison to YOLOv5s, with an increment of 1.1 million parameters. Moreover, a 0.11% improvement in mAP and 0.05% improvement in F1 score was achieved by the proposed model compared to YOLOv8s while having 3 million fewer parameters and 12 gigabytes fewer Giga Floating Point Operation per Second (GFlops). [ABSTRACT FROM AUTHOR]

In recent years, the number of traffic accidents caused by road defects has increased dramatically all over the world, and the repair and prevention of road defects is an urgent task. Researchers in different countries have proposed many models to deal with this task, but most of them are either highly accurate and slow in detection, or the accuracy is low and the detection speed is high. The accuracy and speed have achieved good results, but the generalization of the model to other datasets is poor. Given this, this paper takes YOLOv5s as a benchmark model and proposes an optimization model to solve the problem of road defect detection. First, we significantly reduce the parameters of the model by pruning the model and removing unimportant modules, propose an improved Spatial Pyramid Pooling-Fast (SPPF) module to improve the feature signature fusion ability, and finally add an attention module to focus on the key information. The activation function, sampling method, and other strategies were also replaced in this study. The test results on the Global Road Damage Detection Challenge (GRDDC) dataset show that the FPS of our proposed model is not only faster than the baseline model but also improves the MAP by 2.08%, and the size of this model is also reduced by 6.07 M. [ABSTRACT FROM AUTHOR]

Crack detection is an important task for road maintenance. Currently, convolutional neural-network-based segmentation models with attention blocks have achieved promising results, for the reason that these models can avoid the interference of lights and shadows. However, by carefully examining the structure of these models, we found that these segmentation models usually use down-sampling operations to extract high-level features. This operation reduces the resolution of features and causes feature information loss. Thus, in our proposed method, a Parallel Convolution Module (PCM) was designed to avoid feature information loss caused by down-sampling. In addition, the attention blocks in these models only focused on selecting channel features or spatial features, without controlling feature information flow. To solve the problem, a Self-Gated Attention Block (SGAB) was used to control the feature information flow in the attention block. Therefore, based on the ideas above, a PSNet with a PCM and SGAB was proposed by us. Additionally, as there were few public datasets for detailed evaluation of our method, we collected a large dataset by ourselves, which we named the OAD_CRACK dataset. Compared with the state-of-the-art crack detection method, our proposed PSNet demonstrated competitive segmentation performance. The experimental results showed that our PSNet could achieve accuracies of 92.6%, 81.2%, 98.5%, and 76.2% against the Cracktree200, CRACK500, CFD, and OAD_CRACK datasets, respectively, which were 2.6%, 4.2%, 1.2%, and 3.3% higher than those of the traditional attention models. [ABSTRACT FROM AUTHOR]

Road authorities worldwide can leverage the advances in vehicle technology by continuously monitoring their roads' conditions to minimize road maintenance costs. The existing methods for carrying out road condition surveys involve manual observations using standard survey forms, performed by qualified personnel. These methods are expensive, time-consuming, infrequent, and can hardly provide real-time information. Some automated approaches also exist but are very expensive since they require special vehicles equipped with computing devices and sensors for data collection and processing. This research aims to leverage the advances in vehicle technology in providing a cheap and real-time approach to carry out road condition monitoring (RCM). This study developed a deep learning model using the You Only Look Once, Version 5 (YOLOv5) algorithm that was trained to capture and categorize flexible pavement distresses (FPD) and reached 95% precision, 93.4% recall, and 97.2% mean Average Precision. Using vehicle built-in cameras and GPS sensors, these distresses were detected, images were captured, and locations were recorded. This was validated on campus roads and parking lots using a car featured with a built-in camera and GPS. The vehicles' built-in technologies provided a more cost-effective and efficient road condition monitoring approach that could also provide real-time road conditions. [ABSTRACT FROM AUTHOR]

The real-time detection of cracks is an important part of road maintenance and an important initiative to reduce traffic accidents caused by road cracks. In response to the lack of efficiency of current research results for the real-time detection of road cracks and the low storage and computational capacity of edge devices, a new automatic crack detection algorithm is proposed: BT–YOLO. We combined Bottleneck Transformer with You Only Look Once (YOLO), which is more conducive to extracting the features of small cracks than YOLOv5s. The introduction of DWConv to the feature extraction network reduced the number of parameters and improved the inference speed of the network. We embedded the SimAM (Simple, Parameter-Free Attention Module) non-parametric attention mechanism to make the crack features more prominent. The experimental results showed that the accuracy of BT–YOLO in crack detection was increased by 4.5%, the mapped value was increased by 8%, and the parameter amount was decreased by 24.9%. Eventually, we deployed edge devices for testing. The frame rate reached 89, which satisfied the requirements of real-time crack detection. [ABSTRACT FROM AUTHOR]

In the 21st century, one of the key requirements is to develop and maintain our infrastructure facilities most efficiently using the available resources. Tramways are of significant national economic importance and represent an important national asset. There are currently seven different types of superstructure systems in Hungary, based on the national regulations and the related requirements currently in force. This paper compares the paved tramway superstructure systems in the context of track geometry, through-rolled axle tons of track, and the age of track sections. Paved tracks have many benefits, but the main ones are easier maintenance and road traffic use. Elastically supported continuous rail bedding (ESCRB; in Hungary, this is known as "RAFS") and "large" slab superstructure systems are used to create paved superstructure systems. Road crossings use the latter systems, while heavily loaded lines use several ESCRB systems. This article examines the geometrical changes in several ESCRB superstructure systems. A TrackScan 4.01 instrument was used to take measurements in June and September 2021 and in April 2022, September 2022, and May 2023. Track gauge, alignment, and longitudinal level are examined. Regardless of the ESCRB superstructure system or age, a medium-loaded line's track gauge trendline increases, which means that the track gauge is widening and, regardless of traffic load or age, the average longitudinal level is constantly increasing from year to year. When it is a medium-loaded line, the average value of alignment increases slightly, and the trendline is almost straight, but it decreases when it is an extremely heavily loaded line. The authors will analyze how the reference track section will change in the future. Based on the results, it is important to assess how subsequent measurements affect the trend lines. Because the data evaluations show similar results, comparing open tramway tracks to paved ones is crucial. [ABSTRACT FROM AUTHOR]

Featured Application: Authors are encouraged to provide a concise description of the specific application or a potential application of the work. This section is not mandatory. The latest advances in spatial information technology have led to the emergence of Volunteered Geographic Information (VGI) as enrichment to existing spatial data sources. Additionally, Decision Support Systems (DSS) are among the fields that have seen major advances. Volunteered Geographic Information (VGI) has great potential as a valuable data source to decision support systems. Several studies have been proposed to integrate VGI data into DSS. However, as VGI data may have different levels of quality, integrating VGI data with poor quality may affect the decision-making process. In fact, VGI data with poor quality. that are obsolete or incomplete, could, if integrated into a spatial DSS, lead to inappropriate analysis results. This paper presents an approach that aims to enhance spatial DSS analysis and exploitation by integrating high quality VGI data that are appropriate to the user requirements, and that have a good indicator completeness and time relevance. The approach introduces a conceptual framework that evaluates VGI data quality and integrates only high quality VGI data into spatial DSS. The proposed approach is experimented on a road maintenance project in Grand-Tunis. We develop the Map-Report prototype, and we evaluate the efficiency of our approach in enhancing data analysis and exploitation in spatial DSS by reducing the error rate and providing accurate and precise analysis results. [ABSTRACT FROM AUTHOR]

Road maintenance systems (RMS) are crucial for maintaining safe and efficient road networks. The impact of climate change on road maintenance systems is a concern as it makes them more susceptible to weather events and subsequent damages. To tackle this issue, we propose an RMSDC (Road Maintenance Systems Using Deep Learning and Climate Adaptation) technique to improve road maintenance systems based on Deep learning and Climate Adaptation. RMSDC aims to use the multivariate classification technique and divides the dataset into training and test datasets. The RMSDC combines Convolutional Long Short-Term Memory (ConvLSTM) techniques with road weather information and sensor data. However, in emerging nations, the effects of climate change are already apparent, which makes road networks particularly susceptible to extreme weather, floods, and landslides. Therefore, climate adaptation of road networks is essential, especially in developing nations with limited financial resources. To address this issue, we propose an intelligent and effective RMSDC that utilizes deep learning algorithms based on climate change predictions. The ConvLSTM block effectively captures the relationship between input features over time to calculate the root-mean deviation (RMSD). We evaluate RMSDC performance against frameworks for downscaling climate variables using two metrics: root-mean-square error (RMSE) and mean absolute difference. Through real evaluations, RMSDC consistently outperforms approaches with a reduced RMSE of 0.26. These quantitative results highlight how effective RMSDC is in addressing maintenance systems on road networks leading to proactive road maintenance strategies that enhance traffic safety, reduce costs, and improve environmental sustainability. [ABSTRACT FROM AUTHOR]

As an infrastructure project, the cost and benefits of road maintenance should be measured through social costs. In order to fully consider user interests and make road maintenance decisions more reasonable, two types of user costs that may affect road maintenance benefits have been quantified through analysis of existing literature. At the same time, environmental issues have gradually become the focus of various industries, and in optimizing road maintenance decisions, the impact of environmental issues on decision making should also be considered. This article first analyzes and quantifies the user costs that affect the effectiveness of road maintenance. Secondly, based on the concept of sustainable development of roads, the optimization of road maintenance decisions is divided into two steps: the first step is to determine the minimum maintenance budget funds, and the second step is to determine the optimal plan. The specific optimization method is to use the 0-1 mathematical programming method to establish a network-level pavement maintenance decision optimization model based on a quantitative model. The most reasonable maintenance optimization plan is determined from four aspects: maintenance funds, maintenance performance improvement value, user benefit improvement, and reducing environmental impact. Finally, a provincial road network for a case study is selected. The applicability of the new model is verified through a case study. This study can help decision makers deal with asphalt pavement maintenance arrangements at the network level with four decision objectives: maintenance funds, pavement performance, road users, and environmental impacts. [ABSTRACT FROM AUTHOR]

In this study, we propose an algorithm to improve the accuracy of tiny object segmentation for precise pothole detection on asphalt pavements. The approach comprises a three-step process: MOED, VAPOR, and Exception Processing, designed to extract pothole edges, validate the results, and manage detected abnormalities. The proposed algorithm addresses the limitations of previous methods and offers several advantages, including wider coverage. We experimentally evaluated the performance of the proposed algorithm by filming roads in various regions of South Korea using a UAV at high altitudes of 30–70 m. The results show that our algorithm outperforms previous methods in terms of instance segmentation performance for small objects such as potholes. Our study offers a practical and efficient solution for pothole detection and contributes to road safety maintenance and monitoring. [ABSTRACT FROM AUTHOR]

The research objective for this study is to identify and evaluate techniques for soil stabilization to support military-vehicle ground maneuver in contested environments. Various types of stabilizers mixed with silty sand are evaluated in the laboratory for their compressive strength at various soil moisture contents and in the field for their rutting performance. Field data are analyzed for the ability to withstand trafficking from a military ground vehicle by evaluating the rut depth and measured instrumentation data. The field testing shows that the rapid soil stabilization materials and techniques can produce repairs that withstand required traffic without traditional pavement surface materials. [ABSTRACT FROM AUTHOR]