Search Results (111)

Effective management of airport pavements is essential for maintaining safety and operational efficiency in air travel. An airport pavement management system (APMS) operates at two levels: the network level, which monitors overall pavement performance across the airport, and the project level, which conducts detailed inspections of individual pavements. However, pavement assessments are often costly and labor intensive, necessitating the development of cost-effective and practical models. This paper introduces the Airport Pavement Integrated Risk Management (APIRM) model, which integrates pavement condition assessment criteria with safety risk management (SRM) methodologies. The model was applied at Istanbul Airport. By using APIRM, airports can prioritize high-risk areas, optimizing resource allocation and enhancing safety. The model encourages coordination among various airport departments, offering a holistic approach to pavement management that integrates maintenance requirements with safety considerations. Full article

Roads are essential for economic development, facilitating the circulation of services and resources. This research seeks to provide local governments with a comprehensive framework to enhance road maintenance, focusing on the surface and functional evaluation of pavements. It compares the conventional methods International Roughness Index (IRI) and the Pavement Condition Index (PCI) with novel methodologies that employ smart technologies. The efficiency of such technologies in the maintenance of local roads in Peru is analyzed, taking as a case study a 2 km section of the AR-780 highway in the city of Arequipa. The International Roughness Index (IRI) obtained through the Merlin Roughness Meter and the Roadroid application were compared, finding a minimum variation of 4.0% in the left lane and 8.7% in the right lane. Roadroid turned out to be 60 times faster than the conventional method, with a cost difference of 220.11 soles/km (USD $57.92/km). Both methods classified the Present Serviceability Index (PSI) as good, validating the accuracy of Roadroid. In addition, the Pavement Condition Index (PCI) was evaluated with conventional methods and a DJI Mavic 2 Pro drone, finding a variation of 6.9%. The cost difference between the methodologies was 1047.73 soles/km (USD $275.72/km), and the use of the drone proved to be 10 times faster than visual inspection. This study contributes to closing the knowledge gap regarding the use of smart technologies for better pavement management on local roads, so the actors in charge of such infrastructure make decisions based on science, contributing to the well-being of the population. Full article

Maintaining the integrity of urban road pavements is vital for public safety, transportation efficiency, and economic stability. However, aging infrastructure and limited budgets make it challenging to detect subsurface defects that can lead to pavement collapses. Traditional inspection methods are often inadequate for identifying such underground anomalies. Ground Penetrating Radar (GPR), especially dual-polarized array systems, offers a non-destructive, high-resolution solution for subsurface inspection. Despite its potential, effectively detecting and analyzing areas at risk of collapse in urban pavements remains a challenge. This study employed a dual-polarized array GPR system to inspect road pavements in London. The research involved comprehensive field testing, including data acquisition, signal processing, calibration, background noise removal, and 3D migration for enhanced imaging. Additionally, Short-Fourier Transform Spectrum (SFTS) analysis was applied to detect moisture-related anomalies. The results show that dual-polarized GPR systems effectively detect subsurface issues like voids, cracks, and moisture-induced weaknesses. The ability to capture data in multiple polarizations improves resolution and depth, enabling the identification of collapse-prone areas, particularly in regions with moisture infiltration. This study demonstrates the practical value of dual-polarized GPR technology in urban pavement inspection, offering a reliable tool for early detection of subsurface defects and contributing to the longevity and safety of road infrastructure. Full article

In the rapid development of urbanization, the sustained and healthy development of transportation infrastructure has become a widely discussed topic. The inspection and maintenance of asphalt pavements not only concern road safety and efficiency but also directly impact the rational allocation of resources and environmental sustainability. To address the challenges of modern transportation infrastructure management, this study innovatively proposes a hybrid learning model that integrates deep convolutional neural networks (DCNNs) and support vector machines (SVMs). Specifically, the model initially employs a ShuffleNet architecture to autonomously extract abstract features from various defect categories. Subsequently, the Maximum Relevance Minimum Redundancy (MRMR) method is utilized to select the top 25% of features with the highest relevance and minimal redundancy. After that, SVMs equipped with diverse kernel functions are deployed to perform training and prediction based on the selected features. The experimental results reveal that the model attains a high classification accuracy of 94.62% on a self-constructed asphalt pavement image dataset. This technology not only significantly improves the accuracy and efficiency of pavement inspection but also effectively reduces traffic congestion and incremental carbon emissions caused by pavement distress, thereby alleviating environmental burdens. It is of great significance for enhancing pavement maintenance efficiency, conserving resource consumption, mitigating environmental pollution, and promoting sustainable socio-economic development. Full article

Proper road network maintenance is essential for ensuring safety, reducing transportation costs, and improving fuel efficiency. Traditional pavement condition assessments rely on specialized equipment, limiting the frequency and scope of inspections due to technical and financial constraints. In response, crowdsourcing data from connected and autonomous vehicles (CAVs) offers an innovative alternative. CAVs, equipped with sensors and accelerometers by Original Equipment Manufacturers (OEMs), continuously gather real-time data on road conditions. This study evaluates the feasibility of using CAV data to assess pavement condition through the International Roughness Index (IRI). By comparing CAV-derived data with traditional pavement auscultation results, various thresholds were established to quantitatively and qualitatively define pavement conditions. The results indicate a moderate positive correlation between the two datasets, particularly in segments with good-to-satisfactory surface conditions (IRI 1 to 2.5 dm/km). Although the IRI values from CAVs tended to be slightly lower than those from auscultation surveys, this difference can be attributed to driving behavior. Nonetheless, our analysis shows that CAV data can be used to reliably identify pavement conditions, offering a scalable, non-destructive, and continuous monitoring solution. This approach could enhance the efficiency and effectiveness of traditional road inspection campaigns. Full article

Ferronickel slag (FNS) is a byproduct produced during ferronickel alloy manufacturing, primarily used in the manufacturing of stainless steel and iron alloys. This material is produced by cooling molten slag with water or air, posing significant disposal challenges, as improper storage in industrial yards can lead to environmental contamination. This study investigates the chemical and mineralogical characteristics of reduction ferronickel slag (RFNS) and its potential use as an alternative aggregate in hot mix asphalt (HMA). The research is based on the practical application of HMA containing RFNS in an experimental area, specifically the parking lot used by buses transporting employees of Anglo American, located at the Codemin Industrial Unit in Niquelândia, Goiás, Central Brazil. Chemical analysis revealed that RFNS primarily consists of MgO, Fe₂O₃, and SiO₂, which are elements with minimal environmental impact. The lack of significant calcium content minimizes concerns about expansion issues commonly associated with calcium-rich slags. The X-ray diffractogram indicates a predominantly crystalline structure with minerals like Laihunite and Magnetite, which enhances wear and abrasion resistance. HMA containing 40% RFNS was tested using the Marshall methodology, and a small experimental area was subsequently constructed. The HMA containing RFNS met regulatory specifications and technological controls, achieving an average resilient modulus value of 6323 MPa. Visual inspections conducted four years later confirmed that the pavement remained in excellent condition, validating RFNS as a durable and effective alternative aggregate for asphalt mixtures. The successful application of RFNS not only demonstrates its potential for local road paving near industrial areas but also underscores the importance of sustainable waste management solutions. This research highlights the value of academia–industry collaboration in advancing environmentally responsible practices and reinforces the contribution of RFNS to enhancing local infrastructure and promoting a more sustainable future. Full article

The primary challenge in the operation of steel deck pavement systems lies in the inspection and assessment of their condition. Traditionally, manual inspection methods are employed. However, these approaches are not only time-consuming and labor-intensive but also prone to human error. As a result, integrating data-driven machine learning technologies into the evaluation of pavement systems presents a significant advantage in addressing these issues. This study proposes a decision-making tool for estimating the condition levels of steel bridge deck pavement systems by employing classification techniques. To address the issue of class imbalance in the dataset, the SMOTE algorithm is utilized. Additionally, seven different machine learning methods—Light Gradient Boosting Machine, Extreme Gradient Boosting, Random Forest, Adaptive Boosting, K-Nearest Neighbor, Multilayer Perceptron, and Logistic Regression—are applied for training. Comparative analysis reveals that the Light Gradient Boosting performs optimally, achieving classification accuracies of 0.841 and 0.929 on the original and synthetic datasets, respectively. Full article

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

Timely discovery and disposal of road risk sources constitute the cornerstone of road operation safety. Presently, the detection of road risk sources frequently relies on manual inspections via inspection vehicles, a process that is both inefficient and time-consuming. To tackle this challenge, this paper introduces a novel automated approach for detecting road risk sources, termed the multi-scale lightweight network (MSLN). This method primarily focuses on identifying road surfaces, potholes, and scattered objects. To mitigate the influence of real-world factors such as noise and uneven brightness on test results, pavement images were carefully collected. Initially, the collected images underwent grayscale processing. Subsequently, the median filtering algorithm was employed to filter out noise interference. Furthermore, adaptive histogram equalization techniques were utilized to enhance the visibility of cracks and the road background. Following these preprocessing steps, the MSLN model was deployed for the detection of road risk sources. Addressing the challenges associated with two-stage network models, such as prolonged training and testing times, as well as deployment difficulties, this study adopted the lightweight feature extraction network MobileNetV2. Additionally, transfer learning was incorporated to elevate the model’s training efficiency. Moreover, this paper established a mapping relationship model that transitions from the world coordinate system to the pixel coordinate system. This model enables the calculation of risk source dimensions based on detection outcomes. Experimental results reveal that the MSLN model exhibits a notably faster convergence rate. This enhanced convergence not only boosts training speed but also elevates the precision of risk source detection. Furthermore, the proposed mapping relationship coordinate transformation model proves highly effective in determining the scale of risk sources. Full article

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. Full article

Taiwan’s provincial highways span approximately 5000 km and are crucial for connecting cities and towns. As pavement deteriorates over time and maintenance funds are limited, efficient pavement inspection and maintenance decision-making are challenging. Traditional inspections rely on manual visual assessments, consuming significant human resources and time without providing quantitative results. This study addresses current maintenance practices by introducing automated pavement damage detection technology to replace manual surveys. This technology significantly improves inspection efficiency and reduces costs. For example, traditional methods inspect 1 km per day, while automated survey vehicles cover 4 km per day, increasing efficiency fourfold. Additionally, automated surveys reduce inspection costs per kilometer by about 1.7 times, lowering long-term operational costs. Inspection results include the crack rate, rut depth, and roughness (IRI). Using K-means clustering analysis, maintenance thresholds for these indicators are established for decision-making. This method is applied to real cases and validated against actual maintenance decisions, showing that the introduced detection technology efficiently and objectively guides maintenance decisions and meets the needs of maintenance units. Finally, the inspection results are integrated into a pavement management platform, allowing direct maintenance decision-making and significantly enhancing management efficiency. Full article

Road cracks significantly affect the serviceability and safety of roadways, especially in mountainous terrain. Traditional inspection methods, such as manual detection, are excessively time-consuming, labor-intensive, and inefficient. Additionally, multi-function detection vehicles equipped with diverse sensors are costly and unsuitable for mountainous roads, primarily because of the challenging terrain conditions characterized by frequent bends in the road. To address these challenges, this study proposes a customized Unmanned Aerial Vehicle (UAV) inspection system designed for automatic crack detection. This system focuses on enhancing autonomous capabilities in mountainous terrains by incorporating embedded algorithms for route planning, autonomous navigation, and automatic crack detection. The slide window method (SWM) is proposed to enhance the autonomous navigation of UAV flights by generating path planning on mountainous roads. This method compensates for GPS/IMU positioning errors, particularly in GPS-denied or GPS-drift scenarios. Moreover, the improved MRC-YOLOv8 algorithm is presented to conduct autonomous crack detection from UAV imagery in an on/offboard module. To validate the performance of our UAV inspection system, we conducted multiple experiments to evaluate its accuracy, robustness, and efficiency. The results of the experiments on automatic navigation demonstrate that our fusion method, in conjunction with SWM, effectively enables real-time route planning in GPS-denied mountainous terrains. The proposed system displays an average localization drift of 2.75% and a per-point local scanning error of 0.33 m over a distance of 1.5 km. Moreover, the experimental results on the road crack detection reveal that the MRC-YOLOv8 algorithm achieves an F1-Score of 87.4% and a mAP of 92.3%, thus surpassing other state-of-the-art models like YOLOv5s, YOLOv8n, and YOLOv9 by 1.2%, 1.3%, and 3.0% in terms of mAP, respectively. Furthermore, the parameters of the MRC-YOLOv8 algorithm indicate a volume reduction of 0.19(×10⁶) compared to the original YOLOv8 model, thus enhancing its lightweight nature. The UAV inspection system proposed in this study serves as a valuable tool and technological guidance for the routine inspection of mountainous roads. Full article

Steel truss–arch composite bridge systems are widely used in bridge engineering to provide sufficient space for double lanes. However, a lack of research exists on their mechanical performance throughout their lifespan, resulting in uncertainties regarding bearing capacity and the risk of bridge failure. This paper conducts a numerical study of the structural mechanical performance of a flexible arch composite bridge with steel truss beams throughout its lifespan to determine the critical components and their mechanical behavior. Critical vehicle loads are used to assess the bridge’s mechanical performance. The results show that the mechanical performance of the bridge changes significantly when the temporary piers and the bridge deck pavement are removed, substantially influencing the effects of the vehicle loads on the service life. The compressive axial force of the diagonal bar significantly increases to 33,101 kN near the supports during the two construction stages, and the axial force in the upper chord of the midspan increases by 4.1 times under a critical load. Moreover, the suspender tensions and maximum vertical displacement are probably larger than the limit of this bridge system in the service stage, and this is caused by the insufficient longitudinal bending stiffness of truss beams. Therefore, monitoring and inspection of critical members are necessary during the removal of temporary piers and bridge deck paving, and an appropriate design in steel truss beams is required to improve the life cycle assessment of this bridge system. Full article

Road crack detection is crucial for maintaining and inspecting civil infrastructure, as cracks can pose a potential risk for sustainable road safety. Traditional methods for pavement crack detection are labour-intensive and time-consuming. In recent years, computer vision approaches have shown encouraging results in automating crack localization. However, the classical convolutional neural network (CNN)-based approach lacks global attention to the spatial features. To improve the crack localization in the road, we designed a vision transformer (ViT) and convolutional neural networks (CNNs)-based encoder and decoder. In addition, a gated-attention module in the decoder is designed to focus on the upsampling process. Furthermore, we proposed a hybrid loss function using binary cross-entropy and Dice loss to evaluate the model’s effectiveness. Our method achieved a recall, F1-score, and IoU of 98.54%, 98.07%, and 98.72% and 98.27%, 98.69%, and 98.76% on the Crack500 and Crack datasets, respectively. Meanwhile, on the proposed dataset, these figures were 96.89%, 97.20%, and 97.36%. Full article

This study proposes a deep learning method for pavement defect detection, focusing on identifying potholes and cracks. A dataset comprising 10,828 images is collected, with 8662 allocated for training, 1083 for validation, and 1083 for testing. Vehicle attitude data are categorized based on three-axis acceleration and attitude change, with 6656 (64%) for training, 1664 (16%) for validation, and 2080 (20%) for testing. The Nvidia Jetson Nano serves as the vehicle-embedded system, transmitting IMU-acquired vehicle data and GoPro-captured images over a 5G network to the server. The server recognizes two damage categories, low-risk and high-risk, storing results in MongoDB. Severe damage triggers immediate alerts to maintenance personnel, while less severe issues are recorded for scheduled maintenance. The method selects YOLOv7 among various object detection models for pavement defect detection, achieving a mAP of 93.3%, a recall rate of 87.8%, a precision of 93.2%, and a processing speed of 30–40 FPS. Bi-LSTM is then chosen for vehicle vibration data processing, yielding 77% mAP, 94.9% recall rate, and 89.8% precision. Integration of the visual and vibration results, along with vehicle speed and travel distance, results in a final recall rate of 90.2% and precision of 83.7% after field testing. Full article