Road Infrastructure Maintenance Research Articles

Key pavement condition indicators for assessing highway pavement maintenance quality index

The technical condition of road pavements requires regular monitoring to support accurate maintenance decisions. However, existing pavement maintenance quality index (PQI) assessment methods are often expensive and infrequent, which limits intelligent decision-making. This paper proposes a cost-effective and precise PQI assessment method using data from five highways in Beijing. Key PQI indicators were identified through a hybrid feature selection method, and a CatBoost model optimised by a genetic algorithm (GA-CatBoost) was developed. The model demonstrated superior accuracy, achieving an R2 of 0.938, an MSE of 0.838, and an MAE of 0.576. It achieved 98.46% accuracy compared to traditional methods, confirming its reliability in an on-site application. This approach offers an economical solution for high-frequency PQI assessment, enabling intelligent decision-making in road infrastructure maintenance and supporting the use of lightweight detection equipment.

Urban Road Transport Infrastructure Planning and Development Implications on Socio-Economic Growth in the City of Douala, Cameroon

Cities are the major contributors to socio- economic growth with easy access to amenities which stem through road transport infrastructure development. This study was conducted to assess urban road transport infrastructure planning and examine development implications on socio-economic growth in the City of Douala. A descriptive research design was used with primary and secondary data collected and analyzed using descriptive and inferential statistical techniques. Results revealed that, transport infrastructure in Douala have been structured through urban development planning policy which influences surface spatial expansion of socio- economic activities. The planning policies have contributed to the rapid expansion and growth through the initiations of master plan, land use plan and sector plan. Infrastructural development on road transport depicts a physical expansion and construction of road networks which influences the socio-economic growth of the study area. Stakeholders’ actions on construction, reconstruction and rehabilitation, investments and maintenance of road infrastructure have helped to curb down some transportation challenges and creates easy access to facilities within different sectors of the study area. However, road transport infrastructure which is well-developed boasts socio-economic activities and growth. The state of road transport network, its diversity and characteristic features is defined by its prevalence within different areas in Douala. Contrarily, other transport infrastructures which are not functioning appropriately due to lapses in physical planning possess enormous implications on transportation in the study area. The conclusion drawn was that the manner in which urban planning policies are implemented determines the level of development in road transport infrastructure and socio-economic growth of Douala. The study recommends an effective application of planning policies to be integrated by implicating all the actors of urban managers and transportation planners for improved growth in the study area.

Validating the Climate Resiliency Framework for Road Projects in KPK, Pakistan

This study validates a recently developed framework aimed at assessing the climate resiliency of road projects, recognizing the increasing impact of climate change on infrastructure, especially transportation networks such as roads. The framework provides a standardized and systematic approach for evaluating how well road projects can withstand and adapt to climate change impacts, including challenges like flooding, extreme temperatures, and rising sea levels. Conducting the research through a case study approach, we applied the framework to three specific road projects: Drabo Road, Chino Road, and Shahi-Barawal Road. The results revealed the resiliency scores for each project—Drabo Road scored 55.5%, indicating a partially resilient status; Chino Road received a score of 47.9%, signifying an acceptable status with room for improvement, also classified as partially resilient; Shahi-Barawal Road obtained a score of 50.2%, placing it in an acceptable and partially resilient category. This investigation showcases the framework's effectiveness in identifying vulnerabilities and recommending adaptation measures. The research contributes to the development of a practical tool that can assist engineers, planners, and policymakers in making informed decisions regarding the design, construction, and maintenance of road infrastructure in the face of climate change. Ultimately, the goal is to enhance the overall resilience of road projects and minimize potential damage caused by climate change impacts.

Micro–Macro-Analysis and Model Derivation of Electrical Resistivity of Ningxia Cement–Loess

In recent years, highway infrastructure in the Ningxia region of China has rapidly advanced. Cement–loess is extensively utilized in the roadbed and foundation reinforcement. It is necessary to conduct micro–macro-analysis and model derivation of the electrical resistivity on Ningxia cement–loess, which are beneficial for both the practical application of electrical resistivity and the evaluation of the geotechnical properties of cement–loess. Therefore, a series of electrical resistivity measurements, microstructural observations (scanning electron microscopy), mineral analyses (thermogravimetric analysis), and theoretical analyses were adopted on the cement–loess. The following conclusions can be drawn: The electrical resistivity is negatively related to dry density and water content, while it is positively related to cement dosage and curing age. A cement dosage of 6% exhibits a lower hydration reaction potential compared to 12%, causing a slower increase in electrical resistivity. The formation of calcium silicate gel around particles results in particle clustering and pore filling, reducing the pore area and increasing electrical resistivity. Increased hydration also decreases microscopic orientation, contributing to a higher electrical resistivity of cement–loess. Finally, a new three-dimensional electrical resistivity model was created, finding that the electrical resistivity of Ningxia cement–loess was determined by the dry density, water content (ρd·w), cement dosage, and curing age (aw·T) in an exponential function form. The new three-dimensional electrical resistivity model could be used in the high-efficiency evaluation of the cement–loess geotechnical parameter, offering valuable insights for the monitoring and maintenance of road infrastructure.

Geographic information system for reporting damage and road grouping based on website using waterfall method and k-means method

Roads are a vital land transportation infrastructure and transportation route. One of the functions of roads is as a driving factor in the development process and equitable distribution of development in a region. Road damage that occurs disrupts the activities and routines of residents. Lhokseumawe City as the center of economic and social development in its region requires an efficient system for the management and maintenance of road infrastructure. The reason is that many road damages occur in Lhokseumawe City that have not been repaired due to limited damage information so reports are needed from residents to find out the location of the road damage so that it is easier for the Lhokseumawe City government to follow up on the report. With these problems, a geographic information system for reporting road damage is created. It is hoped that the government can follow up on the report and prioritize damage with the highest group, which groups road damage using the clustering method. This web-based information system is designed using the UML (Unified Modeling Language) model with the PHP programming language and uses MySQL as its database. With this system, the government can receive reports of existing damage and group the damage into three levels to determine the priority of road repairs to be carried out.

Real-Time Monitoring of Road Networks for Pavement Damage Detection Based on Preprocessing and Neural Networks

This paper presents a novel multi-initialization model for recognizing road surface damage, e.g. potholes and cracks, on video using convolutional neural networks (CNNs) in real-time for fast damage recognition. The model is trained by the latest Road Damage Detection dataset, which includes four types of road damage. In addition, the CNN model is updated using pseudo-labeled images from semi-learned methods to improve the performance of the pavement damage detection technique. This study describes the use of the YOLO architecture and optimizes it according to the selected parameters, demonstrating high efficiency and accuracy. The results obtained can enhance the safety and efficiency of road pavement and, hence, its traffic quality and contribute to decision-making for the maintenance and restoration of road infrastructure.

Enhancing autonomous pavement crack detection: Optimizing YOLOv5s algorithm with advanced deep learning techniques

To enhance the safety and comfort of vehicle travel, detecting pavement cracks is a critical task in road management. This article introduces an advanced single-stage target detection method utilizing the YOLOv5s algorithm to enhance real-time performance and accuracy. Initially, Squeeze-and-Excitation Networks are integrated into the model to facilitate self-learning for improved crack characterization. Subsequently, anchors computed through the K-means clustering algorithm are closely aligned with the fracture dataset, achieving an adaptation rate of 99.9 % and enhancing the recall rate of the model. Furthermore, the inclusion of the SimSPPF module from YOLOv6 diminishes memory usage and expedites detection speed. By replacing the original nearest up-sampling method with transposed convolution, optimization of up-sampling for crack datasets is achieved. Performance assessments reveal that the refined YOLOv5s algorithm attains an F1 score of 91 %, a mean Average Precision (mAP) of 93.6 %, and a 1.54 % increase in frames per second (fps) for pavement crack detection. This enhancement in detection technology signifies a substantial advancement in the maintenance and longevity of road infrastructure.

Road surface crack detection and classification based on YOLOv5

The burgeoning need for sustainable urban transportation has magnified the importance of effective road infrastructure maintenance, particularly road crack detection. This study addresses the challenge of accurately detecting road surface cracks through automated methods. To accurately identify road surface cracks, we used the YOLOv5 image recognition algorithm, enhanced by comprehensive data augmentation and meticulous training strategies. The YOLOv5 proved superior to traditional methods in accuracy and recall while maintaining high computational speed and a smaller model size in experimental outcomes. The findings affirm that YOLOv5-based detection technologies significantly improve maintenance efficiency and traffic safety, with future work poised to further refine these approaches for greater robustness and integration into intelligent transportation systems.

Utilization of Nanocenospheres a waste material to improve the physical and rheological properties of SBS asphalt binders

The performance enhancement of asphalt binders by incorporating innovative additives has been a subject of significant research and industry interest. This study emphasizes the potential of Nano polymer composites as a promising additive for improving the performance of asphalt binders, providing valuable insights into developing advanced asphalt materials for sustainable road infrastructure construction and maintenance. It explores the utilization of nano cenospheres to amplify the attributes of Styrene-Butadiene-Styrene (SBS) modified asphalt binders. It introduces a novel approach by incorporating Nano polymer composites (NPC) comprising nanocenosphere particles and Styrene-butadiene-Styrene Particles. Nanocenosphere particles are the waste materials derived from coal combustion in thermal power plants. Styrene-butadiene-styrene is a type of thermoplastic elastomer, a synthetic rubber. A series of experimental tests, penetration, softening point, ductility, performance grading, temperature sweep, multiple stress and creep recovery, frequency sweep, linear amplitude sweep, and rutting aging index, is conducted to evaluate changes in physical and rheological properties. Results indicate that adding nano cenospheres particles in SBS asphalt binder enhances its softening point, elasticity recovery, rutting resistance, fatigue life, storage stability, and aging resistance compared to traditional SBS-modified asphalt binders. Statistical analyses were accomplished to comprehend the influence of the Nano polymer composites. This analysis provides valuable insights into the effects of nanocenospheres on SBS-modified binders. Through rigorous statistical analysis, a thorough interpretation of how the incorporation of nanocenospheres influences the attributes of Nano-polymer composites.

Implementation of wavelet method and backpropagation neural network on road crack detection based on image processing

Road crack detection is critical to road infrastructure maintenance, requiring sophisticated and accurate approaches. This research explores the utilization of a combination of Wavelet and Convolutional Neural Network (CNN) methods to improve efficiency and accuracy in detecting cracks in road images. The wavelet method was chosen for its capability to capture information at different scales, enabling improved feature extraction. Meanwhile, CNN was utilized to comprehend the spatial context and tackle image complexity. The research involves several stages, including data collection, pre-processing, decomposition using the Wavelet method, forming of the CNN architecture model, training, testing, and evaluating the result. The tested images involve three main types of cracks: alligator, linear, and images without cracks. The testing results show that the developed model is capable of classifying cracks with an F1-score of 0.96, recall of 0.96, and precision of 0.96. In real-time detection of road cracks, the testing obtained an F1-score of 0.84, recall of 0.92, and precision of 0.77. This research contributes to the advancement of road crack detection technology by leveraging the capabilities of Wavelet and CNN, enhancing the accuracy and efficiency of crack detection in road maintenance.

Impacts of posted bridges on log truck routing in Mississippi, USA

ABSTRACT Log hauling costs are increased by the presence of closed and posted (restricted weight loading) bridges, as operators are forced to find alternative routes to wood-using mills. The USDA Forest Service Forest Inventory and Analysis (FIA) forest inventory, road network, bridge, and sawmill datasets were obtained for the US state of Mississippi. An 80.5 km buffer was established to define each sawmill’s working area, which occasionally extended across state lines. Anetwork analysis derived 129 feasible shortest optimal trucking routes between 46 FIA plot locations and the sawmills. Thirty of these routes had either closed or posted bridges along the shortest path; only 13 viable alternative routes were identified due to distance and posted weight limits. The additional trucking distance for these workable alternative routes averaged 7.1 km. The affected routes and their legal, full payload alternatives were each fitted to atriangular distribution to better understand how costs were impacted by posted or closed bridges. If no regulated bridges were encountered, haul cost across arange of haul rates and distances ranged from $3.50 to $14.40 USD per tonne. Costs were from $3.90 to $16.00 USD per tonne along routes with posted or closed bridges. On average, the cost difference was $0.98 USD per tonne, which was an increase of 12.0%. These findings have implications for road infrastructure maintenance, log trucking, wood procurement, and ultimately the values of standing timber and timberland.

A Road Crack Segmentation Method Based on Transformer and Multi-Scale Feature Fusion

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.

Road infrastructure maintenance: Operative method for interventions’ ranking

Planned maintenance of transportation infrastructure is a milestone in maintaining the functionality and safety of road networks. This paper is part of this process and aims to propose an easily applicable and versatile operational methodology for prioritizing maintenance interventions generally scheduled on road networks of considerable extent. An operational methodology for ranking and prioritizing maintenance interventions is proposed; this method is applicable to road networks of such size that direct inspection of each situation is impossible. The core of the paper is the analytical description of this methodology, based on a weighted sum function of three blocks: i) Category − is a score related to the purpose of the intervention; ii) Asset − is a score due to the road on which the intervention is located (considering Average Daily Traffic, Accidentality, Social Cost, Road Type, Routes); iii) Typology − is a score related to the element on which the intervention is being carried out. After describing the parameters and the implementation procedure for each block, the calibration of the relative weights is described, and a sensitivity analysis is performed. In the case study, the proposed methodology is applied to the ANAS corporate network in Italy. This case study will highlight the usefulness, versatility, and operability of the methodology with GIS (Geographic Information System) tools for implementing thematic maps. A concluding section concerns the addition of a scoring block − Spatial Context − as an additional differentiating factor due to the spatial context of each road.

Analysis of Pothole Detection Accuracy of Selected Object Detection Models Under Adverse Conditions

Abstract Potholes detection is an essential aspect of road safety and road infrastructure maintenance. Potholes, which are typically caused by a combination of heavy traffic and weather, are depressions or holes in the road surface that can cause damage to specific parts of a vehicle. Autonomous vehicles, in particular, must be capable of detecting and avoiding them. Hitting a deep or sharp-edged pothole at high speed can lead to loss of control or even an accident. This makes pothole detection all the more important. The accuracy of pothole detection systems installed in autonomous vehicles may be significantly impaired by adverse weather and bad light conditions. Therefore, the classification accuracy of selected well-known computer vision models for pothole detection under these specific conditions has been investigated. The results were then compared with state-of-the-art methods. Our findings showed that we outperformed many of them when used under adverse weather and low light situations. This paper presents valuable insights into the precision of various computer vision models for potholes detection. It may aid in selecting the optimal model for a specific application.

ANALISIS NILAI CBR SEBAGAI PARAMETER KRITIS UNTUK PERENCANAAN JALAN RAYA

<p><em>Infrastructure development is one indicator of a country's progress that cannot be ignored. Road infrastructure is an important part of the transportation system that connects different locations, facilitates human mobility, and transports goods. In the construction and maintenance of road infrastructure, soil plays a crucial role, so the condition and strength of the soil must be considered. Soil plays a significant role in construction as it serves as the foundation or base for building structures. Soil that has sufficient strength to support road loads will facilitate road construction. This study was conducted to determine the California Bearing Ratio (CBR) value, so that appropriate actions can be taken to construct roads based on local soil conditions. The CBR method is commonly used in soil geotechnical testing for road planning. This method involves laboratory testing of soil samples to determine the California Bearing Ratio (CBR). The results show a CBR value of 22.5%, which falls under the category of very good. This indicates that there is no need for improvement efforts in the construction of the Jl. Panglima Besar Sudirman, Dsn. Bolawen Ds. Tiron Kec. Banyakan Kab. Kediri highway, allowing resources to be allocated to other areas in need of road infrastructure improvements to enhance accessibility and overall regional development.</em></p>

An efficient pavement distress detection scheme through drone–ground vehicle coordination

Efficient road maintenance is imperative for infrastructure longevity and safety. Conventional ground vehicle-based methods for detecting pavement distress, however, encounter limitations in practice when dealing with complex road structures. Drones, endowed with greater spatial freedom, can access road segments that are hard-to-reach to ground vehicles, thereby enhancing detection efficiency and expanding detection coverage. By harnessing the complementary strengths of both detection modalities, we propose a scheme that capitalizes on the cooperative coordination of drones and ground vehicles for effective pavement distress detection. Our proposed scheme is evaluated using realistic road networks in practice. Results reveal that the coordinated detection scheme strikes a favorable balance between fixed device-related expenses and detection efficiency. This scheme offers promising policy implications, streamlining maintenance across diverse road networks and meeting extensive infrastructure needs, offering policymakers an efficient and viable scheme for road infrastructure maintenance.

Automated Crack Identification, to Ease Maintenance of Reinforced Concrete Bridges

One of the critical factors in the regional economic development and sustainability is the regular maintenance of road infrastructure. The preliminary step in the structural maintenance of bridges is in-situ inspection.Accurate and reliable infrastructure inspection plays a vital role in making the further steps’ decision, whether more assessment needs to put into action or the bridge is in good condition.The visual assessment, at the moment, is performed manually by the engineers, being therefore a too time-consuming and expensive operation, and in case of inspecting the critical points puts the engineers in danger, otherwise those parts will be missing of data. Overcoming these barriers, over the past few decades, considerable efforts have been made to construct the fundamental of implementing artificial intelligent techniques as a tool, to do autonomous inspection.This research engages in the data analysis section of the autonomous inspection with segmenting the cracks on the images acquired by them. This goal is achieved by implementing semantic segmentation deep learning development which is being trained on 8192 images and their binary masks as dataset, using the U-Net network. The model prediction shows 98.2% accuracy in crack segmentation. However, the novelty of our research extends beyond these results, we introduce a unique evaluation method named Pixel Average Error Distance (PAED) instead of the commonly used Intersection over Union (IoU) metric for segmentation assessment. Our methodology aims to forego the IoU metrics unreliable results in case of small deviation of crack patterns. It enhances the understanding of our model real performance with the PAED metrics lower than 0.5 which demonstrates a well-designed model, while the average IoU with 0.65 value shows a poor-designed model.Overall, this method will provide the engineer the possibility to observe all surfaces of a bridge and have a reliable and precise evaluation of the existing cracks.

Network-level Method for Road Infrastructure Maintenance Decision Making

In South Korea, the aging of road infrastructures exceeding 30 years and susceptibility to natural disasters emphasize the imperative for improved maintenance. A novel method was developed to incorporate social factors, such as traffic flow, into maintenance strategies. This method evaluated the significance of road infrastructures within the network by considering changes in network performance resulting from changes in their condition. This method, anticipated to enhance maintenance efficiency, was envisaged for determining regular maintenance and recovery priorities of damaged infrastructures in the wake of natural disasters.

Road slag asphalt concrete

Asphalt and concrete road surfaces are an essential part of the infrastructure of transport systems, and in recent years slag road asphalt has gained popularity as an efficient and environmentally friendly alternative. Slag asphalt, produced by incorporating slag, a by-product of industrial steel production, into asphalt concrete mixes, is the subject of this review article. The article presents a review of researches papers on the use of slag road asphalt. This article reviews the composition, production and physical and mechanical characteristics of slag asphalt concrete. Particular attention is paid to studies of the effect of slag on the strength, fracture resistance and deformation of the material. The authors provide an overview of the various methods of modifying slag asphalt to improve its performance and durability of road surfaces. The article also highlights the environmental importance of slag asphalt, generalized its ability to reduce the use of natural resources and reduce waste from the steel industry. The overall analysis of these articles suggests the potential of using slag road asphalt concrete as an effective and environmentally sustainable material in road construction. However, further research and practical tests are needed to confirm its applicability in different climatic and road conditions. The final part of the article discusses the significance of slag asphalt concrete in the context of sustainable road construction. The potential of slag asphalt to save costs in the construction and maintenance of road infrastructure, as well as to improve its durability and sustainability, is emphasized. In conclusion, the article provides a generalised overview of slag asphalt concrete, its advantages and promising applications in road construction.

Assessing road roughness using UAV-derived dense point clouds

The quality and safety of road networks are of paramount importance in modern transportation infrastructure. Road surface conditions, particularly road roughness, significantly impact vehicular travel safety, user comfort, vehicle operating costs, and overall road infrastructure maintenance. Traditional methods for road roughness analysis, such as manual inspections or image annotation, often present limitations in terms of data completeness, efficiency, and cost-effectiveness, especially for extensive road networks. This study investigates the potential of Unmanned Aerial Vehicles (UAVs) equipped with Structure-from-Motion (SfM) derived point clouds to transform road roughness assessment. By leveraging the capabilities of UAVs, including rapid data acquisition and high-resolution imagery, and employing SfM to generate detailed point clouds, this research aims to provide a comprehensive analysis of road surface conditions. The study, conducted on a road segment within the Harran University Osmanbey campus, systematically examines road roughness at different kernel sizes: 30 cm (smaller), 50 cm (moderate), and 75 cm (larger). Through this investigation, insights are gained into how different scales of analysis influence roughness measurements. The findings highlight the potential of UAV-derived point clouds as a promising avenue for road roughness analysis, offering transportation authorities and road administrators an efficient and cost-effective means of maintaining and enhancing road networks. The integration of this technology could lead to the development of safer, more efficient, and economically sustainable road transportation systems, benefiting both road users and infrastructure managers. As research and technological advancements in UAV-based road roughness assessment continue to progress, the potential for revolutionizing road management practices becomes increasingly apparent, ultimately leading to improved road quality and enhanced travel experiences for road users.