Road Infrastructure Maintenance Research Articles

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.

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.

6G Connected Vehicle Framework to Support Intelligent Road Maintenance Using Deep Learning Data Fusion

The growth of IoT, edge and mobile Artificial Intelligence (AI) is supporting urban authorities exploit the wealth of information collected by Connected and Autonomous Vehicles (CAV), to drive the development of transformative intelligent transport applications for addressing smart city challenges. A critical challenge is timely and efficient road infrastructure maintenance. This paper proposes an intelligent hierarchical framework for road infrastructure maintenance that exploits the latest developments in 6G communication technologies, deep learning techniques, and mobile edge AI training approaches. The proposed framework abides with the stringent requirements of training efficient machine learning applications for CAV, and is able to exploit the vast numbers of CAVs forecasted to be present on future road networks. At the core of our framework is a novel Convolution Neural Networks (CNN) model which fuses imagery and sensory data to perform pothole detection. Experiments show the proposed model can achieve state of the art performance in comparison to existing approaches while being simple, cost-effective and computationally efficient to deploy. The proposed system can form part of a federated learning framework for facilitating large scale real-time road surface condition monitoring and support adaptive resource allocation for road infrastructure maintenance.

IDENTIFICATION OF RELEVANT POINT CLOUD GEOMETRIC FEATURES FOR THE DETECTION OF PAVEMENT CRACKS USING MLS DATA

Abstract. The maintenance of road infrastructures is one of the main challenges that transportation authorities must face to guarantee the safe mobility of people and goods. Novel remote monitoring technologies offer advanced solutions for this issue, allowing the inspection of large sections of the network in a time-effective way. In this paper, we introduce a methodology for the detection of cracks on road pavements using point clouds acquired with a mobile laser scanner. First, the points of the cloud are labelled as pavement or cracks based on field annotations, and local geometric features of the points are calculated using principal component analysis. Two different machine learning classifiers, Support Vector Machine (SVM) and Random Forest, are then trained to identify crack points using the point feature data. The crack points predicted by the classifiers are clustered as individual instances and compared to the corresponding ones from a test dataset. Although pointwise performance of the method is modest, it can correctly identify and measure areas of the pavement affected by cracking.

CTCD-Net: A Cross-Layer Transmission Network for Tiny Road Crack Detection

Crack detection is essential for the safety maintenance of road infrastructure. However, there are two major limitations to detecting road cracks accurately: (1) tiny cracks usually possess less distinctive features and are more susceptible to noises, so they are apt to be ignored; (2) most existing methods extract cracks with coarse and thicker boundaries, which needs further improvement. To address the above limitations, we propose CTCD-Net: a Cross-layer Transmission network for tiny road Crack Detection. Firstly, we propose a cross-layer information transmission module based on an attention mechanism to compensate for the disadvantage of unobvious features of tiny cracks. With this module, the feature information from upper layers is transmitted to the next one, layer by layer, to achieve information enhancement and emphasize the feature representation of tiny crack regions. Secondly, we design a boundary refinement block to further improve the accuracy of crack boundary locations, which refines boundaries by learning the residuals between the label images and the interim coarse maps. Extensive experiments conducted on three crack datasets demonstrate the superiority and effectiveness of the proposed CTCD-Net. In particular, our method largely improves the accuracy and completeness of tiny crack detection.

Gloss to the judgment of the Court of Appeal in Lublin of 30 June 2020 (I ACa 233/19) and to the judgment of the Court of Appeal in Szczecin of 24 March 2021 (I ACa 434/20)No Liability of the Property Owner (Road Manager) for Damage due to the Failure to Maintain Property in Winter Conditions

The gloss refers to judgments rendered by two courts of appeal on the subject of the property owner’s liability for the proper maintenance of pavements and other pedestrianized zones against adverse weather conditions (snow, rain, black ice). The necessity of providing proper maintenance of road infrastructure in winter has been of interest to both civil law scholarship and jurisprudence for a long time, which has resulted in the emergence of different tendencies. The author, who agrees with the position expressed in the glossed judgments that liability for proper maintenance of infrastructure has been regulated in Article 415 of the Polish Civil Code, emphasizes that this provision will apply regardless of the fact whether the liable entity is a private person, a cooperative, a municipality or other unit of local government. In addition to analyzing the provisions in force, the scope of obligations imposed on property owners or road managers and the requirement of exercising due care by the above-mentioned entities, the author also points out that it is possible to present a number of no-liability cases, including events which are actually ‘unfortunate accidents’.

Investigating Sand as Wearing Course in Rural Roads

Road transportation in rural areas is one of the main means of conveying people, farmers, farm implements and farm produce from the rural dwellings and farms unfortunately the dearth of investment and maintenance of road infrastructure in rural communities and also the growing use of these roads has made the plying of such roads to be worrisome because of its implication on the safety of those using the road. The consequence of the badly rutted, sandy or muddy roads is the increase in the cost of transporting agricultural produce to farm which will inevitably contribute to increasing food prices. The need to make sandy rural road to have a suitable wearing course necessitated the collection of sand samples from a community in Yewa South Local Government Area of Ogun State Nigeria. Particle size distribution, in-situ density, compaction, relative density, relative compaction and California bearing ratio (CBR) were conducted on the sand samples. The soil profile was 300mm thick at the point the sample was obtained. The results obtained from the tests indicated that; the sand was well graded (SW) in accordance to the unified soil classification system. The CBR values obtained were 23.23% and 22% and the result of relative compaction was 65% which is less than the 90-95% stipulated by relevant standard and this can be attributed to the natural compaction of the soil done by the flowing water as against when a compactive effort is applied. For the sandy road to be plied all year round, the moisture content of the sand should be kept at a value well above 1.3% and the thickness of the wearing course should not be less than 300mm.

Road condition monitoring using unsupervised learning based bus trajectory processing

The road infrastructure maintenance is crucial for hassle-free transportation. The proposed work leverages the dense connectivity of public transportation buses for road condition monitoring at a large scale. It uses the buses equipped with GPS and accelerometer as mobile sensors to infer the road surface roughness and the damaged road segments. The vibration features are computed from accelerometer data using the in-bus controller, and trip records are processed offline using a centralized server. The proposal applies the unsupervised learning based Self Organizing Map (SOM) and k -means clustering algorithms on the GPS location records and vibration features to infer the road condition. The damaged segments and rough patches of the selected region that requires immediate repairment are suggested. This information can be used to prioritize the repairment based on the available time and budget. The proposed solution is evaluated using more than 1150 km of trip records collected over four routes of Gujarat state of India. The proposed solution accurately infers the road roughness and identifies the damaged road segments for maintenance. Moreover, the ablation analysis of the proposal is carried out to evaluate the utility of combined execution of SOM and k -means algorithms. Further, the feasibility of proposal for a large scale deployment is assessed. The analysis shows that the proposed system is scalable and can process the daily transit data of a metro-city (e.g. 540 buses of the Ahmedabad Municipal Transport Service) using the in-bus controllers and a server.

Engineered and emerged collaboration: vicious and virtuous cycles

Inter-organizational collaboration within the construction industry consists of both engineered and emerged aspects. Engineered formal practices and emerged informal practices interplay in their influence on the overall success of client-contractor collaboration. This interplay has been recognized but is still understudied and requires further research to increase the understanding of how the interplay functions. To enable the study of the emerged aspects of collaboration we applied a practice-based approach in a longitudinal multiple case study, including four projects for operation and maintenance of road infrastructure. This paper contributes to the literature on collaboration in construction by providing detailed examples of how the interplay between engineered and emerged collaboration may unfold, showing how formal and informal practices contribute to the development of vicious and virtuous cycles of collaboration. Furthermore, our findings indicate that a virtuous cycle of only informal practices can become a weakness if unexpected problems occur. Project managers should therefore be encouraged to implement formal collaboration, even if the project team is already in a virtuous cycle of informal collaboration.

Comparing Different Equipment and Applications in Pavement Data Collection as Part of Road Management System

Abstract National roads are the main arteries in road transport infrastructure. Therefore, all agencies or authorities responsible of road infrastructure, pay attention to road management systems. Albania is experiencing an increase in road infrastructure investments and maintenance of this road network. There have been some attempts to establish national and secondary road management systems. These systems attempt to achieve different objectives, such as the provision of an adequate level of service, the preservation of the road infrastructure, etc. A good Road Asset Management System (RAMS), helps to carry out all the actions of inventory, storage and maintenance of road assets as well as, supports the decision-making process. At present, there are several data collection devices and applications that carry out the job efficiently. The purpose of this paper is to present the analysis of the use and comparison of some equipment and Cell Phone Based Systems (MiniROMDAS, PaveProf-V2 and RoadLab_Pro) used for the road pavement data collection, necessary in the calculation of the International Roughness Index (IRI), along the national road network in Albania. The comparison is made, by analyzing the data and results obtained along a 20 km long road segment in Albania, using the various above-mentioned technologies. Also, an overview of the currently available technologies providing information that could assist managers in establishing an appropriate data collection program is given.

Impacts of road and hydrographic networks in the conditions of the Raut river basin environment

Road infrastructure in the Republic of Moldova represents the totality of road networks classified in express, republican and regional, including a complex of objects that ensure the efficiency of road traffic, such as bridges, gas stations, pedestrian crossings, street lighting, etc. Road construction and maintenance also require changes in geographical systems, including within river basins. The research highlights the impact of the river network on road infrastructure construction and maintenance projects and at the same time the impact of the road network on the river network in the environmental conditions within the Răut river basin. Therefore, the most frequent environmental impressions are left by the relief levelling and geological settlement works, by the extraction and displacement of mineral substances (gravel, sand) according to construction or repair projects, river valley modelling for bridge construction, landscaping of the space adjacent to the roads by deforestation of trees, irradiation of the soil layer, by modification of surface runoff and environmental pollution.

Geotechnical characterization of an innovative soil stabilization product (Aggrebind/Road Master) usable in the construction, rehabilitation and maintenance of road infrastructure and the construction of social housing in Cameroon

Building roads in developing countries has always been very expensive. An efficient and more affordable method is needed. AggreBind’s soil stabilization solutions, known as RoadMaster (RM1 / RM2) and AggreBind (AGB-WT/BT) are offered. The objective of the present study was to make a geotechnical characterization of this product with a view towards its use in the construction, rehabilitation and maintenance of road infrastructure in Cameroon.A series of laboratory tests were carried out; soil identification tests (particle size analysis, Atterberg limits), lift tests (Proctor and modified CBR test), determination of the optimum water content of the material, maximum dry density, immediate CBR index and the CBR index after immersion for 4 days.Only the 95% OPM CBR test was carried out on the soil with the additive AGB-WT/RM1 in order to characterize the effects of the product on the soil.In conclusion, AggreBind/RoadMaster can provide stable, dust-free roads and stabilized base courses for general roads and highways that meet or exceed the bearing capacity requirements of international road specifications. In addition, the use of this product reduces the cost of road construction by 40% to60% and increases in-situ load bearing capacity by 400% to 600%.

Development of a Smartphone Application Serving Pavement Management Engineers

Determining the presence and location of pavement defects is vital to the timely maintenance of road infrastructure. Many applications exist for the purpose of tracking user-reported pavement distresses and roadway hazards, but few leverage the capabilities of modern smartphones. This pilot study aims to establish proof of concept that a custom-built smartphone application possesses the ability to utilize onboard smartphone sensors to collect global positioning system (GPS) and vibrational data with accuracy and sensitivity suitable for detecting pavement defects. An application was developed for Android and iOS that utilizes smartphone GPS, accelerometer, and gyroscope sensors to sample and log location, vibrational, and rotational data. The datasets obtained by the application are used to quantitatively establish the application’s ability to detect abnormalities in sampled data when in the presence of pavement defects or abnormal road conditions versus a baseline noise margin under no pavement defects. Additionally, the true sampling frequencies of both platforms are established to compare against user-specified sampling frequencies to determine true sampling performance. Furthermore, the datasets obtained from the Android and iOS platforms are compared to establish that there are differences in sensor hardware used that affect the sensor responses. Results from analysis of the datasets support the conclusion that the application successfully samples smartphone sensors, which can be used to detect defects in pavement when mounted to a vehicle windshield, as well as having the ability to provide relatively accurate and precise location data, given commercial smartphone GPS accuracy limitations.

Relationship between transport infrastructure expenditures and costs and transport indicators - an overview of European and Ukrainian situation

Quality of transport infrastructure determines the quality of the provision of services for the transportation of goods and passengers. The infrastructure sector is a guarantee of efficiency, mobility and uninterrupted economic activity. However, the high quality of the transport infrastructure implies significant and long-term investments. According to a McKinsey research report, to support sustainable economic growth in the world in line with forecasts for 2030, it is necessary to cover the need for additional investment of $ 3.3 trillion annually, equivalent to 3.8% of world GDP. The purpose of this work is to analyze the existing European approach to assessing transport infrastructure expenditures and costs, their impact on transport performance at the country level, as well as trends in Ukrainian policy regarding the support and development of road infrastructure. Transport infrastructure costs include the investment in new infrastructure, the cost of upgrading the existing infrastructure, the cost of maintaining the infrastructure, and the operating costs to enable the use of the transport infrastructure. Directive 2011/76 / EU prescribes that the weighted average infrastructure charges should be linked to construction costs, as well as the costs of operation, maintenance and development of the corresponding infrastructure network. The official Eurostat data of investment and O&M costs of transport infrastructure based on the citizenship principle was taken for this study, namely: - the number of ton-kilometers traveled per year; - the volume of investments in road infrastructure per year; - the amount of funds for the maintenance of road infrastructure per year. The indicators were taken for countries of the EU for which statistics were available for 2016 and 2019. According to the study, the effect of investments in transport infrastructure is strongly manifested both in the same year and affects subsequent years in terms of the volume of annual transport work. That is, an increase in investment in road infrastructure clearly has a positive effect on the volume of transport work performed. On the other hand, the volume of investments in the EU infrastructure brings a much greater positive effect than funds for road maintenance. Thus, the experience of developed countries shows a positive relationship between investment in the maintenance and construction of transport infrastructure and economic performance of the state. Part of the research was devoted to the study of the impact of investments in road infrastructure of Ukraine on the example of the global Ukrainian infrastructure project "Big Construction". It is projected that in 2024, thanks to road works under the Big Construction, Ukraine's GDP will be 4.4% higher than GDP in 2020. But, as the research shows, good transport infrastructure is a necessary, but not a sufficient condition for growth. "Big Construction" project in Ukraine does not show any positive effect on the volume of performed internal transport work now. The reasons may lie both in more long-term effect from improving the infrastructure, and in the facts of corruption and theft of funds allocated for the project.

Performance Assessment of Reference Modelling Methods for Defect Evaluation in Asphalt Concrete.

The deterioration of road conditions and increasing repair deficits pose challenges for the maintenance of reliable road infrastructure, and thus threaten, for example, safety and the fluent flow of traffic. Improved and more efficient procedures for maintenance are required, and these require improved knowledge of road conditions, i.e., improved data. Three-dimensional mapping presents possibilities for large-scale collection of data on road surfaces and automatic evaluation of maintenance needs. However, the development and, specifically, evaluation of large-scale mobile methods requires reliable references. To evaluate possibilities for close-range, static, high-resolution, three-dimensional measurement of road surfaces for reference use, three measurement methods and five instrumentations are investigated: terrestrial laser scanning (TLS, Leica RTC360), photogrammetry using high-resolution professional-grade cameras (Nikon D800 and D810E), photogrammetry using an industrial camera (FLIR Grasshopper GS3-U3-120S6C-C), and structured-light handheld scanners Artec Leo and Faro Freestyle. High-resolution photogrammetry is established as reference based on laboratory measurements and point density. The instrumentations are compared against one another using cross-sections, point–point distances, and ability to obtain key metrics of defects, and a qualitative assessment of the processing procedures for each is carried out. It is found that photogrammetric models provide the highest resolutions (10–50 million points per m2) and photogrammetric and TLS approaches perform robustly in precision with consistent sub-millimeter offsets relative to one another, while handheld scanners perform relatively inconsistently. A discussion on the practical implications of using each of the examined instrumentations is presented.