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There are around 300, 000 kilometer of gravel roads throughout the Swedish countryside, used every day by common people and companies. These roads face constant wear due to harsh weather as well as from heavy traffic, and thus, regular maintenance is required to keep up the road standard. A cost effective maintenance requires knowledge of where support is needed and such data is obtained through inventorying. Today, the road inventory is done primarily by hand using manual tools and requiring trained personel. With new tools, this work could be partially automated which could save on cost as well as open up for more complex analysis. This project aims to investigate the possibility of automating road inventory using computer vision and machine learning. Previous works within the field show promising results using deep convolutional networks to detect and classify road anomalies like potholes and cracks on paved roads. With their results in mind, we try to translate the solutions to also work on unpaved forest roads. During the project, we have collected our own dataset containing 3522 labelled images of gravel and forest roads. There are 203 instances of potholes, 614 bare roads and 3099 snow covered roads. These images were used to train an image segmentation model based on the YOLOv8 architecture for 30 epochs. Using transfer learning we took advantage of pretrained weights gained from training on the COCO dataset. The predicted road segmentation results were also used to estimate the width of the road, using the pinhole camera model and inverse projective geometry. The segmentation model reaches a AP50−95 = 0.746 for the road and 0.813 for the snow covered road. The model shows poor detection of potholes with AP50−95 = 0.048. Using the road segmentations to estimate the road width shows that the model can estimate road width with a mean average error of 0.24 m. The results from this project shows that there are already areas where machine learning could assist human operators with inventory work. Even difficult tasks, like estimating the road width of partially covered roads, can be solved with computer vision and machine learning.

Gravel roads deterioration having absence of maintenance is one of the serious issue and common problem in the world. In order to achieve the objectives of the study, the researchers were used questioner survey. To achieve this, first identified the road sections those having lack of maintenance on time existed in the study area. Then the representative road (Sarbo-Busa, Saka-Ushane and Buxure-Sanxama) and questioner surveys samples have been selected by non-probabilistic purposive and systematic sampling respectively. The questioner’s survey was focused on identifying the causes of absence of maintenance and pre-conditions considered. Questioner survey was analyzed by RII. The absence of maintenance was occurred due to lack of budget fund, poor management, lack of skilled manpower, lack of good material quality, lack of regular inspections, poor working environments. The pre-conditions considered while maintaining the gravel roads are: Cost of maintenance, class as the road, maintenance schedule, skilled manpower, topography, number of road users, traffic volume, and economic standards of the society and location of the road from the center. In Jimma zone the deteriorations of gravel roads has been increasing with the absence of maintenance time.

The focus of this research is to develop a systematic method for ascertaining the level of service (LOS) of gravel roads in the US. To date, such a method does not exist in the highway capacity manual (HCM). In this research, a methodology comprising eight criteria that affect gravel road traffic operations is developed. For each criterion, a rating is assigned. It is either “Good”, “Fair” or “Poor”. The results of each individual criterion rating are combined to develop an overall LOS rating. Furthermore, a non-parametric machine learning technique, namely the classification and regression tree (CART) modeling structure, is employed to quantify the contribution of each criterion to the overall gravel roads’ LOS. As a case study, the developed methodology is implemented to assess traffic operations of gravel roads in Laramie County, Wyoming. The studied gravel road network is compiled and managed in a Geographic information system (GIS) shapefile prepared for the purpose of this research. That is to provide decision-makers in local agencies a computerized tool to efficiently track the performance of their gravel road network. Roughly 63.5% of the gravel road segments are operating at an LOS “Fair”, while 19% are operating at an LOS “Poor” and unexpectedly 17.5% are operating at an LOS “Good”. All gravel roads assessed are low-volume roads with 400 vpd or less. The developed methodology is intended to benefit traffic engineers, decision-makers, and any other stakeholders. The gravel road LOS methodology was developed and applied to Wyoming’s conditions. Minor adjustments might be needed for implementation in other jurisdictions.

In extreme bad weather conditions, vehicles can timely adjust the speed and distance according to the road information, which will effectively reduce the occurrence of traffic accidents. Road type and road adhesion coefficient are the main factors affecting road condition. The traditional identification method of road adhesion coefficient has the characteristics of high cost and low reliability. At the same time, the road surface information recognition technology based on machine vision has become a hot spot in the current research, but the low accuracy and poor robustness are always the difficulties in the research. Based on the semantic segmentation model which is popular in machine vision, a new pavement type recognition technology is proposed by improving the model output network. According to the existing literature, vehicle road surface can be divided into the following 9 categories, including wet asphalt road, dry asphalt road, wet concrete road, dry concrete road, wet soil road, dry soil road, gravel road, compacting snow road and icy road. The standard data set is made by collecting road pictures through taking photos, downloading and other ways, and the pre-processed data set is used to train the improved semantic segmentation network model. Through many tests, training parameters that achieve the desired effect are selected for model parameter solidification, and the semantic segmentation model after solidification is used to predict the road image acquired by the camera. According to the prediction results of the model, the categories of the current driving road surface are obtained. The test results of a large number of road images show that the average classification accuracy of the nine road types is about 94%, which effectively improves the identification accuracy and robustness of the current road types. At the same time, the predicted time of a single frame image on a specific test platform is about 0.028 6 s, which meets the real-time requirements.

The interaction between off-road tires and granular terrain has a great influence on the tractive performance of off-road vehicles. However, the finite element method or the discrete element method cannot effectively study the interaction between off-road tires and granular terrain. The three-dimensional combined finite element and discrete element method is applied to handle this problem. In this study, a calibrated finite element method–discrete element method model is established, in which the finite element model of off-road tire is validated by stiffness tests, while the discrete element model of gravel particles is validated by triaxial compression tests. The calibrated finite element method–discrete element method model can describe the structural mechanics of the off-road tire and the macroscopic mechanical properties of the gravel road. Tractive performance simulations of the off-road tire on gravel road under different slip conditions are performed with the commercial software LS-DYNA. The simulation results are basically corresponded with the soil-bin test results in terms of granular terrain deformation and tractive performance parameters versus the slip rates. Finally, the effects of tread pattern, wheel load, and tire inflation pressure on tractive performance of off-road tire on granular terrain are investigated. It indicates that the calibrated finite element method–discrete element method can be an effective tool for studying the tire–granular terrain interaction and predicting the tractive performance of off-road tire on granular terrain.

Gravel road safety is a crucial area of road safety since gravel roads represent a substantial proportion of the entirety of the nation’s roadway network. Also, gravel roads pose inherent hazards t...

Gravel roads require extensive maintenance and rehabilitation. That is because of the dynamic behavior of gravel road conditions. This study is aimed at investigating road condition factors affecting the traffic-generated dust on gravel roads. The study concerns Laramie County, Wyoming. The parametric binary probit regression structure and the non-parametric Bayesian regularization artificial neural network (BRANN) methods were implemented to model traffic-generated dust levels as a function of the factors that contribute to dust generation. The BRANNs method simplifies the model building process by precluding irrelevant and redundant weights of artificial neural networks (ANNs). In this study, the BRANN method is utilized with one single hidden layer using the MATLAB® function for neural networks. In the hidden layer, multiple neuron counts ranging from three to thirty were attempted. The parametric and non-parametric techniques mentioned were adopted to provide comprehensive insights into important factors that contribute to dust generation. Therefore, both techniques complement each other. A total of 206 gravel road segments were used for model building for both analyses. As per the results of the BRANN model, it was found that twenty neurons produced the most accurate results. Furthermore, it was found that the BRANN model had more variables than the binary probit regression model, whereas the probit model provided general insights into the factors affecting the dust on gravel roads such as average travel speed and soil type. Also, it would be an easy-to-use method to assist local agencies and DOT practitioners in addressing the dust problems on gravel roads.

Our old grey Subaru crept slowly up the dusty gravel road. It was a muggy evening, but you couldn’t turn the air con on or open any windows unless you wanted a car full of dust. I got to sit in the front for this last stretch to Nanny’s, so I oversaw switching out Dad's Prince Tui Teka CDs. Dad went extra slow so as not to scratch the discs, but that was hard on a gravelly road full of potholes.

Road condition evaluation is a critical part of gravel road maintenance. One of the assessed parameters is the amount of loose gravel, as this determines the driving quality and safety. Loose gravel can cause tires to slip and the driver to lose control. An expert assesses the road conditions subjectively by looking at images and notes. This method is labor-intensive and subject to error in judgment, therefore, its reliability is questionable. Road management agencies look for automated and objective measurement systems. In this study, acoustic data on gravel hitting the bottom of a car was used. The connection between the acoustics and the condition of loose gravel on gravel roads was assessed. Traditional supervised learning algorithms and convolution neural network (CNN) were applied, and their performances are compared for the classification of loose gravel acoustics. The advantage of using a pre-trained CNN is that it selects relevant features for training. In addition, pre-trained networks offer the advantage of not requiring days of training or colossal training data. In supervised learning, the accuracy of the ensemble bagged tree algorithm for gravel and non-gravel sound classification was found to be 97.5%, whereas, in the case of deep learning, pre-trained network GoogLeNet accuracy was 97.91% for classifying spectrogram images of the gravel sounds.

Daily traffic on arid gravel roads can easily generate dust. Dust emitted from gravel roads creates several problems such as aggravated asthma, breathing difficulties, reducing crop yields, and even death. Therefore, local agencies tend to track the dust amounts on the gravel roads in order to maintain them in good conditions. An accurate detection of dust amounts is very crucial in Gravel Roads Management System (GRMS). Data collection is considered as one of the main challenges facing local agencies due to budget constraints. This paper establishes a novel method for an automatic recognition of dust amounts on gravel roads. This algorithm, “Simple Dust Classification Algorithm”, uses images taken from smartphone application, “Road roid”, to classify dust amounts by using proprietary Digital Image Processing algorithms. A Dustometer device was used to validate the proposed algorithm. Dustometer measurements, supported by statistical analysis, demonstrate that the proposed algorithm achieves an outstanding dust amount classification accuracy. Hence, the proposed algorithm is a promising alternative to assist local agencies in data collection and maintenance planning.

Maintenance of low volume roads especially gravel roads is costly, and millions of funding annually are spent for aggregate replacement alone. Otta seal, compared to other bituminous surface treatments (BSTs) for low volume roads that require high-quality materials and specialized expertise, can be constructed using cheaper local aggregates and accessible equipment. However, only three states have reported Otta seal constructions and performance in the US. In this study, an economic analysis was conducted to compare the cost of maintaining a gravel road to the cost of upgrading an existing gravel road to a double Otta seal surface. This analysis was conducted at three levels: deterministic life-cycle cost analysis (LCCA), a stochastic Monte Carlo simulation-based LCCA, and a traffic based economic analysis. A generic one-mile rural road in Midwest was considered as a case study location. Although, according to the analysis conducted in this paper, an upgrade to Otta seal investment might be justif...

Due to importance of developing the North and Arctic territories, it is essential to ensure working capacity of machinery in cryolithozone. To solve these problems, it is necessary to study accumulation of structural damage and changes in material properties. The purpose of this article is to study influence of operation in the northern roads conditions on fatigue damage, brittle fracture resistance and the working capacity of a leaf spring of a KAMAZ truck widely used in Yakutia. Methods of mathematical statistics, metallography, microindentation, standard mechanical tests, etc. were applied. Results of analysis of distribution of the KAMAZ vehicle springs failures on the months of the year are given, taking into account the seasonal changes in a profile of the typical for Yakutia gravel road. To assess the fatigue damage of metal, the structural and statistical study of microhardness (microdamage) and porosity (mesodamage) was carried out. Impact toughness values were determined and it was found that the accumulated damage did not lead to metal embrittlement at low temperatures. The more significant impact of the cryolithozone road microprofile on the working capacity of the KAMAZ truck springs as compared to the factor of the low temperatures has been substantiated.

Heavy vehicle traffic on unpaved roads in western North Dakota, due to increasing oil extraction activities, is generating coarse particulate matter (PM10) and fine PM (PM2.5), and total suspended particles (TSP), potentially raising concerns for animal and human health and impacts on crop and plant growth. However, limited quantification and characterization are available for PM from unpaved roads next to oil activities. Therefore, a study was conducted to characterize and quantify the PM concentrations from heavy traffic on unpaved roads due to increasing oil activities in western North Dakota. Three unpaved road sites were selected and paired with application of two dust suppressants (brine and magnesium chloride). MiniVol air samplers were used to quantify PM, scanning electron microscopy (SEM) was used to characterize the minerals in PM samples, and inductively coupled plasma mass spectrometry (ICP-MS) was used to analyze the chemical composition of soil samples. The average PM10 and PM2.5 concentrations were higher at site 2 (untreated loose gravel road) than at site 1 (periodically treated with dust suppressants) during the study period (2015-2016). In addition, the PM concentrations were lower in 2016 because of decreased oil activities at site 2. Statistical analyses revealed that the PM concentrations were mostly correlated with vehicle count in addition to temperature, wind direction, and rainfall. Magnesium chloride was the most effective treatment for reducing PM. The SEM analyses of PM samples revealed that most particulates were quartz, other silicates, or biogenic particles. Soil sample analyses revealed that the concentrations of most elements were lower than the threshold values set by the USGS National Geochemical Survey. Keywords: Air pollution, Air quality, Dust, North Dakota, Oil development, Particulate matter.

In developing countries, improvement of trafficability of rural roads, which are generally earth or graveled, are crucial for rural development. Under the circumstances with financial constraints of those countries, locally available material-based approach towards improvement of trafficability are regarded as one of the practical measures. Do-nou, which is Japanese term for soilbag, have been utilized for reinforcing base course. Do-nou method is requiring only labor even for compaction of base material and used polyester fiber woven bags for crops, fertilizer, etc. as one of geotextile. In 29 countries of Asia, Africa and the Pacific, about 180 km rural road has been improved using the Do-nou method, by the NGO which one of the authors is belong to. Because of the easiness and effectiveness of Do-nou method, it enables minor and small enterprises, generally operated by the youth, to be involved in road construction implemented by public works. In order to improve trafficability of rural roads while solving social problem, high unemployment rate among the youth in developing countries, the method needs to be adopted by road authorities for further extension. In this study, therefore, the performance of gravel road with base course consist of Do-nou have been evaluated through the series of full-size driving tests. The settlement of road surfaces with conventionally designed base course and with that consist of Do-nou with the same thickness subjected to the traffic load were measured and compared. Dynamic cone penetration index of these base course structure was also compared. Load distribution effects were also examined using earth pressure gages located on subgrade and road surface during driving tests. The mechanism of reducing settlement of road surface with Do-nou base course were verified. The performance of base course with Do-nou are defined in comparison to conventional base course structure, which enable road authorities adopt Do-nou method.

Road condition evaluation is a critical part of gravel road maintenance. One of the parameters that are assessed is loose Gravel. An expert does this evaluation by subjectively looking at images taken and written text for deciding on the road condition. This method is labor-intensive and subjected to an error of judgment; therefore, it is not reliable. Road management agencies are looking for more efficient and automated objective measurement methods. In this study, acoustic data of gravel hitting the bottom of the car is used, and the relation between these acoustics and the condition of loose gravel on gravel roads is seen. A novel acoustic classification method based on Ensemble bagged tree (EBT) algorithm is proposed in this study for the classification of loose gravel sounds. The accuracy of the EBT algorithm for Gravel and Nongravel sound classification is found to be 97.5. The detection of the negative classes, i.e., non- gravel detection, is preeminent, which is considerably higher than Boosted Trees, RUSBoosted Tree, Support vector machines (SVM), and decision trees.

Conventional road condition evaluation is an expensive and time-consuming task. Therefore data collection from indirect economical methods is desired by road monitoring agencies. Recently intelligent road condition monitoring has become popular. More studies have focused on automated paved road condition monitoring, and minimal research is available to date on automating gravel road condition assessment. Road roughness information gives an overall picture of the road but does not help in identifying the type of defect; therefore, it cannot be helpful in the more specific road maintenance plan. Road monitoring can be automated using data from conventional sensors, vehicles' onboard devices, and audio and video streams from cost-effective devices. This paper reviews classical and intelligent methods for road condition evaluation in general and, more specifically, reviews studies proposing automated solutions targeting gravel or unpaved roads.

Under the road development programme of Ethiopian Roads Authority (ERA) in Ethiopia, many roads spreading all over the country were upgraded from the existing gravel road to the widened asphalt road. Due to the rugged and mountainous terrain condition, natural and man-made slope failures such as landslides usually occur in almost all the hilly and mountainous terrain in Ethiopia. The main cause for the occurrences of failures at different locations along project roads which were widened either by cutting the hill slope and/or filling the valley slope was high rainfall in the area during the month of June to September. In this paper, the case study on the investigations of landslide failures along hill slope, valley slope, slope of bridge approaches, slope of hillside on upstream and valley side on downstream of culverts along project roads is described. The analysis of landslides and its different methods of mitigation measures, namely flattening of slope with or without provision of intermediate berms, provision of lateral support by gravity retaining wall at toe of hill slope or at road edge on valley slope side including proper slope protection and drainage measures adopted in the rehabilitation works along different project roads in Ethiopia, are presented here.

In gravel roads management systems (GRMS), the need for a holistic approach for detecting the dust amounts on gravel roads has enabled the development of a solution that works based on one ...

This study developed a model to select the optimum sections of unpaved roads for chemical treatments as dust suppressants within a limited budget. In the state of Wyoming, more than 19,312 ...

En beräkningsmodell utvecklas för att skatta miljöpåverkan och ekonomiska effekter vid vägunderhållet grusning. Beräkningsmodellen används för att kunna jämföra alternativa metoder för grusning. Beräkningsmodellen testas genom ett testscenario med tre alternativa metoder för grusning. I alternativ ett används nytt grus och i alternativ två och tre används återvunnet grus. Studiens resultat visar att beräkningsmodellen kan användas för att skatta kostnad och miljöbelastning. Testscenariot visar att det är ekonomiskt och miljömässigt fördelaktigt att använda återvunnet grus vid grusning. Ur ett samhällsperspektiv är det viktigt att belysa fördelarna med att använda återvunnet grus, då detta kan motivera att fler faktiskt väljer detta alternativ och då brytning av naturgrus är en energikrävande process som påverkar miljön negativt. Studiens syfte är att skapa förståelse för miljömässiga och ekonomiska effekter vid återvinning av grus vid grusvägsunderhåll. För att uppnå syftet genomförs en fallstudie som besvarar frågeställningen hur miljömässiga och ekonomiska effekter kan skattas på grusvägsunderhåll. A calculation model is developed to estimate the environmental impact and economic effects of gravel road maintenance. The purpose of the calculation model is to be able to compare alternative methods for graveling. The calculation model is tested through a test scenario with three alternative methods for graveling. In alternative one, new gravel is used and in alternatives two and three recycled gravel is used. The study's results show that the model calculation can be used to estimate cost and environmental impact. The test scenario shows that it is economically and environmentally advantageous to use recycled gravel in graveling. From a societal perspective, it is important to highlight the benefits of using recycled gravel, as this may motivate more people to actually choose this alternative. Because mining of natural gravel is an energy-intensive process that has a negative impact on the environment. The purpose of the study is to create an understanding of the environmental and economic effects of recycling of gravel during gravel road maintenance. To achieve this, a case study is conducted that answers the question how environmental and economic impacts can be estimated on gravel road maintenance.

Small-scale race events are a popular form of recreation in natural areas with the potential for social and environmental impacts. Leave No Trace-based communication and education strategies are the most prevalent form of mitigating recreation impacts on public lands. To understand competitive race participants’ perceptions of Leave No Trace, this exploratory study compared race event participants’ attitudes, evaluative beliefs, and global perceptions of Leave No Trace by event type: gravel road running, trail running, mountain biking, and motorcycle trail racing. Data were collected using online post-event surveys. Participants were sampled from five competitive race events on public land held during fall 2015 to summer 2016. Significant differences between event types were found for all constructs examined. Motorcycle racers were the least likely to agree that Leave No Trace is an effective method for protecting the environment, least likely to believe that humans have the potential to cause social or environmental impacts, and were the least willing to change their behavior if they found out it was damaging the environment. Runners’ attitudes toward specific Leave No Trace behaviors were the least in line with Leave No Trace recommendations, while mountain bikers were the most in-line. Management implications When issuing permits and managing events on public natural areas, managers should approach each affair differently, realizing that the type of event may influence the level of social and ecological impact. Managers should contact event organizers as early as possible to raise awareness of possible impacts, provide tailor-made messaging to participants, and develop management plans specific to the event and location that monitor and adaptively manage impacts to preserve social experiences and ecological resources. This will help managers remain flexible in their event management.

In 2009, pulp mill fly ash was used for stabilizing the road base of a low-volume gravel road. Six years after stabilization, a two-year monitoring program and a complementary study were conducted ...

The government proliferantion in Indonesia become a consequence of the existence of a policy of fiscal desentalisasi. The existence of the government proliferantion is expected to shorten the span of control so that the goal of improving the well-being of society and the public service can be achieved. This study goals to analyze the impact of the government proliferantion on the regional educational and economic outcomes against on 491 Kabupaten/Kota in Indonesia. This research uses the Difference in Difference as a tool of analysis. Data obtained from the Indodapoer-World Bank year 2001 until 2014. The impact of the presence of the government proliferantion against outcomes education is lowering the level of literacy through a decrease in the number of buildings on the SD output though it is insignificant and its impact on economic outcomes effect increasing income per capita community that in proxy through household spending per capita and GDP per capita, increasing the number of workers, and lower levels of poverty through increased output in the electricity, road access to the village dirt road, and the village gravel road. The impact in General of the expansion area is a local Government has succeeded in transferring funds to the poor but not with a business climate that is shown by the results of the constant price GDP as a whole which indicates significant results negative. Keywords: Government Proliferantion, Difference in Difference, Educational, Economic.JEL : R1, H00, I21, I25