This licentiate thesis is part of a Ph.D. project entitled “CDU:T43 Different methods for dust control and evaluating dust control on Nordic gravel roads”. The Ph.D. project is financially supported by the Swedish Road Administration (SRA) through the Centre ofResearch and Education in Operation and Maintenance of the Infrastructure (CDU)within the Swedish Network of Excellence – Road Technology (RT). Much of the research described in this thesis has been carried out in collaboration with SRA Construction and Maintenance, which also contributed financially.One of the most significant problems associated with gravel roads is traffic-generateddust which facilitates the deterioration of the road surface and acts as a major source of particulate matter in the air, thus affecting traffic safety, public economics, and environmental quality.This work describes different programs for evaluating the effectiveness of different dust suppressants and the results obtained from completion of these experiments. Inchapter one, a general introduction into this project and its objective will be offered. The next chapter gives insight into the basic concepts of gravel roads. In the third chapter the existing literature concerning dust control of gravel roads is briefly retold. The fourth chapter gives an account of the research methodology. A field evaluation of different dust suppressants will be described in the fifth chapter. Chapter six describes methods for analysing dust suppressant residual concentration of samples taken from the different test road sections included in the above mentioned field evaluation. The objective is to investigate the longevity of these dust suppressants. Results from the analyses of the horizontal diffusion of gravel road generated dust are presented in chapter eight. The ninth chapter offers a description and evaluation of the objective method used for quantitative dust emission measurements by means of a visual method. In chapter tendust emissions are correlated to other general deformation processes on the gravel road.Chapter eleven defines laboratory trials concerning the leaching of dust suppressants from gravel wearing course material when subjected to water. Concentrations of dust suppressant as well as size distribution of gravel material were factors examined in this context. In chapter twelve, laboratory examinations of the drying rate for different combinations of aggregate gradations and chloride compounds will be presented. The thirteenth chapter gives a description of a developed laboratory equipment for evaluatingdust suppressant effectiveness, while chapter fourteen offers a conclusive summary. / QC 20101115

Ghana, like many African countries, is plagued with unemployment, poverty and annual trade deficits. Unemployment and poverty have led to a socio-economic breakdown. They are believed to be among the causes that led to the 1994 Rwanda genocide. Despite the abundance of an unemployed labor force, Ghana continues to depend on imported equipment, costing $174 million annually for its earthmoving and construction activities. In 1986, the Government of Ghana, the World Bank, the International Labor Organization and the United Nations Development Program introduced labor-based road rehabilitation program in Ghana to help create more jobs and reduce the high unemployment and poverty incidence. The program has not been patronized due to the casual labor usage and labor organizational and management problems. This research formulates the Family-Based Labor Management (FBLM) concept (also referred to as the HPWT-FBLM concept) by incorporating High Performance Work Team (HPWT), the Ghana Family System, and Roles and Responsibilities Matrix (RRM) concepts to make the program more attractive to labor and management. The FBLM concept would equip local contractors with the managerial skills to increase average monthly production from 1.33km to between 4km and 6km gaining competitive advantage over the 3.07km monthly production of the equipment-intensive contractor. Since the HPWT-FBLM concept has not been used, the related concepts HPWT and RRM concepts are used to validate the newly formulated recruitment, training, work method, communication and reward strategies. When adopted, the HPWT-FBLM concept would annually invest 10% of the $174 million for five years and yield employment increase of 23,000-34,000 the first year, growing to a total of 116,000-170,000 in five years. This concept will help reduce import deficit, conserve foreign exchange, and develop a pool of skilled workers and managers in Ghana. It has the potential of boosting the Ghanaian manufacturing industry for making hand-tools in lieu of purchasing imported equipment. The HPWT-FBLM concept can be adopted by the agriculture and building construction and other industries in Ghana that use large supplies of unskilled and semi-skilled labor. / Ph. D.

M.Sc. / The purpose of this study was to determine whether unpaved roads in commercial forests have a detrimental impact on aquatic macro-invertebrates in the receiving rivers associated with these roads. The upper section of the headwaters of the Motlhaka-Semeetse River was chosen as the Study Area because a portion is situated in the Wolkberg Wilderness area (natural area with no roads) and a portion in a commercial forestry plantation (high road network density). Stream conditions based on a rapid bio-assessment of aquatic macro-invertebrates using SASS5 were then compared between the two catchments amid 2002 and 2010. In order to support the SASS5 results, daily rainfall data from December 1959 to October 2010 was collated and compared to daily stream flow data from January 1960 to October 2010. In addition, geomorphological and instream habitat changes in the river between the two catchment areas were recorded, as well as the condition of stream crossings and their linkage to stream networks in the afforested catchment. The SASS5 results revealed that stream conditions at the upper unimpaired site were significantly better, visibly and statistically (p < 0.05) than conditions at the lower site within the forestry plantation. In addition, there were noteworthy changes in the catchment hydrology, the geomorphology and instream habitat between the natural and afforested catchments. Road network densities in commercial forests are mostly considerably higher than suggested in literature and exceed stream network densities. Roads increase the surface area for interception of rainfall and the runoff from this high density of roads results in modification of the catchment hydrology, geomorphology and instream habitat of receiving streams. This physical change to the receiving streams is one of the main reasons for the deterioration in SASS5 results, disproving the hypothesis that road networks in forestry areas have no impact on receiving aquatic ecosystems. Commercial forestry in South Africa needs to improve their road planning, layout, management and maintenance to reduce these environmental impacts. In doing so, the road network density will be reduced with considerable environmental and economic benefits.

Gravel roads connect sparse populations and provide highways for agriculture and the transport of forest goods. Gravel roads are an economical choice where traffic volume is low. In Sweden, 21% of all public roads are state-owned gravel roads, covering over 20,200 km. In addition, there are some 74,000 km of gravel roads and 210,000 km of forest roads that are owned by the private sector. The Swedish Transport Administration (Trafikverket) rates the condition of gravel roads according to the severity of irregularities (e.g. corrugations and potholes), dust, loose gravel, and gravel cross-sections. This assessment is carried out during the summertime when roads are free of snow. One of the essential parameters for gravel road assessment is loose gravel. Loose gravel can cause a tire to slip, leading to a loss of driver control.  Assessment of gravel roads is carried out subjectively by taking images of road sections and adding some textual notes. A cost-effective, intelligent, and objective method for road assessment is lacking. Expensive methods, such as laser profiler trucks, are available and can offer road profiling with high accuracy. These methods are not applied to gravel roads, however, because of the need to maintain cost-efficiency.  In this thesis, we explored the idea that, in addition to machine vision, we could also use machine hearing to classify the condition of gravel roads in relation to loose gravel. Several suitable classical supervised learning and convolutional neural networks (CNN) were tested. When people drive on gravel roads, they can make sense of the road condition by listening to the gravel hitting the bottom of the car. The more we hear gravel hitting the bottom of the car, the more we can sense that there is a lot of loose gravel and, therefore, the road might be in a bad condition. Based on this idea, we hypothesized that machines could also undertake such a classification when trained with labeled sound data. Machines can identify gravel and non-gravel sounds. In this thesis, we used traditional machine learning algorithms, such as support vector machines (SVM), decision trees, and ensemble classification methods. We also explored CNN for classifying spectrograms of audio sounds and images in gravel roads. Both supervised learning and CNN were used, and results were compared for this study. In classical algorithms, when compared with other classifiers, ensemble bagged tree (EBT)-based classifiers performed best for classifying gravel and non-gravel sounds. EBT performance is also useful in reducing the misclassification of non-gravel sounds. The use of CNN also showed a 97.91% accuracy rate. Using CNN makes the classification process more intuitive because the network architecture takes responsibility for selecting the relevant training features. Furthermore, the classification results can be visualized on road maps, which can help road monitoring agencies assess road conditions and schedule maintenance activities for a particular road. / <p>Due to unforeseen circumstances the seminar was postponed from May 7 to 28, as duly stated in the new posting page.</p>

Evaluation of the road's condition and state is essential for its upkeep, especially when discussing gravel roads, for the following reasons, among other. When loose gravel is not adequately maintained, it can pose a hazard to drivers, who can lose control of their vehicle and cause accidents. Current maintenance procedures are either laborious or time-consuming. Road agencies and institutions are on the lookout for more effective techniques. This study seeks to establish an automatic method for estimating loose gravel using acoustic observation. On gravelroads, recordings from a car's interior were evaluated and matched to the road's state. The first strategy examined road sections with a four-tier (multiclass) manual classification, based on their perceived condition of loose gravel, in accordance with the Swedish road administration authority’s guidelines. The second, examined two tier (binary) manual classification, distinguishing between roads with low and high maintenance needs. Sound features were extracted and processed for subsequentanalysis. Several supervised machine learning methods and algorithms, combined with selected data preprocessing strategies, were deployed. The performance of each strategy and model is determined by assessing and evaluating their classification accuracy along with other performance metrics. The SVM classifier had the best performance in classifying both multiclass as well as binary gravel road conditions. SVM achieved an accuracy of 57.8% when classifying on a four-tier scale and an accuracy of 82% when classifying on a two-tier scale. These results indicate some merits of using audio features as predictive features in the automatic classification of loose gravel conditions on gravel roads.