The proliferation of technological capability, miniaturization, and demand for aerial intelligence is pushing unmanned aerial systems (UAS) into the realm of a multi-billion dollar industry. This book surveys the UAS landscape from history to future applications. It discusses commercial applications, integration into the national airspace system (NAS), System function, operational procedures, safety concerns, and a host of other relevant topics. The book is dynamic and well-illustrated with separate sections for terminology and web- based resources for further information.

Road maintenance planning, an essential component of road asset management, preserves the integrity of road networks. Current state of the art pavement management systems exercise optimisation tools, pavement deterioration models, and intervention criteria to forecast the future maintenance requirements of a road network. These tools have been utilised to forecast future maintenance requirements of road networks; however, with this current approach to pavement management, uncertainties associated with the failure of individual sections of road may not always be accounted for explicitly, and therefore the susceptibility of a road network to failure is unknown. Predicting the probability of the end of life of a road pavement involves wholly understanding possible modes of failure and utilising suitable computational techniques, so that engineering knowledge can be well represented in data driven models. To this end, this thesis describes the development of a diagnostic approach that infers engineering knowledge into computational models, to quantify the probability of failure of road pavements and identify the most likely causes of failure. To do so, this research developed a number of failure charts that capture engineering knowledge, such as citing influential failure factors of road pavements including the influence from external environments and internal pavement attributes. Engineering knowledge on road pavement failure was obtained from three sources: literature describing the fundamentals of pavement design and common causes of road failure, expert knowledge from the industry identifying relationships between failure mechanisms and causes, and a data analysis to obtain site-specific causes such as road environments and material properties. Each chart presents a possible failure path, detailing a set of factors contributing to failure. A comparative study evaluated the performance of five classification modelling approaches in order to determine the most suitable technique for this research. Based on performance and user interpretability criteria, the study identified one based on support vector machines as the most suitable. The developed prototype system, consisting of a failure system for rutting, fatigue cracking, and shear, performed well in both the development phase and network testing of the system utilising data from the New Zealand Long-term Pavement Performance Programme. A case study focussing on rural New Zealand roads was carried out, which demonstrated the use of this tool in network and project level applications.

The significant contribution of the research presented in this report lies in the fact that it utilizes the most comprehensive database of pavement conditions that is readily available and promises to provide the sought data in future years. The first part of this report reviews the existing literature covering related topics including pavement roughness, the LLTP background, artificial neural networks, regression analysis and the existing pavement deterioration models. The second part discusses the work done in data analysis and data manipulation in addition to the development of the training of the neural network model. The third part deals with various aspects of the model development using neural networks and regression analysis. The next part concludes the research with summarizing the results of model development. The models developed in this research are then compared to some existing models by applying the models to similar data sets and performing statistical analysis of the results.

The growing interest in pavement management systems (PMSs), both in South Mrica and internationally, has been in response to a shift in importance from the construction of new roads to the maintenance of the existing paved network coupled with increasingly restrictive road funding. In order to develop a balanced expenditure programme for the national roads of South Africa there is a need to predict the rate of deterioration of a pavement and the nature of the changes in its condition so that the timing, type and cost of maintenance needs could be estimated. Internationally these expected changes in pavement condition are predicted by pavement deterioration models, which normally are algorithms developed mathematically or from a study of pavement deterioration. Since no usable pavement deterioration models existed locally, it was necessary to evaluate overseas literature on pavement deterioration prediction models with the aim of identifying models possibly applicable to the national roads of South Africa. Only deterioration models developed from the deterioration results of inservice pavements under a normal traffic spectrum were evaluated. Models developed from accelerated testing were avoided since these models virtually eliminated long?term effects (these are primarily environmental but also include effects of the rest periods between loads), and that the unrepresentative traffic loading regimes can distort the behaviour of the pavement materials, which is often stress dependent. Models developed from the following studies were evaluated: AASHO Road Test The Kenya study Brazil-UNDP study (HDM-ill models) Texas study Of all the above models studied that were developed from major studies it was concluded that the incremental models developed during the Brazil study, were the most appropriate for further evaluation under South African conditions. A sensitivity analysis was conducted on the HDM-III models to evaluate their sensitivity to changes in the different parameters comprising each model. The results obtained from the sensitivity analysis indicate that the incremental roughness prediction model incorporated into the HDM-III model tends to be insensitive to changes in most parameters. Accuracy ranges for input data were, however, also identified for parameters which indicated an increase in sensitivity in certain ranges. The local applicability of the HDM-III deterioration models were finally evaluated by comparing HDM-III model predictions with the actually observed deterioration values of a selected number of national road pavement sections. To enable the above comparison, a validation procedure had to be developed according to which the format of existing data could be transformed to that required by the HDM-ill model, as well as additional information be calculated. From the comparison it was concluded that the HDM-III models are capable of accurately predicting the observed deterioration on South African national roads, but that for most models calibration is needed for local conditions. Guidelines regarding recommended calibration factor ranges for the different HDM-ill models are given. Finally it is recommended that the HDM-III models should be considered for incorporation into a balanced expenditure programme for the national roads of South Africa.