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Approved for public release; distribution is unlimited / The purpose of this thesis is to develop a prototype web-enabled database to improve the process flow of data collection and manipulation in support of Army aviation operations. Data collection is focused around routine aviation operations and aviation maintenance with the intention of identifying a feasible replacement for the existing redundant manual and automated collection procedures. The web interface has the potential to reduce the logistical burden on unit's data collection procedures and provides tailorable, near real time information about aircraft maintenance status, individual training, and unit training to decision makers at all levels as a decision support tool. This thesis will describe the design considerations for a web-enabled database to include the development of detailed data and process models. / Major, United States Army

Transport infrastructure constitutes one of the key factors to a country’s economic growth. Investment in new transport infrastructure might cause potential environmental impacts, and if a project has several alternative corridors open for suggestion then each alternative corridor will have a different impact on the environment. The European Commission has stated that the natural resources are important to the quality of life. Therefore, the efficient use of resources will be a key towards meeting future climate change and reduction in greenhouse gas (GHG) emissions. This implies that in an evergrowing global society the resource efficiency as well as the choice of transport infrastructure corridor becomes even more important to consider. The aim of this research project was to contribute to early transport infrastructure planning by the development of methods for and implementation of easy understandable geological criteria and models for decision support. Moreover, the intention was to assess how geological information can be developed and extracted from existing spatial data and coupled with other areas of interest, such as ecology and life cycle assessment. It has previously been established that geological information plays an important role in transport infrastructure planning, as the geological characteristics of the proposed area as well as the possibilities of material use influences the project. Therefore, in order to couple geological information for early transport infrastructure planning, four studies (Paper I-IV) were undertaken where methods were developed and tested for the inclusion of geological information. The first study (Paper I) demonstate how optional road corridors could be evaluated using geological information of soil thickness, soil type and rock outcrops, bedrock quality and slope in combination with ecological information. The second study (Paper II) shows how geological information of soil thickness and stratigraphy can be combined with life cycle assessments (LCA) to assess the corresponding greenhouse gas emission and energy use for the proposed road corridors. The difficulty of using expert knowledge for susceptibility assessment of natural hazards, i.e. flooding, landslide and debris flow, for early transport infrastructure planning was presented in the third study (Paper III). In this study the expert knowledge was used in a multi-criteria analysis where the analytic hierarchy process (AHP) was chosen as a decision rule. This decision rule was compared to the decision rule weighted linear combination (WLC) using two different schemes of weighting. In all the mentioned studies the importance of soil thickness information was highlighted. Therefore, the fourth and final study (Paper IV) presented a new methodology for modelling the soil thickness in areas where data is sparse. A simplified regolith model (SRM) was developed in order to estimate the regolith thickness, i.e. soil thickness, for previously glaciate terrain with a high frequency of rock outcrops. SRM was based on a digital elevation model (DEM) and an optimized search algorithm. The methods developed in order to couple geological information with other areas of interest is a tentative step towards an earlier geo-environmental planning process. However, the methods need to be tested in other areas with different geological conditions. The combination of geological information in GIS with MCA enabled the integration of knowledge for decision making; it also allowed influencing the importance between various aspects of geological information as well as the importance between geological information and other fields of interest, such as ecology, through the selected weighting schemes. The results showed that synergies exist between ecology and geology, where important geological considerations could also have positive effects on ecological consideration. Soil thickness was very important for GHG emission and energy whereas stratigraphical knowledge had a minor influence. When using expert knowledge the consistency in the expert judgements also needs to be considered. It was shown that experts tended to be inconsistent in their judgements, and that some consistency could be reached if the judgements were aggregated instead of used separately. The results also showed that the developed SRM had relatively accurate results for data sparse areas, and that this model could be used in several projects where the knowledge of soil thickness is important but lacking. It was concluded that geological information should be considered. By using GIS and MCA it is possible to evaluate different aspects of geological information in order to improve decision making. / Environmental assessment of road geology and ecology in a system perspective

by Cheung Wai-Lam, William. / Thesis (M.B.A.)--Chinese University of Hong Kong, 1992. / Includes bibliographical references (leaves 93-96). / Chapter chapter 1: --- introduction / Chapter 1.1 --- INTRODUCTION --- p.1 / Chapter 1.2 --- OBJECTIVES AND SCOPE --- p.2 / Chapter 1.3 --- STRUCTURE OF REPORT --- p.3 / Chapter 1.4 --- CHAPTER SUMMARY --- p.5 / Chapter chapter 2: --- decision support systems (dss) / Chapter 2.1 --- THE DECISION MAKING PROCESS --- p.6 / Chapter 2.2 --- DEFINITION OF DSS --- p.8 / Chapter 2.3 --- STRUCTURE OF DSS --- p.12 / Chapter 2.3.1 --- Users --- p.12 / Chapter 2.3.2 --- Database --- p.12 / Chapter 2.3.3 --- Model Base --- p.14 / Chapter 2.4 --- CHAPTER SUMMARY --- p.15 / Chapter chapter 3: --- dss for stock evaluation / Chapter 3.1 --- STOCK VALUATION: CAPM vs APT --- p.16 / Chapter 3.2 --- DSS FOR STOCK INVESTMENT --- p.21 / Chapter 3.3 --- THE PROPOSED STOCK EVALUATION DSS --- p.23 / Chapter 3.4 --- CHAPTER SUMMARY --- p.26 / Chapter chapter 4: --- analyticheerarchy process (ahp) / Chapter 4.1 --- WHAT IS AHP --- p.27 / Chapter 4.2 --- AN EXAMPLE: PLANT LOCATION SELECTION --- p.27 / Chapter 4.3 --- COMPUTATION PROCESS OF AHP / Chapter 4.3.1 --- Notations --- p.35 / Chapter 4.3.2 --- Principal Eigenvector --- p.35 / Chapter 4.3.3 --- Eigenvalue --- p.36 / Chapter 4.3.4 --- Consistency Ratio --- p.36 / Chapter 4.4 --- CHAPTER SUMMARY --- p.37 / Chapter chapter 5: --- an ahp model for stock evaluation / Chapter 5.1 --- ALTERNATIVES FOR STOCK EVALUATION --- p.39 / Chapter 5.2 --- THE AHP MODEL FOR STOCK SELECTION --- p.41 / Chapter 5.3 --- EXPLANATIONS AND JUSTIFICATIONS FOR PROPOSED HIERARCHY --- p.43 / Chapter 5.3.1 --- Level1 --- p.45 / Chapter 5.3.2 --- Level2 --- p.45 / Chapter 5.3.3 --- Level3 --- p.46 / Chapter 5.3.4 --- Level4 --- p.48 / Chapter 5.3.5 --- Level5 --- p.49 / Chapter 5.3.6 --- Level6 --- p.60 / Chapter 5.4 --- CHAPTER SUMMARY --- p.61 / Chapter chapter 6: --- the development of iris: a prototype / Chapter 6.1 --- SYSTEM FLOWCHART --- p.63 / Chapter 6.2 --- PROGRAM SPECIFICATION --- p.65 / Chapter 6.2.1 --- File Maintenance Module --- p.65 / Chapter 6.2.2 --- Hierarchy Setup --- p.65 / Chapter 6.2.3 --- Eigenvector Computation --- p.67 / Chapter 6.2.4 --- Overall Weight Computation --- p.67 / Chapter 6.3 --- PROTOTYPE OPERATION --- p.67 / Chapter 6.4 --- CHAPTER SUMMARY --- p.79 / Chapter chapter 7: --- user evaluationof model and prototype / Chapter 7.1 --- METHODOLOGY OF EVALUATION --- p.82 / Chapter 7.1.1 --- Participants --- p.82 / Chapter 7.1.2 --- Stock Candidates --- p.83 / Chapter 7.1.3 --- Stock Data --- p.83 / Chapter 7.1.4 --- Process of Model and Prototype Evaluation --- p.84 / Chapter 7.2 --- FINDINGS --- p.85 / Chapter 7.2.1 --- Structure the Stock Evaluation Process --- p.86 / Chapter 7.2.2 --- Time-consuming --- p.87 / Chapter 7.2.3 --- The Consistency Ratio --- p.87 / Chapter 7.2.4 --- Reconsideration of Factors --- p.87 / Chapter 7.2.5 --- Precise Amount Available --- p.88 / Chapter 7.2.6 --- Users Forced to Considered All Factors --- p.88 / Chapter 7.3 --- CONCLUSION OF EVALUATION --- p.89 / Chapter 7.4 --- CHAPTER SUMMARY --- p.90 / Chapter chapter 8: --- summary and conclusion / Chapter 8.1 --- REPORT SUMMARY --- p.91 / Chapter 8.2 --- CONCLUSION --- p.91 / references --- p.93 / appendix --- p.97