Design frequently involves a series of trade-offs to obtain the "optimal" solution to a design problem. Green roofs have many different characteristics based on a variety of variables. Designers typically weigh the impacts of these characteristics in an implicit process based on intuition or past experience. But since vegetated roofing is a relatively complex and comparatively new technology to many practitioners, a rational, explicit method to help organize and rank the trade-offs made during the design process is useful. This research comprises the creation of a framework diagramming the decision process involved in the selection of vegetated roofing systems. Through a series of expert interviews and case studies, the available knowledge is captured and organized to determine the critical parameters affecting design decisions. A set of six case study projects in North America is analyzed and six critically important evaluative categories are identified: storm water management, energy consumption, acoustics, structure, compliance with regulatory guidelines and governmental incentives, and cost. These six factors are key decision-making parameters in the selection of vegetated roofing systems and they form the basis of this study. They are addressed in the context of a decision support system for green roof designers. A summation of the total importance of the advantages represented by each alternative is used to determine the most feasible green roof system for a particular project. The decision-making framework developed in this dissertation will ultimately be adaptable to digital processing and a computer-based design assistance tool. / Ph. D.

This research proposes a new site analysis methodology in the form of an integrated framework. The framework separates the site analysis process into different models, incorporates each model, and considers the interaction between them. The most important models are the environmental models (climate, geology, hydrology, topography, and vegetation models), social-cultural models, economic models, and infrastructure models. Each model also contains several important factors. The study identifies and organizes environmental factors within the framework that influence site analysis and design. Based on the applicability of feng shui principles and their interpretations into measurable factors, this research compares and incorporates feng shui and contemporary environmental design theories, and summarizes essential environmental factors. The emphasis on environmental factors from these models may lead to a better understanding of the relationships between humankind and the natural environment. The proposed framework is implemented into a computer simulation program, titled SiteOne, to demonstrate the concepts and ideas, with an emphasis on environmental factors. This research uses the town of Reston in Fairfax County, Virginia, as the study area because of the availability of information. SiteOne analyzes a range of alternatives and then derives solutions from the suggested site conditions in the form of GIS maps. There are various modules that make up the prototype system: namely an analysis module, a database module, and a result generation module. SiteOne helps professionals collect a wide range of information and select corresponding criteria in the early stages of design. It also provides visual analysis based on selected criteria and models. Therefore, it can act as an educational knowledge component for professionals as well as the general public. / Ph. D.

Fast moving technologies have caused high-tech components to have shortened life cycles, rendering them obsolete quickly. Technology obsolescence creates significant problems for product sectors that use components that are only available for a short period of time for manufacture and maintenance of long field-life systems. Technology obsolescence can make design changes of systems prohibitively expensive, and results in high life cycle costs of systems. While the impact and pervasiveness of obsolescence problems are growing, existing tools and solutions are lacking the needed information and knowledge to do much more than focus on reactively managing obsolescence. Current methods and tools are limited by data conflicts and data inexplicitness, incompleteness, and inconsistency. In response to the drawbacks of current tools, comprehensive knowledge representation that allows information sharing, reuse, and collaboration on obsolescence issues across different organizations is required. Further, decision making tools that can support proactive and strategic obsolescence management are needed. The purpose of this research is to establish an ontology-based knowledge representation scheme for information sharing, reuse, and collaboration on obsolescence issues, and develop decision making models to support proactive and strategic management for overall cost savings in managing obsolescence. Three primary aspects of this research are investigated. First, ontologies for obsolescence knowledge representation are developed in a systematic way with the use of UML diagrams. The generality of the developed ontology is demonstrated with distinct examples. Diminishing Manufacturing Sources and Material Shortages (DMSMS) obsolescence provides the basis for this study. Second, an ontology-based hybrid approach for integrating heterogeneous data resources in existing obsolescence management tools is proposed. Third, decision support models are developed and formalized, and include the obsolescence forecasting method for proactively managing obsolescence, and the mathematical models to determine the optimal design refresh plan to minimize the product life cycle cost for strategic obsolescence management. Finally, the design of the obsolescence management information system is provided along with a system evaluation methodology. Ultimately, the research contributes to the field of knowledge representation as well as design for managing product obsolescence. / Ph. D.

Failure of pipes in water distribution systems is a common occurrence especially in large cities. The failure of a pipe results in: loss of water; property damage; interruption of service; decreased system performance; and the financial cost of restoring the failed pipe. The cost of replacement and rehabilitation in the United States is estimated at 23 plus billion dollars. It is virtually impossible to replace all vulnerable pipes at the same time. As a result, there is a need for methods that can help in progressive system rehabilitation and replacement subject to budgetary constraints. If delaying is considered a good strategy due to the time value of money then, the timing of preventive maintenance becomes a crucial element for system maintenance and operation. The central under pinning element in the decision process for scheduling preventive maintenance is the deteriorating nature of a pipe under a given surrounding. By planning to replace pipes before they fail, proper planning can be put in place for securing of finances and labor force needed to rehabilitate the pipes. With this approach, service interruptions are minimized as the loss of service time is limited to the time used in replacing the pipe. In this research, a mechanistic model for assessing the stage of deterioration of an underground pipe is developed. The developed model consists of three sub-models namely, the Pipe Load Model (PLM), the Pipe Deterioration Model (PDM), and the Pipe Break Model (PBM). The PLM simulates the loads and stresses exerted on a buried water main. These loads include the earth load, traffic load, internal pressure, expansive soil loads, thermal, and frost loads. The PDM simulates the deterioration of the pipe due to corrosion resulting from the physical characteristics of the pipe environment. The pipe deterioration effect is modeled in two stages. First, the thinning of the pipe wall is modeled using a corrosion model. Second, the localized pit growth is used to determine the residual strength of the pipe based on the fracture toughness and the initial design strength of the pipe. The PBM assesses the vulnerability of a pipe at any time in terms of a critical safety factor. The safety factor is defined as the ratio of residual strength to applied stress. For a conservative estimate the multiplier effect due to thermal and frost loads are considered. For a chosen analysis period, say 50 years, the pipes with safety factors less than the critical safety factor are selected and ordered by their rank. Aided by the prioritized list of failure prone pipes, utilities can organize a replacement schedule that minimizes cost over time. Additionally a physically based regression model for determining the optimal replacement time of pipe is also presented. A methodology for assessing the consequences of accelerated and delayed replacement is also provided. The methodologies developed in this dissertation will enable utilities to formulate future budgetary needs compatible with the intended level of service. An application of the model and results are included in the dissertation. / Ph. D.

Over the last two decades, wireless technology has become ubiquitous in the United States and other developed countries. Consumer devices such as AM/FM radios, cordless and cellular telephones, pagers, satellite televisions, garage door openers, and television channel changers are just some of the applications of wireless technology. More recently, wireless computer networking has seen increasing employment. A few reasons for this move toward wireless networking are improved electronics transmitters and receivers, reduced costs, simplified installation, and enhanced network expandability. The objective of the study is to generate understanding of the planning inherent in a broadband, fixed wireless telecommunication network and to implement that knowledge into an SDSS. Intermediate steps toward this goal include solutions to both fixed wireless point-to-multipoint (PMP) and fixed wireless mesh networks, which are developed and incorporated into the SDSS. This study explores the use of a Spatial Decision Support System (SDSS) for broadband fixed wireless connectivity to solve the wireless network planning problem. The spatial component of the DSS is a Geographic Information System (GIS), which displays visibility for specific tower locations. The SDSS proposed here incorporates cost, revenue, and performance capabilities of a wireless technology applied to a given area. It encompasses cost and range capabilities of wireless equipment, the customers' propensity to pay, the market penetration of a given service offering, the topology of the area in which the wireless service is proffered, and signal obstructions due to local geography. This research is both quantitative and qualitative in nature. Quantitatively, the wireless network planning problem may be formulated as integer programming problems (IP). The line-of-sight restriction imposed by several extant wireless technologies necessitates the incorporation of a GIS and the development of an SDSS to facilitate the symbiosis of the mathematics and geography. The qualitative aspect of this research involves the consideration of planning guidelines for the general wireless planning problem. Methodologically, this requires a synthesis of the literature and insights gathered from using the SDSS above in a what-if mode. / Ph. D.

Climate services, often refers as decision support tools, are developed to provide information with a view to aid in decision making and policy planning for adaptation due to climate variability and change. This study investigated different publicly available climate services and decision support tools based on previously proposed evaluation framework. This evaluation framework originally consists of four design elements which are divided into nine evaluation metrics for this study. These evaluation metrics are: identification of decision making context, discussion of the role of climate information in decision making, discussion of non-climatic factors, uncertainty of the data presented, accessibility of information, discussion on the development process, sustainability/ ongoing process, discussion of funding sources, and evaluation of the tool through survey, modeling or contingent valuation method. Tools were then given "High", "Medium", and "Low" score for each of the criterion. A total of 19 tools were evaluation for this study. Tools performed relatively well in "characteristics, tailoring, and communication of the climate information" and "governance, process, and structure of the climate service" whereas they got average scores in "problem identification and the decision-making context" and "value of the service provided". Additionally, four case study evaluation of tools showed detail evaluation of how the tools performed against each of the criterion. The results of this study showed the relative strengths and weakness of the evaluated tools which can be used to improve existing climate services to aid in adaptation decision needs for climate change. This will also help in better decision making and policy planning for different sectors impacted by the changing climate. / Master of Science / Climate services, often refers as decision support tools, are developed to provide information with a view to aid in decision making and policy planning for adaptation due to the adverse impacts caused by climate variability and change. This study investigated a total of 19 publicly available climate services and decision support tools based on previously proposed evaluation framework. This evaluation framework originally consists of four design elements which further classified into nine evaluation metrics and each of tools were given "High", "Medium", and "Low" score against these criteria. These metrics are: identification of decision making context, discussion of the role of climate information in decision making, discussion of non-climatic factors, uncertainty of the data presented, accessibility of information, discussion on the development process, sustainability/ ongoing process, discussion of funding sources, and evaluation of the tool through survey, modeling or contingent valuation method. Evaluated tools performed better than average in terms of uncertainty of the data presented, accessibility of information, discussion on the development process, sustainability/ ongoing process, discussion of funding sources, and feedback/ evaluation criteria whereas they performed below average in problem identification and decision making context, discussion of the role of climate information in decision making, and discussion of non-climatic factors. Four case study evaluation were also presented in this study for better understanding of how the evaluation process works for the tools. The results of this study provide an insight about the relative strengths and weakness of the evaluated tools and these can be used to improve existing climate services tools. This will also help in better decision making and policy planning for different sectors that are being impacted by the changing climate.

Water distribution system pipes span thousands of miles and form a significant part of the total infrastructure of the country. Rehabilitation of this underground infrastructure is one of the biggest challenges currently facing the water industry. Water main deterioration is twofold: the main itself loses strength over time and breaks; also, there is degradation of water quality and hydraulic capacity due to build of material within a main. The increasing repair and damage costs and degrading services demand that a deteriorating water main be replaced at an optimal time instead of continuing to repair it. In addition, expanding business districts, indirect costs, and interruptions including protected areas, waterways and roadways require examination of trenchless technologies for pipe installation. In this thesis a new threshold break rate criterion for the optimal replacement of pipes is provided. As opposed to the traditional present worth cost (PWC) criterion, the derived method uses the equivalent uniform annualized cost (EUAC). It is shown the EUAC based threshold break rate subsumes the PWC based threshold break rate. In addition, practicing engineers need a user-friendly decision support system to aid in the optimal pipeline replacement process. They also need a task-by-task cost evaluation in a project. As a part of this thesis a comprehensive decision support system that includes both technology selection knowledge base and cost evaluation spreadsheet program within a graphical user interface framework is developed. Numerical examples illustrating the theoretical derivations are also included. / Master of Science

Decision making is a big part of our day-to-day lives, both personal and professional. A good decision support can provide a decision process with high quality, efficiency and consistency. In recent years, machine learning has shown outstanding capacity for making complex processes understandable and provide decision support. But what good is this decision support if it is not trusted? Our work tries to improve the usage of machine learning models by making their results more understandable and trustworthy. In this thesis, we investigate the decisions in the Product Development (PD) process at Scania. Two important steps in the PD process is to prioritize a Product Change Request (PCR) and decide if it should be realized or not. Our main objective is to build machine learning models that can be incorporated in this process and help with the decision making. In order to choose the most suitable model, different machine learning models are trained on historical data. The model with the best performance is chosen and can be used to make predictions on new PCRs. The model that performed best when deciding the priority of a given PCR was Extreme Gradient Boosting (XGB), which achieved a F1 score of 46.6% and an accuracy of 48.0%. However, we found that the data was not suitable for making classifications regarding the priorities. The model that performed the best when deciding if a PCR should be realized or not was the random forest, which achieved a F1 score of 67.4% and an accuracy of 79.4%. We found that better classifications could be made regarding if a PCR should be realized or not when additional data was added to the model, and we therefore recommend changes to the collection and storage of data. The random forest achieved a F1 score of 73.5% and an accuracy of 83.8% with the additional data from attachments. We also explain and visualize how the random forest makes its classification and how each feature from the PCRs affect the classification. This is important in order to improve the trust in the decision support provided by the model. / Att ta beslut är en stor del av våra dagliga liv, både personligt och professionellt. Ett bra beslutsstöd kan skapa en beslutsprocess med hög kvalitet, effektivitet och stabilitet. Under de senaste åren har maskininlärning blivit ett viktigt verktyg för att förstå komplexa processer och skapa beslutsstöd. Men vilken nytta gör detta beslutsstöd om människor inte litar på det? Vårt arbete försöker att hantera detta problem och göra resultaten från maskininlärningsmodeller mer förståeliga och tillförlitliga. I den här rapporten undersöker vi besluten som tas i processen för produktutveckling hos Scania. Två viktiga steg i denna process är att prioritera föreslagna produktförändringar och att bestämma ifall dessa ska genomföras eller inte. Vårt huvudmål är att bygga maskininlärningsmodeller som kan användas i denna process och hjälpa till vid beslutstagandet. För att kunna välja den lämpligaste modellen så tränas olika maskininlärningsmodeller på historiska data. Modellen som presterar bäst väljs och kan användas för att förutsäga besluten för nya föreslagna produktförändringar. Den modell som lyckades bäst med att förutsäga vilken prioritet som en föreslagen produktförändring ska ha var Extreme Gradient Boosting (XGB) som uppnådde ett F1-score på 46,6% och en träffsäkerhet på 48,0%. Vi såg däremot att den data som fanns inte var lämplig för att göra klassificeringar gällande prioriteringen. Den modell som lyckades bäst med att bestämma ifall en föreslagen produktförändring borde genomföras eller inte var random forest, som uppnådde ett F1-score på 67,4% och en träffsäkerhet på 79,4%. Vi visar att bättre klassificeringar kan göras gällande om en föreslagen produktförändring ska genomföras eller inte när mer data läggs till i modellen, och vi kan därmed föreslå förändringar av insamlingen och lagringen av data. Random forest uppnådde ett F1-score på 73,5% och en träffsäkerhet på 83,8% med data insamlat från bilagor. Vi förklarar och visar även hur random forest gör sin klassificering och hur varje faktor från den föreslagna produktförändringen påverkar klassificeringen. Detta är viktigt för att kunna öka förtroendet för det beslutsstöd som modellen ger.

The aim of this thesis is to carry out research into the application of experimental design in an activity-based environment; specifically into the methodology and potential application of experimental design and analysis as an activity-based management decision support tool in the general manufacturing, systems engineering and logistics engineering fields. This thesis will detail the author's research into the decision support framework that will be introduced in Chapter 1.0 and developed during the thesis. Chapter 2.0 will introduce activity-based management and develop a systems dynamics model of a US industrial enterprise. Experimental design and analysis will be introduced in Chapter 3.0. The development of a knowledge-based expert system (KBES) trainer called DESIGNS.ART will be covered in Chapter 4.0, and' a copy of the program has been included in Appendix A This KBES trainer has been designed to train engineers in US industry in the application of experimental design and analysis. Chapter 5.0 discusses the potential application of experimental design and analysis in each of the three fields. Chapter 6.0 combines experimental design and analysis with activity-based management by fully developing the activity-based management (ABM) framework introduced in Chapter 1.0. Chapter 6.0 includes a hypothetical case study of the ABM framework in operation. Chapter 7.0 proposes that this ABM framework be integrated into a US firm which has implemented activity-based costing. This research into the integration of experimental design and analysis with activity-based management in systems engineering, logistics engineering and general manufacturing is original, and it results in a contribution to both the systems engineering, logistics engineering and the general manufacturing fields. / Master of Science

Through pollination, bees are responsible for the persistence of many biological systems on our planet. Bees have also been used for thousands of years in agriculture to improve crop quality and yield. Recently, there have been declines in honeybees worldwide. This decline is concerning because it threatens food supplies and global biodiversity. An alternative to alleviating the effect of a honey bee shortage could be to use native bees. Problems with adoption of native bees in agriculture occur because of a lack of large scale analysis methods for native bees, regional species lists and management knowledge. This research explores the use of GIS in modeling native bee habitat to provide a landscape scale analysis method for native bees and develop a systematic sampling method for regional species list development. Raster GIS modeling, incorporating decision support and Poisson statistical methods were used to develop a native bee habitat model. The results show landscape composition is important to bee abundance and diversity. In addition, habitat fragmentation may not be as detrimental to bees as previously thought. Bees are most sensitive to landscape composition at a scale of 250 m, but require large patches of floral resources. GIS proved to be very useful in modeling bee habitat and provides an opportunity to conduct landscape scale bee population analysis. / Master of Science