Thesis (M.S.)--University of Nevada, Reno, 2006. / "December, 2006." Includes bibliographical references. Online version available on the World Wide Web.

Thesis (M.S.)--University of Nevada, Reno, 2007. / "May, 2007." Includes bibliographical references (leaves 71-73). Online version available on the World Wide Web.

Present day air traffc control is reaching its operational limits and accommodating future traffic growth will be a challenging task for air traffic service providers and airline operators. Free Flight is a proposed transition from a highly-structured and centrally-controlled air traffic system to a self-optimized and highly-distributed system. In Free Flight, pilots will have the flexibility of real-time trajectory planning and dynamic route optimization given airspace constraints (traffic, weather etc.). A variety of advanced air traffc management (ATM) concepts are proposed as enabling technologies for the realization of Free Flight. Since these concepts can be exposed to unforeseen and challenging scenarios in Free Flight, they need to be validated and evaluated in order to implement the most effective systems in the field. Evaluation of advanced ATM concepts is a challenging task due to the limitations in the existing scenario generation methodologies and limited availability of a common platform (air traffic simulator) where diverse ATM concepts can be modeled and evaluated. Their rigorous evaluation on safety metrics, in a variety of complex scenarios, can provide an insight into their performance, which can help improve upon them while developing new ones. In this thesis, I propose a non-propriety, non-commercial air traffic simulation system, with a novel representation of airspace, which can prototype advanced ATM concepts such as conflict detection and resolution, airborne weather avoidance and cockpit display of traffic information. I then propose a novel evolutionary computation methodology to algorithmically generate a massive number of conflict scenarios of increasing complexity in order to evaluate conflict detection algorithms. I illustrate the methodology in detail by quantitative evaluation of three conflict detection algorithms, from the literature, on safety metrics. I then propose the use of data mining techniques for the discovery of interesting relationships, that may exist implicitly, in the algorithm's performance data. The data mining techniques formulate the conflict characteristics, which may lead to algorithm failure, using if-then rules. Using the rule sets for each algorithm, I propose an ensemble of conflict detection algorithms which uses a switch mechanism to direct the subsequent conflict probes to an algorithm which is less vulnerable to failure in a given conflict scenario. The objective is to form a predictive model for algorithm's vulnerability which can then be included in an ensemble that can minimize the overall vulnerability of the system. In summary, the contributions of this thesis are: 1. A non-propriety, non-commercial air traffic simulation system with a novel representation of airspace for efficient modeling of advanced ATM concepts. 2. An Ant-based dynamic weather avoidance algorithm for traffic-constrained enroute airspace. 3. A novel representation of 4D air traffic scenario that allows the use of an evolutionary computation methodology to evolve complex conflict scenarios for the evaluation of conflict detection algorithms. 4. An evaluation framework where scenario generation, scenario evaluation and scenario evolution processes can be carried out in an integrated manner for rigorous evaluation of advanced ATM concepts. 5. A methodology for forming an intelligent ensemble of conflict detection algorithms by data mining the scenario space.

The development of technology in project planning has enabled more efficient work through easier interaction and handling of complex problems. Despite the technological advancements, many companies have not yet utilized its potential, and many areas within the construction industry are in need of streamlined work processes. This study examines the possibilities of automating the design process for joints in steel structures using digital tools and artificial intelligence (AI) technologies. The digital tools examined are Tekla Structures and IDEA StatiCa, and how they, along with the developed workflow, can streamline the design process and improve the accuracy and quality of the work. Interviews with designers and AI developers have mapped out the current usage of digital tools and the perspective on automating the design process. Several challenges and limitations with the implementation of AI have been identified, such as safety risks, the need for extensive data, and human integration. To ensure that the joints are correctly designed and constructible, it is important to maintain a balance between automation and human expertise. The report aims to propose a workflow for automating the design process of joints. The proposed workflow may include various AI algorithms to achieve the best possible optimization. Some algorithms mentioned in the report are machine learning, artificial neural networks (ANN), and evolutionary algorithms (EA). These algorithms are suitable for identifying and preparing data, reading, and finding complex patterns. Previous automation of the design process for joints using digital tools has shown to reduce the time required for modeling and designing. The developed workflow can provide improved efficiency, accuracy, and quality in the design process; however, further development is required to overcome the current limitations. / Teknikens utveckling inom projektering har möjliggjort effektiviserade arbeten genom lättare samspel och hantering av komplexa problem. Trots teknikens utveckling är det många företag som ännu inte utnyttjat dess potential och många områden inom byggbranschen är i behov av en effektiviserad arbetsprocess. I denna studie undersöks möjligheterna att automatisera dimensioneringsprocessen för knutpunkter i stålkonstruktion genom användning av digitala verktyg och artificiella intelligenta (AI) teknologier. Digitala verktyg som undersöks är Tekla Structures och IDEA StatiCa och hur de tillsammans med det framtagna arbetsflödet kan effektivisera dimensioneringsprocessen och förbättra noggrannheten och kvaliteten i arbetet. Intervjuer med konstruktörer och AI-utvecklare har kartlagt den nuvarande användningen av digitala verktyg och synen på automatisering av dimensioneringsprocessen. Det har identifierats ett flertal utmaningar och begränsningar med implementeringen av AI, faktorer som säkerhetsrisker, behovet av omfattande data och mänsklig integration. För att säkerställa att knutpunkterna är korrekt dimensionerade och byggbara är det viktigt med en balans mellan automatisering och mänsklig expertis. Rapportens mål är att ta fram ett förslag på ett arbetsflöde för automatisering av dimensioneringsprocessen av knutpunkter. Det förslagna arbetsflödet kan inkludera olika AIalgoritmer för att uppnå bästa möjliga optimering. Några algoritmer som nämns i rapporten är maskininlärning, artificiella neurala nätverk (ANN) och evolutionära algoritmer (EA). Dessa algoritmer är lämpade för att identifiera och förbereda data, läsa av och hitta komplexa mönster. Tidigare automatisering av dimensioneringsprocessen för knutpunkter med hjälp av digitala verktyg har visats minska tidsåtgången för modellering och dimensionering. Det framtagna arbetsflödet kan ge en förbättrad effektivitet, noggrannhet och kvalitet i dimensioneringsprocessen, dock krävs ytterligare utveckling för att överkomma de nuvarande begräsningarna.