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Abstract: School buildings are one of the most important educational and learning environments and the appropriate design of these spaces has a significant impact in enhancing both students’ and teachers’ performance, comfort and satisfaction. As a result, the preliminary design evaluation and optimization of school buildings should be given a significant consideration. The key factor in design optimization of a school building is defining the users' expectations, which is qualitative and subjective in nature. To capture these qualitative and imprecise aspects of the problem, and optimize school building design parameters a multi-criteria fuzzy expert system is employed and the design evaluation and optimization model is developed. Different school building design parameters such as; building orientation and layout, envelope features, indoor air quality as well as day-lighting systems are investigated as part of the design evaluation and optimization process. The fuzzy expert system is used to analyze the optimal values of a list of parameters associated with the building design process to enhance the learning environment for school buildings. This method employs both quantitative and qualitative design performance parameters and allows for comparison analysis between different design alternatives in order to achieve the objectives of the study.

In motor condition monitoring applications, traditional human expert approach for sensor exploitation is not cost-effective. The training requirements for human experts are extensive, and the overall training process is a very time-consuming task. In addition, the performance of human experts has limitations. For human experts, it is difficult to examine all the input-output data from the motor system under varying noise and motor load conditions. With a motor condition monitoring system that can automatically generate rules in the form of interpretable linguistic fuzzy "if-then" rules and membership functions, it would be easier for experts to understand and modify the rule base and also to track the motor condition for maintenance and replacement requirements. In this research, a methodology for fuzzy rule and membership function generation for broken rotor bar detection of squirrel-cage induction motors was developed. The methodology consists of a set of steps that an expert might do for fuzzy rule and membership function design. The methodology is named "H-ROC", since it utilizes histogram analysis with overlapping bins and a weighted cost function based on ROC (Receiver Operating Characteristics) curve analysis. As a second method, an existing fuzzy rule extraction method was extended to broken rotor bar detection problem. The performance and sensitivity analyses of the two methods were conducted.