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Since its development in the 1920s, bolting has become the most dominant support method in underground construction. However, because of the geological environment, the design process for roof bolt systems is an art rather than a science. To quantify the selection of bolting systems a MSBT (minimum solid beam thickness) approach was developed. The ultimate goal of this bolt design paradigm was achieved by optimizing bolt length, bolt density, and bolt pretension during installation. The impact of the number of strata layers within bolting range and pretension applied to bolts upon the stability of an opening was investigated using FLAC model. Four statistical models for predicting optimum bolt supports using a minimum solid beam thickness were established, and based on these results, a design criterion was proposed. To meet support needs in various geological and geotechnical settings, a variety of bolt types have been developed. The installation of such bolt-based support systems is often complex and specialized, and thus imposes a challenge for engineers to identify the specific cause and to take appropriate remedial measures once problems arise. To solve these problems, a knowledge-based expert system (KBES) has been developed. The knowledge base includes the data accumulated from years of laboratory and field investigations conducted by the Mine Safety and Health Administration of the US Department of Labor. A user-friendly Windows-based program was implemented using KAPPA environment. After identifying the problem, the KBES searches its knowledge base and reasons out the most likely, secondary, and other potential causes, then provides solutions according to users' input. The results of this research are validated and demonstrated using case studies. / Ph. D.

Spatial decision making is a process of providing an effective solution for a problem that encompasses semi-structured spatial data. It is a challenging task which involves various factors to consider. For example, in order to build a new industry, an appropriate site must be selected for which several factors have to be taken into consideration. Some of the factors, which can affect the decision in this particular case, are air pollution, noise pollution, and distance from living areas, which makes the decision difficult. The geographic information systems (GIS) and the expert systems (ES) have many advantages in solving problems in their prospective areas. Integrating these two systems will benefit in solving spatial decision making problems. In the past, many researchers have proposed integrating systems which extracts the data from the GIS and saves it in the database for decision making. Most of the frameworks which have been developed were system dependent and are not properly structured. So it is difficult to search the data. This thesis proposes a framework which extracts the GIS data and processes it with the help of ES decision making capabilities to solve the spatial decision making problem. This framework is named GeoFilter. This research classifies various types of mechanisms that can be used to integrate these two systems.

This thesis perpetuates research aimed at deploying a diagnostic expert system for the MK 92 Mod 2 Fire Control System to 28 Oliver Hazard Perry class fast frigates. Referred to as the Maintenance Advisor Expert System (MAES) , this expert system is being jointly developed by the Naval Postgraduate School and Port Hueneme Division, Naval Surface Warfare Center (NSWC PHD). This thesis focuses on the long-term implementation issues related to deploying MAES to the fleet, integrating MAES into the formal training pipeline, and transitioning life cycle Support for MAES to NSWC PHD. MAES long-term implementation issues, which include hardware, software, documentation, and training requirements, are examined within the context of implementation factors and risks historically associated with deploying expert systems. Plans for deploying MAES to the fleet and integrating MAES into the formal training pipeline are provided. As part of the documentation necessary to transition life cycle support of MAES to NSWC, a System Level Description document is also provided

Linear programming is widely used to solve various complex problems with many variables, subject to multiple constraints. Expert systems are created to provide expertise on complex problems through the application of inference procedures and advanced expert knowledge on facts relevant to the problem. The diet problem is well-known for its contribution to the development of linear programming. Over the years many variations and facets of the diet problem have been solved by means of linear programming techniques and expert systems respectively. In this study the feasibility of combining expert system technology and linear programming techniques to solve a diet problem topical to South Africa, is examined. A computer application is created that incorporates goal programming- and multi-objective linear programming models as the inference engine of an expert system. The program is successfully applied to test cases obtained through knowledge acquisition. The system delivers an eating-plan for an individual that conforms to the nutritional requirements of a healthy diet, includes the personal food preferences of that individual, and includes the food items that result in the lowest total cost. It further allows prioritization of the food preference and least cost factors through the use of weights. Based on the results, recommendations and contributions to the linear programming and expert system fields are presented. / MSc (Computer Science), North-West University, Potchefstroom Campus, 2014

Linear programming is widely used to solve various complex problems with many variables, subject to multiple constraints. Expert systems are created to provide expertise on complex problems through the application of inference procedures and advanced expert knowledge on facts relevant to the problem. The diet problem is well-known for its contribution to the development of linear programming. Over the years many variations and facets of the diet problem have been solved by means of linear programming techniques and expert systems respectively. In this study the feasibility of combining expert system technology and linear programming techniques to solve a diet problem topical to South Africa, is examined. A computer application is created that incorporates goal programming- and multi-objective linear programming models as the inference engine of an expert system. The program is successfully applied to test cases obtained through knowledge acquisition. The system delivers an eating-plan for an individual that conforms to the nutritional requirements of a healthy diet, includes the personal food preferences of that individual, and includes the food items that result in the lowest total cost. It further allows prioritization of the food preference and least cost factors through the use of weights. Based on the results, recommendations and contributions to the linear programming and expert system fields are presented. / MSc (Computer Science), North-West University, Potchefstroom Campus, 2014