Extending the Q system's prediction of support in tunnels employing fuzzy logic and extra parameters

Rock mass classifications predict support measures according to expert rules by rating rock mass and taking into account the span of the opening. A similar procedure is adopted, in this work, and computerized using statistics and fuzzy logic. Fuzzy expert systems are trained with data of previously constructed underground openings. Using subtractive clustering the systems have the intelligence to pick up the relations between input and output and define the rules that represent the system's behavior automatically. These systems are found to predict support to be used more successfully than the Q system. With the introduction of extra input variables, which are important in numerical analysis, such as depth and intact rock strength, an extended fuzzy system is developed. This system is suggested for preliminary use as it is able to predict support even better.

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