• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.12
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• pp.3124-3134
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• 1993

Diagnosis is composed of two different but interrelated steps ; retrieving the sensory responses f the system and reasoning the state of the system through the given sensor data. This paper explains the probabilistic nature of reasoning involved in the diagnosis when the uncertainties are inevitably included in experts' diagnostic decision making. Uncertainties in decision making are experts' personal experiences, preferences, and system's coherent characteristics. In order to ensure a consistent decision based on the same responses from the system, expert system technology is adopted with the Bayesian reasoning scheme.

• Geotechnical Engineering
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• v.13 no.2
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• pp.77-90
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• 1997

A resonable and economical method which can predict permeability of rock mass in underground is needed to overcome the uncertainty of groundwater behavior. For this par pose, one prediction method of permeability has been studied. The artificial neural networks model using error back propagation algorithm, . one of the teaching techniques, is utilized for this purpose. In order to verify the applicability of this model, in-situ permeability results are simulated. The simulation results show the potentiality of utilizing the neural networks for effective permeability prediction of rock mass.

• Journal of Korean Tunnelling and Underground Space Association
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• v.5 no.4
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• pp.337-348
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• 2003

The propagation mechanism of a detonation pressure with fully coupled charge is clarified and the blasting pressure propagated in rock mass is derived from the application of shock wave theory. The blasting pressure was a function of detonation velocity, isentropic exponent, explosive density, Hugoniot parameters, and rock density. Probabilistic distribution is obtained by using explosion tests on emulsion and rock property tests on granite in Seoul and then the probabilistic distribution of the blasting pressure is derived from the above mentioned properties. The probabilistic distributions of explosive properties and rock properties show a normal distribution so that the blasting pressure propagated in rock can be also regarded as a normal distribution. Parametric analysis was performed to pinpoint the most influential parameter that affects the blasting pressure and it was found that the detonation velocity is the most sensitive parameter. Moreover, uncertainty analysis was performed to figure out the effect of each parameter uncertainty on the uncertainty of blasting pressure. Its result showed that uncertainty of natural rock properties constitutes the main portion of blasting pressure uncertainty rather than that of explosive properties. In other words, since rock property uncertainty is much larger than detonation velocity uncertainty the blasting pressure uncertainty is more influenced by the former than by the latter even though the detonation velocity is found to be the most influencing parameter on the blasting pressure.