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The classification of surrounding rock stability is the critical problem in tunneling engineering. In order to decrease engineering disasters, the surrounding rock stability should be accurately evaluated. The ideal point method is applied to the classification of surrounding rock stability. Considering the complexity of surrounding rock classification, some factors such as rock uniaxial compressive strengthen, integrality coefficient of rock mass, the angle between tunnel axis and the main joint, joints condition, and seepage measurement of groundwater are selected as evaluation indices. The weight coefficients of these evaluation indices are determined by the objective and subjective weighting method, consisting with the delphi method and the information entropy theory. The objective and subjective weighting method is exact and reliable to determine the weights of evaluation indices, considering not only the expert’s experiences, but also objectivity of the field test data. A new composite model is established for evaluating the surrounding rock stability based on the ideal point method and the objective and subjective weighting method. The present model is applied to Beigu mountain tunnel in Jiangsu province, China. The result is in good agreement with practical situation of surrounding rock, which proves that the ideal point method used to classify the surrounding rock in tunnels is reasonable and effective. The present model is simple and has very strong operability, which possesses a good prospect of engineering application.

This paper presents a new fusion diagnosis based on rough set and BP neural network for the fault diagnosis of gas turbine. The frame is designed to fusion fault diagnosis, which is composed by three parts: the rough set data pre-processor, rough set diagnosis model and BP neural network diagnosis model. Aiming at the difficulty in getting adequate fault samples in fault diagnosis, rough set theory is first used to process the original data, establish the decision table and generate rules, which can eliminate the redundant information and build the rough set diagnosis model. After that, according to the optimal decision attribute pre-treated by rough set, BP neural network is designed for fault diagnosis, which can reduce the scale of neural network, improve the identification rate, and improve the efficiency of the whole fusion diagnosis system. Finally, an example of gas turbine generator sets fuel system is taken as a case study to demonstrate the feasibility and validity of the proposed method in this paper.