| dc.description.abstract | Rock mass classification system is an important criterion for proposing a support type in rock tunneling. Currently rock mass classification systems used in Taiwan are all come from abroad, and it is necessary to modify these methods for complex geologic condition in Taiwan. In this study, we take the advantages of efficiency and quick learning character of a non-linear and supervised classification problem, and uses BPN (Back Propagation Artificial Neural Network) method to perform the rock mass classification. We collected tunnel engineering reports of the Fu-de and the New Guan-yin tunnels as examples for study. We adopted geologic factors from these reports, and normalize the factors as input level. In the output level of BPN model, we designed a fuzzy membership, so as uncertainty could be considered. We adopt some good samples for BPN learning and the parameters judging, and then use the BPN and rest data for testing the performance of the system.
In the case of Fu-de tunnel, we use 300 good samples for BPN training and learning and get a good BPN model. We test the rest 2019 samples with the BNP model, and result reveals that 74.39% cases output are exact by the same type of support as the target type and 96.19% cases output support type within one neighboring class of the target type. In the case of Guan-yin, we picked up 17 geologic factors from the engineering reports and summarized 2099 samples for learning and testing in BPN model. Result reveals that the accuracy rate is 99.05% with the suggestion is exactly as the target type. After these two case studies, the best BPN models are two hind layers, the neural units of hind layers are above 13, training more than 5000 times, and moment factor and learning rate are almost closed to 0.5.
Results from the BPN model of the Fu-de and the New Guan-yin tunnels may conclude: (a) If the overburden factor is exclude for the analysis, it is necessary to remove the test data at portal section of the tunnel. (b) The result in BPN training and testing could be better if we consider more factors for analyses. (c) When the quality of data is good enough, we may use as much data as we have to get the best result. Whereas more data produce more chaotic BPN model, when data quality is bad. (d) BPN model trained from the first 1/3 portion of the tunnel is not good enough to predict the support type and geologic condition of the rest of the tunnel. Field investigation and drilling are necessary for determining the supporting type for the rest potion of tunnel. (e) The fuzzy membership function output from the BPN model can help us making decisions. In the future, we may collect a large number of tunnel geologic and construction data, and establish a more general BPN model. With the assistance of factor analysis and cluster analysis, we may construct a more complete and friendly procedure for tunnel rock mass classification and support prediction. | en_US |