Investigation of deposits for traditional extraction activities (metals and coal) has generally been based on determining grade, or content, of the required material. In order to apply the grade concept to an ornamental rock such as slate, it is first necessary to define the variables that determine both the geotechnical recovery rate for the rock mass — which conditions the size of the extracted blocks – and the aesthetic features of the slate — which define the quality of the slabs as potential roofing material.
For this research, geotechnical and aesthetic data for a slate deposit were collected from 16 continuous core borehole samples. A fuzzy expert system was then developed using this data, defining the rock mass recovery rate and slab quality in accordance with the criteria of a slate expert, producing as a final output a zonation of the deposit in terms of top quality slate, medium quality slate or waste.
A mathematical model based on fuzzy logic was chosen due to the fact that the boundaries between different quality groups in a deposit are not clearly distinguished. Moreover, quality also depends on a company's infrastructures for transformation of the blocks, and also on its commercial strategies. 相似文献
The hydrogeological effectiveness of fracture sets is determined and evaluated by the fuzzy c-mean and hierarchical clustering.
These cluster analyses combine the geological spatial attributes and the hydraulic relevant attributes of fractures. Based
on the results of the clustering the fracture set volumes are estimated. 相似文献
Integrated fuzzy neural network models are developed for the assessment of liquefaction potential of a site. The models are trained with large databases of liquefaction case histories. A two-stage training algorithm is used to develop a fuzzy neural network model. In the preliminary training stage, the training case histories are used to determine initial network parameters. In the final training stage, the training case histories are processed one by one to develop membership functions for the network parameters. During the testing phase, input variables are described in linguistic terms such as ‘high’ and ‘low’. The prediction is made in terms of a liquefaction index representing the degree of liquefaction described in fuzzy terms such as ‘highly likely’, ‘likely’, or ‘unlikely’. The results from the model are compared with actual field observations and misclassified cases are identified. The models are found to have good predictive ability and are expected to be very useful for a preliminary evaluation of liquefaction potential of a site for which the input parameters are not well defined. 相似文献