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
Lunan stone forest is a kind of typical karst in China, which is mainly developed under red soil. In the winter of 1999, three study sites were chosen in stone forest national park according to vegetation cover, geomorphologic location and soil types. CO2 concentration was measured with Gastec pump at different depths of soil (20, 40, 60 cm) and at the same time soil samples were gathered and soil properties such as soil moisture, pH, soil organic content were analyzed and the total number of viable microbes were counted in laboratory. In the study, dependent variable was chosen as the mean soil log (PCO2), and soil properties were chosen as the independent variables. Multiple stepwise regression analysis showed that the total amount of microbes and soil moisture are the best indicators of the CO2 production, with the equation LOG(PCO2) = - 0.039(TNM) - 0.056(Mo) + 1.215 accounting for 86% of the variation of the soil CO2 concentration, where TNM is the total number of microbes in the soil and Mo is the moisture of soil sample. 相似文献
