طراحي مرحله اي شبكه نمونه برداري، بر اساس پارامت رهاي ژئوتكنيكي و خصوصيات كيفي ساختگاه سد سميلان، با استفاده از كريجينگ و شبكه عصبي

Design of Sequential Sampling Network, with Kriging and Neural Network, based on Geotechnical Parameters and Quality of Structures of Semilan Dam Site

Selection of proper number and location of boreholes are major tasks in design of exploration network. Boreholes normally drilled for insitue testing and sampling. A challenging issue in sampling is determination of number and location of samples. The logic of optimum sampling is sequential. This work presents a comparison between single and multiple stages sampling in Semilan dam site. First, the single-stage sampling was tested; using geotechnical data (Lugeon and RQD) of 23 boreholes and indicator function. Based on indicator functions, Lugeon parameter divided to four and RQD to three indexes. The other application of indicator functions, is converting qualitative to quantitative data in order to define indicators of various structures and lithology of the studied dam site. Then, indicator variograms of each parameter, in various directions, were calculated and their variogram parameters were extracted. Based on the average kriging error, Lugeon, RQD and lithology index were also divided into four indexes. Consequently, the function for locations of additional drillings, based on their relation with risk and uncertainty is defined. Function for location of additional drillings is found to be equal to Lugeon multiply to estimation error multiply to index of dam structures; divided by RQD multiply by lithology index. Next, two stages sampling was studied starting with 12 boreholes, according to sample density. All the process was repeated once again and according to function of additional drilling locations, 8 boreholes were selected for the second level or phase of drilling. Based on kriging and neural network estimator, and using normalized data, desired parameters were estimated. In the present study, kriging variance and estimation error in two stages sampling decreased more, compare to one stage sampling, although the numbers of boreholes decreased from 23 to 20. Reduction of estimation error is related to the layout of the first stage boreholes between the second stage ones.