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Digging of geotechnical boreholes and soil resistance tests are time-consuming and expensive activities. Therefore selection of optimum number and suitable location of boreholes can reduce cost of their drilling and soil resistance tests. In this research, a model which is consisting of geo statistics model as an estimator and an optimized model is selected. The kriging calculates the variance of the estimation error of different combinations from available geotechnical boreholes. In each combination, n is number of considered boreholes and N is number of available boreholes (N>n). At the end, the best combination is selected by genetic algorithm (the error variance of this combination is minimum). Also the Kean Shahr of Ahvaz city (in Khuzestan province, Iran) is selected as case study in this research. Location of selected boreholes is in points that soil resistance of these points represents mean soil resistance of total region. Optimum number of boreholes is 15. Also results show that location of selected boreholes depends to soil resistance and diameter and length of applied piles are not important for this purpose.

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Direct drilling method and the use of microtremor studies are among the most commonly used available methods utilized to estimate dynamic parameters for a site. One of the most important parameters is the dominant period of the site whose estimation plays a pivotal role in seismic hazard mitigation. The conventional models obtained are not capable of estimating the parameters that govern the seismic response of a site. Therefore, Artificial Neural Networks (ANNs) are reliable and practical estimation methods that can be used to analyze comprehensive measurements such as dominant period of a site, and improve the data. In this paper, the performance of ANNs has been investigated on calculation of the dominant period for a site. Three different models, namely BP, RBF and ANFIS, have been compared to determine the best model that provides the most accurate estimation for the dominant period. The input parameters have been chosen to be alluvial layer thickness, grain size, specific gravity, effective stress, shear wave velocity, standard penetration number, Atterberg limits. Each of the three models has been trained and tested for these input parameters and a unique output which is the dominant period of the site. The results showed that ANNs successfully model complex relationships between soil parameters and seismic parameters of the site, and provide a robust tool to accurately estimate the dominant period of a site. The accurate estimations can be then used for engineering applications including damage assessment and structural health monitoring. In addition, The obtained emulator of RBF model shows the least model error in estimation of dominant period and has been found to be superior to the other evaluated methods.