An investigation of friction angle correlation with geotechnical properties for granular soils using GMDH type neural networks

The standard penetration test (spt) is one of the most eective tests forquick and inexpensive evaluation of the mechanical properties of soil layers. numerousstudies have been conducted to evaluate correlations between spt blow counts (nspt )and soil properties such as friction angle ('0). in this paper, the relation between and insitu parameters of soil, including nspt , eective stress and ne content, is investigated forgranular soils. in order to demonstrate the relevancy of '0 and corrected spt blow count(n60), a new polynomial model, based on the group method of data handling (gmdh)type neural network (nn), was used based on 195 data sets including three soil parameters.these were recorded after two major earthquakes in turkey and taiwan in 1999. thisstudy addresses the question of whether gmdh-type nn is capable of estimating '0 basedon specied variables. results conrm that gmdh-type nn provide an eective way torecognize data patterns and predict performance over granular soils accurately. finally,the eect of ne content and eective overburden stress on the correlation of n60 and '0has been studied using sensitivity analysis.