Abstract

In this study, a general regression neural network (GRNN) is developed for predicting soil type and standard penetration test (SPT) N (standard penetration resistance) values based on SPT test results. It focuses on soils mainly in Khulna City, Bangladesh that comprise fine-grained alluvium deposits of mostly silt and clay with some organic content and sand. A detailed geological and geotechnical investigation of the city and its surroundings was conducted to generalize the subsoil condition of the study area based on soil type and SPT values. The investigation results showed that the city is divided into four geological units and three geotechnical zones. To develop the GRNN model, more than 2326 field SPT values (N) have been collected from 42 clusters containing 143 boreholes spread over an area of 37 km2. Two trained models were developed: initially the borehole locations were trained with the soil types and after that the borehole location-soil types were trained with the Nc values. The model prediction was compared with the borehole data and the results showed that the GRNN model predicts well compared with the actual site investigation data. Therefore, this model can be used for future planning and expansion of the city.

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