Predicting the spatial distribution of soil profile in Adapazari/Turkey by artificial neural networks using CPT data

The infamous soils of Adapazari, Turkey, that failed extensively during the 46-s long magnitude 7.4 earthquake in 1999 have since been the subject of a research program. Boreholes, piezocone soundings and voluminous laboratory testing have enabled researchers to apply sophisticated methods to determine the soil profiles in the city using the existing database. This paper describes the use of the artificial neural network (ANN) model to predict the complex soil profiles of Adapazari, based on cone penetration test (CPT) results. More than 3236 field CPT readings have been collected from 117 soundings spread over an area of 26 km2. An attempt has been made to develop the ANN model using multilayer perceptrons trained with a feed-forward back-propagation algorithm. The results show that the ANN model is fairly accurate in predicting complex soil profiles. Soil identification using CPT test results has principally been based on the Robertson charts. Applying neural network systems using the chart offers a powerful and rapid route to reliable prediction of the soil profiles.

► Artificial intelligence has opened new horizons in geosciences.
► It is very difficult to determine soil profiles in ancient flood plains due to scores of strata.
► ANN can effectively estimate the complex soil profiles if sufficient reliable data is collected.
► A trial for the earthquake stricken city to realistically guess the profiles has resulted in over 90% success rate.
► The algorithm used for the purpose should be applicable to any terrain.