Elasticity modulus of clayey deposits estimation using group method of data handling type neural network

Abstract

Elasticity modulus (Es) is used to predict the elastic settlement of foundations. Plate load test (PLT) is one of the most commonly applied methods to determine this parameter. Given that the method is relatively costly and time-consuming, especially in depths, various empirical equations have been proposed to relate the Es to the results of standard penetration test (SPT) which is one of the most frequently used tests during the geotechnical investigation. Considering that the existing relationships are not able to estimate Es properly, in the present research, Group Method of Data Handling (GMDH) type neural network (NN) is used to estimate the Es of clayey deposits based on a database including 131 plate load test (PLT) and standard penetration test (SPT) from geotechnical investigation sites in Qazvin, Iran. The comparison between proposed models and existing empirical equations indicate that the GMDH models exhibit higher performance (32–42% improvement) with respect to the other correlations. The sensitivity analysis of the developed models has been conducted to determine the contributions of the parameters affecting Es and shows that N60 is the most influential parameter on the GMDH-based models to predict Es. Also, it has been demonstrated that the Es predicted by the best model is significantly affected by error in the measurement of the LL value. Therefore, measuring this parameter in the laboratory requires high accuracy.