Comparing nonlinear regression analysis and artificial neural networks to predict geotechnical parameters from standard penetration test
Mohammed Amin Benbouras, Ratiba Mitiche Kettab, Hamma Zedira, Fatiha Debiche, Narimane Zaidi
Rezumat/Abstract. At the beginning the twenty-first century, a lot of high-level methods have become available in geotechnical engineering in order to deal with the complexity and heterogeneity encountered in soil, Statistical modeling (i.e. regression analysis method) was used to estimate the relationships among two or more variables, however in the early nineties an application of a new system emerged which gave excellent results in solving a lot of problems by learning from the available data so-called ”artificial neural network”. The aim of this study is to apply both methods, nonlinear regression analysis and artificial neural networks in order to predict geotechnical parameters from standard penetration test in all soil’s types; Comparison of the results using correlation’s coefficient (R) and Root Mean Squared Error (RMSE) is done between both methods; About 400 samples, over 65 boreholes in the Algiers area have been collected and were used in this study, The results show the superiority of ANN method in predicting data that seems closer to experimental values compared to NRA method.
Cuvinte cheie/Key words: artificial neural network, regression analysis, standard penetration number and geotechnical parameters
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