Hamed FALL^1,, Déthié SARR¹, Lamine BAR¹, Abdou Aziz WELLE²

Accurately determining the bearing capacity of shallow foundations is essential in geotechnical engineering. This article proposes an approach using artificial neural networks (ANNs) to estimate the bearing capacity of a continuous footing based on the geotechnical characteristics of the Daraal Peulh site (Senegal). A database of 250 samples was compiled by varying cohesion, the angle of internal friction, the footing width, and the embedment depth. The target values were generated by an equiprobable random mixture of the Terzaghi and Meyerhof formulas. A multilayer perceptron with a hidden layer of 12 neurons, trained using the Levenberg-Marquardt algorithm, yielded a mean squared error (MSE) of 45.97 on the training set and 68.73 on the test set, with correlation coefficients of 0.9932 and 0.9871, respectively. Validation on eight independent samples (four from Terzaghi, four from Meyerhof) yielded relative errors ranging from 3.5% to 38%, with a median error of approximately 13%. This study demonstrates that, despite the inherent variability resulting from the combination of the two theories, the RNA is a rapid and sufficiently reliable estimation tool for the soils of Daraal Peulh, within the limits of the parameter ranges studied.

bearing capacity, shallow foundation, artificial neural networks, MATLAB, external validation, Meyerhof, Daraal Peulh site