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This study was performed to examine pile uplift bearing capacity and to develop a novel approach to simulate pile load-settlement response using a new supervised computational intelligence (CI) approach. To reach the planned aim, a series of experimental studies were conducted on concrete piles subjected to uplift loading. . According to the statistical analysis, pile effective length (lc), applied load (P), pile flexural rigidity (EA), sand-pile friction angle (δ), and pile aspect ratio (lc/d) were pronounced to play a key role, at different contribution levels, on model output. To evaluate and verify the efficiency of the proposed approach, comparison have been made between the employed training algorithm with experimental pile load-test, and with those specified by a number of design procedures. The results revealed an outstanding agreement between the target and predicted pile-load settlement, thus yielding a coefficient of correlation (R), and a minimum root mean square error (RMSE) of 0.985 and 0.059 respectively, thus, in parallel with a relatively insignificant mean square error level (MSE) of 0.0039.

A new approach for Modelling pile settlement of concrete piles under uplift loading using an evolutionary LM training algorithm