invasive weed optimization algorithm

Determining the lateral bearing capacity of piles is one of the important issues that due to factors such as heterogeneity of soil environment in the pile and its geometric characteristics, the correct estimation of lateral bearing capacity of a pile is a problem. Although pile loading testing can be used as a reliable method at various stages of design despite its high accuracy, it incurs high costs and long execution times for construction and mining projects. In this paper, in order to apply new intelligent methods to estimate the lateral bearing capacity of piles in soft clay soils, two bee colony optimization and invasive weed optimization algorithms have been used.Finally, in order to evaluate the accuracy of the models, the indices of square correlation coefficient (R2), variance inclusion (VAF), mean absolute error percentage (MAPE), root mean square error (RMSE) and mean square error (MSE) were used. The evaluation results showed that for the bee colony algorithm, the estimation accuracy using R2 and VAF indices was about 0.98-0.99 and using MAPE, RMSE and MSE indices between 0.000032-0.0056 and also for the algorithm. For invasive weed optimization algorithm, estimation accuracy using R2 and VAF indices was about 0.97-0.98 and using MAPE, RMSE and MSE indices was between 0.0005-0.023. In addition, the results of sensitivity analysis showed that the non-drained shear strength of soil (Su), among other input parameters, has the greatest impact on the lateral bearing capacity of piles and change in it will have the greatest impact on the output.