Swarm-based analysis through social behavior of grey wolf optimization and genetic programming to predict friction capacity of driven piles

The advantage of new data mining-based solutions, and more recently, optimization algorithms (i.e., basically swarm-based solutions) have enhanced traditional models of engineering structural analysis. This paper investigates social behavior of Grey Wolf Optimization (GWO) in improving the neural assessment of friction capacity (f(s)) of concrete driven pile systems. Besides, the genetic programming (GP) algorithm was also proposed to have comparison with the proposed GWO prediction outputs. To achieve this goal, four f(s) influential factors of pile length (m), pile diameter (cm), effective vertical stress (S-v), and undrained shear strength (S-u) are considered for preparing the required dataset. A swarm size-based sensitivity analysis is then carried out to use the best-fitted structures (i.e., more convergency in the final output) of each ensemble. The results of the best prediction network from both above-mentioned sensitivity analyses were compared. The results show that both GWO and GP models presented excellent performance. The findings of neural networks varied based on the number of neurons in a single hidden layer and of course the level of its complexity. Based on R-2 and RMSE, values of (0.9537 and 9.372) and (0.8963 and 7.045) are determined, for the training and testing datasets of MLP-based solution, respectively. On the contrary, for the GP and GWO-MLP proposed predictive models, the R-2 of (0.9783 and 0.982) and (0.913 and 0.892) were found for the training and testing datasets. This proves the better performance of GWO when combined with MLP in predicting engineering solutions comparing to conventional MLP or GP-based combinations.

Automated summary

The paper investigates the social behavior of Grey Wolf Optimization (GWO) in enhancing the neural assessment of friction capacity of concrete driven pile systems, compared with genetic programming (GP) algorithm. Through sensitivity analysis and neural network, it is found that GWO performs better in predicting engineering solutions.