Abstract
Piles are typically designed to ensure the bearing capacity and settlement elastic behavior. However, some projects seem over-designed, leading to unnecessary waste, whereas others experience excessive settlement. This could be caused by various factors, such as site investigation, sampling and testing methods, selection of soil behavior model, and calculation programs. To achieve a successful pile design, engineers must consider, among others, the loads applied to the pile, the resistance capacity of the piles, the pile material's bearing capacity, the pile's displacement, and the soil's settlement. On the other hand, the input parameters for geotechnical problems, in general, and pile design problems, in particular, often do not reflect the actual behavior of the soil due to its heterogeneous and anisotropic nature. To address these challenges, an Artificial Neural Network (ANN) approach is proposed for pile design, using a relatively wide range of soil input data. This study establishes a numerical program for pile design combined with the ANN approach, validated by verifying the pile design of a project constructed in Vietnam. The results indicate that the proposed program can reasonably simulate pile group behavior and assist engineers in deploying appropriate safety factors.
Publisher
Engineering, Technology & Applied Science Research
Reference23 articles.
1. A. Benali, B. Boukhatem, M. N. Hussien, A. Nechnech, and M. Karray, "Prediction of axial capacity of piles driven in non-cohesive soils based on neural networks approach," Journal of Civil Engineering and Management, vol. 23, no. 3, pp. 393–408, Mar. 2017.
2. S. Gao and C. W. de Silva, "Estimation distribution algorithms on constrained optimization problems," Applied Mathematics and Computation, vol. 339, pp. 323–345, Dec. 2018.
3. M. S. Kıran, M. Gündüz, and Ö. K. Baykan, "A novel hybrid algorithm based on particle swarm and ant colony optimization for finding the global minimum," Applied Mathematics and Computation, vol. 219, no. 4, pp. 1515–1521, Nov. 2012.
4. M. Birattari, L. Paquete, T. Stützle, and K. Varrentrapp, "Classification of Metaheuristics and Design of Experiments for the Analysis of Components," Technical Report AIDA-01-05, 2001. Available: http://hdl.handle.net/2013/.
5. J. Liu, C. Wu, G. Wu, and X. Wang, "A novel differential search algorithm and applications for structure design," Applied Mathematics and Computation, vol. 268, pp. 246–269, Oct. 2015.