Optimizing axial bearing capacity estimation of pre-bored grouted planted nodular (PGPN) pile: Enriched dataset and genetic algorithm approach
- Authors: Tan Nguyen 1*, Tuetakoun Aphistih 1, Minh The Quang Nguyen 1, Duy Khuong Ly 2,3, Jim Shiau 4
Affiliations:
1 Ton Duc Thang University (Faculty of Civil Engineering), Hochiminh, Vietnam
2 Van Lang University (Laboratory for Computational Mechanics - Institute for Computational Science and Artificial Intelligence), Hochiminh, Vietnam
3 Van Lang University (Faculty of Civil Engineering - School of Technology), Hochiminh, Vietnam
4 University of Southern Queensland (School of Engineering), Toowoomba, Australia
- *Corresponding:This email address is being protected from spambots. You need JavaScript enabled to view it.
- Keywords: Axial bearing capacity, Empirical Formula, Genetic algorithm optimiza-tion, Static Pile Load Test.
- Received: 23rd-July-2024
- Revised: 29th-Oct-2024
- Accepted: 10th-Nov-2024
- Online: 1st-Dec-2024
- Section: Civil Engineering
Abstract:
This study introduces a practical and efficient methodology to improve the estimation of the ultimate axial bearing capacity of Pre-bored Grouted Planted Nodular (PGPN) piles, addressing a critical need in geotechnical engineering. By enhancing the dataset with 98 additional case histories and pile load test data from various projects across Vietnam, we have developed a more reliable predictive tool for engineers. The use of genetic algorithms has refined existing empirical formulas, significantly improving their accuracy while remaining simple enough for hand calculations. The proposed formula achieves a correlation coefficient of 0.907, a 7.2% improvement over previous methods. This research offers a valuable solution for the challenges faced in predicting the load-bearing capacity of PGPN piles, providing a more dependable method that can streamline design and construction practices.
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