Pile Design using the Modified Unified Method combined with Monte Carlo Simulation

Authors

  • Hoa Cao Van Department of Construction Technology, Faculty of Civil Engineering, Ho Chi Minh City University of Architecture, Vietnam
Volume: 14 | Issue: 3 | Pages: 14275-14281 | June 2024 | https://doi.org/10.48084/etasr.7247

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.

Keywords:

pile design, Monte Carlo simulation, hybrid model, artificial neural networks, modified unified method

Downloads

Download data is not yet available.

References

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.

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.

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.

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/.

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.

Y. Wei, "A Hybrid evolutionary algorithm based on Artificial Bee Colony algorithm and Differential Evolution," in 2021 2nd International Conference on Computer Engineering and Intelligent Control (ICCEIC), Chongqing, China, Nov. 2021, pp. 35–40.

M. S. Kiran, "TSA: Tree-seed algorithm for continuous optimization," Expert Systems with Applications, vol. 42, no. 19, pp. 6686–6698, Nov. 2015.

Y.-C. Yeh, Y.-H. Kuo, and D.-S. Hsu, "Building an expert system for debugging FEM input data with artificial neural networks," Expert Systems with Applications, vol. 5, no. 1, pp. 59–70, Jan. 1992.

J. Ghaboussi, J. H. Garrett, and X. Wu, "Knowledge‐Based Modeling of Material Behavior with Neural Networks," Journal of Engineering Mechanics, vol. 117, no. 1, pp. 132–153, Jan. 1991.

S. J. Alghamdi, "Prediction of Concrete’s Compressive Strength via Artificial Neural Network Trained on Synthetic Data," Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 12404–12408, Dec. 2023.

L. Arabet, M. Hidjeb, and F. Belaabed, "A Comparative Study of Reinforced Soil Shear Strength Prediction by the Analytical Approach and Artificial Neural Networks," Engineering, Technology & Applied Science Research, vol. 12, no. 6, pp. 9795–9801, Dec. 2022.

L. Arabet, M. Hidjeb, and F. Belaabed, "A Comparative Study of Reinforced Soil Shear Strength Prediction by the Analytical Approach and Artificial Neural Networks," Engineering, Technology & Applied Science Research, vol. 12, no. 6, pp. 9795–9801, Dec. 2022.

M. A. Shahin, "Load–settlement modeling of axially loaded steel driven piles using CPT-based recurrent neural networks," Soils and Foundations, vol. 54, no. 3, pp. 515–522, Jun. 2014.

A. Kordjazi, F. Pooya Nejad, and M. B. Jaksa, "Prediction of ultimate axial load-carrying capacity of piles using a support vector machine based on CPT data," Computers and Geotechnics, vol. 55, pp. 91–102, Jan. 2014.

A. Ismail, D.-S. Jeng, and L. L. Zhang, "An optimised product-unit neural network with a novel PSO–BP hybrid training algorithm: Applications to load–deformation analysis of axially loaded piles," Engineering Applications of Artificial Intelligence, vol. 26, no. 10, pp. 2305–2314, Nov. 2013.

A. Ahangar-Asr, A. A. Javadi, A. Johari, and Y. Chen, "Lateral load bearing capacity modelling of piles in cohesive soils in undrained conditions: An intelligent evolutionary approach," Applied Soft Computing, vol. 24, pp. 822–828, Nov. 2014.

R. Eslami, S. H. H. Sadeghi, and H. A. Abyaneh, "A Probabilistic Approach for the Evaluation of Fault Detection Schemes in Microgrids," Engineering, Technology & Applied Science Research, vol. 7, no. 5, pp. 1967–1973, Oct. 2017.

B. H. Fellenius, "Unified Design of Piled Foundations with Emphasis on Settlement Analysis," in Current Practices and Future Trends in Deep Foundations, Apr. 2012, pp. 253–275.

C. V. Hoa, "Pile design with consideration of down drag," in Geotechnics for Sustainable Infrastructure Development, Singapore, 2020, pp. 145–151.

Η. V. Cao and T. A. Nguyen, "Verification and Validation of the Pile Design Method with Consideration of down Drag," International Journal of GEOMATE, vol. 23, no. 96, pp. 145–152, Aug. 2022.

H. C. Van and T. A. Nguyen, "Numerical Simulation of Pile Design Method that Considers Negative Friction," Civil Engineering and Architecture, vol. 11, no. 5, pp. 2285–2292, Sep. 2023.

C. V. Hoa, "Rational Pile Design using Computer-based Program Coding in Matlab: A Case Study," Engineering, Technology & Applied Science Research, vol. 14, no. 2, pp. 13160–13166, Apr. 2024.

P. Anbazhagan, M. Neaz Sheikh, K. Bajaj, P. J. Mariya Dayana, H. Madhura, and G. R. Reddy, "Empirical models for the prediction of ground motion duration for intraplate earthquakes," Journal of Seismology, vol. 21, no. 4, pp. 1001–1021, Jul. 2017.

Downloads

How to Cite

[1]
H. Cao Van, “Pile Design using the Modified Unified Method combined with Monte Carlo Simulation”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 3, pp. 14275–14281, Jun. 2024.

Metrics

Abstract Views: 149
PDF Downloads: 66

Metrics Information