Optimizing Empirical Methods for Calculating the Bearing Capacity of Concrete Piles
Abstract
Designing concrete piles that are low-cost and safe requires reliable methods to predict their bearing capacity. Empirical design methods are a popular alternative, like Meyerhof’s method (MH), which suits better temperate soils, and Décourt-Quaresma’s (DQ), which is more suitable for tropical soils. Coefficients empirically calibrate these methods; nevertheless, they frequently become inaccurate for specific cases. This work aims to recalibrate these two empirical design methods using datasets containing static load tests, all obtained for tropical soil. The Lichtenberg Algorithm (LA) is applied to find optimal coefficients, considering three pile types for MH and two for DQ. The study tested three different objective functions. The new coefficients improved MH concerning R2, RMSE, and MAE. R2 increased from 0.32 to 0.90 for one case of bored piles, the most notable improvement observed throughout the study. The same did not occur for DQ, although RMSE and MAE significantly decreased. The original calibration of the methods can explain this difference once this work uses data from tropical soil.
Keywords:
optimization, meta-heuristics, pile bearing capacity
References
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Meyerhof, G. G. (1976). Bearing capacity and settlements of pile foundations. Journal of the Geotechnical Engineering Division, 102(3):197–228.
Moayedi, H. and Hayati, S. (2019). Artificial intelligence design charts for predicting friction capacity of driven pile in clay. Neural Computing and Applications, 31(11):7429–7445.
Pereira, J. L. J., Brendon, F. M., Ribeiro, R. F., Cunha, S. S., and Gomes, G. F. (2022a). Deep multiobjective design optimization of cfrp isogrid tubes using lichtenberg algorithm. Soft Computing, pages 7195–7209.
Pereira, J. L. J., Francisco, M. B., de Oliveira, L. A., Chaves, J. A. S., Jr, S. S. C., and Gomes, G. F. (2022b). Multi-objective sensor placement optimization of helicopter rotor blade based on feature selection. Mechanical Systems and Signal Processing, 180:109466.
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Pham, T. A., Ly, H.-B., Tran, V. Q., Giap, L. V., Vu, H.-L. T., and Duong, H.-A. T. (2020). Prediction of pile axial bearing capacity using artificial neural network and random forest. Applied Sciences, 10(5).
Pham, T. A. and Tran, V. Q. (2022). Developing random forest hybridization models for estimating the axial bearing capacity of pile. PLoS ONE, 17(3).
Robert, Y. (1997). A few comments on pile design. Canadian Geotechnical Journal, 34(4):560–567.
Salgado, R. (2008). The Engineering of Foundations. McGraw-Hill.
Santos Jr., O. (1988). Previsão do comportamento carga-recalque de estacas pré-moldadas de concreto. Master’s thesis, Universidade de São Paulo, São Carlos, Brasil.
Shahin, M. A. (2010). Intelligent computing for modeling axial capacity of pile foundations. Canadian Geotechnical Journal, 47(2):230–243.
Vianna, A. P. F. (2000). Análise de provas de carga estática em estacas pré-moldadas cravadas na cidade de Curitiba e região metropolitana. PhD thesis, Universidade de São Paulo.
Yang, X.-S. (2014). Nature-Inspired Optimization Algorithms. Elsevier.
Alkroosh, I. S., Bahadori, M., Nikraz, H., and Bahadori, A. (2015). Regressive approach for predicting bearing capacity of bored piles from cone penetration test data. Journal of Rock Mechanics and Geotechnical Engineering, 7(5):584–592.
Ardalan, H., Eslami, A., and Nariman-Zadeh, N. (2009). Piles shaft capacity from cpt and cptu data by polynomial neural networks and genetic algorithms. Computers and Geotechnics, 36(4):616–625.
Brendon Francisco, M., Luiz Junho Pereira, J., Augusto Vilas Boas Vasconcelos, G., Simões da Cunha Jr, S., and Ferreira Gomes, G. (2022). Multiobjective design optimization of double arrowhead auxetic model using lichtenberg algorithm based on metamodelling. Structures, 45:1199–1211.
de Souza, T. A. Z., Pereira, J. L. J., Francisco, M. B., Sotomonte, C. A. R., Ma, B. J., Gomes, G. F., and Coronado, C. J. R. (2022). Multi-objective optimization for methane, glycerol, and ethanol steam reforming using lichtenberg algorithm. International Journal of Green Energy, pages 1–18.
Décourt, L., Albiero, J., and Cintra, J. (1996). Análise e projeto de fundações profundas. Fundações: teoria e prática, 2:265–327.
Décourt, L. and Quaresma, A. R. (1978). Capacidade de carga de estacas a partir de valores de spt. In Congresso Brasileiro de Mecânica dos Solos e Engenharia de Fundações, volume 6, pages 45–53.
Francisco, M. B., Junqueira, D. M., Oliver, G. A., Pereira, J. L. J., da Cunha Jr Jr, S. S., and Gomes, G. F. (2021). Design optimizations of carbon fibre reinforced polymer isogrid lower limb prosthesis using particle swarm optimization and lichtenberg algorithm. Engineering Optimization, 53(11):1922–1945.
Kardani, N., Zhou, A., Nazem, M., and Shen, S.-L. (2020). Estimation of bearing capacity of piles in cohesionless soil using optimised machine learning approaches. Geotechnical and Geological Engineering, 38(2):2271–2291.
Kordjazi, A., Pooya Nejad, F., and Jaksa, M. (2014). Prediction of ultimate axial load-carrying capacity of piles using a support vector machine based on cpt data. Computers and Geotechnics, 55:91–102.
Lobo, B. O. (2005). Método de previsão de capacidade de carga de estacas: aplicação dos conceitos de energia do ensaio spt. Master’s thesis, Universidade Federal do Rio Grande do Sul.
Meyerhof, G. G. (1976). Bearing capacity and settlements of pile foundations. Journal of the Geotechnical Engineering Division, 102(3):197–228.
Moayedi, H. and Hayati, S. (2019). Artificial intelligence design charts for predicting friction capacity of driven pile in clay. Neural Computing and Applications, 31(11):7429–7445.
Pereira, J. L. J., Brendon, F. M., Ribeiro, R. F., Cunha, S. S., and Gomes, G. F. (2022a). Deep multiobjective design optimization of cfrp isogrid tubes using lichtenberg algorithm. Soft Computing, pages 7195–7209.
Pereira, J. L. J., Francisco, M. B., de Oliveira, L. A., Chaves, J. A. S., Jr, S. S. C., and Gomes, G. F. (2022b). Multi-objective sensor placement optimization of helicopter rotor blade based on feature selection. Mechanical Systems and Signal Processing, 180:109466.
Pereira, J. L. J., Francisco, M. B., Diniz, C. A., Oliver, G. A., Cunha, S. S., and Gomes, G. F. (2021). Lichtenberg algorithm: A novel hybrid physics-based meta-heuristic for global optimization. Expert Systems with Applications, 170:114522.
Pham, B. T., Nguyen, D. D., Bui, Q.-A. T., Nguyen, M. D., Vu, T. T., and Prakash, I. (2022). Estimation of load-bearing capacity of bored piles using machine learning models. Science of the Earth, 44(4).
Pham, T. A., Ly, H.-B., Tran, V. Q., Giap, L. V., Vu, H.-L. T., and Duong, H.-A. T. (2020). Prediction of pile axial bearing capacity using artificial neural network and random forest. Applied Sciences, 10(5).
Pham, T. A. and Tran, V. Q. (2022). Developing random forest hybridization models for estimating the axial bearing capacity of pile. PLoS ONE, 17(3).
Robert, Y. (1997). A few comments on pile design. Canadian Geotechnical Journal, 34(4):560–567.
Salgado, R. (2008). The Engineering of Foundations. McGraw-Hill.
Santos Jr., O. (1988). Previsão do comportamento carga-recalque de estacas pré-moldadas de concreto. Master’s thesis, Universidade de São Paulo, São Carlos, Brasil.
Shahin, M. A. (2010). Intelligent computing for modeling axial capacity of pile foundations. Canadian Geotechnical Journal, 47(2):230–243.
Vianna, A. P. F. (2000). Análise de provas de carga estática em estacas pré-moldadas cravadas na cidade de Curitiba e região metropolitana. PhD thesis, Universidade de São Paulo.
Yang, X.-S. (2014). Nature-Inspired Optimization Algorithms. Elsevier.
Published
2024-11-17
How to Cite
RIBEIRO, Dimas B.; PEREIRA, João Luiz Junho; LORENA, Ana C..
Optimizing Empirical Methods for Calculating the Bearing Capacity of Concrete Piles. In: NATIONAL MEETING ON ARTIFICIAL AND COMPUTATIONAL INTELLIGENCE (ENIAC), 21. , 2024, Belém/PA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 132-143.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2024.245084.
