Artificial neural network model for predicting load capacity of driven piles




Load capacity; Deep foundations; Artificial neural networks; Driven piles.


In geotechnics, several models, empirical or not, have been proposed for the calculation of load capacity in deep foundations. These models run mainly through physical assumptions and construction of approximations by mathematical models. Artificial Neural Networks (ANN), in addition to other applications, are excellent computational mechanisms that, based on biological neural learning, can perform predictions and approximations of functions. In this work, an application of artificial neural networks is presented. The objective here is to propose a mathematical model based on artificial intelligence focused on Artificial Neural Network (ANN) learning capable of predicting the load capacity for driven piles. The results obtained through the neural network were compared with actual values of load capacities obtained in the field through load tests. For this quantitative comparison, the following metrics have been chosen: Pearson correlation coefficient and mean squared error. The database used to carry out the project consisted of 233 load tests, carried out in diverse cities and different countries, for which load capacity, hammer weight, hammer drop height, pile length, pile diameter and pile penetration per blow values ​​were available. These values have been used as input values in a multilayer perceptron neural network to estimate the load capacities of the respective piles. It has been found that the proposed neural model presented, in general, correlation with field values above 90%, reaching 96% in the best result.


ABNT, N. B. R. (2010). Projeto e execução de fundações. Associação Brasileira de Normas Técnicas, Rio de Janeiro, Brasil.

Amancio, L. B. (2013). Previsão de Recalques em Fundações Profundas Utilizando Redes Neurais Artificiais do Tipo Perceptron. Dissertação (Mestrado) - Mestraod em Engenharia Civil: Geotecnia. Centro de Tecnologia. Universidade Federal do Ceará, Fortaleza.

Araújo, C. B. C., Neto, S. A. D. & Anjos, G. J. M. dos. (2015). Estimativa de Recalque em Estacas Utilizando Redes Neurais Artificiais. Anais do VII Simpósio Brasileiro de Engenheiros Geotécnicos Jovens.

Azeredo, H. A. (1977). O Edifício Até Sua Cobertura. (2nd ed.) Edgard Blücher, São Paulo.

Batista, C. F. B. (2012). Soluções de Equações Diferenciais Usando Redes Neurais de Múltiplas camadas com os métodos da Descida mais íngreme e Levenberg-Marquardt. Dissertação (Mestrado). Programa de Pós-graduação em Matemática. Universidade Federal do Pará, Belém.

Benesty, J., Chen, J., Huang, Y., & Cohen, I. (2009). Pearson correlation coefficient. In: Noise reduction in speech processing. (pp. 1-4). Springer, Berlin, Heidelberg.

Bowles, J. E. (1996). Foundation Analysis and Design. (5th ed.), The McGraw-Hill Companies, Inc., New York.

Cintra, J. C. A. & Aoki, N. (2011). Fundações por estacas: projeto geotécnico. Oficina de Textos, São Paulo.

Das, B. M. (2010). Principles of the Geotechnical Engineering. (7th ed.), Cengage Learning, Stamford.

Das, B. M. (2011). Principles of Foundation Engineering. Seventh Edition.Cengage Learning, Stamford.

Erzin, Y. & Gul, T. O. (2014). The use of neural networks for the prediction of the settlement of one-way footings on cohesionless soils based on standard penetration test. Neural Computing and Applications, 24(3–4), 891–900.

Fellenius, B. H. (2020). Basics of Foundation Design. British Columbia. < ,%20Basics%20of%20foundation%20design%202020.pdf>

Hachich, W. C., Falconi, F. F., Saes, J., Frota, R. G. Q., Carvalho, C. S. & Niyama, S. (1998). Fundações – Teoria e prática. Ed. Pini, ABMS/ABEF, (2nd. ed.).

Haykin, S. (2001). Redes Neurais: Princípios e Prática. (2nd ed.), Bookman, Porto Alegre.

Jayaweera, M. S. R. (2009). Capacity Estimation of Piles Using Dynamic Methods. Master of Engineering in Foundation Engineering & Earth Retaining Systems. University of Moratuwa. Sri-Lanka

Kalinli, A., Acar, M. C. & Gündüz, Z. (2011). New approaches to determine the ultimate bearing capacity of shallow foundations based on artificial neural networks and ant colony optimization. Engineering Geology, 117(1–2), 29–38.

Khanlari, G. R., Mojtaba, H., Momeni, A.A. & Abdin, Y. (2012). Prediction of shear strength parameters of soils using artificial neural networks and multivariate regression methods. Engineering Geology, 131–132, 11–18, 2012.

Lobo, B. O. (2005). Método de previsão de capacidade de carga de estacas : aplicação dos conceitos de energia do ensaio SPT . Dissertação (Mestrado). Programa de Pós-Graduação em Engenharia Civil. UFRGS, Porto Alegre .

Padmini, D., Ilamparuthi, K. &Sudheer, K. P. (2008). Ultimate bearing capacity prediction of shallow foundations on cohesionless soils using neurofuzzy models. Computers and Geotechnics, v. 35, n. 1, p. 33–46.

Pereira, A. S., Shitsuka, D. M., Parreira, F. J., & Shitsuka, R. (2018). Metodologia da pesquisa científica. [eBook]. Santa Maria. Ed. UAB / NTE / UFSM.

Pessoa, A. D. (2018). Modelo Neuronal para Previsão de Capacidade de Carga em Fundações Profundas. Universidade Federal do Maranhão, São Luis.

SCAC. (2013). Case: conjunto residencial no Rio de Janeiro. <>

Shanazari, H. & Tutunchian, M. A. (2012). Prediction of ultimate bearing capacity of shallow foundations on cohesionless soils: An evolutionary approach. KSCE Journal of Civil Engineering, 16(6), 950–957.

Silva, I. N., Spatti, D. & Flauzino, R. (2016). Redes Neurais Artificiais para Engenharia e Ciências Aplicadas: Curso Prático. (2nd ed.), São Paulo. Artliber Editora.

Tian, H. & Shang, Z. (2006). Artificial neural network as a classification method of mice by their calls. Ultrasonics, 44, e275--e278.

Velloso, D. A. & Lopes, F. de R. (2011). Fundações: critérios de projeto, investigação do subsolo, fundações superficiais, fundações profundas. Oficina de Textos, São Paulo.

Vesic, A. S. (1963). Bearing Capacity of Deep Foundations in Sand. National Academy of Sciences, Highway Research Board, Report No. 39, Washington D.C. pp. 112-153.

Willmott, C. J. & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim Res. 30(1):79–82.

Yan, H., Jiang, Y., Zheng, J., Peng, C. & Li, Q. (2006). A multilayer perceptron-based medical decision support system for heart disease diagnosis. Expert Systems with Applications, 30(2), 272–281.

Zanella, L. C. H. (2011). Metodologia de Pesquisa. (2nd ed.). < texto Metodologia da Pesquisa.pdf>




How to Cite

PESSOA, A. D. .; SOUSA, G. C. L. de .; ARAUJO, R. da C. de .; ANJOS, G. J. M. dos . Artificial neural network model for predicting load capacity of driven piles. Research, Society and Development, [S. l.], v. 10, n. 1, p. e12210111526, 2021. DOI: 10.33448/rsd-v10i1.11526. Disponível em: Acesso em: 27 jan. 2021.