Artificial neural network model for predicting load capacity of driven piles

Authors

DOI:

https://doi.org/10.33448/rsd-v10i1.11526

Keywords:

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

Abstract

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.

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Published

04/01/2021

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: https://rsdjournal.org/index.php/rsd/article/view/11526. Acesso em: 27 jan. 2021.

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Section

Engineerings