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Prediction of axial capacity of piles driven in non-cohesive soils based on neural networks approach

Author(s):




Medium: journal article
Language(s): Latvian
Published in: Journal of Civil Engineering and Management, , n. 3, v. 23
Page(s): 393-408
DOI: 10.3846/13923730.2016.1144643
Abstract:

This paper presents an application of two advanced approaches, Artificial Neural Networks (ANN) and PrinciĀ­pal Component Analysis (PCA) in predicting the axial pile capacity. The combination of these two approaches allowed the development of an ANN model that provides more accurate axial capacity predictions. The model makes use of Back-Propagation Multi-Layer Perceptron (BPMLP) with Bayesian Regularization (BR), and it is established through the incorporation of approximately 415 data sets obtained from data published in the literature for a wide range of un-cemented soils and pile configurations. The compiled database includes, respectively 247 and 168 loading tests on large-and low-displacement driven piles. The contributions of the soil above and below pile toe to the pile base resistance are pre-evaluated using separate finite element (FE) analyses. The assessment of the predictive performance of the new method against a number of traditional SPT-based approaches indicates that the developed model has attractive capabiliĀ­ties and advantages that render it a promising tool. To facilitate its use, the developed model is translated into simple design equations based on statistical approaches.

Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.3846/13923730.2016.1144643.
  • About this
    data sheet
  • Reference-ID
    10354305
  • Published on:
    13/08/2019
  • Last updated on:
    13/08/2019