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Geotechnical and Geological Engineering

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07-03-2018 | Original paper

Settlement Prediction of Model Piles Embedded in Sandy Soil Using the Levenberg–Marquardt (LM) Training Algorithm

Authors: Ameer A. Jebur, William Atherton, Rafid M. Al Khaddar, Ed Loffill

Published in: Geotechnical and Geological Engineering | Issue 5/2018

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Abstract

This investigation aimed to examine the load carrying capacity of model piles embedded in sandy soil and to develop a predictive model to simulate pile settlement using a new artificial neural network (ANN) approach. A series of experimental pile load tests were carried out on model concrete piles, comprised of three piles with slenderness ratios of 12, 17 and 25. This was to provide an initial dataset to establish the ANN model, in attempt at making current, in situ pile-load test methods unnecessary. Evolutionary Levenberg–Marquardt (LM) MATLAB algorithms, enhanced by T-tests and F-tests, were developed and applied in this process. The model piles were embedded in a calibration chamber in three densities of sand; loose, medium and dense. According to the statistical analysis and the relative importance study, pile lengths, applied load, pile flexural rigidity, pile aspects ratio, and sand-pile friction angle were found to play a key role in pile settlement at different contribution levels, following the order: P > δ > lc/d > lc > EA. The results revealed that the optimum model of the LM training algorithm can be used to characterize pile settlement with good degree of accuracy. There was also close agreement between the experimental and predicted data with a root mean square error, (RMSE) and correlation coefficient (R) of 0.0025192 and 0.988, respectively.

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Title: Settlement Prediction of Model Piles Embedded in Sandy Soil Using the Levenberg–Marquardt (LM) Training Algorithm
Authors: Ameer A. Jebur
William Atherton
Rafid M. Al Khaddar
Ed Loffill
Publication date: 07-03-2018
Publisher: Springer International Publishing
Published in: Geotechnical and Geological Engineering / Issue 5/2018
Print ISSN: 0960-3182
Electronic ISSN: 1573-1529
DOI: https://doi.org/10.1007/s10706-018-0511-1

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