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

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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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Acknowledgements

The first author would like to show his gratitude to Dr. William Atherton and Prof. Rafid Al Khaddar for their guidance and to the technical staff from Liverpool John Moores University, UK, who provided insight and expertise for the current study. The authors would like to acknowledge the Iraqi Ministry of Higher Education and Scientific Research and Wasit University for the grant provided to carry out this research.

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Authors and Affiliations

  1. Department of Civil Engineering, Liverpool John Moores University, Henry Cotton Building, Webster Street, Liverpool, L3 2ET, UK

    Ameer A. Jebur

  2. Department of Civil Engineering, Peter Jost Centre, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK

    William Atherton, Rafid M. Al Khaddar & Ed Loffill

Authors
  1. Ameer A. Jebur
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  2. William Atherton
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  3. Rafid M. Al Khaddar
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  4. Ed Loffill
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Correspondence to Ameer A. Jebur.

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Jebur, A.A., Atherton, W., Al Khaddar, R.M. et al. Settlement Prediction of Model Piles Embedded in Sandy Soil Using the Levenberg–Marquardt (LM) Training Algorithm. Geotech Geol Eng 36, 2893–2906 (2018). https://doi.org/10.1007/s10706-018-0511-1

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  • DOI: https://doi.org/10.1007/s10706-018-0511-1

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