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An artificial neural network approach for under-reamed piles subjected to uplift forces in dry sand

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Abstract

The present study is about under-reamed pile subjected to uplift forces. They are known to be very effective especially against uplift forces. The objective is to develop a simple design formula based on an optimized artificial neural network (ANN) predictive approach model. This formula can calculate the ultimate uplift capacity of under-reamed piles (Pul) embedded in dry cohesionless soil with excellent accuracy. The new generated ANN model was developed by taking into account the key factors such as under-reamed base diameter, angle of enlarged base to the vertical axis, shaft diameter, and embedment ratio. The proposed approach shows excellent agreement with a mean absolute error (MAE) less than 0.262, which is better than previous theories.

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Authors and Affiliations

  1. Department of Civil Engineering, Kermanshah University of Technology, Kermanshah, Iran

    Hossein Moayedi

  2. Department of Electrical Engineering, Kermanshah University of Technology, Kermanshah, Iran

    Abbas Rezaei

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  1. Hossein Moayedi
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  2. Abbas Rezaei
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Correspondence to Hossein Moayedi.

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Moayedi, H., Rezaei, A. An artificial neural network approach for under-reamed piles subjected to uplift forces in dry sand. Neural Comput & Applic 31, 327–336 (2019). https://doi.org/10.1007/s00521-017-2990-z

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  • DOI: https://doi.org/10.1007/s00521-017-2990-z

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