Journal
NEURAL COMPUTING & APPLICATIONS
Volume 31, Issue 2, Pages 327-336Publisher
SPRINGER LONDON LTD
DOI: 10.1007/s00521-017-2990-z
Keywords
Artificial neural network; Under-reamed pile; Uplift force; Dry sand
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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 (P-ul) 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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