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

作者：Moayedi, Hossein^*; Rezaei, Abbas

来源：Neural Computing & Applications, 2019, 31(2): 327-336.

DOI：10.1007/s00521-017-2990-z

• Artificial neural network
• Under-reamed pile
• Uplift force
• Dry sand

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.

• 出版日期2019-2