April 18, 2024

Hossein Moayedi

Academic rank:
Address:
Education: Ph.D
Phone:
Faculty: Faculty of Engineering

Research

Title
An artificial neural network approach for under-reamed piles subjected to uplift forces in dry sand
Type Article
Keywords
Artificial neural network, Under-reamed pile, Uplift force, Dry sand
Researchers Hossein Moayedi، Abbas Rezaei

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.