Prediction of Drilling Torque for Full Displacement Pile Construction in Loose Soil Using Neural Networks
Tomme: LVIII (LXII) Fascicle: 4 | 2012
Pages: 97-112
Abstract text:
The paper is aimed at developing a neural network model for predicting the drilling torque necessary to push a drill bit in to the subsurface during the installation of full displacement pile (FDP) based on the cone resistance value of the subsurface. A self-evolving network algorithm is employed developing the model in order to reduce the model complexity without compromising accuracy. The database used to construct and validate the model consists of measurements taken during a drilling operation to install FDP piles for a sedimentation tank of the Brăila Waste Water Treatment Plant. The results indicate that the neural network model predictions of drilling torque are in good agreement with filed observations.
Key Words:
FDP; drilling parameters; cone penetration test; neural network.
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