| Abstract: | A neural network approach for the prediction of pile bearing capacity by the stress-wave matching technique is presented. The main advantage of this approach over the traditional manual or automated matching approach is that it avoids the time-consuming process of iterative adjustment. This makes it feasible to determine the static pile capacity in real time in the field. Another benefit of this approach is that as more case histories become available, the neural network can be improved by learning from these new examples. A three-layer back-propagation network is set up to illustrate the capability of the proposed approach for 70 dynamically tested concrete bored piles. A wave equation model developed at the National University of Singapore and coded in the NUSWAP computer program is used to formulate the problem. Up to 14 of the 70 piles (20 percent) are used in training the network. The NUSWAP program is used to generate simulation training examples based on the manually fitted training examples for further training of the network. Different network configurations are examined. The trained network produces results exhibiting good stress-wave matching qualities compared to those obtained by manual fitting. The pile bearing capacities predicted by the two approaches agree very closely. The load-settlement curve and axial load distribution in the pile computed using the network-predicted soil parameters are in good agreement with the field measurements obtained from a maintained load test. |
