Building KBES for diagnosing PC pile with artificial neural network

Diagnosis of damage of prestressed concrete piles during driving is an important problem in foundation engineering. An effort to build an expert system for the problem is described in this paper. To overcome the bottleneck of knowledge acquisition, an artificial neural network is used as the learning mechanism to transfer engineering experience into usable knowledge. The back-propagation learning algorithm is employed to train the network for extracting knowledge from training examples. The influences of various control parameters (including learning rate and momentum factor) and various network architecture factors (including the number of hidden units and the number of hidden layers) are examined. The results prove that the artificial neural network can work sufficiently as a knowledge-acquisition tool for the diagnosis problem. To apply the knowledge in the trained network, a reasoning strategy that hybridizes forward-and backward-reasoning schemes is proposed to realize the inference mechanism.