Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Modified high-order neural network for invariant pattern recognition
Pattern Recognition Letters
Neural network and neuro-fuzzy assessments for scour depth around bridge piers
Engineering Applications of Artificial Intelligence
An incremental learning algorithm for function approximation
Advances in Engineering Software
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An adaptive high-order neural tree for pattern recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
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An accurate estimation of pile response to loading is a challenging task due to the complexity of the soil-pile interactions and uncertainties in the soil properties. Conventional methods of predicting pile load-settlement relationship either oversimplify the problem or require the parameters that are difficult to determine in the laboratory. In this study, a high-order neural network (HON) is developed to simulate the pile load-settlement curve using properties of the pile and SPT data along the depth of pile embedment as inputs. The results indicated a significant improvement in the quality of HON predictions over that of BPN, RBF and GRNN models. Based on the comparisons with the predictions of elastic and hyperbolic models, the proposed HON model provides better predictions than existing theoretical models.