Settlement prediction of shallow foundations on granular soils using B-spline neurofuzzy models

The design of shallow foundations on granular soils is generally controlled by settlement rather than bearing capacity. As a consequence, settlement prediction is a major concern and is an essential criterion in the design process of shallow foundations. At present, consistent accurate prediction of settlement of shallow foundations on granular soils has yet to be achieved using many numerical modelling techniques. Recently, multi-layer perceptrons (MLPs) trained with the back-propagation algorithm have been applied successfully to settlement prediction of shallow foundations on granular soils. However, a shortcoming of MLPs is that the knowledge that is acquired during training is distributed across their connection weights in a complex manner that is often difficult to interpret. Consequently, the rules governing the relationships between the network input/output variables are difficult to quantify. One way to overcome this problem is to use neurofuzzy networks in which the acquired knowledge can be translated into a set of fuzzy rules that describe the relationships between the network inputs and the corresponding outputs in a transparent fashion. In the present paper, the ability of neurofuzzy networks to predict settlement of shallow foundations on granular soils and to assist with providing a better understanding regarding the relationships be tween settlement and the factors affecting settlement is assessed. The sensitivity of neurofuzzy models to a number of stopping criteria is investigated and the models obtained are compared in terms of prediction accuracy, model parsimony and model transparency. The impact of incorporating existing engineering knowledge on neurofuzzy model performance and interpretation is also investigated. The type of neurofuzzy networks used in this research are B-spline networks that are trained with the adaptive spline modelling of observation data (ASMOD) algorithm. The results indicate that B-spline neurofuzzy networks are capable of predicting well the settlement of shallow foundations on granular soils and are able to provide a transparent understanding of the relationships between settlement and the factors affecting it. It is found from this research that neurofuzzy models that use the Bayesian Information Criterion (BIC) are able to strike a balance between model accuracy, parsimony and transparency. The results also indicate that modifying neurofuzzy networks by incorporating existing engineering knowledge can improve model performance and enhance the interpretation of the constructed model. (C) 2003 Elsevier Ltd. All rights reserved.