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

Abstract 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 between 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.

[1]  Holger R. Maier,et al.  Forecasting cyanobacterium Anabaena spp. in the River Murray, South Australia, using B-spline neurofuzzy models , 2001 .

[2]  John Burland,et al.  Settlement of foundations on sand and gravel , 1985 .

[3]  Martin Brown,et al.  Neurofuzzy Systems Modelling: A Transparent Approach , 1997 .

[4]  Jacek M. Zurada,et al.  Introduction to artificial neural systems , 1992 .

[5]  C. V. Altrock.  Fuzzy logic and neurofuzzy applications explained , 1995 .

[6]  Holger R. Maier,et al.  PREDICTING SETTLEMENT OF SHALLOW FOUNDATIONS USING NEURAL NETWORKS , 2002 .

[7]  Tarek Sayed,et al.  Comparison of Neural and Conventional Approaches to Mode Choice Analysis , 2000 .

[8]  C. H. Juang,et al.  A fuzzy neural network approach to evaluation of slope failure potential , 1996 .

[9]  V. Kůrková,et al.  Dealing with complexity : a neural networks approach , 1998 .

[10]  T. Kavli.  ASMO—Dan algorithm for adaptive spline modelling of observation data , 1993 .

[11]  Rolf Isermann,et al.  Local basis function networks for identification of a turbocharger , 1996 .

[12]  Timothy Masters,et al.  Practical neural network recipes in C , 1993 .

[13]  Martin Brown,et al.  Neurofuzzy adaptive modelling and control , 1994 .

[14]  K. Terzaghi,et al.  Soil mechanics in engineering practice , 1948 .

[15]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[16]  Marek J. Patyra,et al.  Book review: Fuzzy logic and Neuro Fuzzy Applications Explained by Constantin von Altrock (Prentice Hall 1995) , 1997, SGAR.

[17]  Jean-Louis Briaud,et al.  Behavior of Five Large Spread Footings in Sand , 1999 .

[18]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[19]  Martin Brown,et al.  A perspective and critique of adaptive neurofuzzy systems used for modelling and control applications , 1995, Int. J. Neural Syst..

[20]  Holger R. Maier,et al.  Neurofuzzy networks applied to settlement of shallow foundations on granular soils , 2003 .

[21]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1994 .

[22]  Maria Rossella Massimino,et al.  Observed and Computed Settlements of Two Shallow Foundations on Sand , 1998 .

[23]  Holger R. Maier,et al.  DATA DIVISION FOR DEVELOPING NEURAL NETWORKS APPLIED TO GEOTECHNICAL ENGINEERING , 2004 .

[24]  Hg Poulos,et al.  Common Procedures for Foundation Settlement Analysis: Are They Adequate? , 1999 .