Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Mathematical models for handling partial knowledge in artificial intelligence
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Advances in Artificial Neural Systems
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Proper characterization of the dynamic behaviour of soil deposits is of utmost importance in earthquake ground-response analyses. Cone-tip penetration resistances, which are usually obtained in a typical geotechnical study for foundation design, can be used to evaluate the dynamic properties and thus to outline dynamically a given soil deposit. Because of the capacity of neurofuzzy techniques to combine the representational aspect of fuzzy models and the learning mechanisms of neural networks, in this paper a neurofuzzy system is proposed to map the values obtained from cone penetration tests into the dynamic properties of Mexico City clays. Utilizing this methodology, it is possible to achieve profies of shear modulus and damping ratios versus shear strain curves, from which soil deposits can be characterized dynamically at a reduced cost.