Modelling of a footing behaviour using an Adaptive Neuro-Fuzzy Inference System

Musso, A; Provenzano, P.; Ferlisi, Settimio

doi:10.1201/9781439833797-c37

Cited by 3 publications

(1 citation statement)

References 2 publications

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“…Grima (2000) uses also neuro-fuzzy techniques for predicting tunnel boring machine performance in rock and for geological mapping using geophysical data. Musso et al (2002) simulate the load-settlement behavior of a model footing subjected to vertical eccentric load using an Adaptive-Neural-Fuzzy Inference System.…”

Section: Adaptive Inference Modeling In Geotechnical and Structural Ementioning

confidence: 99%

A Fuzzy‐Neural Network Method for Modeling Uncertainties in Soil‐Structure Interaction Problems

Provenzano

2003

Computer aided Civil Eng

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Uncertainty often recurs in structural system characterization as well as in choosing the mechanical model and in calibrating it. When analyzing a structure founded in cohesionless soils, the uncertainty in system modeling comes from soil inherent variability, site conditions, construction tolerance, and failure mechanisms. In this research, a Fuzzy-Neural Network method to predict the behavior of structures built on complex cohesionless soils is proposed. The method is based on an ArtificialNeural Network (ANN) for modeling the soil-foundation interaction. Its learning process analyzes over 200 records of building foundations, tanks, and embankments settlements on sand and gravel. Once validated, ANN is introduced in the soil-foundation-sovrastructure interaction model. Using fuzzy sets to define vague and ambiguous variables, the Fuzzy-Neural Network method predicts the system behavior and quantifies the uncertainty of its response. A numerical example shows the method effectiveness in the case of uncertainty in soil parameters and gives suggestions for successive applications.

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Section: Adaptive Inference Modeling In Geotechnical and Structural Ementioning

confidence: 99%