Settlements and angular distortions of shallow foundations on liquefiable soil

The extensive injury caused to buildings by liquefaction during past earthquakes, with the uneven spatial distribution of damage, raises need for rapid predictive tools, applicable at a large scale, that comprehensively account properties earthquake, subsoil and structure. A method is herein proposed quantify angular distortion framed low-rise based on simple characterization above factors. analysis moves from literature criteria introduced vulnerability under static conditions extends their applicability assessment integrating parametric two-dimensional numerical analyses recent formulas machine learning inference. calculation, performed variable stratigraphic mechanical characteristics subsoil, ground motion equivalent flexural stiffness foundation, quantifies role each factor absolute settlement distortion. Then dependency different factors inferred an artificial neural network, grouping parameters limit number input variables express results charts make prediction more accessible.