The paper investigates experimental error propagation and its effects on critical flutter speeds of pedestrian suspension bridges using three different experimental data sets: pressure coefficients, aerodynamic static forces and flutter derivatives. The three data sets are obtained from section model measurements in three distinct laboratories. Data sets are used to study three different geometries of pedestrian suspension bridges. Critical flutter speed is estimated using finite-element nonlinear analysis, numerical 2-DOF generalized deck model and 3-DOF full-bridge model. Flutter probability, contaminated by various experimental error sources, is examined. Experimental data sets are synthetically expanded to obtain two population sets of deck wind loads with 30 and 5.10(5) realizations, respectively. The first set is obtained using Monte-Carlo simulation approach, whereas the second one is determined using Polynomial chaos expansion theory and a basis of Hermite polynomials. The numerically-determined probability density functions are compared against empirical probability histograms (pdfs) by Kolmogorov-Smirnov tests.

Predicting the flutter speed of a pedestrian suspension bridge through examination of laboratory experimental errors

Fabio Rizzo
;
2018-01-01

Abstract

The paper investigates experimental error propagation and its effects on critical flutter speeds of pedestrian suspension bridges using three different experimental data sets: pressure coefficients, aerodynamic static forces and flutter derivatives. The three data sets are obtained from section model measurements in three distinct laboratories. Data sets are used to study three different geometries of pedestrian suspension bridges. Critical flutter speed is estimated using finite-element nonlinear analysis, numerical 2-DOF generalized deck model and 3-DOF full-bridge model. Flutter probability, contaminated by various experimental error sources, is examined. Experimental data sets are synthetically expanded to obtain two population sets of deck wind loads with 30 and 5.10(5) realizations, respectively. The first set is obtained using Monte-Carlo simulation approach, whereas the second one is determined using Polynomial chaos expansion theory and a basis of Hermite polynomials. The numerically-determined probability density functions are compared against empirical probability histograms (pdfs) by Kolmogorov-Smirnov tests.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/246797
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