Identification, Model Updating, and Validation of a Steel Twin Deck Curved Cable‐Stayed Footbridge

Abstract

To perform a realistic reliability analysis of a complex cable‐stayed steel footbridge subject to natural hazard and corrosion, this article addresses a rational process of modeling and simulation based on identification, model updating, and validation. In particular, the object of this study is the Ponte del Mare footbridge located in Pescara, Italy; this bridge was selected as being a complex twin deck curved footbridge because it is prone to corrosion by the aggressive marine environment. With the modeling and simulation objectives in mind, a preliminary finite element (FE) model was realized using the ANSYS software. However, uncertainties in FE modeling and changes during its construction suggested the use of experimental system identification. As a result, the sensor location was supported by a preliminary FE model of the footbridge, although to discriminate close modes of the footbridge and locate identification sensor layouts, Auto Modal Assurance Criterion (AutoMAC) values and stabilization diagram techniques were adopted. Modal characteristics of the footbridge were extracted from signals produced by ambient vibration via the stochastic subspace identification (SSI) algorithm, although similar quantities were identified with free‐decay signals produced by impulse excitation using the ERA algorithm. All these procedures were implemented in the Structural Dynamic Identification Toolbox (SDIT) code developed in a MATLAB environment. The discrepancies between analytical and experimental frequencies led to a first update of the FE model based on Powell's dog‐leg method that relied on a trust‐region approach. As a result, the identified FE model was capable of reproducing the response of the footbridge subject to realistic gravity and wind load conditions. Finally, the FE was further updated in the modal domain, by changing both the stationary aerodynamic coefficients and the flutter derivatives of deck sections to take into account the effects of the curved deck layout.

Stationarity‐Based Approach for the Selection of Lag Length in Cointegration Analysis Used for Structural Damage Detection

“…To perform a realistic reliability analysis of a complex cable‐stayed steel footbridge subject to natural hazard and corrosion, Bursi et al. () investigated a rational process of modeling and simulation based on stochastic subspace identification, model updating, and validation. Butcher et al.…”

New discrete-time robust H2/H∞ algorithm for vibration control of smart structures using linear matrix inequalities

“…For example, Cha and Buyukozturk (2015) present structural damage detection using modal strain energy and a multi-objective genetic algorithm for optimization (Jia et al, 2014;Luna et al, 2014;Paris et al, 2015). Bursi et al (2014) present health monitoring of cable-stayed bridges. Yeum and Dyke (2015) present vision-based automated crack detection for bridge inspection.…”

A Two-Step Strategy for System Identification of Civil Structures for Structural Health Monitoring Using Wavelet Transform and Genetic Algorithms

“…During the last two decades, a branch of algorithms have been specifically designed for performing the MPI of civil structures using ambient vibrations, including fast Fourier transform (FFT)-based methods [12,13], the autoregressive model-based models [14,15], random decrement technique (RDT) [16], natural excitation technique combined with the eigensystem realization algorithm (NExT-ERA) [17,18], and stochastic subspace identification (SSI) methods [19,20], among others. Despite the excellent results obtained using the abovementioned techniques, some difficulties remain unsolved.…”