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Titre de la revue:Journal Title: Structural Safety
AbstractIt is common to assess the condition of an existing infrastructure using reliability analysis. When, based on the available information, an existing structure has an estimated failure probability above the admissible level, the default solution often is to either strengthen or replace it. Even if this practice is safe, it may not be the most economical.In order to economically restore and improve our existing infrastructure, the engineering community needs to be able to assess the potential gains associated with reducing epistemic uncertainties using measurements, before opting for costly intervention actions, if they become necessary. This paper provides a pre-posterior analysis framework to (1) optimize sequences of actions minimizing the expected costs and satisfying reliability constraints, and (2) quantify the potential gain of making measurements in existing structures. Illustrative examples show that when the failure probability estimated based on the present state of knowledge does not satisfy an admissible threshold, strengthening or replacement interventions can be sub-optimal first actions. The examples also show that significant savings can be achieved by reducing epistemic uncertainties.
It is imperative to study the damage detection methods of steel truss structures that are always employed in extreme environment. Accurate structural damage localization is still a challenge due to high noise and low accuracy of the structural finite element model. To develop a dependable damage localization technique for truss structural health monitoring, a novel idea of damage localization is proposed: the curvature difference method of strain waveform fractal dimension, based on fractal theory and curvature method. To validate the approach, a simply supported bailey steel truss benchmark model has been designed and constructed in the laboratory. Based on the model, both experimental and numerical simulation results using the procedure under pulse excitation indicate that it is feasible and effective to detect the change of boundary conditions and the stiffness reduction of a truss member. In addition, the proposed technique exhibits high-noise insusceptibility (e.g. it works for noise levels up to 20% for a 10% truss member stiffness reduction). Moreover, the proposed technology is robust against the accuracy of the finite element model of measured structures, which decrease the workload of model updating dramatically. All these lay a good foundation for its engineering application.
Optimal sensor placement is essentially a decision problem under uncertainty. The maximum expected utility theory and a Bayesian linear model are used in this paper for robust sensor placement aimedat operational modal identification.To avoid nonlinear relations between modal parameters and measured responses, we choose to optimize the sensor locations relative to identifying modal responses.Since the modal responses contain all the information necessary to identify the modal parameters, the optimal sensor locations for modal response estimation provide at least asuboptimalsolution for identification of modal parameters. First, aprobabilistic model for sensor placement considering model uncertainty, load uncertainty and measurement error is proposed.The maximum expected utility theory is then applied with this model by considering utility functions based on three principles: Shannon information, quadratic loss, and K-L divergence.In addition, the prior covariance of modal responses under band-limited white-noise excitation is derived and the nearest Kronecker product approximation is employed to accelerate evaluation of the utility function. As demonstration and validation examples, sensor placements in a 16-degrees-of-freedom sheartype building and in Guangzhou TV Towerunder ground motion and wind load areconsidered. Placements of individual displacement meter, velocimeter, accelerometer and placement of mixed sensors are illustrated.
Operational modal analysis is the primary tool for modal parameter identification in civil engineering. Bayesian statistics offers an ideal framework for analyzing uncertainties associated with the identified modal parameters. However, the exact Bayesian formulation is usually intractable due to the high computational demand in obtaining the posterior distributions of modal parameters. In this paper, the variational Bayes method is employed to provide an approximate solution. Unlike the Laplace approximation and Monte Carlo sampling, the variational Bayes approach provides a gradient-free algorithm to analytically approximate the posterior distributions. Working with the state-space representation of a dynamical system, the variational Bayes approach for identification of modal parameters is derived by ignoring statistical correlation between latent variables and the model parameters. In this approach, the joint distribution of the state-transition and observation matrices as well as the joint distribution of the process noise and measurement error are firstly calculated analytically using conjugate priors. The distribution of modal parameters is extracted from these obtained joint distributions using a first-order Taylor series expansion. A robust implementation of the method is discussed by using square-root filtering and Cholesky decomposition. The proposed approach is illustrated by its application to an example mass-spring system and the One Rincon Hill Tower in San Francisco.
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