No abstract

The classical nonuniqueness problem exists due to uncertainty in the finite element (FE) calibration field. Namely, multiple models with different intrinsic parameters may all fit the observed data well, thus the selected single “best” model probably is not the truly best model to reflect the structural intrinsic property. A probability-based method using a population of FE models, not the single “best” method, is proposed to deal with the nonuniqueness problem. In this method, the Markov Chain Monte Carlo (MCMC) technique is first performed to sample the key structural parameters representing the main sources of uncertainty. Then a FE model population is generated using the samples, and the posterior probability of each model is evaluated by calculating the correlation between the simulation results and measurements through the Bayesian theorem. Finally, all the FE models from the stochastic sampling with their posterior probabilities are used for structural identification (St-Id) and performance evaluation. The advantage of the proposed method is that it not only identifies the magnitudes of structural parameters, but also generates their probability distributions for subsequent probability-based reliability analysis and risk evaluation. The feature provided by the stochastic sampling and statistical techniques makes the proposed method suitable for dealing with uncertainty. The example of the Phase I IASC-ASCE benchmark structure investigated demonstrates the effectiveness of the proposed method for probability-based structural health monitoring.

No abstract

Impact testing is an effective means of identifying structural flexibility. However, most flexibility identification methods have strict requirements on the type of input forces. For instance, methods operated in the frequency domain may generate incorrect flexibility identification results when double or multiple clicks occur in an impact test. This article proposes a method to estimate the structural modal scaling coefficients and flexibility characteristics using a subspace identification algorithm in the time domain. The advantage of the proposed method is that it adapts to the input force type and thus has the potential to be widely used in engineering practice. Numerical and experimental examples are presented to illustrate the effectiveness and robustness of the proposed method.

In contrast to traditional modal identification methods processing structural accelerations for modal parameter identification, a multiple reference impact testing method using long-gauge fiber optical sensors and the related data processing approach are proposed. First, macro strains of key structural elements during the impact testing are measured through long-gauge fiber optic sensors, which subsequently are used to estimate the strain frequency response functions (FRFs) of the structure. Second, an approach is proposed to estimate strain mode shapes from the strain FRFs, and corresponding displacement mode shapes are calculated by applying an improved conjugate beam method. Finally, the strain flexibility character of the structure is identified by using the identified strain and displacement mode shapes with normalization. The advantage of long-gauge fiber optic sensors measuring averaged strains in a long length (e.g., 1 meter) provides the opportunity for strain modal analysis. The output of the proposed method, strain flexibility, is meaningful for static strain prediction and structural capacity evaluation. Examples investigated successfully verified the effectiveness of the proposed method for structural strain flexibility identification.