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In finite-element (FE) model updating using response surface (RS) models as surrogate, the procedure of finding an appropriate design to build the RS models requires a number of trial-and-error approaches with different designs and subset models. To address this issue, a procedure is proposed in this paper to design and fit proper RS models in FE model updating problems. Also, formulation of the problem in an iterative format in time domain is proposed to extract more information from measured signals and compensate for the error present in the regressed models. This procedure is applicable to both linear and nonlinear models under static or dynamic analysis. The proposed methodology is applied to a numerical case study of a steel frame with global nonlinearity. Appropriate design and model order are successfully established and optimization in time performs well in all the simulated scenarios. Finally, the performance of this method in presence of measurement noise is compared with a method based on sensitivity analysis in terms of required time and accuracy.

Abstract-The joint source-channel coding problem for softdecision demodulated time-correlated fading channels is investigated without the use of channel coding and interleaving. For the purpose of system design, the recently introduced non-binary noise discrete channel with queue based noise (NBNDC-QB) is adopted. This analytically tractable Markovian model has been shown to effectively represent correlated fading channels that are hard to handle analytically. Optimal sequence maximum a posteriori (MAP) detection of a discrete Markov source sent over the NBNDC-QB is first studied. When the Markov source is binary and symmetric, a necessary and sufficient condition under which the MAP decoder is reduced to a simple instantaneous symbol-by-symbol decoder is established. Two robust lossy source coding schemes with low-encoding delay are next proposed for the NBNDC-QB. The first scheme consists of a scalar quantizer, a proper index assignment, and a sequence MAP decoder designed to harness the redundancy left in the quantizer's indices, the channel's soft-decision output and noise correlation. The second scheme is the classical noise resilient vector quantizer known as the channel optimized vector quantizer. It is demonstrated that both systems can successfully exploit the channel's memory and soft-decision information. Signal-to-distortion (SDR) gains of more than 1.7 dB are obtained over hard-decision demodulation by using only 2 bits for soft-decision. Furthermore, gains as high as 4.4 dB can be achieved for a strongly correlated channel, in comparison with systems designed for the ideally interleaved (memoryless) channel. Finally, it is numerically observed that for low coding rates the NBNDC-QB model can accurately approximate discrete fading channels in terms of SDR performance.

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