A stochastic model updating method for parameter variability quantification based on response surface models and Monte Carlo simulation

Stochastic model updating must be considered for quantifying uncertainties inherently existing in real-world engineering structures. By this means the statistical properties,instead of deterministic values, of structural parameters can be sought indicating the parameter variability. However, the implementation of stochastic model updating is much more complicated than that of deterministic methods particularly in the aspects of theoretical complexity and low computational efficiency. This study attempts to propose a simple and cost-efficient method by decomposing a stochastic updating process into a series of deterministic ones with the aid of response surface models and Monte Carlo simulation. The response surface models are used as surrogates for original FE models in the interest of programming simplification, fast response computation and easy inverse optimization. Monte Carlo simulation is adopted for generating samples from the assumed or measured probability distributions of responses. Each sample corresponds to an individual deterministic inverse process predicting the deterministic values of parameters. Then the parameter means and variances can be statistically estimated based on all the parameter predictions by running all the samples. Meanwhile, the analysis of variance approach is employed for the evaluation of parameter variability significance. The proposed method has been demonstrated firstly on a numerical beam and then a set of nominally identical steel plates tested in the laboratory. It is found that compared with the existing stochastic model updating methods, the proposed method presents similar accuracy while its primary merits consist in its simple implementation and cost efficiency in response computation and inverse optimization.

Elimination of variability sources in NIR spectra for the determination of fat content in olive fruits

Models for prediction of oil content as percentage of dried weight in olive fruits were comput- ed through PLS regression on NIR spectra. Spectral preprocessing was carried out by apply- ing multiplicative signal correction (MSC), Sa vitzky–Golay algorithm, standard normal variate correction (SNV), and detrending (D) to NIR spectra. MSC was the preprocessing technique showing the best performance. Further reduction of variability was performed by applying the Wold method of orthogonal signal correction (OSC). The calibration model achieved a R 2 of 0.93, a SEPc of 1.42, and a RPD of 3.8. The R 2 obtained with the validation set remained 0.93, and the SEPc was 1.41.