Sampling methods for predicting a reliability index such as Monte Carlo or Latin Hypercube Sampling are very time consuming thus advanced simulation techniques are frequently used. The asymptotic sampling is one of new techniques based on asymptotic results from reliability theory supported with a simple regression. Since the high sensitivity of the asymptotic sampling method to the control parameters has been reported, this contribution is focused on a study of the optimal parameter setting. The well-known truss structure is introduced and results with different parameter settings are presented.
This paper deals with a reconstruction of random media via multi-objective optimization. Two statistical descriptors, namely a two-point probability function and a two-point lineal path function, are repetitively evaluated for the original medium and the reconstructed image to appreciate the improvement in the optimization progress. Because of doubts of the weights setting in the weighted-sum method, purely multi-objective optimization routine Non-dominated Sorting Genetic Algorithm~II is utilized. Three operators are compared for creating new offspring populations that satisfy a prescribed volume fraction constraint. The main contribution is in the testing of the proposed methodology on several benchmark images.
The microstructure of hardened cement pastes comprises of a heterogeneous agglomeration of distinct quasihomogeneous domains with variable physical, chemical, and morphological features, denoted here as material phases. Accurate material characterization rests on a precise description and quantification of underlying principal phases, focusing, in particular, on their volumetric proportions and spatial configuration within the microstructure, as these affect, to a large extent, the macroscopic properties of the composite material. A realistic cement paste microstructure used in this study was obtained from micro-computed X-ray tomography, following the application of suitable segmentation filters, highlighting and isolating the sought phase – anhydrous cement grains – for statistical analysis. The present paper then compares and assesses several implementations of a lineal path function, all applied to quantifying the phase connectedness and short-range order characteristics of the grains. The main emphasis rests on assessing the accuracy and evaluation speed of the implemented algorithms.
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