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Showing 2 results for Calibration

Baziar M.h., Asna Ashari M.,
Volume 2, Issue 3 (9-2004)
Abstract

An experimental study was carried out to evaluate the liquefaction resistance of silty sand utilizing laboratory techniques. In this study, liquefaction potential of silty sand by using cyclic triaxial tests on frozen samples retrieved from calibration chamber and constructed samples by dry pouring method were investigated. Correlation between cone penetration resistance and cyclic strength of undisturbed silty sand samples are also examined using CPT calibration chamber and cyclic triaxial tests. The cone penetration tests were performed on silty sand samples with fine contents ranging from 0% to 50% and overburden stresses in the range of 100-300 kPa. Then the soil sample in calibration chamber, in the same way that soil samples were prepared during CPT sounding, was frozen and undisturbed soil specimen retrieved from frozen soil sample were tested using cyclic triaxial tests. Analysis of results indicates that the quality of frozen samples is affected by fine content and overburden pressures. Also, using data obtained in this research, the relationship between cone tip resistance and cyclic resistance ratio (CRR) for silty sand soils will be presented. These correlations are in relatively good agreement with field case history data. Also increasing confining pressure in silty sand material increases the cone tip resistance and generally, cyclic resistance ratio increases by increasing silt content.
Kourosh Behzadian, Abdollah Ardeshir, Zoran Kapelan, Dragan Savic,
Volume 6, Issue 1 (3-2008)
Abstract

A novel approach to determine optimal sampling locations under parameter uncertainty in a water distribution system (WDS) for the purpose of its hydraulic model calibration is presented. The problem is formulated as a multi-objective optimisation problem under calibration parameter uncertainty. The objectives are to maximise the calibrated model accuracy and to minimise the number of sampling devices as a surrogate of sampling design cost. Model accuracy is defined as the average of normalised traces of model prediction covariance matrices, each of which is constructed from a randomly generated sample of calibration parameter values. To resolve the computational time issue, the optimisation problem is solved using a multi-objective genetic algorithm and adaptive neural networks (MOGA-ANN). The verification of results is done by comparison of the optimal sampling locations obtained using the MOGA-ANN model to the ones obtained using the Monte Carlo Simulation (MCS) method. In the MCS method, an equivalent deterministic sampling design optimisation problem is solved for a number of randomly generated calibration model parameter samples.The results show that significant computational savings can be achieved by using MOGA-ANN compared to the MCS model or the GA model based on all full fitness evaluations without significant decrease in the final solution accuracy.

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