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Probabilistic Analysis of Water Retention Characteristic Curve of Fly Ash
AbstractThis paper presents a comprehensive framework to understand the uncertainties associated with the water retention characteristic curve (WRCC) of fly ash, which is necessary for studying the unsaturated behavior of the fly ash. The measuring devices, range of measured suction, and water content play important roles in inducing the uncertainties associated with WRCC. To account for these uncertainties, a univariate probabilistic modeling was first adopted. Measured suction and volumetric water content were modeled as univariate random variables, the parameters of which were determined using quantile-quantile plots alongside the estimations of the best-fit probability distribution. To handle a wide range of uncertainties associated with WRCC zones and their measurements, the measured data were partitioned according to (1) saturation, desaturation, and residual zones and (2) the measurement range of four instruments. The bivariate dependencies were incorporated using a copula-based modeling and simulation approach wherein the marginals were chosen from the results of the univariate modeling. Results show that the univariate modeling provided a good firsthand estimate of the uncertainties of WRCCs, and when integrated with bivariate modeling and simulation, can yield representative WRCCs under considerably restricted measurement options. The study demonstrates the usefulness of copula-based probabilistic modeling for determining a realistic WRCC of fly ash under limited measured data availability.
Probabilistic Analysis of Water Retention Characteristic Curve of Fly Ash
AbstractThis paper presents a comprehensive framework to understand the uncertainties associated with the water retention characteristic curve (WRCC) of fly ash, which is necessary for studying the unsaturated behavior of the fly ash. The measuring devices, range of measured suction, and water content play important roles in inducing the uncertainties associated with WRCC. To account for these uncertainties, a univariate probabilistic modeling was first adopted. Measured suction and volumetric water content were modeled as univariate random variables, the parameters of which were determined using quantile-quantile plots alongside the estimations of the best-fit probability distribution. To handle a wide range of uncertainties associated with WRCC zones and their measurements, the measured data were partitioned according to (1) saturation, desaturation, and residual zones and (2) the measurement range of four instruments. The bivariate dependencies were incorporated using a copula-based modeling and simulation approach wherein the marginals were chosen from the results of the univariate modeling. Results show that the univariate modeling provided a good firsthand estimate of the uncertainties of WRCCs, and when integrated with bivariate modeling and simulation, can yield representative WRCCs under considerably restricted measurement options. The study demonstrates the usefulness of copula-based probabilistic modeling for determining a realistic WRCC of fly ash under limited measured data availability.
Probabilistic Analysis of Water Retention Characteristic Curve of Fly Ash
Prakash, A (author) / Deka, A / Sreedeep, S / Hazra, B
2017
Article (Journal)
English
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