A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Modeling SWCC for Coarse-Grained and Fine-Grained Soil
Following Chap. 10.1007/978-981-15-9105-1_2, the work is extended to predict SWCC of a soil with different initial densities by using Bayesian method, for both coarse-grained and fine-grained soil. An adjustment parameter (β) is introduced to express the relationships between the matric suctions of two soil samples. The parameter β is a function of the initial void ratio, matric suction or volumetric water content. Bayesian model class selection is adopted to determine the optimal predictive models of β. The optimal models of β are validated by comparing the estimated matric suction and measured data. The comparisons show that the newly proposed method produces more accurate SWCCs than the other models. Furthermore, the influence of the model parameters of β on the predicted matric suction and SWCC is evaluated using Latin hypercube sampling.
Modeling SWCC for Coarse-Grained and Fine-Grained Soil
Following Chap. 10.1007/978-981-15-9105-1_2, the work is extended to predict SWCC of a soil with different initial densities by using Bayesian method, for both coarse-grained and fine-grained soil. An adjustment parameter (β) is introduced to express the relationships between the matric suctions of two soil samples. The parameter β is a function of the initial void ratio, matric suction or volumetric water content. Bayesian model class selection is adopted to determine the optimal predictive models of β. The optimal models of β are validated by comparing the estimated matric suction and measured data. The comparisons show that the newly proposed method produces more accurate SWCCs than the other models. Furthermore, the influence of the model parameters of β on the predicted matric suction and SWCC is evaluated using Latin hypercube sampling.
Modeling SWCC for Coarse-Grained and Fine-Grained Soil
Zhou, Wan-Huan (author) / Yin, Zhen-Yu (author) / Yuen, Ka-Veng (author)
Practice of Bayesian Probability Theory in Geotechnical Engineering ; Chapter: 3 ; 59-88
2020-11-14
30 pages
Article/Chapter (Book)
Electronic Resource
English
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