This paper presents an investigation on strength of cement deep mixing （CDM） mixture. Four typical works of offshore or land-based projects are introduced. With samples from these projects and laboratory tests, stat...This paper presents an investigation on strength of cement deep mixing （CDM） mixture. Four typical works of offshore or land-based projects are introduced. With samples from these projects and laboratory tests, statistical analysis is made on the increment law of the strength of cement-soil mixture with different amount of cement, and strengths under different working conditions are compared. It is found that the amount of cement in the cement-soil mixture is closely related to the unconfined compressive strength of the mixture. At the age of 90 d,the unconfined compressive strength of the cement-soil mixture increased by 0.054 MPa—0.124 MPa with each cement increasing 10 kg/m3 in the cement-soil mixture, averagely increased by 0.085 MPa, while that at the age of 120 d increased by 1100 in comparison.The quality of the cement-soil mixture should be comprehensively evaluated in accordance with the trimmed average of strength, coefficient of variation and rock quality designation （RQD） indicators of sampling ratio.展开更多
Structural equation model(SEM) is a multivariate analysis tool that has been widely applied to many fields such as biomedical and social sciences. In the traditional SEM, it is often assumed that random errors and exp...Structural equation model(SEM) is a multivariate analysis tool that has been widely applied to many fields such as biomedical and social sciences. In the traditional SEM, it is often assumed that random errors and explanatory latent variables follow the normal distribution, and the effect of explanatory latent variables on outcomes can be formulated by a mean regression-type structural equation. But this SEM may be inappropriate in some cases where random errors or latent variables are highly nonnormal. The authors develop a new SEM, called as quantile SEM(QSEM), by allowing for a quantile regression-type structural equation and without distribution assumption of random errors and latent variables. A Bayesian empirical likelihood(BEL) method is developed to simultaneously estimate parameters and latent variables based on the estimating equation method. A hybrid algorithm combining the Gibbs sampler and Metropolis-Hastings algorithm is presented to sample observations required for statistical inference. Latent variables are imputed by the estimated density function and the linear interpolation method. A simulation study and an example are presented to investigate the performance of the proposed methodologies.展开更多
文摘This paper presents an investigation on strength of cement deep mixing （CDM） mixture. Four typical works of offshore or land-based projects are introduced. With samples from these projects and laboratory tests, statistical analysis is made on the increment law of the strength of cement-soil mixture with different amount of cement, and strengths under different working conditions are compared. It is found that the amount of cement in the cement-soil mixture is closely related to the unconfined compressive strength of the mixture. At the age of 90 d,the unconfined compressive strength of the cement-soil mixture increased by 0.054 MPa—0.124 MPa with each cement increasing 10 kg/m3 in the cement-soil mixture, averagely increased by 0.085 MPa, while that at the age of 120 d increased by 1100 in comparison.The quality of the cement-soil mixture should be comprehensively evaluated in accordance with the trimmed average of strength, coefficient of variation and rock quality designation （RQD） indicators of sampling ratio.
基金supported by the National Natural Science Foundation of China under Grant No.11165016
文摘Structural equation model(SEM) is a multivariate analysis tool that has been widely applied to many fields such as biomedical and social sciences. In the traditional SEM, it is often assumed that random errors and explanatory latent variables follow the normal distribution, and the effect of explanatory latent variables on outcomes can be formulated by a mean regression-type structural equation. But this SEM may be inappropriate in some cases where random errors or latent variables are highly nonnormal. The authors develop a new SEM, called as quantile SEM(QSEM), by allowing for a quantile regression-type structural equation and without distribution assumption of random errors and latent variables. A Bayesian empirical likelihood(BEL) method is developed to simultaneously estimate parameters and latent variables based on the estimating equation method. A hybrid algorithm combining the Gibbs sampler and Metropolis-Hastings algorithm is presented to sample observations required for statistical inference. Latent variables are imputed by the estimated density function and the linear interpolation method. A simulation study and an example are presented to investigate the performance of the proposed methodologies.
