A Simple Estimator for Dynamic Models with Serially Correlated Unobservables

We present a method for estimating Markov dynamic models with unobserved state variables which can be serially correlated over time. We focus on the case where all the model variables have discrete support. Our estimator is simple to compute because it is noniterative, and involves only elementary matrix manipulations. Our estimation method is nonparametric, in that no parametric assumptions on the distributions of the unobserved state variables or the laws of motions of the state variables are required. Monte Carlo simulations show that the estimator performs well in practice, and we illustrate its use with a dataset of doctors' prescription of pharmaceutical drugs.

Evaluation of Alteration in Cement Paste Due to Calcium Leaching withIntegrated CT-XRD Method and Its Effect on Ionic Diffusions

In 2011, Tohoku earthquake and subsequent tsunami hit Fukushima Daiichi Nuclear Power Plant, and they lead to Fukushima nuclear disaster. For more than a decade after the disaster, concrete materials in submerged structures of nuclear power plants have been chronically in contact with water and resulted in calcium leaching. To assess the alteration of the concrete property, it’s necessary to evaluate the permeability change due to calcium leaching and its effect on radioactive ion diffusion. In this study, small scale cylindrical cement paste specimens with 3mm in diameter and 6mm in height were prepared with water to cement ratios of 0.5 and 0.6 respectively, and they were tested in the static leaching. Specimens were subjected to carbonation before immersed into deionized water for different periods. CT image of each specimen at a resolution of 2.46μm/voxel was acquired in SPring-8, Hyogo, Japan. Combining CT images and X-ray diffraction data, dissolution front of portlandite under different leaching periods was determined, and the time dependent development law of dissolution front was evaluated. Introducing Buil’s model as a local equilibrium, time and position dependent porosity of cement paste due to leaching was calculated and correspondingly diffusion coefficient of Sr ion in the numerical simulation was modified. Simulative results on Sr ion diffusivity for 10-year showed the impact of carbonation followed by leaching on radioactive ion diffusion

非破壊CT-XRD連成法を用いた水の作用を受けたセメント系材料の輸送特性の評価

Concrete is used in underground radioactive waste disposal facilities due to its stable chemical properties to fulfill the requirement of structural stability. However, these facilities are inevitably in contact with ground water for a long time, which brings the danger of concrete structure being damaged by leaching. Leaching refers to the process by which soluble compounds in the cement hydrated system are dissolved and carried away by water. This process can increase the porosity and permeability of the concrete, making it more susceptible to further leaching and the ingress of harmful substances. Therefore, it is of great significance to analyze the transport properties of concrete under the impact of long-term leaching deterioration. However, as a heterogeneous material, the different phases in concrete, including aggregates, bulk cement paste, the interfacial transition zone (ITZ) and pores, have different responses to the leaching process, which together form the picture of the leaching process of concrete. This response is firstly reflected in the change in transport of substances in different phase, that is, the change in transport properties. Although research efforts have been made on evaluating leaching deterioration in the past, quantitative analysis on transport properties considering the alteration of different phases from the microscopic point of view is still needed. This research aims to study the alteration of cement-based materials under leaching from a microscopic perspective using non-destructive integrated CT-XRD method. Integrated CT-XRD technique using synchrotron radiation X-ray source can provide high-precision three-dimensional geometric information and phase distribution information of specimen non-destructively and simultaneously. The addition of random walk algorithm to the CT image analysis made it possible to quantify transport properties of different phases in concrete. Among them, ITZ is difficult to quantify due to its irregular and tiny geometric shape. To solve this problem, a reconstructed method of CT images in adjacent aggregate region was originally proposed, which made the transport properties analysis on ITZ possible. In the experiments, a series of natural leaching tests under different conditions were conducted. The dissolution front of portlandite was identified, enabling to separate the leached region and non-leached region in the specimen after the leaching tests. Then, transport properties of different regions were analyzed, and corresponding diffusion coefficients of calcium ions were calculated including in the ITZ. It is found that the diffusion coefficient of calcium ion in the leached region was about 50 times larger than that of the non-leached region, while it was about 10 times larger in ITZ as compared with the non-leached region. In this way, this study successfully provided the changing diffusion coefficients in altered specimens due to leaching. The obtained results were verified by simulating with the SiTraM2D software. Using the quantitative transport properties obtained from CT-XRD method and random walk algorithm, the dissolution of portlandites in concrete after 10 years immersion was predicted. Results showed that the dissolution front ranged from 6.2 to 7.2 mm deep inward the concrete, indicating non negligible effect of ITZ. In addition, it was implied that the diffusion of Sr2+ ion was influenced by the carbonation as well as the portlandite dissolution

A Simple Estimator for Dynamic Models with Serially Correlated Unobservables

We present a method for estimating Markov dynamic models with unobserved state variables which can be serially correlated over time. We focus on the case where all the model variables have discrete support. Our estimator is simple to compute because it is noniterative, and involves only elementary matrix manipulations. Our estimation method is nonparametric, in that no parametric assumptions on the distributions of the unobserved state variables or the laws of motions of the state variables are required. Monte Carlo simulations show that the estimator performs well in practice, and we illustrate its use with a dataset of doctors’ prescription of pharmaceutical drugs