Reinforced concrete is generally used as a shielding wall for synchrotron radiation facilities where high-energy gamma rays are generated. The shielding effect of high-energy gamma rays should vary depending on the diameter of the reinforcing bars (rebars) in the reinforced concrete. The consistency among four types of Monte Carlo radiation transport codes was confirmed through concrete calculations of 1.25 and 10 MeV gamma-ray sources and the analyses of the results of Bishop experiment. We compared the leakage neutron and gamma-ray effective dose rates from the concrete (100 cm thickness) with and without rebars for 10 to 3000 MeV gamma-ray plane sources using four types of Monte Carlo codes. The shielding effect was evaluated from the ratio (decrease ratio) of the leakage radiation dose of the reinforced concrete to that of the concrete without rebars. The decrease ratio of high-energy gamma rays by MCNP6 decreased linearly as the diameter of the rebars increased.

In this paper, we propose a method to quantify the uncertainty of the macroscopic permeability coefficient (permeability coefficient of a bentonite layer as a whole), an inspection indicator of the low permeability of bentonite layers in radioactive waste disposal facilities, based on a Bayesian inference method. The Bayesian inference method makes it possible to evaluate the probability that the estimated macroscopic permeability coefficient of the bentonite layer after construction will satisfy a criterion of the macroscopic permeability coefficient by expressing statistical values such as the mean and standard deviation as a probability distribution. This explicitly explains the conservatism contained in the estimated macroscopic permeability coefficient. Furthermore, by utilizing the proposed method, we reasonably determine the sample size for the microscopic permeability coefficient (permeability coefficient obtained from laboratory permeability tests using undisturbed samples collected locally from the bentonite layer) required for inspecting the bentonite layer. This method can potentially provide a more fully underpinned justification for sampling plans and inspection results on a statistical basis, compared with classical frequentist statistical methods such as the maximum likelihood estimation method.

We propose a survey method for accessing the interior of the reactor pressure vessel (RPV) in Unit 1 of the Fukushima Daiichi Nuclear Power Plant (1F-1) through a presumed bottom opening, aiming to collect crucial environmental data such as internal conditions, video imagery, and spatial dose rate maps to inform decommissioning strategies. To enable this, a prototype UAV was developed for stable flight under harsh conditions, including high radiation, darkness, confined spaces, high humidity, and fog. The UAV incorporates dual redundant sensors for position control and combines autonomous hovering with collision avoidance systems to enhance stability in narrow spaces. Performance evaluations demonstrated that the UAV met all requirements and achieved stable flight in fog-free environments, while stable operation was largely maintained even under foggy conditions, despite minor reductions in control precision. The bottom-access survey method proved to be safe and efficient, with no issues in data collection or communication. These findings confirm the feasibility of spatial dose rate mapping inside the RPV and highlight the potential for acquiring detailed internal data that had previously been difficult to obtain.