Experimental shielding evaluation of the radiation protection provided by the structurally significant components of residential structures.

The human health and environmental effects following a postulated accidental release of radioactive material to the environment have been a public and regulatory concern since the early development of nuclear technology. These postulated releases have been researched extensively to better understand the potential risks for accident mitigation and emergency planning purposes. The objective of this investigation is to provide an updated technical basis for contemporary building shielding factors for the US housing stock. Building shielding factors quantify the protection from ionising radiation provided by a certain building type. Much of the current data used to determine the quality of shielding around nuclear facilities and urban environments is based on simplistic point-kernel calculations for 1950s era suburbia and is no longer applicable to the densely populated urban environments realised today. To analyse a building’s radiation shielding properties, the ideal approach would be to subject a variety of building types to various radioactive sources and measure the radiation levels in and around the building. While this is not entirely practicable, this research analyses the shielding effectiveness of ten structurally significant US housing-stock models (walls and roofs) important for shielding against ionising radiation. The experimental data are used to benchmark computational models to calculate the shielding effectiveness of various building configurations under investigation from two types of realistic environmental source terms. Various combinations of these ten shielding models can be used to develop full-scale computational housing-unit models for building shielding factor calculations representing 69.6 million housing units (61.3%) in the United States. Results produced in this investigation provide a comparison between theory and experiment behind building shielding factor methodology.