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Estimating the probability distributions of radioactive concrete in the building stock using Bayesian networks
RISE Research Institutes of Sweden, Safety and Transport, Measurement Technology. Lund University, Sweden.ORCID iD: 0000-0002-2178-5391
RISE Research Institutes of Sweden, Built Environment, System Transition and Service Innovation.ORCID iD: 0000-0002-8107-7768
RISE Research Institutes of Sweden, Built Environment, System Transition and Service Innovation.ORCID iD: 0000-0002-5044-6989
RISE Research Institutes of Sweden, Safety and Transport, Measurement Technology.ORCID iD: 0000-0002-9860-4472
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2023 (English)In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 222, article id 119812Article in journal (Refereed) Published
Abstract [en]

The undesirable legacy of radioactive concrete (blue concrete) in post-war dwellings contributes to increased indoor radon levels and health threats to occupants. Despite continuous decontamination efforts, blue concrete still remains in the Swedish building stock due to low traceability as the consequence of lacking systematic documentation in technical descriptions and drawings and resource-demanding large-scaled radiation screening. The paper aims to explore the predictive inference potential of learning Bayesian networks for evaluating the presence probability of blue concrete. By integrating blue concrete records from indoor radon measurements, pre-demolition audit inventories, and building registers, it is possible to estimate buildings with high probabilities of containing blue concrete and encode the dependent relationships between variables. The findings show that blue concrete is estimated to be present in more than 30% of existing buildings, more than the current expert assumptions of 18–20%. The probability of detecting blue concrete depends on the distance to historical blue concrete manufacturing plants, building class, and construction year, but it is independent of floor area and basements. Multifamily houses and buildings built between 1960 and 1968 or nearby manufacturing plants are more likely to contain blue concrete. Despite heuristic, the data-driven approach offers an overview of the extent and the probability distribution of blue concrete-prone buildings in the regional building stock. The paper contributes to method development for pattern identification for hazardous building materials, i.e., blue concrete, and the trained models can be used for risk-based inspection planning before renovation and selective demolition. © 2023 The Author(s)

Place, publisher, year, edition, pages
Elsevier Ltd , 2023. Vol. 222, article id 119812
Keywords [en]
Bayesian network, Building stock, Methodology, Predictive inference, Radioactive concrete, Risk-based inspection, Concretes, Demolition, Health risks, Probability distributions, Radioactivity, Risk perception, Bayesia n networks, Building stocks, Indoor radon, Manufacturing plant, Predictive inferences, Probability: distributions, Risk-based, Bayesian networks
National Category
Building Technologies
Identifiers
URN: urn:nbn:se:ri:diva-64306DOI: 10.1016/j.eswa.2023.119812Scopus ID: 2-s2.0-85150056393OAI: oai:DiVA.org:ri-64306DiVA, id: diva2:1755454
Note

Correspondence Address: Wu, P.-Y., RISE Research Institutes of Sweden, Sweden; email: pei-yu.wu@ri.se; Funding details: Stiftelsen för Strategisk Forskning, SSF, FID18-0021; Funding details: Sveriges Geologiska Undersökning, SGU; Funding details: Energimyndigheten, 957026, P2022-00304; Funding text 1: The work is part of the PhD project “Prediction of Hazardous Materials in Buildings using Machine Learning” supported by RISE Research Institutes of Sweden. Special thanks are sent to Cecilia Jelinek from the Geological Survey of Sweden (SGU), who provided information on the radiation measurements with vehicles in the Swedish municipalities.; Funding text 2: The research fund comes from the Swedish Foundation for Strategic Research (SSF) with grant number FID18-0021, the Re:Source project from the Swedish Energy Agency with grant number P2022-00304, and the EU BuiltHub project with grant agreement ID of 957026.

Available from: 2023-05-08 Created: 2023-05-08 Last updated: 2024-02-14Bibliographically approved

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Wu, Pei-YuJohansson, TimMangold, MikaelSandels, ClaesMjörnell, Kristina

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