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Wu, P.-Y., Johansson, T., Mundt-Petersen, S. O. & Mjörnell, K. (2025). Predictive modeling and estimation of moisture damages in Swedish buildings: A machine learning approach. Sustainable cities and society, 118, Article ID 105997.
Open this publication in new window or tab >>Predictive modeling and estimation of moisture damages in Swedish buildings: A machine learning approach
2025 (English)In: Sustainable cities and society, ISSN 2210-6707, Vol. 118, article id 105997Article in journal (Refereed) Published
Abstract [en]

Identifying potential moisture damage is crucial for maintenance practices and assurance of well-being of oc cupants. However, due to limited information availability and standardization, assessing damage prevalence on the building stock scale remains understudied. By combining investigation records and building databases, this study leverages data analytic techniques and machine learning modeling to characterize damage pathology and predict its occurrence in Swedish buildings. The interrelationships between damage-specific attributes and their associations with building parameters of several damage types were analyzed using feature selection, forming the basis for developing predictive models. Results show that multilabel classifiers outperform binary classifiers for every damage type, with lead tree ensemble models achieving minimum average AUCPR and F2 of 0.85 for microbial growth, 0.87 for deformation, 0.91 for odor, and 0.95 for water leakage. The identified patterns were interpreted and verified against descriptive statistics. The binary relevance models estimate that one-third of school buildings, 20 % of commercial and office buildings, and 15 % of residential dwellings in regional building stock contain moisture damage. These findings advance the quantification of moisture damage by providing new knowledge and approaches for appraising moisture damage likelihood at aggregated and individual building levels, thereby aiding in moisture safety evaluations and preventive maintenance efforts

Place, publisher, year, edition, pages
Elsevier, 2025
National Category
Civil Engineering
Identifiers
urn:nbn:se:ri:diva-76269 (URN)10.1016/j.scs.2024.105997 (DOI)
Note

The research fund comes from Lansf ¨ ors ¨ ¨akringar (County Insurance) for the project predicting moisture damage in existing and new buildings using AI (machine learning) with the program ID P4:22

Available from: 2024-12-13 Created: 2024-12-13 Last updated: 2024-12-13Bibliographically approved
Mjörnell, K. (2024). Energy Efficiency in Seasonal Homes: A Study on the Occupancy, Energy Use, and Renovation of Second Homes in Sweden. Energies, 17(17), Article ID 4493.
Open this publication in new window or tab >>Energy Efficiency in Seasonal Homes: A Study on the Occupancy, Energy Use, and Renovation of Second Homes in Sweden
2024 (English)In: Energies, E-ISSN 1996-1073, Vol. 17, no 17, article id 4493Article in journal (Refereed) Published
Abstract [en]

The escalating utilisation of second homes has led to an extension in heating periods and, to a certain degree, renovations to elevate the standard, resulting in augmented energy and resource consumption. A comprehensive survey was conducted in Sweden, examining user patterns across different seasons, heating systems, and implemented energy efficiency measures. The results indicate that second homes are occupied for extended periods during the summer season and intermittently throughout the year. Over half of the second homes are heated even when unoccupied, with 12% maintaining a temperature above 16 °C. The predominant heating method is direct electricity (32.2%), followed by heat pumps (29.5%) and stoves (17.5%). A variety of renovations are undertaken, primarily to enhance the standard and technical performance, but also to implement energy efficiency measures such as window replacement, additional insulation, or heat pump installation. Based on the reported user and heating patterns, and the energy renovations carried out, the potential energy savings with different energy renovation strategies were estimated for the Swedish second home stock. The results show that though lowering the temperature when a second home is unoccupied emerges as the most efficient measure, both in terms of cost-effectiveness and climate impact, it needs to be complemented with intermittent heating or dehumidification to ensure that the relative humidity is below critical levels, to avoid the risk of damages caused by, for example, mould growth. Installing a heat pump is the second most energy- and cost-effective measure and has the advantage that the indoor temperature can be maintained at rather high levels. 

Place, publisher, year, edition, pages
Multidisciplinary Digital Publishing Institute (MDPI), 2024
Keywords
Clean energy; Heat pump systems; Heating equipment; Efficiency measure; Energy; Energy renovation; Energy use; Energy-consumption; Heat pumps; Heating period; Heating system; Second home; User pattern; Energy efficiency
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:ri:diva-75099 (URN)10.3390/en17174493 (DOI)2-s2.0-85203633214 (Scopus ID)
Note

This study was conducted within the research project “Energieffektivisering och varsam renovering av fritidshus” P2022-00125, funded by Spara&Bevara through The Swedish Energy Agency.

Available from: 2024-10-29 Created: 2024-10-29 Last updated: 2024-10-29Bibliographically approved
Wu, P.-Y., Mjörnell, K. & Johansson, T. (2024). Förutsäga fuktskador i befintliga och nya byggnader med hjälp av AI (maskininlärning). RISE Research Institutes of Sweden
Open this publication in new window or tab >>Förutsäga fuktskador i befintliga och nya byggnader med hjälp av AI (maskininlärning)
2024 (Swedish)Report (Other academic)
Abstract [en]

Predicting modeling and analysis of moisture damages in Swedish buildings Moisture damage in buildings poses significant challenges, leading to costly repairs and negative impacts on indoor environments. Understanding the patterns and prevalence of such damage is crucial for implementing effective preventive and mitigative measures. This report synthesizes findings from the comprehensive study on moisture damage in Swedish buildings, highlighting the complex interplay of multidimensional variables that contribute to these issues. Analyzing 2,100 moisture-related damage records from empirical damage investigations from 2014 to 2020, the study employs data-driven analytical techniques to identify primary moisture damage profiles and evaluate their prevalence. Multivariate analyses reveal diverse associations among factors related to moisture damage, with significant variations in damage types across different building components, phases, and actors responsible. The findings indicate that moisture damage is most prevalent in buildings constructed between 1960-1980 and 2000-2020, with common issues including microbial growth, deformation of building envelopes and roofs, odors caused by humidity-related problems and wind-driven rain. Buildings with non-ventilated crawlspaces and non-ventilated attics are particularly susceptible. By characterizing moisture damage patterns and their associated factors, these findings enhance our understanding of moisture damage occurrences and informs strategies for relevant actors to improve their practices on moisture damage prevention along building lifecycles. Furthermore, various predictive modeling techniques and machine learning algorithms were explored to predict the presence of prevalent moisture damage types, including microbial growth, deformation, odor, and water leakage. The binary and multilabel classification models achieve high prediction rates of over 0.9 average AUCPR and F2 scores by training with building-related features in cross-validation and validation. The lead binary relevance models estimate that approximately one-third of school buildings, 20% of commercial and office buildings, and 15% of residential dwellings in the uninvestigated Swedish building stock may contain unreported moisture damage. These findings advance quantitative research on moisture damage, providing new insights and tools for assessing the extent and patterns of in-situ moisture damage, thereby aiding existing moisture safety evaluations, and building maintenance strategies. Specifically, the results can help building owners, insurance companies, and damage investigators predict and address potential moisture damage in buildings. They also support developers, designers, and contractors in making informed decisions about construction solutions, helping to avoid designs with a high risk of damage. By preventing moisture damage in both existing and new buildings, we can significantly reduce costs and the environmental impact of remediation and material replacement, which currently result in substantial expenses and CO2 emissions.

Place, publisher, year, edition, pages
RISE Research Institutes of Sweden, 2024
Series
RISE Rapport ; 2024:101
Keywords
Moisture damage, Multivariate analysis, Machine learning, Estimation, Building stock
National Category
Civil Engineering
Identifiers
urn:nbn:se:ri:diva-76268 (URN)978-91-89971-68-4 (ISBN)
Note

Detta projekt genomfördes i samarbete mellan forskare vid RISE Research Institutes of Sweden och avdelningen för Byggnadsfysik vid Lunds universitet, med finansiering från Länsförsäkringar (projekt-ID: P4:22). Skadedatabasen, som samlades in från verkliga skadeanmälningar av Polygon AB, digitaliserades och sammanställdes av S. Olof Mundt-Petersen.

Available from: 2024-12-13 Created: 2024-12-13 Last updated: 2024-12-13Bibliographically approved
Ericsson, F., Mjörnell, K. & Janson, U. (2024). Reuse of building materials—the perspective of Swedish clients. Cleaner Engineering and Technology, 23, Article ID 100848.
Open this publication in new window or tab >>Reuse of building materials—the perspective of Swedish clients
2024 (English)In: Cleaner Engineering and Technology, ISSN 2666-7908, Vol. 23, article id 100848Article in journal (Refereed) Published
Abstract [en]

In the context of the circular economy, there is an urgent need for transformation into circular material flows by avoiding waste, reducing extraction of virgin raw materials, and extending product life cycles. Within the construction and real estate industry, the reuse of building materials stands out as a critical strategy for value retention. The objective of this paper is to localise the forefront of the practical implementation of reused materials within the Swedish construction and real estate industry. To achieve this, the current state of reuse practices through the perspective of property companies as clients in decision-making positions was conducted through semi-structured interviews to identify key barriers and enablers associated with reuse. The three most significant barriers that emerged were a lack of measurable economic incentives, the absence of a professional reuse market, and obsolete project management. Conversely, the three most significant enablers were new and improved project management methods, enhancing competence and learning within and beyond organisations, and introduce reuse at an early stage. The results emphasise the need for project management to develop and adopt circular economy principles. This is further understood due to responders highlighting the industry’s linear approach as a major obstacle to circularity together with uncertainties related to product performance, responsibilities and economics characterising reuse efforts. However, an industry in transition is witnessed, e.g. by the emergence of new roles which suggests a continued need for focused research in organisational matters.

Place, publisher, year, edition, pages
Elsevier Ltd, 2024
National Category
Civil Engineering
Identifiers
urn:nbn:se:ri:diva-76300 (URN)10.1016/j.clet.2024.100848 (DOI)2-s2.0-85210709724 (Scopus ID)
Note

The research is funded by two projects: Bolokal – Hållbar renovering och konvertering till bost¨ader, grant P2022-00857 funded by the Swedish Energy Agency and ASSURE - Adaptation of urban space through sustainable regeneration, grant 2022-01903 funded by the Swedish Research Council for Sustainable Development.

Available from: 2025-01-03 Created: 2025-01-03 Last updated: 2025-01-14Bibliographically approved
Mjörnell, K. & Johansson, D. (2024). The use, energy use and renovation of Swedish second homes in winter sport areas. Paper presented at 2024 World Sustainable Built Environment Conference, WSBE 2024. Virtual, Online. 12 June 2024 through 14 June 2024.. IOP Conference Series: Earth and Environment, 1363(1), Article ID 012058.
Open this publication in new window or tab >>The use, energy use and renovation of Swedish second homes in winter sport areas
2024 (English)In: IOP Conference Series: Earth and Environment, ISSN 1755-1307, E-ISSN 1755-1315, Vol. 1363, no 1, article id 012058Article in journal (Refereed) Published
Abstract [en]

The growing utilization of second homes has led to extended heating periods and, to some extent, renovations to enhance their standards, resulting in increased energy and resource consumption. This study, conducted in Sweden, investigates user patterns across various seasons, heating systems, and implemented energy renovation measures. Findings reveal that 40% of second homes in winter sport areas are inhabited prolonged periods during the winter, and more than half are used for shorter durations throughout all seasons, surpassing the usage frequency of second homes in general. Additionally, more than half of these second homes are heated to temperatures exceeding 16°C even when unoccupied. The predominant heating method is direct electricity (48%), followed by heat pumps (32%). Renovation activities primarily focus on interior surfaces, kitchens, and bathrooms to elevate standards, with less than 15% implemented energy-efficient measures like heat pump installation, added insulation, new lighting, or control systems. Considering reported user and heating patterns, along with energy renovations undertaken, the study estimates the energy-saving potential associated with various energy renovation strategies. By installing heat pump the energy consumption can be reduced by more than 50% and by lowering the temperature when the house is unoccupied the energy consumption may be reduced by almost 50%. 

Place, publisher, year, edition, pages
Institute of Physics, 2024
National Category
Civil Engineering
Identifiers
urn:nbn:se:ri:diva-74645 (URN)10.1088/1755-1315/1363/1/012058 (DOI)2-s2.0-85198450370 (Scopus ID)
Conference
2024 World Sustainable Built Environment Conference, WSBE 2024. Virtual, Online. 12 June 2024 through 14 June 2024.
Note

This study has been conducted within and funded by the research project “Energieffektivisering och varsam renovering av fritidshus” P2022-00125 funded by Spara&Bevara through The Swedish Energy Agency. 

Available from: 2024-08-06 Created: 2024-08-06 Last updated: 2024-08-06Bibliographically approved
Johansson, T. & Mjörnell, K. (2023). Data-driven prediction of PVC flooring in the Swedish building stock.
Open this publication in new window or tab >>Data-driven prediction of PVC flooring in the Swedish building stock
2023 (English)Report (Other academic)
Abstract [en]

PVC flooring accounts for a significant share of PVC use in the construction sector and has great potential for recycling. Nevertheless, the actual recycling rate of PVC flooring spillage in 2018 was less than 20%, according to the national system for the separate collection and recycling of material residues from the installation of PVC floorings, developed by flooring manufacturer Tarkett AB and now used by all manufacturers in the flooring industry. To improve the sorting and recycling process of old PVC flooring it is necessary to identify where the material is located and evaluate its recycling potential. Such information is crucial for demolition waste recycling companies and flooring manufacturers to improve recycling practices for PVC flooring and then use the recycled PVC materials in the new flooring production. The challenge is to find out in which buildings there is PVC flooring and when it was installed which will indicate when it is planned to be dismantled and replaced. Since the PVC flooring manufactures do not keep track on where their products are laid such information is lacking. The best source of information that was made available for the researchers appeared to be the public building owners´ maintenance plans. Therefore, it was decided to focus on the presence of PVC flooring in public preschools as an example. By combining data from maintenance plans with national building registers, the PVC flooring in the Swedish preschools have been forecasted. The project results show an example how limited data sources can be used to predict presence of materials in larger stocks and is therefore expected to contribute to a climate-neutral supply chain with recycled PVC flooring. Based on the results of this study, dialogue, recommendations and guidelines can be developed for the flooring industry, the waste and recycling industry and the Swedish real estate and construction sector.

Publisher
p. 33
Series
RISE Rapport ; 2023:146
Keywords
PVC flooring, recycling potential, data-driven prediction
National Category
Building Technologies
Identifiers
urn:nbn:se:ri:diva-70098 (URN)978-91-89896-36-9 (ISBN)
Note

The project has been financed with support from Vinnova, the Swedish Innovation Agency.

Available from: 2024-01-17 Created: 2024-01-17 Last updated: 2024-02-14Bibliographically approved
Olsson, L., Lang, L., Bok, G. & Mjörnell, K. (2023). Development of laboratory experiments to determine critical moisture condition of CLT constructions. Journal of Physics, Conference Series, 2654(1), Article ID 012022.
Open this publication in new window or tab >>Development of laboratory experiments to determine critical moisture condition of CLT constructions
2023 (English)In: Journal of Physics, Conference Series, ISSN 1742-6588, E-ISSN 1742-6596, Vol. 2654, no 1, article id 012022Article in journal (Refereed) Published
Abstract [en]

There is an increased interest in using Cross-laminated timber (CLT) in construction, but many buildings are erected without weather protection, which poses a risk of moisture impact if wood is exposed to precipitation during construction. The construction industry argues that there are no documented critical moisture levels for CLT and no specific test method. In the study, a laboratory test set-up was developed to study mould growth under realistic and controlled climatic conditions after exposure to distilled water and spore suspension. In the experiments, small test specimens of CLT structures were exposed to distilled water for 1 day or 1 week. During the development of the method it was found that exposed for one day and then given the opportunity for open drying did not give rise to mould growth. On the other hand, growth occurred on surfaces that could not dry immediately, for example at connection points. For specimens exposed for one week, mould growth arose regardless of whether the surfaces could dry immediately or not. The conclusions apply primarily to the climates studied. The methodology needs to be further developed, with other scenarios being studied, and calibrated against samples exposed to outdoor air, dust, dirt and rainwater. 

Place, publisher, year, edition, pages
Institute of Physics, 2023
Keywords
Moisture; Molds; ’Dry’ [; Cross laminated; Distilled water; Exposed to; Laboratory experiments; Laminated timber; Moisture conditions; Mould growth; Timber construction; Weather protections; Construction industry
National Category
Building Technologies
Identifiers
urn:nbn:se:ri:diva-69246 (URN)10.1088/1742-6596/2654/1/012022 (DOI)2-s2.0-85181173951 (Scopus ID)
Note

The main support provided by Smart Housing Småland is gratefully acknowledged. 

Available from: 2024-01-15 Created: 2024-01-15 Last updated: 2024-01-15Bibliographically approved
Mjörnell, K., Johansson, D., Femenias, P., Eriksson, P., Donarelli, A. & Johansson, T. (2023). Energy use patterns and renovations of Swedish second homes. Paper presented at 13th Nordic Symposium on Building Physics, NSB 2023. Aalborg, Denmark. 12 June 2023 through 14 June 2023. Journal of Physics, Conference Series, 2654(1), Article ID 012011.
Open this publication in new window or tab >>Energy use patterns and renovations of Swedish second homes
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2023 (English)In: Journal of Physics, Conference Series, ISSN 1742-6588, E-ISSN 1742-6596, Vol. 2654, no 1, article id 012011Article in journal (Refereed) Published
Abstract [en]

During and post pandemic more people spent time in their second homes, which is expected to have led to higher energy use for heating. The knowledge of energy performance, heating systems, energy renovation and use patterns of second homes is still poor. The aim of the research is therefore to compile available information from building registers but also to empirically investigate user patterns, heating source and the renovation and energy efficiency measures carried out in second homes. A first step is to synthesize existing knowledge and develop a method for a broad mapping in a next step. The methods used are analysing statistics from national building registers and collecting information from owners/users through a pre-survey that is developed and tested. In this paper statistics on Swedish second homes and results from a pre-survey responded by 92 second homes owners/users are reported. From statistics, the energy performance and the main heating source for second homes with an EPC are identified. Despite the limited sample, the results from the pre-survey give an indication of user patterns, energy renovation measures carried out, and also whether the owners care about cultural values. Based on the experience from the pre-survey, a national survey has been initiated in Sweden.

Place, publisher, year, edition, pages
Institute of Physics, 2023
Keywords
Efficiency measure; Energy performance; Energy use; Energy use patterns; Heating source; Heating system; Home owners; Owner/user; Swedishs; System energy; Energy efficiency
National Category
Building Technologies
Identifiers
urn:nbn:se:ri:diva-69322 (URN)10.1088/1742-6596/2654/1/012011 (DOI)2-s2.0-85181172938 (Scopus ID)
Conference
13th Nordic Symposium on Building Physics, NSB 2023. Aalborg, Denmark. 12 June 2023 through 14 June 2023
Note

Authors wishing to acknowledge financial support from the research programme Spara&Bevara by the Swedish Energy Agency.

Available from: 2024-01-15 Created: 2024-01-15 Last updated: 2024-02-14Bibliographically approved
Wu, P.-Y., Johansson, T., Mangold, M., Sandels, C. & Mjörnell, K. (2023). Estimating the probability distributions of radioactive concrete in the building stock using Bayesian networks. Expert systems with applications, 222, Article ID 119812.
Open this publication in new window or tab >>Estimating the probability distributions of radioactive concrete in the building stock using Bayesian networks
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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
Keywords
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:nbn:se:ri:diva-64306 (URN)10.1016/j.eswa.2023.119812 (DOI)2-s2.0-85150056393 (Scopus ID)
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
Wu, P.-Y., Johansson, T., Sandels, C., Mangold, M. & Mjörnell, K. (2023). Indoor radon interval prediction in the Swedish building stock using machine learning. Building and Environment, 245, Article ID 110879.
Open this publication in new window or tab >>Indoor radon interval prediction in the Swedish building stock using machine learning
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2023 (English)In: Building and Environment, ISSN 0360-1323, E-ISSN 1873-684X, Vol. 245, article id 110879Article in journal (Refereed) Published
Abstract [en]

Indoor radon represents a health hazard for occupants. However, the indoor radon measurement rate is low in Sweden because of no mandatory requirements. Measuring indoor radon on an urban scale is complicated, machine learning exploiting existing data for pattern identification provides a cost-efficient approach to estimate indoor radon exposure in the building stock. Extreme gradient boosting (XGBoost) models and deep neural network (DNN) models were developed based on indoor radon measurement records, property registers, and geogenic information. The XGBoost models showed promising results in predicting indoor radon intervals for different types of buildings with macro-F1 between 0.93 and 0.96, whereas the DNN models attained macro-F1 between 0.64 and 0.74. After that, the XGBoost models trained on the national indoor radon dataset were transferred to fit building registers in metropolitan regions to estimate the indoor radon intervals in non-measured and measured buildings by regions and building classes. By comparing the prediction results and the statistical summary of indoor radon intervals in measured buildings, the model uncertainty and validity were determined. The study ascertains the prediction performance of machine learning models in classifying indoor radon intervals and discusses the benefits and limitations of the data-driven approach. The research outcomes can assist preliminary large-scale indoor radon distribution estimation for relevant authorities and guide onsite measurements for prioritized building stock prone to indoor radon exposure. 

Place, publisher, year, edition, pages
Elsevier Ltd, 2023
Keywords
Sweden; Buildings; Forecasting; Health hazards; Learning systems; Neural network models; Radon; Uncertainty analysis; Building stocks; Deep learning; Exposure estimation; Indoor radon; Machine-learning; Predictive models; Radon exposure; Radon exposure estimation; Regional building stock; Xgboost; building; geogenic source; indoor radon; machine learning; prediction; Deep neural networks
National Category
Civil Engineering
Identifiers
urn:nbn:se:ri:diva-67658 (URN)10.1016/j.buildenv.2023.110879 (DOI)2-s2.0-85172459457 (Scopus ID)
Note

This work has received funding from the Swedish Foundation for Strategic Research (SSF) [ FID18-0021 ] and the Maj and Hilding Brosenius Research Foundation .

Available from: 2023-11-27 Created: 2023-11-27 Last updated: 2024-02-14Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-3863-0740

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