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Publications (10 of 15) Show all publications
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
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
Mangold, M., Bohman, H., Johansson, T. & von Platten, J. (2023). Increased rent misspent?: How ownership matters for renovation and rent increases in rental housing in Sweden. International journal of housing policy
Open this publication in new window or tab >>Increased rent misspent?: How ownership matters for renovation and rent increases in rental housing in Sweden
2023 (English)In: International journal of housing policy, ISSN 1949-1247, E-ISSN 1949-1255Article in journal (Refereed) Epub ahead of print
Abstract [en]

Renovations of the housing rental stock have become a political concern since they have been claimed to drive gentrification and affect tenants’ everyday lives as well as long-term housing conditions. Furthermore, new actors have entered the market, partly as a result of high supply on the international capital markets creating a flow of capital into market segments. This has led to a critique of private equity in the housing sector, and raised the question of the extent to which ownership of the rental stock matters for housing affordability. Yet there seems to be little systematic research on this topic. This study uses a unique dataset covering the entire rental housing stock in Sweden to address whether there are differences in renovation investments between different ownership groups. The purpose of this article is to increase understanding of how ownership affects renovation processes, and specifically to analyse to what extent, and how, private and public actors differ in renovation and rent setting decisions. Our results demonstrate that public housing companies raised rents less and renovated more, particularly in the lower-income segments of the multi-family building stock between 2014 and 2020. © 2023 The Author(s). 

Place, publisher, year, edition, pages
Routledge, 2023
Keywords
affordable housing, Commercialisation, financialisation, ownership, renovation, rents
National Category
Human Geography
Identifiers
urn:nbn:se:ri:diva-65969 (URN)10.1080/19491247.2023.2232205 (DOI)2-s2.0-85166927309 (Scopus ID)
Note

 Correspondence Address: M. Mangold; Division of Built Environment, RISE Research Institutes of Sweden, Gothenburg, Sweden; email: mikael.mangold@ri.se.  This work was conducted with the financial support of the Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (Formas) (grant number 2017-01546) within the project National Building-Specific Information (NBI).

Available from: 2023-08-22 Created: 2023-08-22 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
Wu, P.-Y., Sandels, C., Johansson, T., Mangold, M. & Mjörnell, K. (2023). Machine learning models for the prediction of polychlorinated biphenyls and asbestos materials in buildings. Resources, Conservation and Recycling, 199, Article ID 107253.
Open this publication in new window or tab >>Machine learning models for the prediction of polychlorinated biphenyls and asbestos materials in buildings
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2023 (English)In: Resources, Conservation and Recycling, ISSN 0921-3449, E-ISSN 1879-0658, Vol. 199, article id 107253Article in journal (Refereed) Published
Abstract [en]

Hazardous materials in buildings cause project uncertainty concerning schedule and cost estimation, and hinder material recovery in renovation and demolition. The study aims to identify patterns and extent of polychlorinated biphenyls (PCBs) and asbestos materials in the Swedish building stock to assess their potential presence in pre-demolition audits. Statistics and machine learning pipelines were generated for four PCB and twelve asbestos components based on environmental inventories. The models succeeded in predicting most hazardous materials in residential buildings with a minimum average performance of 0.79, and 0.78 for some hazardous components in non-residential buildings. By employing the leader models to regional building registers, the probability of hazardous materials was estimated for non-inspected building stocks. The geospatial distribution of buildings prone to contamination was further predicted for Stockholm public housing to demonstrate the models’ application. The research outcomes contribute to a cost-efficient data-driven approach to evaluating comprehensive hazardous materials in existing buildings.

Place, publisher, year, edition, pages
Elsevier B.V., 2023
Keywords
Demolition; Forecasting; Hazardous materials; Hazards; Housing; Machine learning; Polychlorinated biphenyls; Building stocks; Cost estimations; In-buildings; Machine learning models; Machine-learning; Material recovery; Pre-demolition audit; Probability: distributions; Project uncertainty; Residential building; asbestos; building; demolition; machine learning; modeling; PCB; prediction; probability; Probability distributions
National Category
Building Technologies
Identifiers
urn:nbn:se:ri:diva-67646 (URN)10.1016/j.resconrec.2023.107253 (DOI)2-s2.0-85174186956 (Scopus ID)
Available from: 2023-11-03 Created: 2023-11-03 Last updated: 2024-02-14Bibliographically approved
von Platten, J., Mangold, M., Johansson, T. & Mjörnell, K. (2022). Energy efficiency at what cost?: Unjust burden-sharing of rent increases in extensive energy retrofitting projects in Sweden. Energy Research & Social Science, 92, Article ID 102791.
Open this publication in new window or tab >>Energy efficiency at what cost?: Unjust burden-sharing of rent increases in extensive energy retrofitting projects in Sweden
2022 (English)In: Energy Research & Social Science, ISSN 2214-6296, E-ISSN 2214-6326, Vol. 92, article id 102791Article in journal (Refereed) Published
Abstract [en]

Although renovation costs can lead to rent increases in energy retrofitting, it is often assumed that reductions in energy costs will counterbalance the rent increase. In Swedish multifamily housing, energy costs for heating are however generally included as a fixed component in the monthly rent, meaning that the rent increase after energy retrofitting corresponds to the net change in rent level as well as energy costs for heating. This makes Sweden a methodologically advantageous setting for studying tenants' cost burden of energy retrofitting. The aim of this study was thus to investigate how energy performance improvement has affected rent increases in Swedish renovation projects between 2013 and 2019. Utilising a national database of multifamily housing, it was found that energy retrofitting entailed a cost relief for tenants in renovation projects with smaller investments. However, in renovation projects with larger investments, energy retrofitting entailed a cost burden for tenants. Moreover, public housing companies had conducted a high share of the extensive energy retrofits, leading to low-income tenant groups being disproportionately subjected to cost burdens of energy retrofitting. On the contrary, light energy retrofits with a cost relief for energy efficiency had been rather evenly distributed across income groups. These results indicate ongoing conflicts with the ability-to-pay principle in the energy transition of Swedish multifamily housing, and suggest that if low-investment energy retrofits are not sufficient for upcoming objectives and requirements, subsidies could be needed to compensate low-income tenants for the cost burden of extensive energy retrofitting. © 2022 The Author(s)

Place, publisher, year, edition, pages
Elsevier Ltd, 2022
National Category
Construction Management
Identifiers
urn:nbn:se:ri:diva-60152 (URN)10.1016/j.erss.2022.102791 (DOI)2-s2.0-85137010833 (Scopus ID)
Note

Funding details: Svenska Forskningsrådet Formas, 2017-01449; Funding text 1: The authors would like to thank the Swedish National Board of Housing, Building and Planning for their collaboration around this research. This work was supported by The Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (Formas) [grant number 2017-01449] within the project National Building-Specific Information (NBI). The authors have no conflicts of interest to declare.; Funding text 2: The authors would like to thank the Swedish National Board of Housing, Building and Planning for their collaboration around this research. This work was supported by The Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (Formas) [grant number 2017-01449 ] within the project National Building-Specific Information (NBI). The authors have no conflicts of interest to declare.

Available from: 2022-09-29 Created: 2022-09-29 Last updated: 2024-02-14Bibliographically approved
Wu, P.-Y., Mangold, M., Sandels, C., Johansson, T. & Mjörnell, K. (2022). Modeling Artificial Neural Networks to Predict Asbestos-containing Materials in Residential Buildings. Paper presented at SBEfin2022 Emerging Concepts for Sustainable Built Environment (SBEfin2022) 23/11/2022 - 25/11/2022. IOP Conference Series: Earth and Environmental Science, 1122, Article ID 012050.
Open this publication in new window or tab >>Modeling Artificial Neural Networks to Predict Asbestos-containing Materials in Residential Buildings
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2022 (English)In: IOP Conference Series: Earth and Environmental Science, Vol. 1122, article id 012050Article in journal (Refereed) Published
Abstract [en]

The presence of hazardous materials inhibits material circularity. The existing residential buildings are exposed to the risk of the unforeseen presence of asbestos-containing materials during the demolition or renovation process. Estimating the potential occurrence of contaminated building components can therefore facilitate semi-selective demolition and decontamination planning. The study aims to investigate the prediction possibility of seven frequently detected asbestos-containing materials by using artificial neural networks based on a hazardous material dataset from pre-demolition audit inventories and national building registers. Through iterative model evaluation and careful hyperparameter tuning, the prediction performance for each asbestos-containing material was benchmarked. A high level of accuracy was obtained for asbestos pipe insulation and ventilation channel, yet barely any patterns were found for asbestos floor mats. Artificial neural networks show potential for classifying specific asbestos components and can enhance the knowledge of their detection patterns. However, more quality data are needed to bring the models into practice for risk assessment for not yet inventoried residential buildings. The proposed screening approach for in situ asbestos-containing materials has high applicability for the quality assurance of recycled materials in circular value chains.

National Category
Building Technologies
Identifiers
urn:nbn:se:ri:diva-63227 (URN)10.1088/1755-1315/1122/1/012050 (DOI)
Conference
SBEfin2022 Emerging Concepts for Sustainable Built Environment (SBEfin2022) 23/11/2022 - 25/11/2022
Note

The work is part of the PhD project “Prediction of Hazardous Materials in Buildings using Machine Learning” supported by RISE Research Institutes of Sweden. The research fund comes from the Swedish Foundation for Strategic Research (SSF) with grant number FID18-0021, and from the EU BuiltHub project with the grant agreement ID 957026. 

Available from: 2023-01-30 Created: 2023-01-30 Last updated: 2024-02-14Bibliographically approved
Wu, P.-Y., Sandels, C., Mjörnell, K., Mangold, M. & Johansson, T. (2022). Predicting the presence of hazardous materials in buildings using machine learning. Building and Environment, 213, Article ID 108894.
Open this publication in new window or tab >>Predicting the presence of hazardous materials in buildings using machine learning
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2022 (English)In: Building and Environment, ISSN 0360-1323, E-ISSN 1873-684X, Vol. 213, article id 108894Article in journal (Refereed) Published
Abstract [en]

Identifying in situ hazardous materials can improve demolition waste recyclability and reduce project uncertainties concerning cost overrun and delay. With the attempt to characterize their detection patterns in buildings, the study investigates the prediction potential of machine learning techniques with hazardous waste inventories and building registers as input data. By matching, validating, and assuring the quality of empirical data, a hazardous material dataset for training, testing, and validation was created. The objectives of the explorative study are to highlight the challenges in machine learning pipeline development and verify two prediction hypotheses. Our findings show an average of 74% and 83% accuracy rates in predicting asbestos pipe insulation in multifamily houses and PCB joints or sealants in school buildings in two major Swedish cities Gothenburg and Stockholm. Similarly, 78% and 83% of recall rates were obtained for imbalanced classification. By correlating the training sample size and cross-validation accuracy, the bias and variance issues were assessed in learning curves. In general, the models perform well on the limited dataset, yet collecting more training data can improve the model's generalizability to other building stocks, meanwhile decreasing the chance of overfitting. Furthermore, the average impact on the model output magnitude of each feature was illustrated. The proposed applied machine learning approach is promising for in situ hazardous material management and could support decision-making regarding risk evaluation in selective demolition work. © 2022 The Author(s)

Place, publisher, year, edition, pages
Elsevier Ltd, 2022
Keywords
Asbestos, Circular economy, Hazardous materials, Machine learning, PCB, Pre-demolition audit, Decision making, Demolition, Forecasting, Hazards, Organic pollutants, Cost-overruns, Demolition wastes, Hazardous wastes, In-buildings, Input datas, Machine learning techniques, Project uncertainty, Recyclability, Polychlorinated biphenyls
National Category
Building Technologies
Identifiers
urn:nbn:se:ri:diva-58771 (URN)10.1016/j.buildenv.2022.108894 (DOI)2-s2.0-85124704384 (Scopus ID)
Note

Funding details: Stiftelsen för Strategisk Forskning, SSF, FID18-0021; Funding text 1: This research was funded by the Swedish Foundation for Strategic Research (SSF) , grant number FID18-0021 .

Available from: 2022-03-04 Created: 2022-03-04 Last updated: 2024-02-14Bibliographically approved
Wu, P.-Y., Mjörnell, K., Mangold, M., Sandels, C. & Johansson, T. (2021). A Data-Driven Approach to Assess the Risk of Encountering Hazardous Materials in the Building Stock Based on Environmental Inventories. Sustainability, 13(14), Article ID 7836.
Open this publication in new window or tab >>A Data-Driven Approach to Assess the Risk of Encountering Hazardous Materials in the Building Stock Based on Environmental Inventories
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2021 (English)In: Sustainability, E-ISSN 2071-1050, Vol. 13, no 14, article id 7836Article in journal (Refereed) Published
Abstract [en]

The presence of hazardous materials hinders the circular economy in construction and demolition waste management. However, traditional environmental investigations are costly and time-consuming, and thus lead to limited adoption. To deal with these challenges, the study investigated the possibility of employing registered records as input data to achieve in situ hazardous building materials management at a large scale. Through characterizing the eligible building groups in question, the risk of unexpected cost and delay due to acute abatement could be mitigated. Merging the national building registers and the environmental inventory from renovated and demolished buildings in the City of Gothenburg, a training dataset was created for data validation and statistical operations. Four types of inventories were evaluated to identify the building groups with adequate data size and data quality. The observations’ representativeness was described by plotting the distribution of building features between the Gothenburg dataset and the training dataset. Evaluating the missing data and the positive detection rates affirmed that reports and protocols could locate hazardous materials in the building stock. The asbestos and polychlorinated biphenyl (PCB)-containing materials with high positive detection rates were highlighted and discussed. Moreover, the potential inventory types and building groups for future machine learning prediction were delineated through the cross-validation matrix. The novel study contributes to the method development for assessing the risk of residual hazardous materials in buildings.

Place, publisher, year, edition, pages
MDPI, 2021
Keywords
hazardous materials, asbestos, PCB, environmental investigation, statistical inference, cross-validation, machine learning pre-processing
National Category
Environmental Analysis and Construction Information Technology
Identifiers
urn:nbn:se:ri:diva-55650 (URN)10.3390/su13147836 (DOI)
Available from: 2021-08-05 Created: 2021-08-05 Last updated: 2024-02-14Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-8107-7768

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