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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.
Öppna denna publikation i ny flik eller fönster >>Estimating the probability distributions of radioactive concrete in the building stock using Bayesian networks
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2023 (Engelska)Ingår i: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 222, artikel-id 119812Artikel i tidskrift (Refereegranskat) 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)

Ort, förlag, år, upplaga, sidor
Elsevier Ltd, 2023
Nyckelord
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
Nationell ämneskategori
Husbyggnad
Identifikatorer
urn:nbn:se:ri:diva-64306 (URN)10.1016/j.eswa.2023.119812 (DOI)2-s2.0-85150056393 (Scopus ID)
Anmärkning

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.

Tillgänglig från: 2023-05-08 Skapad: 2023-05-08 Senast uppdaterad: 2024-02-14Bibliografiskt granskad
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
Öppna denna publikation i ny flik eller fönster >>Increased rent misspent?: How ownership matters for renovation and rent increases in rental housing in Sweden
2023 (Engelska)Ingår i: International journal of housing policy, ISSN 1949-1247, E-ISSN 1949-1255Artikel i tidskrift (Refereegranskat) 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). 

Ort, förlag, år, upplaga, sidor
Routledge, 2023
Nyckelord
affordable housing, Commercialisation, financialisation, ownership, renovation, rents
Nationell ämneskategori
Kulturgeografi
Identifikatorer
urn:nbn:se:ri:diva-65969 (URN)10.1080/19491247.2023.2232205 (DOI)2-s2.0-85166927309 (Scopus ID)
Anmärkning

 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).

Tillgänglig från: 2023-08-22 Skapad: 2023-08-22 Senast uppdaterad: 2024-02-14Bibliografiskt granskad
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.
Öppna denna publikation i ny flik eller fönster >>Indoor radon interval prediction in the Swedish building stock using machine learning
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2023 (Engelska)Ingår i: Building and Environment, ISSN 0360-1323, E-ISSN 1873-684X, Vol. 245, artikel-id 110879Artikel i tidskrift (Refereegranskat) 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. 

Ort, förlag, år, upplaga, sidor
Elsevier Ltd, 2023
Nyckelord
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
Nationell ämneskategori
Samhällsbyggnadsteknik
Identifikatorer
urn:nbn:se:ri:diva-67658 (URN)10.1016/j.buildenv.2023.110879 (DOI)2-s2.0-85172459457 (Scopus ID)
Anmärkning

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

Tillgänglig från: 2023-11-27 Skapad: 2023-11-27 Senast uppdaterad: 2024-02-14Bibliografiskt granskad
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.
Öppna denna publikation i ny flik eller fönster >>Machine learning models for the prediction of polychlorinated biphenyls and asbestos materials in buildings
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2023 (Engelska)Ingår i: Resources, Conservation and Recycling, ISSN 0921-3449, E-ISSN 1879-0658, Vol. 199, artikel-id 107253Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
Elsevier B.V., 2023
Nyckelord
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
Nationell ämneskategori
Husbyggnad
Identifikatorer
urn:nbn:se:ri:diva-67646 (URN)10.1016/j.resconrec.2023.107253 (DOI)2-s2.0-85174186956 (Scopus ID)
Tillgänglig från: 2023-11-03 Skapad: 2023-11-03 Senast uppdaterad: 2024-02-14Bibliografiskt granskad
Mangold, M. & Mjörnell, K. (2023). Swedish public and private housing companies’ access to the capital market for financing energy renovation. Journal of Housing and the Built Environment, 38, 673-697
Öppna denna publikation i ny flik eller fönster >>Swedish public and private housing companies’ access to the capital market for financing energy renovation
2023 (Engelska)Ingår i: Journal of Housing and the Built Environment, ISSN 1566-4910, E-ISSN 1573-7772, Vol. 38, s. 673-697Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

The financing of energy efficiency measures and renovations is key to reaching energy efficiency targets for the housing sector. The purpose of this article is to add the Swedish case of how capital market funds have become accessible and used by public and private housing companies, in particular for energy efficiency measures. The core of this article are interviews with representatives of Swedish housing companies made during the spring of 2021 with the purpose of mapping how public and larger private housing companies finance renovation and energy efficiency measures, and to what extent funds from the capital market are used for these purposes. In this article, we have found that capital market funds are commonly used by the Swedish public and the largest private housing companies. Bonds are less costly compared to bank loans, and green bonds are 0.02–0.03 percentage points less costly than conventional bonds. Furthermore, control systems that investigate the values of building portfolios as security for bonds are poor. A conclusion is that governmental control systems over the capital market issuing bonds for the housing market could be needed to avert future housing bubbles. © 2022, The Author(s).

Ort, förlag, år, upplaga, sidor
Springer Science and Business Media B.V., 2023
Nyckelord
Capital market, Energy efficiency, Financing energy renovation, Housing ownership, Multifamily buildings, Renovation
Nationell ämneskategori
Husbyggnad
Identifikatorer
urn:nbn:se:ri:diva-61422 (URN)10.1007/s10901-022-09996-4 (DOI)2-s2.0-85142215266 (Scopus ID)
Anmärkning

Funding details: Svenska Forskningsrådet Formas, 2017-01546, 2017‐01449; Funding text 1: This work was funded by The Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (Formas), Grant Numbers 2017‐01449 and 2017-01546, within the project National Building‐Specific Information (NBI).

Tillgänglig från: 2022-12-07 Skapad: 2022-12-07 Senast uppdaterad: 2023-07-06Bibliografiskt granskad
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.
Öppna denna publikation i ny flik eller fönster >>Energy efficiency at what cost?: Unjust burden-sharing of rent increases in extensive energy retrofitting projects in Sweden
2022 (Engelska)Ingår i: Energy Research & Social Science, ISSN 2214-6296, E-ISSN 2214-6326, Vol. 92, artikel-id 102791Artikel i tidskrift (Refereegranskat) 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)

Ort, förlag, år, upplaga, sidor
Elsevier Ltd, 2022
Nationell ämneskategori
Byggproduktion
Identifikatorer
urn:nbn:se:ri:diva-60152 (URN)10.1016/j.erss.2022.102791 (DOI)2-s2.0-85137010833 (Scopus ID)
Anmärkning

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.

Tillgänglig från: 2022-09-29 Skapad: 2022-09-29 Senast uppdaterad: 2024-02-14Bibliografiskt granskad
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.
Öppna denna publikation i ny flik eller fönster >>Modeling Artificial Neural Networks to Predict Asbestos-containing Materials in Residential Buildings
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2022 (Engelska)Ingår i: IOP Conference Series: Earth and Environmental Science, Vol. 1122, artikel-id 012050Artikel i tidskrift (Refereegranskat) 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.

Nationell ämneskategori
Husbyggnad
Identifikatorer
urn:nbn:se:ri:diva-63227 (URN)10.1088/1755-1315/1122/1/012050 (DOI)
Konferens
SBEfin2022 Emerging Concepts for Sustainable Built Environment (SBEfin2022) 23/11/2022 - 25/11/2022
Anmärkning

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. 

Tillgänglig från: 2023-01-30 Skapad: 2023-01-30 Senast uppdaterad: 2024-02-14Bibliografiskt granskad
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.
Öppna denna publikation i ny flik eller fönster >>Predicting the presence of hazardous materials in buildings using machine learning
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2022 (Engelska)Ingår i: Building and Environment, ISSN 0360-1323, E-ISSN 1873-684X, Vol. 213, artikel-id 108894Artikel i tidskrift (Refereegranskat) 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)

Ort, förlag, år, upplaga, sidor
Elsevier Ltd, 2022
Nyckelord
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
Nationell ämneskategori
Husbyggnad
Identifikatorer
urn:nbn:se:ri:diva-58771 (URN)10.1016/j.buildenv.2022.108894 (DOI)2-s2.0-85124704384 (Scopus ID)
Anmärkning

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 .

Tillgänglig från: 2022-03-04 Skapad: 2022-03-04 Senast uppdaterad: 2024-02-14Bibliografiskt granskad
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.
Öppna denna publikation i ny flik eller fönster >>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 (Engelska)Ingår i: Sustainability, E-ISSN 2071-1050, Vol. 13, nr 14, artikel-id 7836Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
MDPI, 2021
Nyckelord
hazardous materials, asbestos, PCB, environmental investigation, statistical inference, cross-validation, machine learning pre-processing
Nationell ämneskategori
Miljöanalys och bygginformationsteknik
Identifikatorer
urn:nbn:se:ri:diva-55650 (URN)10.3390/su13147836 (DOI)
Tillgänglig från: 2021-08-05 Skapad: 2021-08-05 Senast uppdaterad: 2024-02-14Bibliografiskt granskad
Wu, P.-Y., Mjörnell, K., Sandels, C. & Mangold, M. (2021). Machine Learning in Hazardous Building Material Management : Research Status and Applications. Recent Progress in Materials, 3(2)
Öppna denna publikation i ny flik eller fönster >>Machine Learning in Hazardous Building Material Management : Research Status and Applications
2021 (Engelska)Ingår i: Recent Progress in Materials, E-ISSN 2689-5846, Vol. 3, nr 2Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Assessment of the presence of hazardous materials in buildings is essential for improving material recyclability, increasing working safety, and lowering the risk of unforeseen cost and delay in demolition. In light of these aspects, machine learning has been viewed as a promising approach to complement environmental investigations and quantify the risk of finding hazardous materials in buildings. In view of the increasing number of related studies, this article aims to review the research status of hazardous material management and identify the potential applications of machine learning. Our exploratory study consists of a two-fold approach: science mapping and critical literature review. By evaluating the references acquired from a literature search and complementary materials, we have been able to pinpoint and discuss the research gaps and opportunities. While pilot research has been conducted in the identification of hazardous materials, source separation and collection, extensive adoption of the available machine learning methods was not found in this field. Our findings show that (1) quantification of asbestos-cement roofing is possible from the combination of remote sensing and machine learning algorithms, (2) characterization of buildings with asbestos-containing materials is progressive by using statistical methods, and (3) separation and collection of asbestos-containing wastes can be addressed with a hybrid of image processing and machine learning algorithms. Analysis from this study demonstrates the method applicability and provides an orientation to the future implementation of the European Union Construction and Demolition Waste Management Protocol. Furthermore, establishing a comprehensive environmental inventory database is a key to facilitating a transition toward hazard-free circular construction

Ort, förlag, år, upplaga, sidor
Lidsen Publ., 2021
Nyckelord
Machine learning technique; hazardous material management; construction and demolition waste; systematic review; asbestos-containing material; polychlorinated biphenyls (PCB)
Nationell ämneskategori
Byggproduktion
Identifikatorer
urn:nbn:se:ri:diva-53115 (URN)10.21926/rpm.2102017 (DOI)
Tillgänglig från: 2021-05-19 Skapad: 2021-05-19 Senast uppdaterad: 2023-11-01Bibliografiskt granskad
Organisationer
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0002-5044-6989

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