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Application of Machine Learning in Building Performance and Building Stock...
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Application of Machine Learning in Building Performance and Building Stock Research

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: closed (22 July 2021) | Viewed by 3695

Special Issue Editor

Dr. Mikael Mangold

E-Mail Website
Guest Editor
Built environments, RISE, Gothenburg, Sweden
Interests: statistics; machine learning; building performance; building stock research; social sustainability; humanitarian aid

Special Issue Information

Dear Colleagues,

Thanks to the rapid increase of data availability, as well as increasing computational capacities and simplified programing methods, machine learning tools are being progressively applied in the research fields of building performance and building stock research. This Special Issue will provide a space for this emerging research topic.

More specifically, the purpose of this Special Issue is to gather scientific ideas, research methods, and innovative applications related to ”Application of Machine Learning in Building Performance and Building Stock Research”.

Examples of relevant techniques are:

  • Image recognition for detection of building features (satellite, drone or open data);
  • Statistical prediction of unknown building features in otherwise comprehensive datasets;
  • Pattern recognition in flowing data logs of measurements in buildings;
  • Decision support tools for building owners and decision makers using machine learning.

References:

Bilal, Muhammad, Lukumon O Oyedele, Junaid Qadir, Kamran Munir, Saheed O Ajayi, Olugbenga O Akinade, Hakeem A Owolabi, Hafiz A Alaka, and Maruf Pasha. 2016. “Big Data in the Construction Industry: A Review of Present Status, Opportunities, and Future Trends.” Advanced Engineering Informatics 30 (3): 500–521.

Hong, Tianzhen, Zhe Wang, Xuan Luo, and Wanni Zhang. 2020. “State-of-the-Art on Research and Applications of Machine Learning in the Building Life Cycle.” Energy and Buildings, 109831 

Dr. Mikael Mangold
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • artificial intelligence
  • big data
  • building performance
  • building stock
  • energy efficiency
  • statistics

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

23 pages, 1871 KiB  
Article
A Data-Driven Approach to Assess the Risk of Encountering Hazardous Materials in the Building Stock Based on Environmental Inventories
by Pei-Yu Wu, Kristina Mjörnell, Mikael Mangold, Claes Sandels and Tim Johansson
Sustainability 2021, 13(14), 7836; https://doi.org/10.3390/su13147836 - 13 Jul 2021
Cited by 11 | Viewed by 2890
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Application of Machine Learning in Building Performance and Building Stock Research)
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