Special Issue "Data and the Built Environment: Reconciling Resources and Information for a Sustainable Living"

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

Deadline for manuscript submissions: 30 September 2022.

Special Issue Editors

Dr. Bernardino D’Amico
E-Mail Website
Guest Editor
School of Engineering and the Built Environment, Edinburgh Napier University, Edinburgh EH11 4BN, UK
Interests: structural engineering; architectural engineering; sustainability science; low carbon design
Special Issues and Collections in MDPI journals
Dr. Suha Jaradat
E-Mail Website
Guest Editor
School of Engineering and the Built Environment, Edinburgh Napier University, Edinburgh EH11 4BN, UK
Interests: building information modelling (BIM); historic building information modelling (HBIM); architectural design and practice; thermal comfort and spatial behaviour; sustainable design
Special Issues and Collections in MDPI journals
Dr. Masoud Sajjadian
E-Mail Website
Guest Editor
School of Engineering and the Built Environment, Edinburgh Napier University, Edinburgh EH11 4BN, UK
Interests: climate-sensitive architecture; building performance simulation; data-driven design

Special Issue Information

Dear Colleagues,

We are living an unprecedented moment in time. For the first time in human history, about half of the world population lives in urban areas, a proportion that is forecasted to reach about 70% by 2050, when 2 more billion people are expected to be living on this planet compared to current estimates. Countless more buildings and infrastructures will be constructed, which will further drive up the increasing consumption of raw materials and the energy required to extract, process, and move them around the globe. Recent research shows that the mass of all artificially built products, from high-rise buildings to plastic bottles, is now approximately equal to the mass of all living organisms on the planet and could soon be surpassed if the business-as-usual model of endless growth remains unchecked. Such a sustained model of resource consumption is materialising into a range of environmentally negative impacts, from climate change to the pollution of natural land and sea ecosystems, as well as biodiversity losses and mass extinctions.

Alongside this ever-increasing consumption of physical matter, the creation and extraction of information has also skyrocketed in recent times. About 90% of all available data created since human history first began have been generated in the last two years alone. The trend is only set to accelerate with planned deployments of more advanced technologies for data communication (5G) and data capturing in our buildings (IoT), our neighbourhoods and towns (smart cities), and above our heads (remote sensing). 

Some scholars have argued that this explosion in the availability of machine-readable data, combined with tremendous advancements in artificial intelligence, has opened the doors to new forms of business relying on commoditisation of data as the ‘new oil’. Whether the exponential availability of data and machine learning technologies may be leading us towards a path of endless wealth, or a global state of ‘surveillance capitalism’, is beyond anyone’s guess. However, the knowledge potential offered by data-driven methods to the enterprise of scientific enquiry is something that cannot be ignored if we are to avoid an environmental catastrophe while providing all the material needs required for humans to live in a built environment that remains within planetary boundaries.

The aim of this Special Issue is to gather contributions from the wider spectrum of academic research related to the built environment, both across scales (from the individual building or component, to entire cites and regions) as well as systems (e.g., occupant’s and user’s behaviour, urban forms, and urban metabolism). The thematic range is left intentionally broad to facilitate and encourage the submission of works that are inherently multidisciplinary in nature, provided that any methodological component of the work relying on data-driven and machine learning methods is made strongly evident.

Dr. Bernardino D’Amico
Dr. Suha Jaradat
Dr. Masoud Sajjadian
Guest Editors

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 papers will be 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 1900 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

  • data-driven
  • machine learning
  • occupant behaviour
  • urban mining
  • low carbon
  • construction
  • buildings

Published Papers (1 paper)

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Research

Article
Circular Construction Process: Method for Developing a Selective, Low CO2eq Disassembly and Demolition Plan
Sustainability 2021, 13(16), 8815; https://doi.org/10.3390/su13168815 - 06 Aug 2021
Viewed by 522
Abstract
With the increasing focus on the construction sector (e.g., following the European Green Deal initiative) with the aim to reduce emissions by 55% by 2030 (compared to 1990 levels), as well as achieve full decarbonisation by 2050, the built environment remains a strategic [...] Read more.
With the increasing focus on the construction sector (e.g., following the European Green Deal initiative) with the aim to reduce emissions by 55% by 2030 (compared to 1990 levels), as well as achieve full decarbonisation by 2050, the built environment remains a strategic domain for the R&I (Research and Innovation) agenda. Indeed, the building and construction sector is the main contributor to greenhouse gas emissions (39% of global emissions as of 2018), highlighting the need to start a process of decarbonisation of this sector. The overall reduction in the environmental impact of building materials is achieved by establishing sustainable continuity between the end-of-life phase of the building and the production phase of individual building components. In particular, with reference to the end-of-life phase of the building (BS EN 15978: 2011), the Minimum Environmental Criteria foresee the preparation of a plan for the disassembly and selective demolition of the building, which allows the reuse or recycling of materials, building components and prefabricated elements used. According to the guidelines of a low-carbon construction design, which takes into account a circular economy, the following thesis deals with a methodological proposal to study “dry” construction systems (wood and steel). In particular, the study intends to reach the development of such an elaboration by carrying out an assessment of the environmental impact of a process of selective disassembly and demolition of steel building systems. The model is developed on the basis of a reading of the level of sustainability of emblematic case studies, appropriately identified, i.e., ‘quality’ architectures, built with ‘dry’ (steel) building systems. Full article
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