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AI-Aided Carbon Engineering in the AEC Industry
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AI-Aided Carbon Engineering in the AEC Industry

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Construction Management, and Computers & Digitization".

Deadline for manuscript submissions: closed (20 June 2022) | Viewed by 12374

Special Issue Editors

Prof. Dr. Yujie Lu

E-Mail Website
Guest Editor
Department of Structural Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, China
Interests: computer vision and construction safety management; intelligent construction and operation management; engineering management and project management; sustainable and green ecological towns; low-carbon and energy-efficient buildings
Dr. Peixian Li

E-Mail Website
Guest Editor
College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China
Interests: sustainable built environment; low-carbon and energy-efficient buildings; indoor environmental quality; smart building operation; Internet of Things (IoT); data analysis
Dr. Qiuchen Lu

E-Mail Website
Guest Editor
The Bartlett School of Sustainable Construction, University College London, London WC1E 6BT, UK
Interests: digital twins; image processing; building information modelling; built asset management
Special Issues, Collections and Topics in MDPI journals
Dr. Haibo Feng

E-Mail Website
Guest Editor
Department of Mechanical and Construction Engineering, Northumbria University, Newcastle, UK
Interests: building performance; life cycle assessment (LCA); building information modeling (BIM); digital technologies; sustainable construction; smart buildings with IoTs
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In view of the increasingly frequent extreme climate events all over the world, global warming has become a well-acknowledged, imperative issue that every country is fighting against. However, as one of the main sources of energy consumption, the building sector accounts for almost 40% of global carbon emissions, and the CO2 emissions of the building sector hit a record high in 2019 despite years of energy-saving efforts in the building industry. It is extremely challenging to reduce building-related carbon emissions to achieve governments’ goals of carbon neutrality in 2050 or even 2060. Hence, the complex issues associated with how to achieve carbon peak and carbon neutrality in the building and construction industry require a major research effort involving all stakeholders.

Advanced technologies such as artificial intelligence (AI) can provide a new and perhaps more efficient way to solve carbon-related problems in the building sector, but exploration in this field is still at an early stage. This Special Issue is therefore intended to encourage researchers and practitioners to apply advanced technologies to solve carbon-related problems. Advanced technologies include, but are not limited to, computer vision, machine learning, deep learning, Internet of Things (IoT), Building Information Modeling (BIM), digital twin, AR/VR, etc. Carbon-related problems include, but are not limited to, the assessment/prediction of carbon reduction potential, carbon peak/neutrality pathway design, carbon reduction techniques, etc. The research objects may vary from products and components to buildings and cities, ranging from the planning and design phase to the operation phase.

Prof. Dr. Yujie Lu
Dr. Peixian Li
Dr. Qiuchen Lu
Dr. Haibo Feng
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 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. Buildings is an international peer-reviewed open access monthly 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 2600 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

  • artificial intelligence
  • BIM
  • carbon neutrality
  • computer vision
  • circular economy
  • digital twin
  • energy efficiency
  • green building
  • Internet of Things
  • low-carbon
  • smart construction

Published Papers (3 papers)

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21 pages, 3813 KiB  
Article
A gbXML Reconstruction Workflow and Tool Development to Improve the Geometric Interoperability between BIM and BEM
by Yikun Yang, Yiqun Pan, Fei Zeng, Ziran Lin and Chenyu Li
Buildings 2022, 12(2), 221; https://doi.org/10.3390/buildings12020221 - 16 Feb 2022
Cited by 11 | Viewed by 5084
Abstract
The BIM-based building energy simulation plays an important role in sustainable design on the track of achieving the net-zero carbon building stock by 2050. However, the issues on BIM-BEM interoperability make the design process inefficient and less automatic. The insufficient semantic information may [...] Read more.
The BIM-based building energy simulation plays an important role in sustainable design on the track of achieving the net-zero carbon building stock by 2050. However, the issues on BIM-BEM interoperability make the design process inefficient and less automatic. The insufficient semantic information may lead to results inaccurate while the error-prone geometry will terminate the simulation engine. Defective models and authoring tools lagging behind the standard often cause failures in creating a clean geometry that is acceptable to the simulation engine. This project aims to develop a workflow that helps with the documentation of a lightweight geometry in gbXML format. The implemented workflow bypasses the modeling inaccuracies and irrelevant details by reconstructing the model based on extrusions on patched floor plans. Compared with other gbXML files exported by BIM authoring tools, the resulting gbXML is more lightweight with airtight space boundaries. The gbXML has been further tested against EnergyPlus to demonstrate its capability in aiding a seamless geometry exchange between BIM and BEM. Full article
(This article belongs to the Special Issue AI-Aided Carbon Engineering in the AEC Industry)
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19 pages, 697 KiB  
Review
Digital Tools for Revealing and Reducing Carbon Footprint in Infrastructure, Building, and City Scopes
by Jiayi Yan, Qiuchen Lu, Junqing Tang, Long Chen, Jingke Hong and Tim Broyd
Buildings 2022, 12(8), 1097; https://doi.org/10.3390/buildings12081097 - 26 Jul 2022
Cited by 8 | Viewed by 4181
Abstract
The climate change issue has been striking and bringing pressure on all countries and industries. The responsibility of the Architecture, Engineering, Construction and Facility Management (AEC/FM) industry is heavy because it accounts for over one-third of global energy use and greenhouse gas emissions. [...] Read more.
The climate change issue has been striking and bringing pressure on all countries and industries. The responsibility of the Architecture, Engineering, Construction and Facility Management (AEC/FM) industry is heavy because it accounts for over one-third of global energy use and greenhouse gas emissions. At the same time, the development of digital technology brings the opportunity to mitigate environmental issues. Therefore, this study intended to examine the state-of-the-art of digital development and transformation in the AEC/FM industry by collecting and reviewing the developed digital carbon footprint analysis tools in infrastructure, building, and city scopes. Specifically, this study (1) generated a review methodology for carbon footprint analysis results; (2) demonstrated the review results from the infrastructure, building, and city scopes, analysed and compared the results crossing the scopes from four aspects: carbon footprint analysis strategy, standards and protocols, rating systems, and general development level of digital tools; and (3) discussed the potential directions in the industry to address the environmental issues. This study indicated that the digitalisation level regarding carbon-related areas is still at an early stage, and efforts should be taken both academically and practically to drive the digital development confronting the harsh climate change issue. Full article
(This article belongs to the Special Issue AI-Aided Carbon Engineering in the AEC Industry)
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19 pages, 2843 KiB  
Article
Daily Carbon Assessment Framework: Towards Near Real-Time Building Carbon Emission Benchmarking for Operative and Design Insights
by Mingyu Zhu and Philip James
Buildings 2022, 12(8), 1129; https://doi.org/10.3390/buildings12081129 - 29 Jul 2022
Cited by 3 | Viewed by 1541
Abstract
The energy consumption and its related carbon emission of non-domestic complex buildings in an urban context are complicated due to their wide variety of functions and services. A detailed assessment of the carbon emission of such buildings can contribute to decision making for [...] Read more.
The energy consumption and its related carbon emission of non-domestic complex buildings in an urban context are complicated due to their wide variety of functions and services. A detailed assessment of the carbon emission of such buildings can contribute to decision making for in-operation building management and schematic designs of future proposals. Concurrently, advances in smart meter data analytics and sensor-enabled operational data streams offer the opportunity to investigate this problem at a finer temporal resolution. This research developed a daily carbon emission benchmarking system of a mixed-use building in a UK university. The research period was set at an annual range from 1 January 2019 to 31 December 2019 and was segmented by strategic periods in line with the operation schedule of the building. The daily benchmark revealed the fluctuation of the building’s energy consumption and associated carbon emissions. Based on this, a digital twin framework was developed to identify the possible time periods when the building is less carbon efficient and potential building characters that can lead to increased carbon emission in the operational stage compared with what originally expected at the design stage. We discuss how these insights can offer actionable knowledge for user groups such as asset managers and architects. Full article
(This article belongs to the Special Issue AI-Aided Carbon Engineering in the AEC Industry)
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