Artificial Intelligence Applications in Sustainable Built Environments

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 (31 March 2024) | Viewed by 1667

Special Issue Editors


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Guest Editor
Architecture and Built Environment, University of Northumbria, Newcastle upon Tyne, UK
Interests: indoor environmental quality; sustainability; digital construction

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Guest Editor
School of Architecture, Technology and Engineering, University of Brighton, Brighton, UK
Interests: offsite construction; sustainable built environment

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Guest Editor
Construction Management, School of Planning, Design and Construction and Civil Engineering, Michigan State University, East Lansing, MI 48823, USA
Interests: construction project management; sustainable construction

Special Issue Information

Dear Colleagues,

Integrating Artificial Intelligence (AI) and Internet of Things (IoT) technologies in the built environment can revolutionize the design, construction, and operation of buildings. With the rise of AI applications in various industries, it is becoming increasingly important to explore how these technologies can be used to create sustainable built environments that are energy-efficient, resource-efficient, and resilient. Climate change has brought an urgency to the need for sustainable built environments. The integration of AI and IoT technologies can play a crucial role in reducing carbon emissions and achieving a more sustainable future. In this Special Issue, we aim to explore the ways in which AI and IoT can be used to create sustainable built environments that are energy-efficient, resource-efficient, and resilient, and thus make an impact in tackling the global challenge of climate change and promoting social sustainability. Submissions should be focused on the resolution of a range of issues in different phases of a building's lifetime, with outcomes contributing to the sustainability of the built environment.

Potential areas of interest include, but are not limited to:

Design phase
  • AI-based building design optimization: The use of AI in designs for energy efficiency and net zero emissions, indoor environmental quality, building envelopes, and material usage.
  • AI applications to improve design collaboration and integration in building information modelling processes and technology.
  • AI-based building lifecycle management: The use of AI and machine learning algorithms for building lifecycle management, including retrofitting, renovation, and decommissioning.
Construction phase
  • AI-based construction planning and scheduling: The use of AI and machine learning algorithms for construction planning, scheduling, and resource allocation.
  • AI-based construction quality control and safety: The use of AI and machine learning algorithms for construction quality control and safety management.
  • AI-based construction site monitoring and control: The use of IoT devices, robotics, and AI algorithms for the real-time monitoring and control of construction site operations.
Operation phase
  • AI-based building energy management: The use of AI and machine learning algorithms for energy management, demand response, and energy forecasting in buildings.
  • AI-based building occupancy and behaviour: The use of AI and machine learning algorithms for understanding and predicting building occupancy and behaviour patterns.
  • IoT-enabled building performance monitoring and control: The use of IoT devices and sensors to monitor and control building systems for energy efficiency, lighting, HVAC, and other building functions.
  • AI-based building safety and security: The use of AI and machine learning algorithms for fire detection, security surveillance, and emergency responses in buildings.

We welcome the submission of original research articles, review articles, case studies, and short communications that address the areas mentioned above or other relevant topics. All submissions will be peer-reviewed by experts in the field, and papers addressing other related topics will also be considered.

We look forward to receiving your submissions and contributing to advancing sustainable built environments through integrating AI and IoT technologies.

Dr. Amit Kaushik
Prof. Dr. Mohammed Arif
Prof. Dr. M. G. Matt Madan Syal
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
  • Internet of Things
  • sustainable built environment
  • net zero
  • sustainable buildings
  • net zero buildings
  • climate change

Published Papers (1 paper)

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Research

24 pages, 11516 KiB  
Article
Vision-Based Guiding System for Autonomous Robotic Corner Cleaning of Window Frames
by Tzu-Jan Tung, Mohamed Al-Hussein and Pablo Martinez
Buildings 2023, 13(12), 2990; https://doi.org/10.3390/buildings13122990 - 30 Nov 2023
Cited by 1 | Viewed by 873
Abstract
Corner cleaning is the most important manufacturing step of window framing to ensure aesthetic quality. After the welding process, the current methods to clean the welding seams lack quality control and adaptability. This increases rework, cost, and the waste produced in manufacturing and [...] Read more.
Corner cleaning is the most important manufacturing step of window framing to ensure aesthetic quality. After the welding process, the current methods to clean the welding seams lack quality control and adaptability. This increases rework, cost, and the waste produced in manufacturing and is largely due to the use of CNC cutting machines, as well as the reliance on manual inspection and weld seam cleaning. Dealing with manufacturing imperfections becomes a challenging task, as CNC machines rely on predetermined cleaning paths and frame information. To tackle such challenges using Industry 4.0 approaches and automation technology, such as robots and sensors, in this paper, a novel intelligent system is proposed to increase the process capacity to adapt to variability in weld cleaning conditions while ensuring quality through a combined approach of robot arms and machine vision that replaces the existing manual-based methods. Using edge detection to identify the window position and its orientation, artificial intelligence image processing techniques (Mask R-CNN model) are used to detect the window weld seam and to guide the robot manipulator in its cleaning process. The framework is divided into several modules, beginning with the estimation of a rough position for the purpose of guiding the robot toward the window target, followed by an image processing and detection module used in conjunction with instance segmentation techniques to segment the target area of the weld seam, and, finally, the generation of cleaning paths for further robot manipulation. The proposed robotic system is validated two-fold: first, in a simulated environment and then, in a real-world scenario, with the results obtained demonstrating the effectiveness and adaptability of the proposed system. The evaluation of the proposed framework shows that the trained Mask R-CNN can locate and quantify weld seams with 95% mean average precision (less than 1 cm). Full article
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