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The Integration of Building Information Modeling (BIM) Technology and Artificial...
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The Integration of Building Information Modeling (BIM) Technology and Artificial Intelligence (AI) in Smart Buildings

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

Deadline for manuscript submissions: 30 June 2024 | Viewed by 819

Special Issue Editors

Dr. Guoqian Ren

E-Mail Website
Guest Editor
School of Architecture and Urban Planning, Tongji University, Shanghai, China
Interests: building information modelling; digital twin; open BIM; artificial intelligence; ontology
Special Issues, Collections and Topics in MDPI journals
Dr. Yang Zou

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of Auckland, Auckland, New Zealand
Interests: smart infrastructure; building information modelling; digital twin
Dr. Xiaoran Huang

E-Mail Website
Guest Editor
School of Architecture and Art, North China University of Technology, Beijing, China
Interests: digital design; behaviour simulation; artificial intelligence; virtual reality

Special Issue Information

Dear Colleagues,

This Special Issue delves into the innovative applications of building information modeling (BIM) technology within smart buildings, exploring its synergistic integration with digital twins and cutting-edge artificial intelligence algorithms. We aim to provide in-depth analysis of how these technologies intersect with crucial domain, such as knowledge management, information exchange, human behavior simulation, and advanced digital design methods. 

As a groundbreaking integrated methodology, BIM offers a holistic digital portrayal of architectural environments, revolutionizing the efficiency and precision of architectural design, construction, and operational management. These BIM models are capable of simulating and analyzing the real-time performance of buildings, thus offering invaluable insights. The integration of artificial intelligence further elevates this process, streamlining the handling of building data and decision making. This not only boosts managerial efficiency, but also significantly enhances the overall user experience. 

This Special Issue places a spotlight on the practical applications and implications of the BIM methodology in conjunction with emerging technologies in the context of digital building. It aims to provide a comprehensive understanding of how these technological advancements are reshaping the future of building design and management.

Dr. Guoqian Ren
Dr. Yang Zou
Dr. Xiaoran Huang
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

  • building information modelling
  • digital twin
  • artificial intelligence
  • building simulation
  • information exchange
  • knowledge management
  • virtual reality

Published Papers (1 paper)

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Research

22 pages, 15580 KiB  
Article
Factors Influencing the Usage Frequency of Community Elderly Care Facilities and Their Functional Spaces: A Multilevel Based Study
by Fang Wen, Yan Zhang, Pengcheng Du, Ziqi Zhang, Bo Zhang and Yuyang Zhang
Buildings 2024, 14(6), 1827; https://doi.org/10.3390/buildings14061827 - 15 Jun 2024
Viewed by 393
Abstract
The construction of community elderly care facilities (CECF) is pivotal for promoting healthy aging and “aging in place” for older people. This study focuses on the low utilization rates of community elderly care facilities in the Dongcheng and Xicheng Districts, core areas of [...] Read more.
The construction of community elderly care facilities (CECF) is pivotal for promoting healthy aging and “aging in place” for older people. This study focuses on the low utilization rates of community elderly care facilities in the Dongcheng and Xicheng Districts, core areas of Beijing. The explainable machine learning method is used to analyze data across three dimensions: the elderly’s individual attributes, characteristics of the community elderly care station (CECS), and features of the built environment around CECS and subdistrict, to identify the important factors that influence the usage frequency of overall CECS and its different functional spaces, and also the correlation between factors and usage frequency of CECS. It shows that the most important factors are the features of CSCF, including the degree of space acceptance and satisfaction with services provided, which influence the usage frequency of nine functional spaces (R2 ≥ 0.68) and overall (R2 = 0.56). In addition, older people’s individual factors, such as age and physical condition, significantly influence the usage of specific spaces such as rehabilitation therapy rooms and assistive bathing rooms. The influence of built environment characteristics is relatively low, with factors such as the density of bus stations and housing prices within the subdistrict and the mean distance from CECF to the nearest subway stations being more important. These findings provide a reference for the construction of indoor environments, management of service quality, and optimal site selection for future community elderly care facilities. Full article
(This article belongs to the Special Issue The Integration of Building Information Modeling (BIM) Technology and Artificial Intelligence (AI) in Smart Buildings)
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