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Buildings
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Advance in BIM-Based Technologies for Sustainable Building Performance Predictions
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Advance in BIM-Based Technologies for Sustainable Building Performance Predictions

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 891

Special Issue Editors

Dr. Woon Seong Jeong

E-Mail Website
Guest Editor
Department of Architectural Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea
Interests: sustainable design and construction management system using building information model and energy simulation model; intelligent and sustainable project management using emerging information and communication technology; decision support system to support the collaborative design process; data modeling to support reliable project planning and control; object-oriented physical modeling for integrated building performance simulation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Sang-Guk Yum

E-Mail Website
Guest Editor
Department of Civil Engineering, Gangneung-Wonju National University, Gangneung 25457, Republic of Korea
Interests: deep learning and machine learning models to predict the return period of multi-natural disaster; fragility analysis of properties to estimate damages; 3D laser scanner and building information modeling-aided vulnerability analysis; deformation monitoring and structural health evaluation of infrastructure facilities using SAR and GIS data

Special Issue Information

Dear Colleagues,

Building performance predictions are among the critical factors in implementing sustainable building construction and project management successfully. Consequently, a feasible and reliable baseline should be obtained for successful construction project management through building performance predictions, especially in the design stage. The construction industry is moving to adopting innovative technologies such as Industry 4.0 technologies involving building information modeling (BIM), artificial intelligence, smart construction, simulation-based construction scheduling and virtual construction, and so on. In these circumstances, research on the state-of-the-art techniques used to predict reliable building performance predictions has been conducted; however, there are still emerging lines of inquiry capable of enhancing construction performance predictions.

The primary objective of this Special Issue is to publish original theoretical, methodological, and empirical research papers focusing on techniques to support building performance predictions. Specific topics include, but are not limited to, sustainable construction methods and technologies, digital twins in the construction industry, artificial intelligence in scheduling, and simulation-based methods for construction scheduling and virtual construction.

Dr. Woon Seong Jeong
Prof. Dr. Sang-Guk Yum
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 design
  • construction processes
  • construction management
  • building information modeling (BIM)
  • off-site construction
  • modular construction
  • design for manufacturing and assembly (DFMA)
  • sustainable construction methods and technologies
  • digital twins in the construction industry
  • artificial intelligence in scheduling
  • simulation-based methods for construction scheduling and virtual construction

Published Papers (2 papers)

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Research

17 pages, 3875 KiB  
Article
Construction Safety Risk Assessment of High-Pile Wharf: A Case Study in China
by Ziwen Wang and Yuan Yuan
Buildings 2024, 14(5), 1189; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings14051189 - 23 Apr 2024
Viewed by 269
Abstract
The complexity of the wharf components and the harshness of the offshore construction environment increase the safety risk of hazards, which has highlighted the importance and urgency of safety risk management in high-pile wharf constructions. This paper established a visualized digital construction safety [...] Read more.
The complexity of the wharf components and the harshness of the offshore construction environment increase the safety risk of hazards, which has highlighted the importance and urgency of safety risk management in high-pile wharf constructions. This paper established a visualized digital construction safety risk model for high-pile wharf based on a so-called FAHP method (the combination of fuzzy comprehensive evaluation (FCE) and analytic hierarchy process (AHP) methods). The construction safety risk indicators were constructed as the target layer, the principle layer and the scheme layer, and then the corresponding safety risk assessment algorithm was established. The physical, functional and safety risk assessment parameters of the component in the BIM model were employed to the safety risk assessment algorithm, and the risk assessment level of each sub-process was subsequently classified. The case study indicated that the high-pile wharf construction project included five elements in principle layer and 15 risk indicators in the scheme layer. Moreover, it was demonstrated that the sub-processes with the highest construction risk level were steel pipe pile sinking in wharf construction and steel pipe pile, steel sheath-immersed pile sinking and embedded rock pile construction in approaches to bridge construction with a risk level of III. In this way, the quantitative visualization of the construction safety risk was effectively realized, which facilitates the safety risk management of construction sites and timely warning and response to unexpected safety accidents. Full article
(This article belongs to the Special Issue Advance in BIM-Based Technologies for Sustainable Building Performance Predictions)
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17 pages, 10311 KiB  
Article
The Development of a Framework for the Automated Translation of Sketch-Based Data into BIM Models
by WoonSeong Jeong, ByungChan Kong, Manik Das Adhikari and Sang-Guk Yum
Buildings 2024, 14(4), 916; https://0-doi-org.brum.beds.ac.uk/10.3390/buildings14040916 - 27 Mar 2024
Viewed by 390
Abstract
At the foundational phase of architectural design, it is of the utmost importance to precisely capture and articulate the visions and requirements of stakeholders, including building owners. This critical step ensures that professionals, including architects, can effectively translate the initial concepts into actionable [...] Read more.
At the foundational phase of architectural design, it is of the utmost importance to precisely capture and articulate the visions and requirements of stakeholders, including building owners. This critical step ensures that professionals, including architects, can effectively translate the initial concepts into actionable designs. This research was directed towards developing a framework to facilitate the decision-making process by efficiently depicting the client’s intentions. This study demonstrates a framework that leverages deep learning to automate the creation of Building Information Modeling (BIM) models from sketched data. The framework’s methodology includes defining the necessary processes, system requirements, and data for system development, followed by the actual system implementation. It involves several key phases: (1) developing a process model to outline the framework’s operational procedures and data flows, (2) implementing the framework to translate sketched data into a BIM model through system and user interface development, and, finally, (3) validating the framework’s ability to precisely convert sketched data into BIM models. Our findings demonstrate the framework’s capacity to automatically interpret sketched lines as architectural components, thereby accurately creating BIM models. In the present study, the methodology and framework proposed enable clients to represent their understanding of spatial configuration through Building Information Modeling (BIM) models. This approach is anticipated to enhance the efficiency of communication with professionals such as architects. Full article
(This article belongs to the Special Issue Advance in BIM-Based Technologies for Sustainable Building Performance Predictions)
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