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Selected Papers from the 20th International Conference on Computing in...
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Selected Papers from the 20th International Conference on Computing in Civil and Building Engineering (ICCCBE 2024)

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

Deadline for manuscript submissions: 31 May 2026 | Viewed by 1825

Special Issue Editor

Prof. Dr. Adel Francis

E-Mail Website
Guest Editor
Construction Engineering Department, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada
Interests: graphical modelling; construction optimization; construction management; visualization techniques; building information modelling (BIM); civil information modelling (CIM)

Special Issue Information

Dear Colleagues,

The 20th International Conference on Computing in Civil and Building Engineering (ICCCBE 2024), held in Montreal, highlighted the crucial role of computational methods in advancing construction operation planning. The integration of visualization, optimization, and graphical modelling has emerged as a cornerstone for improving efficiency, safety, and sustainability in different construction processes. These technologies enable stakeholders to simulate complex scenarios, optimize resource allocation, and enhance decision-making through intuitive graphical representations.

This Special Issue, titled "Selected Papers from the 20th International Conference on Computing in Civil and Building Engineering (ICCCBE 2024)", aims to consolidate cutting-edge research presented at the ICCCBE 2024. It will focus on innovative approaches to visualizing construction processes, optimizing workflows, and leveraging graphical models to address challenges in planning and execution. Submissions may include theoretical or applied research, case studies, or reviews that explore the intersection of digital tools and construction management.

Topics covered in this Special Issue include, but are not limited to, the following:

  • Visualization techniques for digital twins in construction planning.
  • Optimization algorithms for resource allocation and scheduling.
  • Graphical modelling for construction site monitoring and control.
  • The application of virtual and augmented reality in construction operation rehearsals.
  • Machine learning and AI-driven tools for predictive planning and visualization.
  • The integration of BIM and CIM for enhanced construction workflow management.

We invite contributions that advance the state of the art in these areas, drawing from the diverse themes of the ICCCBE 2024, which included building information modelling (BIM), digital twins, visualization, civil information modelling (CIM), and advancing construction planning and scheduling. Research articles, comprehensive reviews, and case studies are welcome.

Prof. Dr. Adel Francis
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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 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 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

  • visualization techniques
  • construction optimization
  • graphical modelling
  • digital twins
  • building information modelling (BIM)
  • civil information modelling (CIM)
  • virtual reality in construction
  • construction planning and scheduling
  • machine learning in construction
  • resource allocation

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

27 pages, 4964 KB  
Article
A Seven-Step BIM Collaboration Model for AEC Education: Bridging Disciplinary Silos Through BIM Maturity Level 3 Implementation
by Jean-Pierre Basson and John Smallwood
Buildings 2026, 16(7), 1282; https://doi.org/10.3390/buildings16071282 - 24 Mar 2026
Viewed by 267
Abstract
The growing implementation of Building Information Modelling (BIM) within the architecture, engineering, and construction (AEC) industries has placed increased pressure on higher education institutions to prepare graduates for interdisciplinary digital collaboration. In many emerging higher education environments, such as South Africa, structured pedagogical [...] Read more.
The growing implementation of Building Information Modelling (BIM) within the architecture, engineering, and construction (AEC) industries has placed increased pressure on higher education institutions to prepare graduates for interdisciplinary digital collaboration. In many emerging higher education environments, such as South Africa, structured pedagogical frameworks for BIM Level 3 collaboration are less well established. This paper addresses this gap by introducing and evaluating a seven-step BIM collaboration framework in an interdisciplinary final year undergraduate project. A comparative cohort case study design was adopted, analysing two cohorts: the 2022 cohort operating within a traditional siloed design model, and the 2023 cohort applying the proposed framework. Grounded in Habermas’s theory of communicative action, student design projects and self-reflection narratives from both the traditional siloed design process and the BIM-enabled framework were analysed deductively according to communication frequency, content, and quality as key categories. Communication quality was evaluated through intrinsic, contextual, representational, and accessibility information dimensions. Findings show that the BIM group had higher levels of established collaboration, better-quality contextually available information, more accessible structured data, and more effective communication. The findings indicate that structured BIM-based collaboration enhances a transformation from mere data exchange to constructive participation and comprehensive information development among students. Rather than functioning solely as a technical tool, BIM served as a structured communication environment that supported critical engagement and interdisciplinary workflows. This study offers a transferable pedagogical model for interdisciplinary BIM education and provides evidence supporting communication-oriented approaches to digital collaboration within built environment curricula. Full article
(This article belongs to the Special Issue Selected Papers from the 20th International Conference on Computing in Civil and Building Engineering (ICCCBE 2024))
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17 pages, 1120 KB  
Article
Professional Perceptions of Integrated Project Delivery in Brazil: Conceptual Dissonance Between Governance Innovation and Technological Adoption
by Paula Heloisa da Silva, Nathalia de Paula, Érik Poirier, Sergio Scheer and Silvio Burrattino Melhado
Buildings 2026, 16(4), 881; https://doi.org/10.3390/buildings16040881 - 22 Feb 2026
Viewed by 336
Abstract
Integrated Project Delivery (IPD) is a collaborative approach proposed to address fragmentation and performance issues in the AEC industry, yet its adoption remains limited. This study examines Brazilian professionals’ perceptions of IPD and identifies the barriers, challenges, and enablers associated with it. Drawing [...] Read more.
Integrated Project Delivery (IPD) is a collaborative approach proposed to address fragmentation and performance issues in the AEC industry, yet its adoption remains limited. This study examines Brazilian professionals’ perceptions of IPD and identifies the barriers, challenges, and enablers associated with it. Drawing on a survey and a systematic review, the findings indicate that although benefits such as improved collaboration are recognized, concerns about contractual feasibility, shared risks, and organizational readiness persist. Technological aspects are seen as more familiar than contractual or managerial changes, diverging from international empirical evidence, which typically identifies contractual and governance-related challenges as the primary barriers to IPD adoption. The study reveals both shared global challenges and unique Brazilian issues, particularly regarding implementation complexity. Adoption depends more on organizational and contractual preparedness than on technology, informing strategies for introducing collaborative models in emerging markets. Full article
(This article belongs to the Special Issue Selected Papers from the 20th International Conference on Computing in Civil and Building Engineering (ICCCBE 2024))
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Graphical abstract

23 pages, 3388 KB  
Article
Explainable Machine Learning for Hospital Heating Plants: Feature-Driven Modeling and Analysis
by Marjan Fatehijananloo and J. J. McArthur
Buildings 2026, 16(2), 397; https://doi.org/10.3390/buildings16020397 - 18 Jan 2026
Viewed by 465
Abstract
Hospitals are among the most energy-intensive buildings, yet their heating systems often operate below optimal efficiency due to outdated controls and limited sensing. Existing facilities often provide only a few accessible measurement points, many of which are locked within proprietary master controllers and [...] Read more.
Hospitals are among the most energy-intensive buildings, yet their heating systems often operate below optimal efficiency due to outdated controls and limited sensing. Existing facilities often provide only a few accessible measurement points, many of which are locked within proprietary master controllers and not integrated into the Building Automation System (BAS). To address these limitations, this study proposes a data-driven feature selection approach that supports the development of gray-box emulators for complex, real-world central heating plants. A year of operational and weather data from a large hospital was used to train multiple machine learning models to predict the heating demand and return water temperature of a hospital heating plant system. The model’s performance was evaluated under reduced-sensor conditions by intentionally removing unpredictable values such as the VFD speed and flow rate. XGBoost achieved the highest accuracy with full sensor data and maintained a strong performance when critical sensors were omitted. An explainability analysis using Shapley Additive Explanations (SHAP) is applied to interpret the models, revealing that outdoor temperature and time of day (as an occupancy proxy) are the dominant predictors of boiler load. The results demonstrate that, even under sparse instrumentation, an AI-driven digital twin of the heating plant can reliably capture system dynamics. Full article
(This article belongs to the Special Issue Selected Papers from the 20th International Conference on Computing in Civil and Building Engineering (ICCCBE 2024))
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