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BIM and Smart Technologies in Building Design, Construction, and Lifecycle...
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BIM and Smart Technologies in Building Design, Construction, and Lifecycle Management

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

Deadline for manuscript submissions: 26 April 2026 | Viewed by 1104

Special Issue Editors

Dr. Mingkai Li

E-Mail Website
Guest Editor
Department of the Built Environment, National University of Singapore, 4 Architecture Drive, SDE1, Singapore 117566, Singapore
Interests: scan-to-BIM; computational design; AI; digital twins; construction informatics
Dr. Xingyu Tao

E-Mail Website
Guest Editor
Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong
Interests: information security; construction management; BIM
Dr. Boyu Wang

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong
Interests: scan-to-BIM; construction digital twin
Dr. Hao Liu

E-Mail Website
Guest Editor
Faculty of Architecture, The University of Hong Kong, Hong Kong
Interests: BIM; knowledge graph; machine learning; large language model; blockchain; digital design; construction informatics

Special Issue Information

Dear Colleagues,

The rapid advancement of digitalization is reshaping the architecture, engineering, and construction (AEC) industry. Building Information Modeling (BIM), together with a wide range of smart technologies, is enabling more efficient, collaborative, and sustainable practices across the lifecycle of buildings, from design and construction to operation and facility management. These innovations not only enhance productivity and quality, but also contribute to sustainability, resilience, and the creation of smarter built environments.

This Special Issue provides a platform for cutting-edge research and practical applications that explore how BIM and smart technologies are transforming building processes and management throughout building lifecycles. We welcome contributions presenting new methods, frameworks, tools, and case studies that integrate digital technologies with design, construction, and lifecycle management to improve efficiency, coordination, decision-making, and innovation. Topics of interest include BIM-enabled planning and construction, digital collaboration, data-driven decision-making, and the automation and optimization of building processes, as well as innovative approaches for sustainable, resilient, and smart buildings.

Dr. Mingkai Li
Dr. Xingyu Tao
Dr. Boyu Wang
Dr. Hao Liu
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 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

  • building information model (BIM)
  • large language models (LLMs)
  • digital twin
  • AR/VR
  • AI and deep learning
  • 3D scanning
  • IoT
  • blockchain
  • IT in construction
  • construction robotics

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

Published Papers (2 papers)

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Research

19 pages, 3336 KB  
Article
Deep Ensemble Learning with CNNs, DPCNNs, and LSTMs for Construction Cost Classification
by Huajian Sun, Lin Qin and Guoqian Ren
Buildings 2025, 15(23), 4239; https://doi.org/10.3390/buildings15234239 - 24 Nov 2025
Viewed by 227
Abstract
With the advancement of cost informationization in construction, the automatic classification of building project costs has become a key step to improving management efficiency. Traditional rule-based or manual methods are insufficient to handle increasingly complex engineering texts. To address this issue, this study [...] Read more.
With the advancement of cost informationization in construction, the automatic classification of building project costs has become a key step to improving management efficiency. Traditional rule-based or manual methods are insufficient to handle increasingly complex engineering texts. To address this issue, this study proposes a deep learning framework that integrates Convolutional Neural Networks (CNNs), Deep Pyramid Convolutional Neural Networks (DPCNNs), and Long Short-Term Memory networks (LSTMs). A standardized dataset of 12,838 records was constructed based on expert annotation. Six baseline models were trained under both character-level and word-level tokenization, and their predictions were combined through a majority voting strategy. Experimental results show that the ensemble model achieved an accuracy of 97.59% on the test set, outperforming single models, with character-level tokenization performing better. The findings confirm the effectiveness of model ensembling in enhancing classification accuracy and robustness, providing a feasible solution for intelligent text classification in cost management, and offering practical reference for digitalization and intelligent applications. Full article
(This article belongs to the Special Issue BIM and Smart Technologies in Building Design, Construction, and Lifecycle Management)
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25 pages, 7154 KB  
Article
Performance Optimization of Expanded Polystyrene Lightweight Concrete Using a Multi-Objective Physically Interpretable Algorithm with Random Forest
by Sen Li, Di Hu, Fei Yu, Qiang Jin and Zihua Li
Buildings 2025, 15(21), 3944; https://doi.org/10.3390/buildings15213944 - 1 Nov 2025
Viewed by 559
Abstract
Expanded polystyrene (EPS) concrete has broad application potential in energy-efficient buildings due to its low density and excellent thermal insulation performance. However, a significant nonlinear trade-off exists between its compressive strength and thermal conductivity. Existing studies are mainly based on empirical mix design [...] Read more.
Expanded polystyrene (EPS) concrete has broad application potential in energy-efficient buildings due to its low density and excellent thermal insulation performance. However, a significant nonlinear trade-off exists between its compressive strength and thermal conductivity. Existing studies are mainly based on empirical mix design or single-objective optimization, and the employed modeling methods generally lack interpretability. To address this challenge, this study proposes a multi-objective optimization model (MOPIA-RA) based on physics-informed constraints and an intelligent evolutionary algorithm, aiming to solve the nonlinear contradiction among compressive strength, thermal conductivity, and production cost encountered in practical engineering. A comprehensive dataset covering different cementitious materials, EPS contents, and particle sizes was established based on experimental data, and a surrogate model (PIA-RA) was developed using this dataset. Finally, the Shapley additive explanation (SHAP) method was used to quantitatively evaluate the effects of key materials on compressive strength and thermal conductivity. The results show that the proposed PIA-RA model achieved coefficients of determination (R2) of 0.95 and 0.98 for predicting compressive strength and thermal conductivity, respectively; EPS particle size was the main factor affecting performance, with a contribution rate of 69%, while EPS content also played an important regulatory role, with a contribution rate of 29%. Based on the constructed MOPIA-RA model, it is possible to effectively resolve the multi-objective trade-offs among strength, thermal performance, and cost in EPS concrete and achieve precise mix design. The proposed MOPIA-RA model not only realizes multi-objective optimization among compressive strength, thermal performance, and cost, but also establishes a physics-informed and interpretable methodology for concrete material design. This model provides a scientific basis for the mix-design optimization of EPS concrete. Full article
(This article belongs to the Special Issue BIM and Smart Technologies in Building Design, Construction, and Lifecycle Management)
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