Submit to Special Issue Submit Abstract to Special Issue Review for Buildings Propose a Special Issue

Journal Menu

Journal Browser

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

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

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 October 2026 | Viewed by 6541

Share This Special Issue

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

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 (8 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

45 pages, 8638 KB

Open AccessArticle

Advancing Sustainable Building Construction Through Immersive Digital Technologies: Towards Digital Transformation in the Nigerian Context

by Oluwagbemiga Paul Agboola and Abdulaziz Mislat Alsharif

Buildings 2026, 16(7), 1441; https://doi.org/10.3390/buildings16071441 - 5 Apr 2026

Cited by 1 | Viewed by 527

Abstract

Rapid urbanisation and resource constraints necessitate the adoption of sustainable construction practices in developing economies, yet empirical evidence on the effectiveness of digital technologies remains limited. This study develops and validates an integrated framework to evaluate the contribution of immersive digital technologies to sustainable construction performance in Nigeria. Data were collected through a structured questionnaire survey of 353 construction professionals across Lagos, Abuja, and Port Harcourt. Key constructs—immersive technologies (Building Information Modelling, Virtual Reality, and Augmented Reality), sustainability outcomes, and adoption barriers were measured using multi-item Likert-scale indicators adapted from prior studies. The data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM), which was selected for its suitability in handling complex models and for prediction-oriented analysis. The measurement model demonstrated satisfactory reliability and validity, with average variance extracted (AVE) and composite reliability (CR) values ranging from 0.62 to 0.88. The structural model explained a substantial proportion of variance in sustainable construction outcomes (R² = 0.89), with all hypothesised relationships statistically significant (p < 0.01). Immersive technologies showed strong positive effects (β = 0.63–0.82), while barriers such as high costs, limited technical expertise, and inadequate infrastructure constrained adoption. This study’s findings indicate the significant potential of immersive technologies to support sustainable construction in developing economies. Full article

(This article belongs to the Special Issue BIM and Smart Technologies in Building Design, Construction, and Lifecycle Management)

► Show Figures

Figure 1

22 pages, 10652 KB

Open AccessArticle

Digital Image-Based Rapid Determination and Analysis of Grain Size Distribution of Concrete Aggregates and Rock Fills

by Muhammet Karabulut, Tugba Palabas and Dragan Marinkovic

Buildings 2026, 16(5), 912; https://doi.org/10.3390/buildings16050912 - 25 Feb 2026

Cited by 1 | Viewed by 575

Abstract

Digital image-based determination of aggregate and rock gradation has been only limitedly addressed in the existing literature despite its considerable potential to transform conventional material characterization practices in civil engineering. Rapid and accurate estimation of aggregate and rock particle size distributions using advanced image-based analytical methods can significantly improve efficiency, consistency, and scalability in design, construction, and quality control processes, particularly in large-scale structural and geotechnical engineering projects where traditional sieve analysis is time-consuming, labor-intensive, and difficult to apply under field conditions. In this study, an image-based methodology is proposed to rapidly detect aggregate particles and determine their size-based proportions within a pile by employing image enhancement, segmentation, and boundary detection algorithms. The results obtained from digital image processing are comparatively evaluated against experimental sieve analysis data, demonstrating a strong correlation between the two approaches. Low RMSE values achieved for larger aggregate sizes, such as 25.4 mm and 19 mm, indicate high detection accuracy, while the relatively higher yet acceptable RMSE values obtained for smaller particles, including 12.7 mm and 9.5 mm, confirm that the method maintains practical sensitivity across different size ranges. By analyzing samples collected from various aggregate and rock piles, the study further demonstrates the originality, robustness, and effectiveness of the proposed approach in evaluating heterogeneous material groups. Overall, the findings highlight that digital image-based determination offers a fast, reproducible, and non-destructive alternative to traditional sieve analysis, making it particularly valuable for reinforced concrete aggregate assessment and port fill rock characterization in large-scale structural and geotechnical engineering applications. Full article

(This article belongs to the Special Issue BIM and Smart Technologies in Building Design, Construction, and Lifecycle Management)

► Show Figures

Figure 1

24 pages, 16653 KB

Open AccessArticle

Evaluation of Compressive Strength of Expanded Polystyrene Concrete Based on Broad Learning System

by Zhenhao Zhou, Wanfen Cao, Qiang Jin and Sen Li

Buildings 2026, 16(4), 795; https://doi.org/10.3390/buildings16040795 - 14 Feb 2026

Viewed by 450

Abstract

Expanded polystyrene (EPS) concrete, with excellent properties such as light weight, thermal insulation, and soundproofing, is widely applied in construction engineering. However, its complex heterogeneous internal structure makes it difficult to quickly and accurately assess compressive strength. Existing testing methods struggle to meet the real-time demands of on-site quality control in terms of both operational efficiency and accuracy. To address this, the present study proposes a method for predicting the compressive strength of EPS concrete based on image processing and Deep Convolutional Neural Networks (DCNN). By constructing a dataset consisting of 5600 preprocessed concrete slice images and addressing the issue of parameter redundancy in fully connected layers, the Broad Learning System (BLS) was employed to reconstruct and optimize the network architecture, thereby improving computational efficiency and enhancing prediction accuracy. The experimental results indicate that after introducing the BLS and related training optimization mechanisms, the training time was reduced by approximately 15%. Among all models, the BLS-Xception model performed the best, requiring only 1.9 s per training image. The coefficient of determination (R²) on the test set reached 0.95, representing an 18.7% improvement over traditional models. The study also indicates that the appropriate incorporation of coal ash, silica fume, and mineral powder significantly enhances the compressive strength of EPS concrete, with smaller EPS particles contributing more substantially to strength improvement. The model demonstrates excellent accuracy and reliability in predictions, providing an effective method for the rapid, non-destructive evaluation of the compressive strength of EPS concrete on construction sites. Full article

(This article belongs to the Special Issue BIM and Smart Technologies in Building Design, Construction, and Lifecycle Management)

► Show Figures

Figure 1

23 pages, 3879 KB

Open AccessArticle

Simultaneous Digital Twin: Chaining Climbing-Robot, Defect Segmentation, and Model Updating for Building Facade Inspection

by Changhao Song, Chang Lu, Yilong Shi, Aili He, Jiarui Lin and Zhiliang Ma

Buildings 2026, 16(3), 646; https://doi.org/10.3390/buildings16030646 - 4 Feb 2026

Cited by 1 | Viewed by 755

Abstract

The rapid deterioration of building facades presents substantial safety hazards in urban environments, necessitating advanced, automated inspection solutions. While computer vision (CV) and deep learning (DL) techniques have shown promise for defect analysis, critical gaps remain in achieving real-time, quantitative, and generalizable damage assessment suitable for robotic deployment. Current methods often lack precise metric quantification, struggle with diverse material appearances, and are computationally intensive for on-site processing. To address these limitations, this paper introduces a fully automated, end-to-end inspection framework integrating a wall-climbing robot, a real-time vision-based analysis system, and a digital twin management platform. The primary contributions are threefold: (1) a novel, fully integrated robotic framework for autonomous navigation, multi-sensor data collection, and real-time analysis; (2) a lightweight, synthetic data-augmented DL model for real-time defect segmentation and metric quantification, achieving a mean Average Precision (mAP) of 0.775 for segmentation, an average defect length error of 1.140 cm, and an average center position error of 0.826 cm; (3) a cloud-based digital twin platform enabling quantitative defect visualization, spatiotemporal traceability, and data-driven project management, with the on-site inspection cycle demonstrating a responsive latency of 2.8–4.8 s. Validated through laboratory tests and real building projects, the framework demonstrates significant improvements in inspection efficiency, quantitative accuracy, and decision support over conventional methods. Full article

(This article belongs to the Special Issue BIM and Smart Technologies in Building Design, Construction, and Lifecycle Management)

► Show Figures

Figure 1

24 pages, 7289 KB

Open AccessArticle

Human–Machine Collaborative Management of Pre-Embedded Components for Submerged Tunnel Segments Based on BIM and AR

by Ben Wang, Xiaokai Song, Junwei Gao, Guoxu Zhao, Chao Pei, Yi Tan, Yufa Zhang, Xu Xiang, Xiangyu Wang and Youde Zheng

Buildings 2026, 16(1), 121; https://doi.org/10.3390/buildings16010121 - 26 Dec 2025

Viewed by 635

Abstract

In submerged tunnel construction, the installation accuracy of pre-embedded components directly impacts subsequent engineering quality and operational safety. However, current on-site construction still primarily relies on manual measurement and two-dimensional drawings for guidance, resulting in significant positioning errors, delayed information transmission, and inefficient installation inspections. To enhance the digitalization and intelligence of submerged tunnel construction, this paper proposes a BIM- and AR-based human–machine collaborative management method for pre-embedded components in submerged tunnel segments. This method establishes a site-wide panoramic model as its foundation, enabling intelligent matching of component model geometry and semantic information. It facilitates human–machine interaction applications such as AR-based visualization for positioning and verification of pre-embedded components, information querying, and progress simulation. Additionally, the system supports collaborative operations across multiple terminal devices, ensuring information consistency and task synchronization among diverse roles. Its application in the Mingzhu Bay Submerged Tunnel Project in Nansha, Guangzhou, validates the feasibility and practical utility of the proposed workflow in a pilot case, and indicates potential for further validation in broader construction settings. Full article

(This article belongs to the Special Issue BIM and Smart Technologies in Building Design, Construction, and Lifecycle Management)

► Show Figures

Figure 1

30 pages, 5730 KB

Open AccessArticle

Blockchain-Based Platform for Secure Second-Hand Housing Trade: Requirement Identification, Functions Analysis, and Prototype Development

by Yi-Hsin Lin, Zhicong Hou, Jun Zhang, Xingyu Tao, Jack C. P. Cheng and Heng Li

Buildings 2025, 15(24), 4563; https://doi.org/10.3390/buildings15244563 - 17 Dec 2025

Viewed by 833

Abstract

Most current second-hand housing sales, contract signing, and other processes require the participation of intermediaries. However, suppose the intermediary refuses to disclose all information to the parties involved in the transactions. In that case, this traditional model can lead to weak supervision and punishment, adverse selection, moral hazards, and weak contract enforcement. Blockchain technology can not only secure the information intermediaries share, encouraging them to disclose information, but can also generate irreversible records of housing transactions for data traceability. Therefore, this study aims to develop a framework based on blockchain technology for the trading of second-hand housing. In this study, a second-hand housing online trading framework (SHHOTF) based on smart contract development is proposed for the second-hand housing business process, aiming to promote second-hand housing transactions. The contributions of this study lie in (1) determining the framework requirements, (2) proposing the functional module of a framework based on the blockchain and designing a complete business process, (3) developing an architecture for integrating blockchain and second-hand housing transaction processes, and developing technical components that support the framework functions, and (4) demonstrating the use case in Britain, analyzing the effectiveness and innovation of the framework. Furthermore, the framework demonstrated a 24% increase in transaction speed compared to the traditional Ethereum public network. The proposed process is highly adaptable within the current second-hand housing domain, and the developed framework can serve as a reference for introducing blockchain technology into other industries or application scenarios. Full article

(This article belongs to the Special Issue BIM and Smart Technologies in Building Design, Construction, and Lifecycle Management)

► Show Figures

Figure 1

19 pages, 3336 KB

Open AccessArticle

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 837

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 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)

► Show Figures

Figure 1

25 pages, 7154 KB

Open AccessArticle

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

Cited by 1 | Viewed by 1135

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 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)

► Show Figures

Journal Menu

Journal Browser

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

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (8 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI