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

Deadline for manuscript submissions: closed (10 April 2026) | Viewed by 12082

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Special Issue Editors

Dr. Ye Zhang

E-Mail Website
Guest Editor

Faculty of Water Resources and Hydroelectric Engineering, Xi’an University of Technology, Xi’an 710048, China
Interests: construction informatics; computer vision; automation in construction; geological engineering; hydraulic engineering

Dr. Shuai Han

E-Mail Website
Co-Guest Editor

Faculty of Construction and Environment, Hong Kong Polytechnic University, Kowloon 100872, Hong Kong
Interests: construction informatics; artificial intelligence; building information modeling; automation in construction
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Special Issue Information

Dear Colleagues,

Emerging technologies, such as digital twins, computer vision, natural language processing, the Internet of Things, and blockchain, have been driving transformative changes in the infrastructure and construction engineering industry. Within this landscape, the advent of intelligent construction has become increasingly significant, addressing the limitations of traditional construction methods such as time-consuming processes and labour-intensive tasks. As intelligent construction enters a phase of rapid growth, it brings forth numerous opportunities, accompanied by significant challenges that demand further breakthroughs.

In this Special Issue entitled “Recent Advances in Intelligent Infrastructure and Construction Engineering”, we encourage researchers and scholars to share their recent research results related to smart construction. The main topics covered by this Special Issue include, but are not limited to, the following:

Civil structural safety and health monitoring;
Intelligence application in construction;
Four-dimensional BIM and construction simulation;
Civil engineering advancements;
Construction robots;
Blockchain application in civil engineering;
Construction environment perception;
Computer simulation
Health and safety monitoring and measuring;
Measurement and risk warning.

For more examples of Special Issues in Buildings see:
https://www.mdpi.com/journal/buildings/special_issues

Dr. Ye Zhang
Dr. Shuai Han
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

automation in construction
deep learning
natural language processing
BIM
monitoring
digital twins
intelligence

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Published Papers (8 papers)

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Research

37 pages, 9138 KB

Open AccessArticle

Scan-to-BrIM Workflow for High-Detail Parametric Modelling of a Steel Pedestrian Structure from Point Clouds

by Massimiliano Pepe, Donato Palumbo, Alfredo Restuccia Garofalo, Vincenzo Saverio Alfio, Ahmed Kamal Hamed Dewedar, Luciano Caroprese, Cristina Cantagallo, Andrei Crisan and Domenica Costantino

Buildings 2026, 16(9), 1838; https://doi.org/10.3390/buildings16091838 - 5 May 2026

Viewed by 207

Abstract

This paper presents a computationally feasible/time-effective Scan-to-BrIM workflow for generating a highly detailed digital model of a complex steel pedestrian bridge. The proposed methodology integrates rapid and accurate point cloud acquisition with advanced parametric modelling and structural information management. First, a high-resolution point cloud is produced using a fast survey strategy that ensures the geometric precision required for a faithful representation of the existing structure. Second, the point cloud is processed in Rhinoceros/Grasshopper, where a custom Python (version 3.13) algorithm automatically detects and generates reference planes containing the structural components, enabling the creation of a consistent and fully parametric BrIM model. The latter approach includes metric normalization, voxel-based downsampling, reliable under tested conditions ground and outlier removal, and PCA (Principal Component Analysis)-based reorientation, followed by guided slicing of the point cloud and projection of each slice onto its section plane. The proposed workflow achieved a geometric RMSE of 2.5 mm with a total processing time of 7.3 h. The resulting parametric model achieves geometric consistency with the source point cloud within an operational tolerance range of approximately 5–10 mm, in line with the requirements of structural applications. Finally, the model is organised and managed within the BrIM environment and then transferred to a downstream FEM environment for preliminary structural application. The workflow is tested on a case study of a 40-m steel pedestrian bridge located in central Italy. Results demonstrate that the integrated approach provides a reproducible and semi-automated solution that reduces manual intervention in Scan-to-BrIM processes for producing accurate parametric models of steel pedestrian bridges, supporting structural assessment, asset management, and future maintenance strategies. Full article

(This article belongs to the Special Issue Recent Advances in Intelligent Infrastructure and Construction Engineering)

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24 pages, 2114 KB

Open AccessArticle

An Integrated Framework for Automated Identification of Workers’ Safety Violation Based on Knowledge Graph

by Yifan Zhu, Yewei Ouyang, Rui Pan, Zhanhui Sun, Yang Zhou, Rui Ma, Baoquan Cheng and Wen Wang

Buildings 2026, 16(5), 1037; https://doi.org/10.3390/buildings16051037 - 6 Mar 2026

Viewed by 505

Abstract

Automatic identification of worker safety violations can substantially strengthen construction-site safety management by enabling continuous, real-time monitoring. Although recent advances have made automated detection feasible, many existing systems still suffer from poor adaptability and limited extensibility. To address these limitations, this study proposes an integrated, knowledge graph-based framework for automatic identification of workers’ safety violations. The framework comprises two principal components: (1) a knowledge graph construction module that encodes domain knowledge (safety regulations, task–hazard relationships, and contextual constraints) into a machine-readable graph structure and (2) a graph-enabled violation identification module that maps structured scene descriptions of worker and environmental states to the knowledge graph and performs semantic inference to detect violations. In this study, these structured scene descriptions are manually specified and simulated as subject–predicate–object triplets; integration with raw sensing data is left for future work. For validation, we construct a knowledge graph containing 1200 safety rules and evaluate the violation identification module on 500 annotated examples representing realistic worker scenarios. Using this curated knowledge graph and structured inputs, the proposed approach achieves an identification accuracy of 97.6% for unsafe worker behaviors. Experimental analysis shows that the knowledge graph representation substantially improves the system’s expandability and interpretability compared with traditional hard-coded rules, facilitating easier incorporation of new rules and multimodal sensing inputs. The results indicate that knowledge graph-driven reasoning offers a practical, scalable pathway for robust, context-aware safety violation detection in varied construction environments. Full article

(This article belongs to the Special Issue Recent Advances in Intelligent Infrastructure and Construction Engineering)

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27 pages, 3381 KB

Open AccessArticle

Fusion of Stereo Matching and Spatiotemporal Interaction Analysis: A Detection Method for Excavator-Related Struck-By Hazards in Construction Sites

by Yifan Zhu, Hainan Chen, Rui Pan, Mengqi Yuan, Pan Zhang and Wen Wang

Buildings 2026, 16(5), 1002; https://doi.org/10.3390/buildings16051002 - 4 Mar 2026

Viewed by 431

Abstract

In the construction industry, struck-by accidents involving heavy equipment such as crawler excavators are a leading cause of worker fatalities and injuries. Existing vision-based hazard detection methods are limited by approximate evaluations, reliance on specific references, and neglect of spatial relationships between equipment and workers, making them inadequate for complex dynamic construction environments. This study aims to address these limitations by proposing a precise and adaptable struck-by hazard detection method. The method integrates four core modules: object tracking via the YOLOv5-DeepSORT model to detect workers, excavators, and their key components; activity recognition to identify the operational states of excavators, working or static, and workers, driver or field worker; proximity estimation based on stereo vision using the BGNet model and camera calibration to calculate 3D spatial distances; and safety identification to assess worker safety status in real time. Validated through three virtual construction scenarios, flat ground, rugged terrain, slope, the method achieved high safety status identification accuracies of 92.71%, 90.04%, and 94.25% respectively. The results demonstrate its robustness in adapting to diverse construction environments and accurately capturing equipment–worker spatial interactions. This research expands the application scope of hazard monitoring in complex settings, enhances safety identification efficiency, and provides a reliable technical solution for improving construction site safety management. Full article

(This article belongs to the Special Issue Recent Advances in Intelligent Infrastructure and Construction Engineering)

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27 pages, 3098 KB

Open AccessArticle

Research on the Systematic Analysis of Safety Risk in Metro Deep Foundation Pit Construction

by Guoqing Guo, Shuai Han, Chao Tang and Chuxiong Shen

Buildings 2026, 16(3), 634; https://doi.org/10.3390/buildings16030634 - 3 Feb 2026

Cited by 1 | Viewed by 647

Abstract

With its advantages such as large capacity, punctuality and low environmental impact, the metro has become one of the primary means of alleviating urban traffic congestion. However, safety accidents still occur frequently during the construction of metro deep foundation pits. A review of domestic and international studies reveals that safety risk management for metro deep foundation pit construction remains insufficient, particularly in terms of comprehensive risk identification, analysis of risk interrelationships and systematic risk assessment. To improve the level of safety risk management in metro deep foundation pit construction, this study analyzes safety risk factors using Chinese word segmentation, AHP, ISM, and MICMAC methods. Based on text mining and literature review, a case database comprising 156 metro deep foundation pit construction safety accidents reports was established and integrated into a unified text corpus. Chinese word segmentation was then performed on the corpus, and through risk interpretation combined with relevant standards and codes, 29 safety risk factors were identified and classified into five categories: technology, management, material, personal and environment. On this basis, 22 main safety risk factors were extracted using the AHP method. The results indicate that management-related factors constitute the most critical type of safety risk. Subsequently, the ISM method was employed to identify the interactions among the main safety risk factors and to construct a five-level hierarchical model, in which the top level contains nine safety risk factors, while the bottom level consists of two factors. Through MICMAC analysis, the safety risk factors were classified into three categories, based on which a safety risk management framework for metro deep foundation pit construction was established, and specific control measures were proposed for six representative safety risk factors. Full article

(This article belongs to the Special Issue Recent Advances in Intelligent Infrastructure and Construction Engineering)

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24 pages, 1735 KB

Open AccessArticle

Research on the Risk Factors and Promotion Strategies of BIM Application in China

by Chao Tang, Chuxiong Shen, Shuai Han, Yufeng Zhang and Yuchen Gan

Buildings 2025, 15(14), 2421; https://doi.org/10.3390/buildings15142421 - 10 Jul 2025

Viewed by 2044

Abstract

Building Information Modeling (BIM) is an emerging information technology tool and management concept in the construction industry, enabling the transition from traditional 2D drawings to 3D models. It helps improve efficiency and promote industrial upgrading in the construction sector. However, in actual project practices, the effectiveness of BIM application has not been as expected, and the return on investment (ROI) may even be negative. Through a literature review, we found that risk identification, correlation analysis, and risk assessment related to BIM implementation require further research. To better promote the application of BIM in the construction industry, this study employs relevant methods to analyze the risk factors of BIM implementation. Through the literature review, 31 BIM implementation risk factors were identified, and 24 major risk factors were extracted using the AHP (Analytic Hierarchy Process) method. The ISM (Interpretative Structural Modeling) method was then used to determine the interrelationships among these major risk factors, establishing a hierarchical model with seven levels. Through MICMAC (Matrices Impacts Corises-Multiplication Appliance Classment) analysis, the BIM implementation risk factors were categorized into three groups, and three-tiered response strategies were proposed at the industry, organizational, and project levels. By analyzing the main risk factors of BIM application in China’s construction industry and formulating corresponding response strategies to promote its successful application, this study contributes to the knowledge system. The findings also provide a reference for other countries and regions to clarify major risk factors and their interrelationships, thereby improving the effectiveness of BIM implementation. Full article

(This article belongs to the Special Issue Recent Advances in Intelligent Infrastructure and Construction Engineering)

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19 pages, 541 KB

Open AccessArticle

Receptiveness of Young Singaporeans Towards Smart Features in Public Residential Buildings (SPRBS): Drivers and Barriers

by Gao Shang, Low Sui Pheng and Kock Ho Ying

Buildings 2025, 15(7), 1181; https://doi.org/10.3390/buildings15071181 - 3 Apr 2025

Viewed by 1526

Abstract

The development of smart and sustainable cities (SSCs) is a global focus to ensure cities remain resilient in a challenging environment. In Singapore, various initiatives have been introduced to maintain its competitiveness as an SSC. This study investigates the drivers and barriers affecting the receptiveness of young Singaporeans (aged 18 to 35) towards smart features in public residential buildings (SPRBs). Questionnaires were distributed to young Singaporeans, and 213 valid responses were collected over three months in 2023. It is worth noting over 40% of the respondents are 25 years old and below, classified as Generation Y. The results showed that among 80.3% of respondents who were familiar with SPRBs in Singapore, 68.1% of them either had a minimal or moderate understanding of SPRBs. The top five drivers were ease of access, safety-related factors, and psychological needs, while the top five barriers included cyberattacks, privacy and security concerns, overdependence, and task perception. Research findings have presented meaningful insights for relevant stakeholders to understand different perspectives of young Singaporeans arising from the implementation of SPRBs. It is hoped that public authorities will use this study to assess the feasibility of SPRBs and improve the concept to meet the evolving needs of future homebuyers in Singapore. Full article

(This article belongs to the Special Issue Recent Advances in Intelligent Infrastructure and Construction Engineering)

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12 pages, 3638 KB

Open AccessArticle

Exploring Edge Computing for Sustainable CV-Based Worker Detection in Construction Site Monitoring: Performance and Feasibility Analysis

by Xue Xiao, Chen Chen, Martin Skitmore, Heng Li and Yue Deng

Buildings 2024, 14(8), 2299; https://doi.org/10.3390/buildings14082299 - 25 Jul 2024

Cited by 4 | Viewed by 2433

Abstract

This research explores edge computing for construction site monitoring using computer vision (CV)-based worker detection methods. The feasibility of using edge computing is validated by testing worker detection models (yolov5 and yolov8) on local computers and three edge computing devices (Jetson Nano, Raspberry Pi 4B, and Jetson Xavier NX). The results show comparable mAP values for all devices, with the local computer processing frames six times faster than the Jetson Xavier NX. This study contributes by proposing an edge computing solution to address data security, installation complexity, and time delay issues in CV-based construction site monitoring. This approach also enhances data sustainability by mitigating potential risks associated with data loss, privacy breaches, and network connectivity issues. Additionally, it illustrates the practicality of employing edge computing devices for automated visual monitoring and provides valuable information for construction managers to select the appropriate device. Full article

(This article belongs to the Special Issue Recent Advances in Intelligent Infrastructure and Construction Engineering)

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21 pages, 15526 KB

Open AccessArticle

Dam Deformation Prediction Considering the Seasonal Fluctuations Using Ensemble Learning Algorithm

by Mingkai Liu, Yanming Feng, Shanshan Yang and Huaizhi Su

Buildings 2024, 14(7), 2163; https://doi.org/10.3390/buildings14072163 - 14 Jul 2024

Cited by 11 | Viewed by 2145

Abstract

Dam deformation is the most visual and relevant monitoring quantity that reflects the operational condition of a concrete dam. The seasonal variations in the external environment can induce seasonal fluctuations in the deformation of concrete dams. Hence, preprocessing the deformation monitoring series to identify seasonal fluctuations within the series can effectively enhance the accuracy of the predictive model. Firstly, the dam deformation time series are decomposed into the seasonal and non-seasonal components based on the seasonal decomposition technique. The advanced ensemble learning algorithm (Extreme Gradient Boosting model) is used to forecast the seasonal and non-seasonal components independently, as well as employing the Tree-structured Parzen Estimator (TPE) optimization algorithm to tune the model parameters, ensuring the optimal performance of the prediction model. The results of the case study indicate that the predictive performance of the proposed model is intuitively superior to the benchmark models, demonstrated by a higher fitting accuracy and smaller prediction residuals. In the comparison of the objective evaluation metrics RMSE, MAE, and R², the proposed model outperforms the benchmark models. Additionally, using feature importance measures, it is found that in predicting the seasonal component, the importance of the temperature component increases, while the importance of the water pressure component decreases compared to the prediction of the non-seasonal component. The proposed model, with its elevated predictive accuracy and interpretability, enhances the practicality of the model, offering an effective approach for predicting concrete dam deformation. Full article

(This article belongs to the Special Issue Recent Advances in Intelligent Infrastructure and Construction Engineering)

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