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Recent Advances in Intelligent Infrastructure and Construction Engineering
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Recent Advances in Intelligent Infrastructure and Construction Engineering

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

Deadline for manuscript submissions: 10 July 2025 | Viewed by 4370

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
Special Issues, Collections and Topics in MDPI journals

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 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 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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  • 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

19 pages, 541 KiB  
Article
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 244
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 [...] Read more.
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 KiB  
Article
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 2 | Viewed by 1382
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 [...] Read more.
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 KiB  
Article
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 5 | Viewed by 1329
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 [...] Read more.
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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