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Advances in AI, Digitization, Robotics, IoT, BIM, and Spatial Modeling...
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Advances in AI, Digitization, Robotics, IoT, BIM, and Spatial Modeling in Building Sciences

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 July 2025 | Viewed by 5341

Special Issue Editors

Prof. Dr. Fuan Tsai

E-Mail Website
Guest Editor
Center for Space and Remote Sensing Research & Department of Civil Engineering, National Central University, Taoyuan 320317, Taiwan
Interests: remote sensing; spatial analysis; image analysis; 3D metrology and reconstruction, geovisualization
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Tee-Ann Teo

E-Mail Website
Guest Editor
Department of Civil Engineering, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan
Interests: geomatics; GeoBIM; GeoAI; photogrammetry; remote sensing

Special Issue Information

Dear Colleagues,

The developments and advances in computer and information sciences, geospatial information, sensors and Internet of Things devices, artificial intelligence, and other related sectors have significant impacts on the building and construction sciences, engineering, and management. These advanced technologies provide new opportunities for researchers and engineers to formulate innovative solutions in order to address complicated issues more effectively, efficiently, or economically. Nevertheless, the effective incorporation of assorted new technologies and their implementation in different domains for sophisticated applications is also a great challenge. This Special Issue aims to provide insights into how advanced technologies can be effectively adopted and successfully implemented in various kinds of applications in buildings and built environments. Topics of interest include, but are not limited to:

  • Artificial intelligence applications;
  • Robotics and autonomous technology;
  • Digitization, visualization, and 3D printing;
  • Multi-dimensional/multi-LOD building models;
  • High-definition building models;
  • SLAM (simultaneous localization and mapping/modelling);
  • Automation in construction;
  • Computer-aided design and engineering;
  • Building information modeling;
  • Spatial analysis in built environments;
  • Sensors and the Internet of Things;
  • Indoor/outdoor positioning, navigation, and location-based services;
  • Ontology and societal impacts.

Prof. Dr. Fuan Tsai
Prof. Dr. Tee-Ann Teo
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

  • digital building models
  • multi-LOD building models
  • high-definition 3D models
  • building information modelling
  • construction management
  • automation in construction
  • building spatial information
  • indoor location services
  • CAD/CAE
  • artifical intelligence/machine learning/deep learning

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.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (6 papers)

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Research

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23 pages, 6214 KiB  
Article
An Image-Based Intelligent System for Addressing Risk in Construction Site Safety Monitoring Within Civil Engineering Projects
by Hijratullah Sharifzada, You Wang, Said Ikram Sadat, Hamza Javed, Khalid Akhunzada, Sidra Javed and Sadiq Khan
Buildings 2025, 15(8), 1362; https://doi.org/10.3390/buildings15081362 - 19 Apr 2025
Viewed by 146
Abstract
In the construction industry, safety is of paramount importance given the complex and dynamic nature of construction sites, which are prone to various hazards, like falls from heights, being hit by falling objects, and structural collapses. Traditional safety management strategies, such as manual [...] Read more.
In the construction industry, safety is of paramount importance given the complex and dynamic nature of construction sites, which are prone to various hazards, like falls from heights, being hit by falling objects, and structural collapses. Traditional safety management strategies, such as manual inspections and safety training, have shown significant limitations. This study presents an intelligent monitoring and analysis system for construction site safety based on an image dataset. A specifically designed Construction Site Safety Image Dataset, comprising 10 distinct classes of objects, is utilized and divided into training, validation, and test subsets. InceptionV3 and MobileNetV2 are chosen as pre-trained models for feature extraction and are modified through truncation and compression to better suit the task. A novel feature fusion architecture is introduced, integrating these modified models, along with a Squeeze-and-Excitation block. Experimental results demonstrate that the proposed model achieves a mean average precision (mAP) of 0.90 at an IoU threshold of 0.5, with high accuracies for classes like “Safety Cone” (91%) and “Machinery” (93%) but relatively lower accuracy for “Vehicle” (57%). The training process exhibits smooth convergence, and compared to prior methods, such as YOLOv4 and SSD, the proposed framework shows superiority in regard to precision and recall. Despite its achievements, the system has limitations, including reliance on visual data and dataset imbalance. Future research directions involve incorporating multi-modal data, conducting real-world deployments, and optimizing for edge deployment, aiming to further enhance construction site safety. Full article
(This article belongs to the Special Issue Advances in AI, Digitization, Robotics, IoT, BIM, and Spatial Modeling in Building Sciences)
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27 pages, 20838 KiB  
Article
Building Change Detection in Aerial Imagery Using End-to-End Deep Learning Semantic Segmentation Techniques
by Tee-Ann Teo and Pei-Cheng Chen
Buildings 2025, 15(5), 695; https://doi.org/10.3390/buildings15050695 - 23 Feb 2025
Viewed by 640
Abstract
Automatic building change detection is essential for updating geospatial data, urban planning, and land use management. The objective of this study is to propose a transformer-based UNet-like framework for end-to-end building change detection, integrating multi-temporal and multi-source data to improve efficiency and accuracy. [...] Read more.
Automatic building change detection is essential for updating geospatial data, urban planning, and land use management. The objective of this study is to propose a transformer-based UNet-like framework for end-to-end building change detection, integrating multi-temporal and multi-source data to improve efficiency and accuracy. Unlike conventional methods that focus on either spectral imagery or digital surface models (DSMs), the proposed method combines RGB color imagery, DSMs, and building vector maps in a three-branch Siamese architecture to enhance spatial, spectral, and elevation-based feature extraction. We chose Hsinchu, Taiwan as the experimental site and used 1:1000 digital topographic maps and airborne imagery from 2017, 2020, and 2023. The experimental results demonstrated that the data fusion model significantly outperforms other data combinations, achieving higher accuracy and robustness in detecting building changes. The RGB images provide spectral and texture details, DSMs offer structural and elevation context, and the building vector map enhances semantic consistency. This research advances building change detection by introducing a fully transformer-based model for end-to-end change detection, incorporating diverse geospatial data sources, and improving accuracy over traditional CNN-based methods. The proposed framework offers a scalable and automated solution for modern mapping workflows, contributing to more efficient geospatial data updating and urban monitoring. Full article
(This article belongs to the Special Issue Advances in AI, Digitization, Robotics, IoT, BIM, and Spatial Modeling in Building Sciences)
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19 pages, 3525 KiB  
Article
Hyperparameter Tuning Technique to Improve the Accuracy of Bridge Damage Identification Model
by Su-Wan Chung, Sung-Sam Hong and Byung-Kon Kim
Buildings 2024, 14(10), 3146; https://doi.org/10.3390/buildings14103146 - 2 Oct 2024
Viewed by 1104
Abstract
In recent years, active research has been conducted using deep learning to evaluate damage to aging bridges. However, this method is inappropriate for practical use because its performance deteriorates owing to numerous classifications, and it does not use photos of actual sites. To [...] Read more.
In recent years, active research has been conducted using deep learning to evaluate damage to aging bridges. However, this method is inappropriate for practical use because its performance deteriorates owing to numerous classifications, and it does not use photos of actual sites. To this end, this study used image data from an actual bridge management system as training data and employed a combined learning model for each member among various instance segmentation models, including YOLO, Mask R-CNN, and BlendMask. Meanwhile, techniques such as hyperparameter tuning are widely used to improve the accuracy of deep learning, and this study aimed to improve the accuracy of the existing model through this. The hyperparameters optimized in this study are DEPTH, learning rate (LR), and iterations (ITER) of the neural network. This technique can improve the accuracy by tuning only the hyperparameters while using the existing model for bridge damage identification as it is. As a result of the experiment, when DEPTH, LR, and ITER were set to the optimal values, mAP was improved by approximately 2.9% compared to the existing model. Full article
(This article belongs to the Special Issue Advances in AI, Digitization, Robotics, IoT, BIM, and Spatial Modeling in Building Sciences)
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17 pages, 3423 KiB  
Article
Spatial Analysis with Detailed Indoor Building Models for Emergency Services
by Min-Lung Cheng, Fuan Tsai and Tee-Ann Teo
Buildings 2024, 14(9), 2798; https://doi.org/10.3390/buildings14092798 - 5 Sep 2024
Cited by 1 | Viewed by 1127
Abstract
This paper presents a systematic approach to perform spatial analysis with detailed indoor building models for emergency service decision supports. To achieve a more realistic spatial application, this research integrates three-dimensional (3D) indoor building models and their attributes to simulate an emergency evacuation [...] Read more.
This paper presents a systematic approach to perform spatial analysis with detailed indoor building models for emergency service decision supports. To achieve a more realistic spatial application, this research integrates three-dimensional (3D) indoor building models and their attributes to simulate an emergency evacuation scenario. Indoor building models of a complicated train station with different levels of detail are generated from two-dimensional (2D) floor plans and Building Information Model (BIM) datasets. In addition to the 3D building models, spatial and non-spatial attributes are also associated with the created building models and the objects within them. The ant colony optimization (ACO) algorithm is modified to analyze the indoor building models for emergency service decision support applications. The detailed indoor models and the proposed spatial analysis algorithms are tested in simulated emergency evacuation scenarios to select the best routes during emergency services. The experimental results demonstrate that the proposed system is helpful for selecting the optimal route with the least cost at varying time stamps. Together with the developed spatial analysis framework, they have a great potential for effective decision support during emergency situations. Full article
(This article belongs to the Special Issue Advances in AI, Digitization, Robotics, IoT, BIM, and Spatial Modeling in Building Sciences)
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Review

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22 pages, 1817 KiB  
Review
Human-Computer Interaction Empowers Construction Safety Management: Breaking Through Difficulties to Achieving Innovative Leap
by Hao Peng, Xiaolin Wang, Han Wu and Bo Huang
Buildings 2025, 15(5), 771; https://doi.org/10.3390/buildings15050771 - 26 Feb 2025
Viewed by 602
Abstract
This paper focuses on the application of human–computer interaction technology in construction project safety management. Through bibliometric methods, we carried out an in-depth analysis of 286 relevant papers from Web of Science and Google Scholar from 2000 to 2024. The research results indicate [...] Read more.
This paper focuses on the application of human–computer interaction technology in construction project safety management. Through bibliometric methods, we carried out an in-depth analysis of 286 relevant papers from Web of Science and Google Scholar from 2000 to 2024. The research results indicate that human–computer interaction technology has achieved remarkable development in four aspects: intelligent monitoring systems, risk assessment and management, ergonomics and cognitive psychology, as well as computer simulation and virtual reality. Meanwhile, this research has given rise to a series of new research topics, such as the safety operation decision-making method for intelligent construction machinery, the application of human action behavior recognition technology, and the application of Internet of Things technology in the safety control of smart construction sites. Additionally, future research modules have been identified, including personalized safety training, digital twin technology, and multimodal data analysis. This study not only summarizes the existing research achievements but also puts forward targeted suggestions for future development trends in the field of construction safety management from a practical perspective, aiming to promote the in-depth application and development of human–computer interaction technology in construction safety management. Full article
(This article belongs to the Special Issue Advances in AI, Digitization, Robotics, IoT, BIM, and Spatial Modeling in Building Sciences)
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18 pages, 10551 KiB  
Review
The Application of Intelligent Construction Technology to Modern Wood Structures in China: A Critical Review
by Jiachen Li, Hua Chen, Long Wang, Yazhou Ou, Tengteng Yin, Jin Zhang, Dekai Qin and Yanchao Du
Buildings 2025, 15(4), 535; https://doi.org/10.3390/buildings15040535 - 10 Feb 2025
Viewed by 1010
Abstract
This paper explores the current application status and development trends of intelligent construction technologies in modern timber structures in China. By using VOSviewer software for bibliometric analysis, the research focused on three key areas: BIM technology, machine vision, and lifecycle information management. The [...] Read more.
This paper explores the current application status and development trends of intelligent construction technologies in modern timber structures in China. By using VOSviewer software for bibliometric analysis, the research focused on three key areas: BIM technology, machine vision, and lifecycle information management. The study found that BIM technology has significantly improved the design accuracy and construction efficiency of timber buildings through 3D visualization, parametric design, and interdisciplinary collaboration. Machine vision technology enhances the quality control and damage assessment efficiency through automation. Lifecycle information management promotes the sustainable development of timber buildings in line with carbon neutrality goals. The paper further analyzes the challenges and difficulties of applying intelligent construction technologies to modern timber structures and proposes the development of technology solutions tailored to the characteristics of timber structures, including deepening the integration with environmental science and urban planning and enhancing user feedback optimization. These studies provide new perspectives for the intelligent and low-carbon development of timber buildings and offer support for achieving sustainability goals and carbon neutrality. Full article
(This article belongs to the Special Issue Advances in AI, Digitization, Robotics, IoT, BIM, and Spatial Modeling in Building Sciences)
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