Topic Editors

Prof. Dr. Junxing Zheng
School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, China
Dr. Peng Cao
College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China

3D Computer Vision and Smart Building and City, 3rd Edition

Abstract submission deadline
30 November 2025
Manuscript submission deadline
31 January 2026
Viewed by
3909

Topic Information

Dear Colleagues,

This is the third edition of the successful topic “3D Computer Vision and Smart Building and City” (https://www.mdpi.com/topics/0J25AOPO4H). Three-dimensional computer vision is an interdisciplinary subject involving computer vision, computer graphics, artificial intelligence, and other fields. Its main contents include 3D perception, 3D understanding, and 3D modeling. In recent years, 3D computer vision technology has developed rapidly and has been widely used in unmanned aerial vehicles, robots, autonomous driving, AR, VR, and other fields. Smart buildings and cities use various information technologies or innovative concepts to connect various systems and services so as to improve the efficiency of resource utilization, optimize management and services, and improve quality of life. Smart buildings and cities can involve some frontier techniques, such as 3D CV for building information models, digital twins, city information models, simultaneous localization and mapping, and robots. The application of 3D computer vision in smart buildings and cities is a valuable research direction, but it still faces many major challenges in theory and technology. This topic focuses on the theory and technology of 3D computer vision in smart buildings and cities. We invite the research community to publish papers and provide innovative technologies, theories or case studies.

Prof. Dr. Junxing Zheng
Dr. Peng Cao
Topic Editors

Keywords

  • smart buildings and cities
  • 3D computer vision
  • SLAM
  • building information model
  • city information model
  • robots

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci
2.5 5.3 2011 18.4 Days CHF 2400 Submit
Buildings
buildings
3.1 3.4 2011 15.3 Days CHF 2600 Submit
Intelligent Infrastructure and Construction
iic
- - 2025 15.0 days * CHF 1000 Submit
ISPRS International Journal of Geo-Information
ijgi
2.8 6.9 2012 35.8 Days CHF 1900 Submit
Sensors
sensors
3.4 7.3 2001 18.6 Days CHF 2600 Submit
Smart Cities
smartcities
7.0 11.2 2018 28.4 Days CHF 2000 Submit
Urban Science
urbansci
2.1 4.3 2017 20.7 Days CHF 1600 Submit

* Median value for all MDPI journals in the second half of 2024.


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Published Papers (6 papers)

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14 pages, 7929 KiB  
Article
Fatigue Damage Study of Reinforced Concrete T-Beam Bridge Considering Bearing Defect
by Lian-Xiang Wang, Lei Tian, Jin Guo, Qiang Zhang, Jun-Xing Zheng and Hai-Bin Huang
Buildings 2025, 15(7), 1169; https://doi.org/10.3390/buildings15071169 - 2 Apr 2025
Viewed by 199
Abstract
Vehicle load is one of the primary factors influencing fatigue damage in reinforced concrete beam bridges, and diseases of bearings will accelerate the fatigue damage of beam bridges, but most of the current studies only consider the influence of vehicle load on the [...] Read more.
Vehicle load is one of the primary factors influencing fatigue damage in reinforced concrete beam bridges, and diseases of bearings will accelerate the fatigue damage of beam bridges, but most of the current studies only consider the influence of vehicle load on the fatigue damage of beam bridges. In order to evaluate the fatigue damage of T-beam bridges more comprehensively, a numerical analysis method for the entire fatigue damage process of T-beam bridges is proposed, which considers the impact of bearing diseases and vehicle loads. The research results indicate the following: (1) During the fatigue calculation process of the T-beam, the maximum tensile fatigue damage value grows rapidly in the early stages. The effects of different bearing diseases and severities on the T-beam are generally similar. In the mid-stage, the growth of the maximum tensile fatigue damage value slows down, and the deeper the bearing disease, the greater the impact on the T-beam. In the later stage, the damage value grows rapidly until failure. (2) The displacement of bearings leads to stress concentration in the beam and the steel plates of bearings. The larger the slippage amplitude, the greater the stress value, which leads to a shortened lifespan of the concrete beam. (3) Bearing maintenance should focus on the damage of the beam. If displacement of bearings occurs, measures should be taken as early as possible (preferably within the mid-stage, 10–65 years); otherwise, it will result in a significant decrease in the beam’s life. Full article
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15 pages, 2560 KiB  
Article
A Multi-Objective Sensor Placement Method Considering Modal Identification Uncertainty and Damage Detection Sensitivity
by Xue-Yang Pei, Yuan Hou, Hai-Bin Huang and Jun-Xing Zheng
Buildings 2025, 15(5), 821; https://doi.org/10.3390/buildings15050821 - 5 Mar 2025
Viewed by 513
Abstract
Structural Health Monitoring relies on accurate modal identification and effective damage detection to assess structural performance and safety. However, traditional sensor placement methods struggle to balance modal identification uncertainty, which arises from limited sensor coverage and measurement noise and damage detection sensitivity, which [...] Read more.
Structural Health Monitoring relies on accurate modal identification and effective damage detection to assess structural performance and safety. However, traditional sensor placement methods struggle to balance modal identification uncertainty, which arises from limited sensor coverage and measurement noise and damage detection sensitivity, which requires sensors to be optimally positioned to capture structural stiffness variations. To address this challenge, this study proposes a multi-objective sensor placement optimization method based on the Non-Dominated Sorting Genetic Algorithm. The method introduces two key objective functions: minimizing modal identification uncertainty by leveraging Bayesian modal identification theory and information entropy and maximizing damage detection sensitivity by incorporating an entropy-based measure to quantify the uncertainty in stiffness variation estimation. By formulating the problem as Pareto-based multi-objective optimization, the method efficiently explores a trade-off between the two competing objectives and provides a diverse set of optimal sensor placement solutions. The proposed approach is validated through numerical experiments on a simply supported beam and a benchmark bridge structure, demonstrating that different optimization objectives lead to distinct sensor placement patterns. The results show that solutions prioritizing modal identification distribute sensors across the structure to improve global response estimation, while solutions favoring damage detection concentrate sensors in critical areas to enhance sensitivity. The proposed method significantly improves sensor placement strategies by offering a systematic and flexible framework for SHM applications, enabling engineers to tailor monitoring strategies based on specific structural assessment needs. Full article
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21 pages, 7316 KiB  
Article
Enhancing Bolt Object Detection via AIGC-Driven Data Augmentation for Automated Construction Inspection
by Jie Wu, Beilin Han, Yihang Zhang, Chuyue Huang, Shengqiang Qiu, Wang Feng, Zhiwei Liu and Chao Zou
Buildings 2025, 15(5), 819; https://doi.org/10.3390/buildings15050819 - 5 Mar 2025
Viewed by 664
Abstract
In the engineering domain, the detection of damage in high-strength bolts is critical for ensuring the safe and reliable operation of equipment. Traditional manual inspection methods are not only inefficient but also susceptible to human error. This paper proposes an automated bolt damage [...] Read more.
In the engineering domain, the detection of damage in high-strength bolts is critical for ensuring the safe and reliable operation of equipment. Traditional manual inspection methods are not only inefficient but also susceptible to human error. This paper proposes an automated bolt damage identification method leveraging AIGC (Artificial Intelligence Generated Content) technology and object detection algorithms. Specifically, we introduce the application of AIGC in image generation, focusing on the Stable Diffusion model. Given that the quality of bolt images generated directly by the Stable Diffusion model is suboptimal, we employ the LoRA fine-tuning technique to enhance the model, thereby generating a high-quality dataset of bolt images. This dataset is then used to train the YOLO (You Only Look Once) object detection algorithm, demonstrating significant improvements in both accuracy and recall for bolt damage recognition. Experimental results show that the LoRA fine-tuned Stable Diffusion model significantly enhances the performance of the YOLO algorithm, providing an efficient and accurate solution for automated bolt damage detection. Future work will concentrate on further optimizing the model to improve its robustness and real-time performance, thereby better meeting the demands of practical industrial applications. Full article
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21 pages, 8550 KiB  
Article
Analysis of Structural Performance and Design Optimization of Prefabricated Cantilever Systems Under Traffic Loads
by Liang Chen, Shengwei Yang, Haihui Xie and Zhifei Tan
Appl. Sci. 2025, 15(5), 2781; https://doi.org/10.3390/app15052781 - 5 Mar 2025
Viewed by 487
Abstract
Prefabricated cantilever systems (PCSs) are essential for mountainous road infrastructure, yet their structural behavior under traffic loads remains insufficiently studied. This study innovatively integrates scaled experiments, finite element simulations, and field test data to develop and validate a full-scale PCS model under extreme [...] Read more.
Prefabricated cantilever systems (PCSs) are essential for mountainous road infrastructure, yet their structural behavior under traffic loads remains insufficiently studied. This study innovatively integrates scaled experiments, finite element simulations, and field test data to develop and validate a full-scale PCS model under extreme traffic conditions. The results reveal that the beam–column junction is highly vulnerable to stress concentrations, risking concrete cracking. To address this, a novel prestressed reinforcement design is proposed, optimizing rebar placement to reduce local stresses and enhance structural integrity. Ultimate load analysis confirms that prestressing improves stiffness, load resistance, and ductility. This study provides a systematic framework for PCS optimization, promoting its application in complex engineering environments. Full article
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17 pages, 10263 KiB  
Article
A Deep Learning-Based Structural Damage Identification Method Integrating CNN-BiLSTM-Attention for Multi-Order Frequency Data Analysis
by Xue-Yang Pei, Yuan Hou, Hai-Bin Huang and Jun-Xing Zheng
Buildings 2025, 15(5), 763; https://doi.org/10.3390/buildings15050763 - 26 Feb 2025
Viewed by 608
Abstract
Structural health monitoring commonly uses natural frequency analysis to assess structural conditions, but direct frequency shifts are often insensitive to minor damage and susceptible to environmental influences like temperature variations. Traditional methods, whether based on absolute frequency changes or theoretical models like PCA [...] Read more.
Structural health monitoring commonly uses natural frequency analysis to assess structural conditions, but direct frequency shifts are often insensitive to minor damage and susceptible to environmental influences like temperature variations. Traditional methods, whether based on absolute frequency changes or theoretical models like PCA and GMM, face challenges in robustness and reliance on model selection. These limitations highlight the need for a more adaptive and data-driven approach to capturing the intrinsic nonlinear correlations among multi-order modal frequencies. This study proposes a novel approach that leverages the nonlinear correlations among multi-order natural frequencies, which are more sensitive to structural state changes. A deep learning framework integrating CNN-BiLSTM-Attention is designed to capture the spatiotemporal dependencies of multi-order frequency data, enabling the precise modeling of intrinsic correlations. The model was trained exclusively on healthy-state frequency data and validated on both healthy and damaged conditions. A probabilistic modeling approach, incorporating Gaussian distribution and cumulative probability functions, was used to evaluate the estimation accuracy and detect correlation shifts indicative of structural damage. To enhance the robustness, a moving average smoothing technique was applied to reduce random noise interference, and damage identification rates over extended time segments were calculated to mitigate transient false alarms. Validation experiments on a mass-spring system and the Z24 bridge dataset demonstrated that the proposed method achieved over 95% damage detection accuracy while maintaining a false alarm rate below 5%. The results validate the ability of the CNN-BiLSTM-Attention framework to effectively capture both structural and environmental nonlinearities, reducing the dependency on explicit theoretical models. By leveraging multi-order frequency correlations, the proposed method provides a robust and highly sensitive approach to structural damage identification. These findings confirm the practical applicability of deep learning in damage identification during the operational phase of structures. Full article
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20 pages, 8870 KiB  
Article
Near Real-Time 3D Reconstruction of Construction Sites Based on Surveillance Cameras
by Aoran Sun, Xuehui An, Pengfei Li, Miao Lv and Wenzhe Liu
Buildings 2025, 15(4), 567; https://doi.org/10.3390/buildings15040567 - 12 Feb 2025
Viewed by 967
Abstract
The 3D reconstruction of construction sites is of great importance for construction progress, quality, and safety management. Currently, most of the existing 3D reconstruction methods are unable to conduct continuous and uninterrupted perception, and it is difficult to achieve registration with real coordinates [...] Read more.
The 3D reconstruction of construction sites is of great importance for construction progress, quality, and safety management. Currently, most of the existing 3D reconstruction methods are unable to conduct continuous and uninterrupted perception, and it is difficult to achieve registration with real coordinates and dimensions. This study proposes a hierarchical registration framework for 3D reconstruction of construction sites based on surveillance cameras. This method can quickly perform on-site 3D reconstruction and restoration by taking surveillance camera images as inputs. It combines 2D and 3D features and does not need transfer learning or camera calibration. By experimenting on one construction site, we found that this framework can complete the 3D point cloud estimation and registration of construction sites within an average of 3.105 s through surveillance images. The average RMSE of the point cloud within the site is 0.358 m, which is better than most point cloud registration methods. Through this method, 3D data within the scope of surveillance cameras can be quickly obtained, and the connection between 2D and 3D can be effectively established. Combined with visual information, it is beneficial to the digital twin management of construction sites. Full article
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