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

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

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 (7 papers)

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All Applied Sciences Buildings Intelligent Infrastructure and Construction IJGI Sensors Smart Cities Urban Science

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35 pages, 4528 KiB  

Open AccessArticle

Probabilistic Prediction Model for Ultimate Conditions Under Compression of FRP-Wrapped Concrete Columns Based on Bayesian Inference

by Feng Cao, Ran Zhu, Jun-Xing Zheng, Hai-Bin Huang and Dong Liang

Buildings 2025, 15(10), 1720; https://doi.org/10.3390/buildings15101720 - 19 May 2025

Viewed by 37

Abstract

The compressive strength and ultimate strain of FRP-confined concrete cylinders are the key indicators for evaluating their mechanical properties. Accurate prediction of compressive strength and ultimate strain is essential for reliability analysis and design of such components. However, the existing ultimate condition under [...] Read more.

The compressive strength and ultimate strain of FRP-confined concrete cylinders are the key indicators for evaluating their mechanical properties. Accurate prediction of compressive strength and ultimate strain is essential for reliability analysis and design of such components. However, the existing ultimate condition under compression models lack sufficient prediction accuracy, and the results exhibit significant uncertainty. This study proposes a Bayesian model updating method based on Markov Chain Monte Carlo (MCMC) sampling to improve the prediction accuracy of the ultimate condition under compression for FRP-confined concrete cylinders and to quantify the uncertainty of the prediction results. First of all, 1016 sets of experimental data on the ultimate condition under compression of FRP-confined concrete cylinders from previous studies were collected. Subsequently, the probabilistic updating model and evaluation system were established based on Bayesian parameter estimation principle, MCMC sampling, WAIC, and DIC. Then, several representative empirical models for predicting the ultimate condition under compression are selected, and their prediction performance is evaluated using the experimental data. Finally, a Bayesian updating problem is established for typical ultimate condition under compression models, and the posterior distributions of model parameters are obtained using MCMC sampling to select the best model, and the prediction performance of the optimal model is assessed using the experimental data. The results show that, compared with existing empirical models, the Bayesian inference-based probabilistic calculation model provides predictions closer to the experimental values, while also reasonably quantifying the uncertainty of the ultimate condition under compression prediction. Full article

(This article belongs to the Topic 3D Computer Vision and Smart Building and City, 3rd Edition)

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14 pages, 7929 KiB  

Open AccessArticle

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 248

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

(This article belongs to the Topic 3D Computer Vision and Smart Building and City, 3rd Edition)

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15 pages, 2560 KiB  

Open AccessArticle

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

Cited by 1 | Viewed by 587

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

(This article belongs to the Topic 3D Computer Vision and Smart Building and City, 3rd Edition)

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21 pages, 7316 KiB  

Open AccessArticle

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 769

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

(This article belongs to the Topic 3D Computer Vision and Smart Building and City, 3rd Edition)

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21 pages, 8550 KiB  

Open AccessArticle

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 578

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

(This article belongs to the Topic 3D Computer Vision and Smart Building and City, 3rd Edition)

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17 pages, 10263 KiB  

Open AccessArticle

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 739

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

(This article belongs to the Topic 3D Computer Vision and Smart Building and City, 3rd Edition)

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20 pages, 8870 KiB  

Open AccessArticle

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

Cited by 1 | Viewed by 1147

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

(This article belongs to the Topic 3D Computer Vision and Smart Building and City, 3rd Edition)

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