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Study of Building Detection, Assessment, and Management: Based on Computer...
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Study of Building Detection, Assessment, and Management: Based on Computer and Information Technologies

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 (20 October 2024) | Viewed by 9303

Special Issue Editors

Dr. Ting-Kwei Wang

E-Mail Website
Guest Editor
Department of Civil Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City 807618, Taiwan
Interests: building information modeling; computer vision; augmented reality; generative design; blockchain
Dr. Pin-Chao Liao

E-Mail Website
Guest Editor
Department of Construction Management, School of Civil Engineering, Tsinghua University, Beijing 100084, China
Interests: safety and quality; neuromanagement; construction risks
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaowei Luo

E-Mail Website
Guest Editor
Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong 999077, China
Interests: construction digitalization; safety management; digital twin; construction automation; building energy management; life-cycle assessment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are excited to announce a Special Issue of Buildings that focuses specifically on the application of computer and information technologies in the field of building detection, assessment, and management.

Computer and information technology can automate the process of building detection, assessment, and management, which is essential for ensuring safety and efficiency in various domains. However, applying these technologies in complex and dynamic environments poses new challenges and risks that require innovative solutions and methods. To address these issues, this Special Issue invites original research on the utilization of computer and information technology in building detection, assessment, and management, with a focus on the progress, methodologies, and practical applications that drive innovation in this particular field.

Topics of interest include, but are not limited to, the following:

  • Automated building detection;
  • Advanced building assessment techniques;
  • Intelligent building management systems;
  • Data integration and decision support systems;
  • Real-time monitoring and emergency response.

Dr. Ting-Kwei Wang
Dr. Pin-Chao Liao
Dr. Xiaowei Luo
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

  • computer vision and remote sensing
  • building detection
  • deep learning
  • LiDAR
  • aerial images
  • mobile robot
  • performance assessment
  • sustainable building
  • building information modeling

Benefits of Publishing in a Special Issue

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  • 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 (5 papers)

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Research

24 pages, 11251 KiB  
Article
A Novel YOLOv10-DECA Model for Real-Time Detection of Concrete Cracks
by Chaokai Zhang, Ningbo Peng, Jiaheng Yan, Lixu Wang, Yinjia Chen, Zhancheng Zhou and Ye Zhu
Buildings 2024, 14(10), 3230; https://doi.org/10.3390/buildings14103230 - 11 Oct 2024
Cited by 3 | Viewed by 1596
Abstract
The You Only Look Once (YOLO) series algorithms have been widely adopted in concrete crack detection, with attention mechanisms frequently being incorporated to enhance recognition accuracy and efficiency. However, existing research is confronted by two primary challenges: the suboptimal performance of attention mechanism [...] Read more.
The You Only Look Once (YOLO) series algorithms have been widely adopted in concrete crack detection, with attention mechanisms frequently being incorporated to enhance recognition accuracy and efficiency. However, existing research is confronted by two primary challenges: the suboptimal performance of attention mechanism modules and the lack of explanation regarding how these mechanisms influence the model’s decision-making process to improve accuracy. To address these issues, a novel Dynamic Efficient Channel Attention (DECA) module is proposed in this study, which is designed to enhance the performance of the YOLOv10 model in concrete crack detection, and the effectiveness of this module is visually demonstrated through the application of interpretable analysis algorithms. In this paper, a concrete dataset with a complex background is used. Experimental results indicate that the DECA module significantly improves the model’s accuracy in crack localization and the detection of discontinuous cracks, outperforming the existing Efficient Channel Attention (ECA). When compared to the similarly sized YOLOv10n model, the proposed YOLOv10-DECA model demonstrates improvements of 4.40%, 3.06%, 4.48%, and 5.56% in precision, recall, mAP50, and mAP50-95 metrics, respectively. Moreover, even when compared with the larger YOLOv10s model, these performance indicators are increased by 2.00%, 0.04%, 2.27%, and 1.12%, respectively. In terms of speed evaluation, owing to the lightweight design of the DECA module, the YOLOv10-DECA model achieves an inference speed of 78 frames per second, which is 2.5 times faster than YOLOv10s, thereby fully meeting the requirements for real-time detection. These results demonstrate that an optimized balance between accuracy and speed in concrete crack detection tasks has been achieved by the YOLOv10-DECA model. Consequently, this study provides valuable insights for future research and applications in this field. Full article
(This article belongs to the Special Issue Study of Building Detection, Assessment, and Management: Based on Computer and Information Technologies)
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20 pages, 6830 KiB  
Article
Deep Learning-Based Intelligent Detection Algorithm for Surface Disease in Concrete Buildings
by Jing Gu, Yijuan Pan and Jingjing Zhang
Buildings 2024, 14(10), 3058; https://doi.org/10.3390/buildings14103058 - 25 Sep 2024
Cited by 2 | Viewed by 1488
Abstract
In this study, the extent of concrete building distress is used to determine whether a building needs to be demolished and maintained, and the study focuses on accurately identifying target distress in different complex contexts and accurately distinguishing between their categories. To solve [...] Read more.
In this study, the extent of concrete building distress is used to determine whether a building needs to be demolished and maintained, and the study focuses on accurately identifying target distress in different complex contexts and accurately distinguishing between their categories. To solve the problem of insufficient feature extraction of small targets in bridge disease images under complex backgrounds and noise, we propose the YOLOv8 Dynamic Plus model. First, we enhanced attention on multi-scale disease features by implementing structural reparameterization with parallel small-kernel expansion convolution. Next, we reconstructed the relationship between localization and classification tasks in the detection head and implemented dynamic selection of interactive features using a feature extractor to improve the accuracy of classification and recognition. Finally, to address problems of missed detection, such as inadequate extraction of small targets, we extended the original YOLOv8 architecture by adding a layer in the feature extraction phase dedicated to small-target detection. This modification integrated the neck part more effectively with the shallow features of the original three-layer YOLOv8 feature extraction stage. The improved YOLOv8 Dynamic Plus model demonstrated a 7.4 percentage-point increase in performance compared to the original model, validating the feasibility of our approach and enhancing its capability for building disease detection. In practice, this improvement has led to more accurate maintenance and safety assessments of concrete buildings and earlier detection of potential structural problems, resulting in lower maintenance costs and longer building life. This not only improves the safety of buildings but also brings significant economic benefits and social value to the industries involved. Full article
(This article belongs to the Special Issue Study of Building Detection, Assessment, and Management: Based on Computer and Information Technologies)
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20 pages, 8874 KiB  
Article
Feature Selection-Based Method for Scaffolding Assembly Quality Inspection Using Point Cloud Data
by Jie Zhao, Junwei Chen, Yangze Liang and Zhao Xu
Buildings 2024, 14(8), 2518; https://doi.org/10.3390/buildings14082518 - 15 Aug 2024
Cited by 2 | Viewed by 1187
Abstract
The stability of scaffolding structures is crucial for quality management in construction. Currently, scaffolding assembly quality monitoring relies on visual inspections performed by designated on-site personnel, which are highly subjective, inaccurate, and inefficient, hindering the advancement of intelligent construction practices. This study proposes [...] Read more.
The stability of scaffolding structures is crucial for quality management in construction. Currently, scaffolding assembly quality monitoring relies on visual inspections performed by designated on-site personnel, which are highly subjective, inaccurate, and inefficient, hindering the advancement of intelligent construction practices. This study proposes an automated method for scaffolding assembly quality inspection using point cloud data and feature selection algorithms. High-precision point cloud data of the scaffolding are captured by a Trimble X7 3D laser scanner. After registration with the forward design model, a 2D slicing comparison method is developed to measure geometric dimensions with an accuracy controlled within 0.1 mm. The collected data are used to build an SVM model for automated assembly quality inspection. To combat the curse of dimensionality associated with high-dimensional data, an optimized genetic algorithm is employed for the dimensionality reduction in the raw sample data, effectively eliminating data redundancy and significantly enhancing convergence speed and classification accuracy of the detection model. Case studies indicate that the proposed method can reduce feature dimensionality by 70% while simultaneously improving classification accuracy by 13.9%. The proposed method enables high-precision automated inspection of scaffolding assembly quality. By identifying the optimal feature subset, the method differentiates the priority of various structural parameters during inspection, providing insights for optimizing the quality inspection process. Full article
(This article belongs to the Special Issue Study of Building Detection, Assessment, and Management: Based on Computer and Information Technologies)
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12 pages, 9804 KiB  
Article
A Fast and Robust Safety Helmet Network Based on a Mutilscale Swin Transformer
by Changcheng Xiang, Duofen Yin, Fei Song, Zaixue Yu, Xu Jian and Huaming Gong
Buildings 2024, 14(3), 688; https://doi.org/10.3390/buildings14030688 - 5 Mar 2024
Cited by 4 | Viewed by 1434
Abstract
Visual inspection of the workplace and timely reminders of unsafe behaviors (e.g, not wearing a helmet) are particularly significant for avoiding injuries to workers on the construction site. Video surveillance systems generate large amounts of non-structure image data on site for this purpose; [...] Read more.
Visual inspection of the workplace and timely reminders of unsafe behaviors (e.g, not wearing a helmet) are particularly significant for avoiding injuries to workers on the construction site. Video surveillance systems generate large amounts of non-structure image data on site for this purpose; however, they require real-time recognition automation solutions based on computer vision. Although various deep-learning-based models have recently provided new ideas for identifying helmets in traffic monitoring, few solutions suitable for industry applications have been discussed due to the complex scenarios of construction sites. In this paper, a fast and robust network based on a mutilscale Swin Transformer is proposed for safety helmet detection (FRSHNet) at construction sites, which contains the following contributions. Firstly, MAE-NAS with the variant of MobileNetV3’s MobBlock as a basic block is applied to implement feature extraction. Simultaneously, a multiscale Swin Transformer module is utilized to obtain the spatial and contexture relationships in the multiscale features. Subsequently, in order to meet the scheme requirements of real-time helmet detection, efficient RepGFPN are adopted to integrate refined multiscale features to form a pyramid structure. Extensive experiments were conducted on the publicly available Pictor-v3 and SHWD datasets. The experimental results show that FRSHNet consistently provided a favorable performance, outperforming the existing state-of-the-art models. Full article
(This article belongs to the Special Issue Study of Building Detection, Assessment, and Management: Based on Computer and Information Technologies)
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26 pages, 10994 KiB  
Article
Robust Building Identification from Street Views Using Deep Convolutional Neural Networks
by Robin Roussel, Sam Jacoby and Ali Asadipour
Buildings 2024, 14(3), 578; https://doi.org/10.3390/buildings14030578 - 21 Feb 2024
Cited by 2 | Viewed by 2699
Abstract
Street view imagery (SVI) is a rich source of information for architectural and urban analysis using computer vision techniques, but its integration with other building-level data sources requires an additional step of visual building identification. This step is particularly challenging in architecturally homogeneous, [...] Read more.
Street view imagery (SVI) is a rich source of information for architectural and urban analysis using computer vision techniques, but its integration with other building-level data sources requires an additional step of visual building identification. This step is particularly challenging in architecturally homogeneous, dense residential streets featuring narrow buildings, due to a combination of SVI geolocation errors and occlusions that significantly increase the risk of confusing a building with its neighboring buildings. This paper introduces a robust deep learning-based method to identify buildings across multiple street views taken at different angles and times, using global optimization to correct the position and orientation of street view panoramas relative to their surrounding building footprints. Evaluating the method on a dataset of 2000 street views shows that its identification accuracy (88%) outperforms previous deep learning-based methods (79%), while methods solely relying on geometric parameters correctly show the intended building less than 50% of the time. These results indicate that previous identification methods lack robustness to panorama pose errors when buildings are narrow, densely packed, and subject to occlusions, while collecting multiple views per building can be leveraged to increase the robustness of visual identification by ensuring that building views are consistent. Full article
(This article belongs to the Special Issue Study of Building Detection, Assessment, and Management: Based on Computer and Information Technologies)
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