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Buildings
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Intelligent Monitoring and Detecting Methodologies for Building Structures
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Intelligent Monitoring and Detecting Methodologies for Building Structures

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Structures".

Deadline for manuscript submissions: closed (10 September 2024) | Viewed by 3947

Special Issue Editors

Dr. Weihang Gao

E-Mail Website
Guest Editor
School of Civil Engineering, Tongji University, Shanghai 200092, China
Interests: structural health monitoring; nondestructive testing; concrete structure; piezoelectric sensing; percussion acoustic; scene reconstruction
Dr. Lin Chen

E-Mail
Guest Editor
School of Civil Engineering, Tongji University, Shanghai 200092, China
Interests: embedded system development; STEM education; precision instruction; interactive teaching aids
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Ensuring the safety and operational reliability of building structures is a critical concern. Structural health monitoring provides a proactive and real-time approach to understanding the behavior of a structure, enabling the early detection of potential issues and preventing unexpected failures. Meanwhile, nondestructive testing methods offer an approach to assess structural integrity without causing additional damage, ensuring that evaluations can be conducted without compromising the safety of the structure. The importance of research in these domains lies in its capacity to enhance our understanding of structural performance, contributing to the development of robust maintenance strategies. Investigating health monitoring and nondestructive testing methods becomes imperative in establishing resilient cities, extending the lifespan of buildings, and minimizing risks associated with structural deterioration. Furthermore, with fast strides being made into the era of artificial intelligence, improvements in computing power, the emergence of algorithms, and the accumulation of monitoring data enable more possibilities for the use of intelligent structural monitoring and detection.

The main aim of this Special Issue is to explore the recent challenges and developments of intelligent monitoring and detecting approaches for building structures. Topics of interest include, but are not limited to, the following:

  • Structural health monitoring;
  • Nondestructive testing;
  • Building structures;
  • Sensing approach;
  • Signal and data processing;
  • Advanced equipment development;
  • Artificial intelligence.

Dr. Weihang Gao
Dr. Lin Chen
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 monthly 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

  • structural health monitoring
  • nondestructive testing
  • building structure
  • sensing approach
  • signal and data processing
  • advanced equipment development
  • artificial intelligence

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Published Papers (4 papers)

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Research

16 pages, 7010 KiB  
Article
A Three-Step Computer Vision-Based Framework for Concrete Crack Detection and Dimensions Identification
by Yanzhi Qi, Zhi Ding, Yaozhi Luo and Zhi Ma
Buildings 2024, 14(8), 2360; https://doi.org/10.3390/buildings14082360 - 31 Jul 2024
Viewed by 606
Abstract
Crack detection is significant to building repair and maintenance; however, conventional inspection is a labor-intensive and time-consuming process for field engineers. This paper proposes a three-step computer vision-based framework to quickly recognize concrete cracks and automatically identify their length, maximum width, and area [...] Read more.
Crack detection is significant to building repair and maintenance; however, conventional inspection is a labor-intensive and time-consuming process for field engineers. This paper proposes a three-step computer vision-based framework to quickly recognize concrete cracks and automatically identify their length, maximum width, and area in damage images. In step one, a region-based convolutional neural network (YOLOv8) is applied to train the crack localizing model. In step two, Gaussian filtering, Canny, and FindContours are integrated to extract the reference contour (a pre-designed seal) to obtain the conversion scale between pixels and millimeter-wise sizes. In step three, the recognized crack bounding box is cropped, and the ApproxPolyDP function and Hough transform are performed to quantify crack dimensions based on the conversion ratio. The developed framework was validated on a dataset of 4630 crack images, and the model training took 150 epochs. Results show that the average crack detection accuracy reaches 95.7%, and the precision of quantified dimensions is over 90%, while the error increases as the crack size grows smaller (increasing to 8% when the crack width is within 1 mm). The proposed method can help engineers to efficiently achieve crack information at building inspection sites, while the reference frame must be pre-marked near the crack, which may limit the scope of application scenarios. In addition, the robustness and accuracy of the developed image processing techniques-based crack quantification algorithm need to be further improved to meet the requirements in real cases when the crack is located within a complex background. Full article
(This article belongs to the Special Issue Intelligent Monitoring and Detecting Methodologies for Building Structures)
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28 pages, 6904 KiB  
Article
Automated Building Information Modeling Compliance Check through a Large Language Model Combined with Deep Learning and Ontology
by Nanjiang Chen, Xuhui Lin, Hai Jiang and Yi An
Buildings 2024, 14(7), 1983; https://doi.org/10.3390/buildings14071983 - 1 Jul 2024
Cited by 1 | Viewed by 1492
Abstract
Ensuring compliance with complex industry standards and regulations during the design and implementation phases of construction projects is a significant challenge in the building information modeling (BIM) domain. Traditional manual compliance checking methods are inefficient and error-prone, failing to meet modern engineering demands. [...] Read more.
Ensuring compliance with complex industry standards and regulations during the design and implementation phases of construction projects is a significant challenge in the building information modeling (BIM) domain. Traditional manual compliance checking methods are inefficient and error-prone, failing to meet modern engineering demands. Natural language processing (NLP) and deep learning methods have improved efficiency and accuracy in rule interpretation and compliance checking. However, these methods still require extensive manual feature engineering, large, annotated datasets, and significant computational resources. Large language models (LLMs) provide robust language understanding with minimal labeled data due to their pre-training and few-shot learning capabilities. However, their application in the AEC field is still limited by the need for fine-tuning for specific tasks, handling complex texts with nested clauses and conditional statements. This study introduces an innovative automated compliance checking framework that integrates LLM, deep learning models, and ontology knowledge models. The use of LLM is motivated by its few-shot learning capability, which significantly reduces the need for large, annotated datasets required by previous methods. Deep learning is employed to preliminarily classify regulatory texts, which further enhances the accuracy of structured information extraction by the LLM compared to directly feeding raw data into the LLM. This novel combination of deep learning and LLM significantly enhances the efficiency and accuracy of compliance checks by automating the processing of regulatory texts and reducing manual intervention. This approach is crucial for architects, engineers, project managers, and regulators, providing a scalable and adaptable solution for automated compliance in the construction industry with broad application prospects. Full article
(This article belongs to the Special Issue Intelligent Monitoring and Detecting Methodologies for Building Structures)
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16 pages, 6082 KiB  
Article
MFRWA: A Multi-Frequency Rayleigh Wave Approximation Method for Concrete Carbonation Depth Evaluation
by Xiangtao Sun, Yongxiang Cui, Jiawei Chen, Shanchang Yi, Xiuquan Li and Lin Chen
Buildings 2024, 14(6), 1821; https://doi.org/10.3390/buildings14061821 - 15 Jun 2024
Viewed by 488
Abstract
Carbonation depth is essential to determine the durability and predict the remaining service life of concrete structures. This study proposes a multi-frequency Rayleigh wave approximation method (MFRWA) to evaluate carbonation depth by exploiting the frequency-dependent penetration depths of ultrasonic Rayleigh waves. A series [...] Read more.
Carbonation depth is essential to determine the durability and predict the remaining service life of concrete structures. This study proposes a multi-frequency Rayleigh wave approximation method (MFRWA) to evaluate carbonation depth by exploiting the frequency-dependent penetration depths of ultrasonic Rayleigh waves. A series of numerical simulations are conducted to investigate the effective penetration depth of Rayleigh waves and the feasibility of the proposed MFRWA method on carbonation depth evaluation. Subsequently, the accelerated carbonation experiment is conducted to evaluate the carbonation depth using low-frequency and high-frequency Rayleigh waves, and the measured results from the Rayleigh wave method are compared with the ones from the phenolphthalein indicator and thermalgravimetric analysis (TGA) method. The results show that carbonation depth measured by Rayleigh wave method meets well with the one from TGA technique, demonstrating that the proposed method could provide a non-destructive and precise carbonation depth estimation. The proposed MFRWA method contributes a novel scheme for concrete carbonation evaluation and holds substantial potential in both laboratory and field applications. Full article
(This article belongs to the Special Issue Intelligent Monitoring and Detecting Methodologies for Building Structures)
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19 pages, 3597 KiB  
Article
Surface Deformation Calculation Method Based on Displacement Monitoring Data
by Lin He and Yibin Yao
Buildings 2024, 14(5), 1417; https://doi.org/10.3390/buildings14051417 - 14 May 2024
Viewed by 640
Abstract
Considering the importance of calculating surface deformation based on monitoring data, this paper proposes a method for calculating horizontal deformation based on horizontal displacement monitoring data. This study first analyzes the characteristics of horizontal displacement monitoring data, then proposes a scheme for obtaining [...] Read more.
Considering the importance of calculating surface deformation based on monitoring data, this paper proposes a method for calculating horizontal deformation based on horizontal displacement monitoring data. This study first analyzes the characteristics of horizontal displacement monitoring data, then proposes a scheme for obtaining the surface horizontal displacement field through corresponding discrete point interpolation. Subsequently, the calculation method for surface horizontal strain is introduced, along with relevant examples. The study also systematically summarizes the calculation methods for surface curvature and surface tilt deformation values, forming a set of surface deformation calculation methods based on monitoring data. The research results indicate that when there is a large number of on-site monitoring points, effective monitoring points can be selected based on the direction of horizontal displacement. When interpolating the surface horizontal displacement field, the interpolation accuracy of the radial basis function method is slightly higher than that of ordinary Kriging. The form of coordinate expression has a significant impact on interpolation accuracy. The accuracy of interpolation using horizontal displacement vectors expressed in polar coordinates is higher than that using vectors expressed in Cartesian coordinates. The calculated surface horizontal strain has effective upper and lower limits, with lower-limit strain on the contour line conforming to the typical surface deformation patterns around mined-out areas. Full article
(This article belongs to the Special Issue Intelligent Monitoring and Detecting Methodologies for Building Structures)
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