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Buildings
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Concrete Structural Safety and Health Monitoring
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Concrete Structural Safety and Health Monitoring

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Materials, and Repair & Renovation".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 6355

Special Issue Editors

Dr. Demi Ai

E-Mail Website
Guest Editor
School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: structural health monitoring
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hongyou Cao

E-Mail Website
Guest Editor
School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, China
Interests: structural analysis theory; structural optimization of cable-supported bridges
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xiaowei Ye

E-Mail Website
Guest Editor
Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China
Interests: structural health monitoring; machine vision; shield tunnel; fatigue analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Common damages in concrete structures, such as cracks and corrosion caused by operational load or erosion environment, are usually of a small size and invisible at the initial stage. They will continuously accumulate and deteriorate the capacity of the structures or components and consequently pose a threat to structural safety. To this end, diverse structural damage detection and health monitoring techniques have been developed in recent decades. Those techniques, including global/local, static/dynamic, and destructive/nondestructive ones, facilitate the measurements of the loading/operating environment and the critical responses of the structure to track and estimate operational incidents, anomalies, and deterioration or damages. While the online, real-time, and in situ monitoring of concrete infrastructures still confronts with challenges of dealing with a considerable amount of data, inaccurate detection results, or labor-intensive cost, which motivates state-of-the-art technologies such as artificial intelligence. This Special Issue aims to present recent advances in concrete structural damage detection, safety evaluation, and health monitoring, particularly those enhanced by machine learning, computational intelligence, or data mining. This Issue will cover topics of interest that include, but are not limited to, the following topics:

  • Concrete structural damage detection;
  • Concrete structural health monitoring;
  • Concrete structural safety evaluation;
  • Application of machine learning for damage detection;
  • Application of computational intelligence for signal processing;
  • Application of smart materials in structural monitoring.

Dr. Demi Ai
Prof. Dr. Hongyou Cao
Prof. Dr. Xiaowei Ye
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

  • concrete structures
  • damage detection
  • structural health monitoring
  • machine learning
  • computational intelligence
  • signal processing
  • building monitoring

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Published Papers (4 papers)

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Research

20 pages, 2334 KiB  
Article
Resistance Factor Spectra for the Ultimate Limit State of the National Building Code of Canada
by Sami W. Tabsh and Moussa Leblouba
Buildings 2024, 14(3), 855; https://doi.org/10.3390/buildings14030855 - 21 Mar 2024
Viewed by 977
Abstract
Over the years, structural engineering codes and specifications in Canada and elsewhere have moved from an allowable stress design (ASD) approach to a load and resistance factor design (LRFD) philosophy. LRFD methodology takes better account of the inherent variability in both loading and [...] Read more.
Over the years, structural engineering codes and specifications in Canada and elsewhere have moved from an allowable stress design (ASD) approach to a load and resistance factor design (LRFD) philosophy. LRFD methodology takes better account of the inherent variability in both loading and resistance by providing different factors of safety for loads of distinct natures with regard to their probability of overload, frequency of occurrences and changes in point of application. The method also results in safer structures because it considers the behavior at collapse. While resistance factors for traditional construction materials based on LRFD in the National Building Code (NBC) of Canada are available, they cannot be used for non-conventional ones. This is because the resistance of such materials due to various load effects has unique bias factors (λR) and coefficients of variation (VR), which greatly impact their reliability index (β). In this study, relationships between the resistance factor ϕ and critical load effects from the NBC load combinations at ultimate limit states are developed for a wide range of resistance bias factors and coefficients of variation. The relationships are presented in the form of charts that are useful for researchers and code-writing professionals who have expertise in the various fields of structural engineering but lack proper background in reliability theory. The developed spectra showed that for the same ϕ, β increases with an increase in the live-to-dead load (L/D) ratio until it reaches 1; thereafter, the shape of the relationship will depend on the statistics of the resistance as well as on the magnitude of ϕ. For a small ϕ and VR, β will keep increasing with an increase in the L/D ratio from 1 until 3, albeit at a lesser rate. For L/D > 3, the relationship between the critical β and applied load is just about constant. This finding is also true for load combinations involving snow and wind. Application of the method is illustrated by a practical example involving the shear strength of a corrugated web steel beam. Full article
(This article belongs to the Special Issue Concrete Structural Safety and Health Monitoring)
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17 pages, 7376 KiB  
Article
A Subpixel Concrete Crack Measurement Method Based on the Partial Area Effect
by Jiayan Zheng, Yan Liu, Renwei Luo, Haijing Liu, Zhixiang Zhou and Ji He
Buildings 2024, 14(1), 151; https://doi.org/10.3390/buildings14010151 - 8 Jan 2024
Cited by 2 | Viewed by 1405
Abstract
To improve the accuracy of concrete crack measurement with a machine vision method in structural health monitoring and in technical status evaluation, a subpixel crack measurement method based on the partial area effect was proposed. (1) First, a pixelwise crack image segmentation method [...] Read more.
To improve the accuracy of concrete crack measurement with a machine vision method in structural health monitoring and in technical status evaluation, a subpixel crack measurement method based on the partial area effect was proposed. (1) First, a pixelwise crack image segmentation method was established through a multi-step process of multi-threshold fusion and morphology operation, and a novel pixel degree crack width calculation method was developed with the extraction of the middle points, the center line and its normal, and the intersection of the center line normal and crack edges. (2) Then, a subpixel algorithm based on the partial area effect was introduced to locate vertical, horizontal, and oblique cracks in subpixel crack edges, and the subpixel crack width could be calculated along the crack center line pixelwise. (3) Finally, the proposed method was verified by indoor concrete beam crack measurement tests with a digital microscope, and the results show that the maximum relative errors of the subpixel width of the horizontal, vertical, and oblique straight cracks measured by the proposed method were 3.06%, 8.97%, and 5.16%, respectively. The absolute error of the crack length was less than 0.30 mm, and the measurement accuracy could reach 0.01 pixels. The subpixel crack measurement method provides a novel possible solution for structural health monitoring. Full article
(This article belongs to the Special Issue Concrete Structural Safety and Health Monitoring)
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15 pages, 5574 KiB  
Article
Deep-Learning- and Unmanned Aerial Vehicle-Based Structural Crack Detection in Concrete
by Tao Jin, Wen Zhang, Chunlai Chen, Bin Chen, Yizhou Zhuang and He Zhang
Buildings 2023, 13(12), 3114; https://doi.org/10.3390/buildings13123114 - 15 Dec 2023
Cited by 2 | Viewed by 1776
Abstract
Deep-learning- and unmanned aerial vehicle (UAV)-based methods facilitate structural crack detection for tall structures. However, contemporary datasets are generally established using images taken with handheld or vehicle-mounted cameras. Thus, these images might be different from those taken by UAVs in terms of resolution [...] Read more.
Deep-learning- and unmanned aerial vehicle (UAV)-based methods facilitate structural crack detection for tall structures. However, contemporary datasets are generally established using images taken with handheld or vehicle-mounted cameras. Thus, these images might be different from those taken by UAVs in terms of resolution and lighting conditions. Considering the difficulty and complexity of establishing a crack image dataset, making full use of the current datasets can help reduce the shortage of UAV-based crack image datasets. Therefore, the performance evaluation of existing crack image datasets in training deep neural networks (DNNs) for crack detection in UAV images is essential. In this study, four DNNs were trained with different architectures based on a publicly available dataset and tested using a small UAV-based crack image dataset with 648 +pixel-wise annotated images. These DNNs were first tested using the four indices of precision, recall, mIoU, and F1, and image tests were also conducted for intuitive comparison. Moreover, a field experiment was carried out to verify the performance of the trained DNNs in detecting cracks from raw UAV structural images. The results indicate that the existing dataset can be useful to train DNNs for crack detection from UAV images; the TransUNet achieved the best performance in detecting all kinds of structural cracks. Full article
(This article belongs to the Special Issue Concrete Structural Safety and Health Monitoring)
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20 pages, 8103 KiB  
Article
Up-Floating Destruction and Reinforcement Measures of Damaged Basement Based on the Bending Moment and Deformation Analysis
by Zhifeng Wu, Zhiyong Li, Jian Fan, Hongyou Cao, Bo Huang and Hui Liu
Buildings 2023, 13(8), 1918; https://doi.org/10.3390/buildings13081918 - 28 Jul 2023
Cited by 3 | Viewed by 1259
Abstract
Based on the up-floating incident of the basement in a high-rise residential building, the finite element (FE) model of the up-floating destruction region is established to investigate the damage mechanism. The stress states and the deformations of the basement structure are obtained under [...] Read more.
Based on the up-floating incident of the basement in a high-rise residential building, the finite element (FE) model of the up-floating destruction region is established to investigate the damage mechanism. The stress states and the deformations of the basement structure are obtained under complex loads including water buoyancy forces, vehicle loads and construction loads. To assess the extent of damage, a novel damage indicator is defined based on two levels: the cracking bending moments and the yield bending moment. The first-level cracking bending moment, second-level cracking bending moment and the yield bending moment can be determined using the section stratification method. By comparing the maximum bending moment of the component with its corresponding cracking moment, one can determine whether the cracks have occurred and assess their severity. Meanwhile, the antifloating failure model is constructed to analyze the mechanism of the up-floating destruction. Finally, a detailed reinforcement treatment plan of ‘decompression first and then reinforcement’ is presented to reinforce and repair the damaged basement structure. The mechanism analysis of the up-floating destruction and the comprehensive reinforcement treatments ensure the simulation of the life cycle of emergence, development and treatment to ensure structural safety. Full article
(This article belongs to the Special Issue Concrete Structural Safety and Health Monitoring)
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