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Buildings
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Advances in Building Structure Analysis and Health Monitoring
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Advances in Building Structure Analysis and Health Monitoring

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

Deadline for manuscript submissions: 30 September 2025 | Viewed by 900

Special Issue Editors

Prof. Dr. Gongfa Chen

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Guest Editor
School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China
Interests: artificial intelligence; structural vibration analysis; building information modeling; structural damage identification
Prof. Dr. David H. Bassir

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Guest Editor
Laboratory IRAMAT-UMR-7065 CNRS, University of Bourgogne Franche-Comté (UTBM), 90010 Belfort, France
Interests: structural optimization; multiscale modeling; applications of artificial intelligence in civil engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Shuai Teng

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Guest Editor
Research Centre for Wind Engineering and Engineering Vibration, Guangzhou University, Guangzhou 510006, China
Interests: structural health monitoring; damage identification; defect detection of building; artificial intelligence technology; signal analysis

Special Issue Information

Dear Colleagues,

Rapid advancements in artificial intelligence (AI) have opened new frontiers in building structure analysis and health monitoring. This Special Issue focuses on the integration of AI technologies to enhance the safety, efficiency, and longevity of building structures. Contributions that explore innovative AI-based techniques, such as machine learning, deep learning, and data-driven models, for structural analysis, damage detection, and predictive maintenance, are invited. We are particularly interested in interdisciplinary approaches that combine AI with traditional structural engineering methods, enabling real-time monitoring and adaptive solutions to structural challenges. Our aim is to provide a platform for groundbreaking research that contributes to the evolution of intelligent building systems.

Prof. Dr. Gongfa Chen
Prof. Dr. David H. Bassir
Dr. Shuai Teng
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

  • artificial intelligence
  • building structures
  • structural health monitoring
  • machine learning
  • predictive maintenance
  • deep learning
  • structural analysis
  • damage detection
  • data-driven models
  • smart buildings

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

18 pages, 2431 KiB  
Article
A Dynamic Interaction Analysis of a Straddle Monorail Train and Steel–Concrete Composite Bridge
by Zhiyong Yao, Zongchao Liu and Zilin Zhong
Buildings 2025, 15(13), 2333; https://doi.org/10.3390/buildings15132333 - 3 Jul 2025
Viewed by 1
Abstract
Train–bridge dynamic interaction analysis is critical for the dynamic design of bridges and the safety and comfort assessment of trains. This study introduces a train–bridge dynamic model of a straddle monorail train and a steel–concrete composite track beam to investigate the dynamic performance [...] Read more.
Train–bridge dynamic interaction analysis is critical for the dynamic design of bridges and the safety and comfort assessment of trains. This study introduces a train–bridge dynamic model of a straddle monorail train and a steel–concrete composite track beam to investigate the dynamic performance of the bridge and train. It explores the influence of track irregularities and passenger loads on the dynamic response of train–bridge systems at various traveling speeds. The numerical results indicate that there is no significant resonance between the straddle monorail train and the steel–concrete composite bridge. However, track irregularities and train speed significantly amplify the responses of the train and bridge, including displacement, acceleration, and impact coefficient. Additionally, increased passenger load leads to a substantial rise in the vertical displacement of the bridge while reducing the vibration of the train, thereby improving riding comfort. The findings of this study provide valuable scientific insights and have significant practical applications for the use of steel–concrete composite bridges in straddle monorail systems. Full article
(This article belongs to the Special Issue Advances in Building Structure Analysis and Health Monitoring)
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20 pages, 3212 KiB  
Article
Unsupervised Restoration of Underwater Structural Crack Images via Physics-Constrained Image Translation and Multi-Scale Feature Retention
by Xianfeng Zeng, Wenji Ai, Zongchao Liu and Xianling Wang
Buildings 2025, 15(13), 2150; https://doi.org/10.3390/buildings15132150 - 20 Jun 2025
Viewed by 213
Abstract
Accurate visual inspection of underwater infrastructure, such as bridge piers and retaining walls, is often hindered by severe image degradation due to light attenuation and scattering. This paper introduces an unsupervised enhancement framework tailored for restoring underwater images containing structural cracks. The method [...] Read more.
Accurate visual inspection of underwater infrastructure, such as bridge piers and retaining walls, is often hindered by severe image degradation due to light attenuation and scattering. This paper introduces an unsupervised enhancement framework tailored for restoring underwater images containing structural cracks. The method combines a physical modeling of underwater light transmission with a deep image translation architecture that operates without requiring paired training samples. To address the loss of fine structural details, this paper incorporates a multi-scale feature integration module and a region-focused discriminator that jointly guide the enhancement process. Moreover, a physics-guided loss formulation is designed to promote optical consistency and texture fidelity during training. The proposed approach is validated on a real-world dataset collected from submerged structures under varying turbidity and illumination levels. Both objective evaluations and visual results show substantial improvements over baseline models, with better preservation of crack boundaries and overall visual quality. This work provides a robust solution for preprocessing underwater imagery in structural inspection tasks. Full article
(This article belongs to the Special Issue Advances in Building Structure Analysis and Health Monitoring)
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16 pages, 4163 KiB  
Article
Experimental and Theoretical Investigation on Cracking Behavior and Influencing Factors of Steel-Reinforced Concrete Deep Beams
by Gaoxing Hu, Lei Zeng, Buqing Chen and Shuai Teng
Buildings 2025, 15(11), 1812; https://doi.org/10.3390/buildings15111812 - 25 May 2025
Viewed by 367
Abstract
Steel-reinforced concrete (SRC) deep beams have been widely used in engineering applications such as high-rise buildings and long-span bridges, with their structural behavior and mechanical properties attracting significant research attention. To investigate the shear cracking behavior of SRC deep beams, seven specimens with [...] Read more.
Steel-reinforced concrete (SRC) deep beams have been widely used in engineering applications such as high-rise buildings and long-span bridges, with their structural behavior and mechanical properties attracting significant research attention. To investigate the shear cracking behavior of SRC deep beams, seven specimens with a scale of 0.4 times were designed for static loading tests, and the influence of the shear-span-to-depth ratio λ, the width ratio of the steel flange, and the height ratio of the steel web on the width and spacing of the diagonal crack was considered. The cracking behavior of the diagonal cracks in the shear span area were recorded by the digital image correlation (DIC) technique. The results show the following: (1) the use of the DIC technology revealed the entire process of crack occurrence, development, and evolution and obtained the distribution characteristics of crack development; (2) the steel flange width has a slight effect on the spacing and width of the diagonal cracks. The diagonal crack width increased with the improvement of the height of the steel web, but the influence of the steel web on the spacing of diagonal cracks was not significant. When the height ratio increased from 0.3 to 0.45 and 0.6, the maximum oblique crack width increased by 13% and 14.5%. Based on the above experimental results and relevant analysis conclusions, an improved method was proposed to calculate the diagonal crack width of composite deep beams by further considering the influence of the crack angle. Finally, the experimental results verified its high accuracy in a qualitative analysis. The calculation method proposed in this article can be used to predict and estimate the width of diagonal cracks in SRC deep beams. Full article
(This article belongs to the Special Issue Advances in Building Structure Analysis and Health Monitoring)
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