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Buildings
Special Issues
Advanced Damage Detection and State Monitoring Technologies for Engineering Structures
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Advanced Damage Detection and State Monitoring Technologies for Engineering Structures

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

Deadline for manuscript submissions: 10 November 2025 | Viewed by 322

Special Issue Editors

Prof. Dr. Jun Chen

E-Mail Website
Guest Editor
School of Transportation Science and Engineering, Beihang University, Beijing 100191, China
Interests: nondestructive evaluation; structural health monitoring; signal analysis; deep learning application
Dr. Lunan Wei

E-Mail Website
Guest Editor
College of Civil Engineering, Anhui Jianzhu University, Hefei 230601, China
Interests: structural health monitoring; nondestructive testing; linear/nonlinear ultrasonics; composite structures; damage detection; intelligent detection; defect resonance; numerical modeling
Dr. Chenglong Yang

E-Mail Website
Guest Editor
School of Civil and Environmental Engineering, Changsha University of Science and Technology, Changsha 410114, China
Interests: structural health monitoring; structural damage imaging; material performance evaluation; ultrasonic array detection; intelligent detection

Special Issue Information

Dear Colleagues,

With the rapidly evolving environment, engineering materials and structures face unprecedented challenges. Aging infrastructure, environmental hazards, and escalating demands for safety and durability underscore the pressing need for advanced monitoring and diagnostic methodologies. In this context, innovative nondestructive testing techniques integrated with intelligent algorithms have emerged as transformative tools, enhancing detection accuracy while facilitating proactive maintenance and timely decision-making.

This Special Issue seeks to present state-of-the-art research and recent advancements in nondestructive evaluation (NDE) and structural health monitoring (SHM) technologies and intelligent algorithms tailored to structural engineering applications. We invite high-quality original research articles and comprehensive review papers addressing, but not limited to, the following topics:

  • Damage characterization of engineering materials;
  • Intelligent monitoring and sensing;
  • Structural state evaluation based on signal analysis;
  • Applications of deep learning in NDE and SHM;
  • Big data methods.

Prof. Dr. Jun Chen
Dr. Lunan Wei
Dr. Chenglong Yang
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

  • structural health monitoring
  • non-destructive evaluation
  • deep learning algorithm
  • damage detection
  • big data method
  • signal processing

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

Published Papers (1 paper)

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Research

19 pages, 2359 KiB  
Article
Research on Concrete Crack Damage Assessment Method Based on Pseudo-Label Semi-Supervised Learning
by Ming Xie, Zhangdong Wang and Li’e Yin
Buildings 2025, 15(15), 2726; https://doi.org/10.3390/buildings15152726 - 1 Aug 2025
Viewed by 192
Abstract
To address the inefficiency of traditional concrete crack detection methods and the heavy reliance of supervised learning on extensive labeled data, in this study, an intelligent assessment method of concrete damage based on pseudo-label semi-supervised learning and fractal geometry theory is proposed to [...] Read more.
To address the inefficiency of traditional concrete crack detection methods and the heavy reliance of supervised learning on extensive labeled data, in this study, an intelligent assessment method of concrete damage based on pseudo-label semi-supervised learning and fractal geometry theory is proposed to solve two core tasks: one is binary classification of pixel-level cracks, and the other is multi-category assessment of damage state based on crack morphology. Using three-channel RGB images as input, a dual-path collaborative training framework based on U-Net encoder–decoder architecture is constructed, and a binary segmentation mask of the same size is output to achieve the accurate segmentation of cracks at the pixel level. By constructing a dual-path collaborative training framework and employing a dynamic pseudo-label refinement mechanism, the model achieves an F1-score of 0.883 using only 50% labeled data—a mere 1.3% decrease compared to the fully supervised benchmark DeepCrack (F1 = 0.896)—while reducing manual annotation costs by over 60%. Furthermore, a quantitative correlation model between crack fractal characteristics and structural damage severity is established by combining a U-Net segmentation network with the differential box-counting algorithm. The experimental results demonstrate that under a cyclic loading of 147.6–221.4 kN, the fractal dimension monotonically increases from 1.073 (moderate damage) to 1.189 (failure), with 100% accuracy in damage state identification, closely aligning with the degradation trend of macroscopic mechanical properties. In complex crack scenarios, the model attains a recall rate (Re = 0.882), surpassing U-Net by 13.9%, with significantly enhanced edge reconstruction precision. Compared with the mainstream models, this method effectively alleviates the problem of data annotation dependence through a semi-supervised strategy while maintaining high accuracy. It provides an efficient structural health monitoring solution for engineering practice, which is of great value to promote the application of intelligent detection technology in infrastructure operation and maintenance. Full article
(This article belongs to the Special Issue Advanced Damage Detection and State Monitoring Technologies for Engineering Structures)
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