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Buildings
Special Issues
Building Structure Health Monitoring and Damage Detection
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Building Structure Health Monitoring and Damage Detection

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

Deadline for manuscript submissions: closed (10 April 2026) | Viewed by 1738

Special Issue Editors

Dr. Fidel Lozano

E-Mail Website
Guest Editor
Department of Civil Engineering, Universidad of Castilla-La Mancha (UCLM), 13071 Ciudad Real, Spain
Interests: BIM; structural health monitoring; artificial intelligence; Construction 4.0; sensors
Dr. Edison Atencio

E-Mail Website
Guest Editor
School of Civil Engineering, Pontificia Universidad Católica de Valparaíso, Av. Brasil 2147, Valparaíso, Chile
Interests: BIM; Construction 4.0; enterprise architecture; business process management (BPM); project management; project governance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Structural health monitoring and damage detection are crucial to ensure the safety and durability of buildings. This Special Issue gathers innovative research on the behavior of structures under various conditions, aiming to improve damage prevention and mitigation.

Topics of interest include the analysis of dynamic structural response and the application of artificial intelligence in damage detection, real-time monitoring, and structural integrity assessment. Advanced methods for fault identification and the prediction of structural behavior under extreme events like earthquakes and vibrations are also explored.

This Special Issue welcomes original research and review studies that provide innovative and practical solutions for structural monitoring and damage detection, promoting safety and sustainability in the construction field.

Dr. Fidel Lozano
Dr. Edison Atencio
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 250 words) can be sent to the Editorial Office for assessment.

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
  • damage detection
  • structural integrity
  • dynamic structural response
  • artificial intelligence
  • real-time monitoring

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

Published Papers (2 papers)

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Research

33 pages, 5134 KB  
Article
Dynamic Structural Early Warning for Bridge Based on Deep Learning: Methodology and Engineering Application
by Fentao Guo, Yufeng Xu, Qingzhong Quan and Zhantao Zhang
Buildings 2026, 16(4), 823; https://doi.org/10.3390/buildings16040823 - 18 Feb 2026
Viewed by 363
Abstract
In bridge health monitoring, structural responses are strongly coupled with temperature effects and vehicle load effects, making it difficult for conventional fixed thresholds and single data-driven approaches to simultaneously achieve environmental adaptability and quantitative reliability assessment. To address this issue, this study proposes [...] Read more.
In bridge health monitoring, structural responses are strongly coupled with temperature effects and vehicle load effects, making it difficult for conventional fixed thresholds and single data-driven approaches to simultaneously achieve environmental adaptability and quantitative reliability assessment. To address this issue, this study proposes a deep-learning-based dynamic early-warning method for bridge structures, using health-monitoring data from an in-service long-span cable-stayed bridge as the research background. First, a two-month mid-span deflection time series is processed using variational mode decomposition optimized by the Porcupine Optimization Algorithm to separate temperature-induced effects. Subsequently, a hybrid prediction model integrating Informer and SEnet is constructed. Temperature and temperature-induced deflection components are used as input features, and a sliding-window strategy is adopted to achieve high-accuracy prediction of the temperature-induced deflection trend, which serves as the time-varying baseline of the dynamic threshold. On this basis, vehicle load effects are modeled by combining Pareto extreme value theory with finite element analysis and superimposed to establish a two-level dynamic early-warning threshold system that satisfies code requirements. Furthermore, a stochastic finite element Monte Carlo method is introduced to probabilistically model uncertainties associated with material parameters, load effects, and model prediction errors. The threshold failure probability at each time instant is taken as the evaluation metric, enabling quantitative characterization of threshold reliability. The results indicate that under combined multiple working conditions, the proposed method reduces the maximum failure probability of the first-level warning by 32.68% and that of the second-level warning by 93.48%, with more stable and consistent probabilistic responses. In engineering applications, simulation experiments based on stochastic traffic loading show that the warning accuracy is improved by up to 19.27%, while the error rate is reduced by up to 16.16%. The study demonstrates that the proposed method possesses a clear physical and statistical foundation as well as good engineering feasibility and provides a viable pathway for transforming bridge early-warning systems from experience-based schemes toward data-driven and risk-oriented frameworks. Full article
(This article belongs to the Special Issue Building Structure Health Monitoring and Damage Detection)
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20 pages, 6383 KB  
Article
Post-Earthquake Damage Detection and Safety Assessment of the Ceiling Panoramic Area in Large Public Buildings Using Image Stitching
by Lichen Wang, Yapeng Liang and Shihao Yan
Buildings 2025, 15(21), 3922; https://doi.org/10.3390/buildings15213922 - 30 Oct 2025
Viewed by 803
Abstract
With the development of artificial intelligence, intelligent assessment methods have been applied in post-earthquake emergency rescue. These methods enable rapid and accurate identification and localization of earthquake-induced damage to ceilings in large public buildings, which often serve as emergency shelters. However, in practical [...] Read more.
With the development of artificial intelligence, intelligent assessment methods have been applied in post-earthquake emergency rescue. These methods enable rapid and accurate identification and localization of earthquake-induced damage to ceilings in large public buildings, which often serve as emergency shelters. However, in practical applications, challenges remain: damage recognition accuracy is low when using wide-field distant shots, while close-up local shots are unsuitable for identifying panoramic regional damage. As a result, high-precision intelligent safety assessment of the entire ceiling area cannot be achieved. Therefore, this study proposes a panoramic image stitching method based on SIFT feature point detection and registration, optimized by the RANSAC algorithm, to generate high-resolution, wide-angle panoramic images of ceilings in large public buildings. The BRISQUE values of the stitched images range between 20 and 30, indicating good stitching quality. Subsequently, by integrating damage recognition and image stitching techniques, a safety assessment test was conducted on 227 stitched images of earthquake-induced ceiling damage captured in real scenes, using evaluation indicators such as damage type and severity quantification. The safety assessment achieved an overall accuracy of 98.7%, demonstrating the effectiveness of ceiling damage detection technology based on image stitching. This technology enables intelligent post-earthquake safety assessment of ceilings in large public buildings across the entire area. Full article
(This article belongs to the Special Issue Building Structure Health Monitoring and Damage Detection)
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