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Buildings
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AI-Powered Structural Health Monitoring: Innovations and Applications
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AI-Powered Structural Health Monitoring: Innovations and Applications

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

Deadline for manuscript submissions: 20 April 2026 | Viewed by 2363

Special Issue Editors

Dr. Yanda Shao

E-Mail Website
Guest Editor
Faculty of Science and Engineering, Curtin University, Perth 6845, Australia
Interests: computer vision; machine learning; structural health monitoring; displacement measurement
Dr. Qilin Li

E-Mail Website
Guest Editor
Faculty of Science and Engineering, Curtin University, Perth 6845, Australia
Interests: machine learning; physics-informed machine learning; computer vision; structural health monitoring; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Fei He

E-Mail Website
Guest Editor
Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
Interests: physics informed machine learning; fluid-structure interaction

Special Issue Information

Dear Colleagues,

The integration of artificial intelligence (AI) into structural health monitoring (SHM) is reshaping how we assess and maintain the integrity of critical infrastructure. Advanced AI techniques, including machine learning, deep learning, and computer vision, enable more accurate damage detection, early warning systems, and real-time decision-making with minimal human intervention.

This Special Issue seeks contributions that explore the use of AI/ML in SHM in terms of both theoretical advancements and practical implementations. Topics may include vision-based inspection, anomaly detection, sensor data fusion, AI-enabled digital twins, and AI-supported non-destructive testing (NDT) methods. We welcome high-quality original research, reviews, and case studies that address emerging challenges and opportunities in AI-driven SHM.

We look forward to your valuable contributions to this Special Issue.

Dr. Yanda Shao
Dr. Qilin Li
Dr. Fei He
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 (SHM)
  • artificial intelligence (AI)
  • machine learning (ML)
  • computer vision
  • smart structure
  • digital twin
  • physics-informed machine learning
  • sensor fusion anomaly detection

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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

13 pages, 2928 KB  
Article
Application Research on General Technology for Safety Appraisal of Existing Buildings Based on Unmanned Aerial Vehicles and Stair-Climbing Robots
by Zizhen Shen, Rui Wang, Lianbo Wang, Wenhao Lu and Wei Wang
Buildings 2025, 15(22), 4145; https://doi.org/10.3390/buildings15224145 - 17 Nov 2025
Viewed by 346
Abstract
Structure detection (SD) has emerged as a critical technology for ensuring the safety and longevity of infrastructure, particularly in housing and civil engineering. Traditional SD methods often rely on manual inspections, which are time-consuming, labor-intensive, and prone to human error, especially in complex [...] Read more.
Structure detection (SD) has emerged as a critical technology for ensuring the safety and longevity of infrastructure, particularly in housing and civil engineering. Traditional SD methods often rely on manual inspections, which are time-consuming, labor-intensive, and prone to human error, especially in complex environments such as dense urban settings or aging buildings with deteriorated materials. Recent advances in autonomous systems—such as Unmanned Aerial Vehicles (UAVs) and climbing robots—have shown promise in addressing these limitations by enabling efficient, real-time data collection. However, challenges persist in accurately detecting and analyzing structural defects (e.g., masonry cracks, concrete spalling) amidst cluttered backgrounds, hardware constraints, and the need for multi-scale feature integration. The integration of machine learning (ML) and deep learning (DL) has revolutionized SD by enabling automated feature extraction and robust defect recognition. For instance, RepConv architectures have been widely adopted for multi-scale object detection, while attention mechanisms like TAM (Technology Acceptance Model) have improved spatial feature fusion in complex scenes. Nevertheless, existing works often focus on singular sensing modalities (e.g., UAVs alone) or neglect the fusion of complementary data streams (e.g., ground-based robot imagery) to enhance detection accuracy. Furthermore, computational redundancy in multi-scale processing and inconsistent bounding box regression in detection frameworks remain underexplored. This study addresses these gaps by proposing a generalized safety inspection system that synergizes UAV and stair-climbing robot data. We introduce a novel multi-scale targeted feature extraction path (Rep-FasterNet TAM block) to unify automated RepConv-based feature refinement with dynamic-scale fusion, reducing computational overhead while preserving critical structural details. For detection, we combine traditional methods with remote sensor fusion to mitigate feature loss during image upsampling/downsampling, supported by a structural model GIOU [Mathematical Definition: GIOU = IOU − (C − U)/C] that enhances bounding box regression through shape/scale-aware constraints and real-time analysis. By siting our work within the context of recent reviews on ML/DL for SD, we demonstrate how our hybrid approach bridges the gap between autonomous inspection hardware and AI-driven defect analysis, offering a scalable solution for large-scale housing safety assessments. In response to challenges in detecting objects accurately during housing safety assessments—including large/dense objects, complex backgrounds, and hardware limitations—we propose a generalized inspection system leveraging data from UAVs and stair-climbing robots. To address multi-scale feature extraction inefficiencies, we design a Rep-FasterNet TAM block that integrates RepConv for automated feature refinement and a multi-scale attention module to enhance spatial feature consistency. For detection, we combine dynamic-scale remote feature fusion with traditional methods, supported by a structural GIOU model that improves bounding box regression through shape/scale constraints and real-time analysis. Experiments demonstrate that our system increases masonry/concrete assessment accuracy by 11.6% and 20.9%, respectively, while reducing manual drawing restoration workload by 16.54%. This validates the effectiveness of our hybrid approach in unifying autonomous inspection hardware with AI-driven analysis, offering a scalable solution for SD in housing infrastructure. Full article
(This article belongs to the Special Issue AI-Powered Structural Health Monitoring: Innovations and Applications)
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13 pages, 1556 KB  
Article
Prediction of Plate End Debonding of FRP-Strengthened RC Beams Based on Explainable Machine Learning
by Sheng Zheng and Woubishet Zewdu Taffese
Buildings 2025, 15(19), 3576; https://doi.org/10.3390/buildings15193576 - 4 Oct 2025
Viewed by 634
Abstract
This research explores the phenomenon of plate-end (PE) debonding in reinforced concrete (RC) beams strengthened with fiber-reinforced polymer (FRP) composites. This type of failure represents a key mechanism that undermines the structural performance and efficiency of FRP reinforcement systems. Despite the widespread use [...] Read more.
This research explores the phenomenon of plate-end (PE) debonding in reinforced concrete (RC) beams strengthened with fiber-reinforced polymer (FRP) composites. This type of failure represents a key mechanism that undermines the structural performance and efficiency of FRP reinforcement systems. Despite the widespread use of FRP in structural repair due to its high strength and corrosion resistance, PE debonding—often triggered by shear or inclined cracks—remains a major challenge. Traditional computational models for predicting PE debonding suffer from low accuracy due to the nonlinear relationship between influencing parameters. To address this, the research employs machine learning techniques and SHapley Additive exPlanations (SHAP), to develop more accurate and explainable predictive models. A comprehensive database is constructed using key parameters affecting PE debonding. Machine learning algorithms are trained and evaluated, and their performance is compared with existing normative models. The study also includes parameter importance and sensitivity analyses to enhance model interpretability and guide future design practices in FRP-based structural reinforcement. Full article
(This article belongs to the Special Issue AI-Powered Structural Health Monitoring: Innovations and Applications)
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