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Structural Health Monitoring Through Advanced Artificial Intelligence
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Structural Health Monitoring Through Advanced Artificial Intelligence

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

Deadline for manuscript submissions: 10 August 2026 | Viewed by 9151

Special Issue Editors

Dr. Chia-Ming Chang

E-Mail Website
Guest Editor
Department of Civil Engineering, National Taiwan University, Taipei 10617, Taiwan
Interests: structural control; advanced large-scale structural testing; smart structures; earthquake engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Tzu-Kang Lin

E-Mail Website
Guest Editor
Department of Civil Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan
Interests: structural health monitoring; artificial intelligence; information theory; bridge engineering; smart structural control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapidly emerging field of resilient and smart cities is redefining urban development and preserving existing infrastructure against natural hazards. Central to this field is structural health monitoring through advanced artificial intelligence, which involves the use of networked sensing and monitoring to collect and analyze vast amounts of data from sensors and digital images. Artificial intelligence methodologies, including machine learning, have shown superiority in model updating, diagnostics, data interpretation, and damage detection. These advancements are transforming traditional SHM approaches, making them more efficient and accurate. With the integration of technologies such as artificial intelligence, the Internet of Things (IoT), and big data analytics, structural health monitoring can be advanced and significantly improved, offering better damage assessment, precise localization, and enhanced monitoring capabilities. Therefore, structural health monitoring through advanced artificial intelligence is needed to ensure the longevity, reliability, and performance of structures.

Dr. Chia-Ming Chang
Prof. Dr. Tzu-Kang Lin
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
  • artificial intelligence
  • machine learning
  • damage detection
  • feature extraction
  • system identification
  • lifecycle assessment
  • real-time monitoring
  • advanced monitoring techniques

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Published Papers (5 papers)

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Research

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28 pages, 4754 KB  
Article
Integration of Machine Learning Models and Tiering Technique in Predicting the Compressive Strength of FRP-Strengthened Circular Concrete Columns
by Anh Duc Pham, Quynh Chau Truong, Quang Trung Nguyen, Cong Luyen Nguyen, Thi Thao Nguyen Nguyen and Anh Duc Mai
Buildings 2026, 16(1), 204; https://doi.org/10.3390/buildings16010204 - 2 Jan 2026
Viewed by 657
Abstract
This study aims to investigate the performance of the combined machine learning (ML) models and tiering technique for predicting the compressive strength of FRP-strengthened circular concrete columns. A dataset consisting of 725 experimental results has been assembled from available research studies to evaluate [...] Read more.
This study aims to investigate the performance of the combined machine learning (ML) models and tiering technique for predicting the compressive strength of FRP-strengthened circular concrete columns. A dataset consisting of 725 experimental results has been assembled from available research studies to evaluate the prediction models. Pearson’s correlation analysis has been carried out to investigate the relationship between seven input parameters and the target parameter. The Taylor diagram has been plotted to deter-mine the best design-oriented strength model. The prediction performance of the combined ML models and tiering technique was compared with that of single ML models and ten design-oriented strength models. The research outcomes revealed that applying the tiering technique significantly improved the prediction accuracy of the ML models. It was also found that the best ML model for predicting the compressive strength of FRP-strengthened circular concrete columns was the combined random forest model and tiering technique, which outperformed single ML and design-oriented strength models. Full article
(This article belongs to the Special Issue Structural Health Monitoring Through Advanced Artificial Intelligence)
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41 pages, 7185 KB  
Article
Two-Stage Dam Displacement Analysis Framework Based on Improved Isolation Forest and Metaheuristic-Optimized Random Forest
by Zhihang Deng, Qiang Wu and Minshui Huang
Buildings 2025, 15(24), 4467; https://doi.org/10.3390/buildings15244467 - 10 Dec 2025
Cited by 2 | Viewed by 603
Abstract
Dam displacement monitoring is crucial for assessing structural safety; however, conventional models often prioritize single-task prediction, leading to an inherent difficulty in balancing monitoring data quality with model performance. To bridge this gap, this study proposes a novel two-stage analytical framework that synergistically [...] Read more.
Dam displacement monitoring is crucial for assessing structural safety; however, conventional models often prioritize single-task prediction, leading to an inherent difficulty in balancing monitoring data quality with model performance. To bridge this gap, this study proposes a novel two-stage analytical framework that synergistically integrates an improved isolation forest (iForest) with a metaheuristic-optimized random forest (RF). The first stage focuses on data cleaning, where Kalman filtering is applied for denoising, and a newly developed Dynamic Threshold Isolation Forest (DTIF) algorithm is introduced to effectively isolate noise and outliers amidst complex environmental loads. In the second stage, the model’s predictive capability is enhanced by first employing the LASSO algorithm for feature importance analysis and optimal subset selection, followed by an Improved Reptile Search Algorithm (IRSA) for fine-tuning RF hyperparameters, thereby significantly boosting the model’s robustness. The IRSA incorporates several key improvements: Tent chaotic mapping during initialization to ensure population diversity, an adaptive parameter adjustment mechanism combined with a Lévy flight strategy in the encircling phase to dynamically balance global exploration and convergence, and the integration of elite opposition-based learning with Gaussian perturbation in the hunting phase to refine local exploitation. Validated against field data from a concrete hyperbolic arch dam, the proposed DTIF algorithm demonstrates superior anomaly detection accuracy across nine distinct outlier distribution scenarios. Moreover, for long-term displacement prediction tasks, the IRSA-RF model substantially outperforms traditional benchmark models in both predictive accuracy and generalization capability, providing a reliable early risk warning and decision-support tool for engineering practice. Full article
(This article belongs to the Special Issue Structural Health Monitoring Through Advanced Artificial Intelligence)
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32 pages, 2623 KB  
Article
Physics-Guided Self-Supervised Few-Shot Learning for Ultrasonic Defect Detection in Concrete Structures
by Mehmet Esen Eren
Buildings 2025, 15(23), 4227; https://doi.org/10.3390/buildings15234227 - 23 Nov 2025
Cited by 2 | Viewed by 1447
Abstract
This study introduces a physics-guided self-supervised framework for few-shot ultrasonic defect detection in concrete structures, addressing the dual challenges of scarce labels and domain variability in structural health monitoring (SHM). Our method integrates physics-informed augmentations, contrastive representation learning, and adversarial domain alignment within [...] Read more.
This study introduces a physics-guided self-supervised framework for few-shot ultrasonic defect detection in concrete structures, addressing the dual challenges of scarce labels and domain variability in structural health monitoring (SHM). Our method integrates physics-informed augmentations, contrastive representation learning, and adversarial domain alignment within a mutually reinforcing cycle, enabling robust defect classification with minimal supervision. A Physics-Informed Augmentation Module synthesizes realistic ultrasonic signals, training a Transformer encoder to extract invariant features while suppressing sensor noise. An Adversarial Feature Aligner further improves cross-domain generalization by mitigating distribution shifts across heterogeneous concretes. Experimental validation on three benchmark datasets demonstrates 63–66% accuracy in one-shot cross-domain tasks and up to 89% in five-shot settings. These results represent 12–15 percentage point gains over modern few-shot baselines, with improvements statistically significant at p < 0.001. Compatible with existing ultrasonic hardware, the proposed framework bridges physics-based modeling and machine learning while paving the way for scalable, field-ready SHM solutions for aging infrastructure and resilient smart cities. Full article
(This article belongs to the Special Issue Structural Health Monitoring Through Advanced Artificial Intelligence)
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19 pages, 2577 KB  
Article
Damage Detection of Seismically Excited Buildings Using Neural Network Arrays with Branch Pruning Optimization
by Jau-Yu Chou, Chia-Ming Chang and Chieh-Yu Liu
Buildings 2025, 15(12), 2052; https://doi.org/10.3390/buildings15122052 - 14 Jun 2025
Cited by 1 | Viewed by 1058
Abstract
In structural health monitoring, visual inspection remains vital for detecting damage, especially in concealed elements such as columns and beams. To improve damage localization, many studies have investigated and implemented deep learning into damage detection frameworks. However, the practicality of such models is [...] Read more.
In structural health monitoring, visual inspection remains vital for detecting damage, especially in concealed elements such as columns and beams. To improve damage localization, many studies have investigated and implemented deep learning into damage detection frameworks. However, the practicality of such models is often limited by their computational demands, and the relative accuracy may suffer if input features lack sensitivity to localized damage. This study introduces an efficient method for estimating damage locations and severity in buildings using a neural network array. A synthetic dataset is first generated from a simplified building model that includes floor flexural behavior and reflects the target dynamics of the structures. A dense, single-layer neural network array is initially trained with full floor accelerations, then pruned iteratively via the Lottery Ticket Hypothesis to retain only the most effective sub-networks. Subsequently, critical event measurements are input into the pruned array to estimate story-wise stiffness reductions. The approach is validated through numerical simulation of a six-story model and further verified via shake table tests on a scaled twin-tower steel-frame building. Results show that the pruned neural network array based on the Lottery Ticket Hypothesis achieves high accuracy in identifying stiffness reductions while significantly reducing computational load and outperforming full-input models in both efficiency and precision. Full article
(This article belongs to the Special Issue Structural Health Monitoring Through Advanced Artificial Intelligence)
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Review

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38 pages, 1954 KB  
Review
Bridge Structural Health Monitoring: A Multi-Dimensional Taxonomy and Evaluation of Anomaly Detection Methods
by Omar S. Sonbul and Muhammad Rashid
Buildings 2025, 15(19), 3603; https://doi.org/10.3390/buildings15193603 - 8 Oct 2025
Cited by 4 | Viewed by 4414
Abstract
Bridges are critical to national mobility and economic flow, making dependable structural health monitoring (SHM) systems essential for safety and durability. However, the SHM data quality is often affected by sensor faults, transmission noise, and environmental interference. To address these issues, anomaly detection [...] Read more.
Bridges are critical to national mobility and economic flow, making dependable structural health monitoring (SHM) systems essential for safety and durability. However, the SHM data quality is often affected by sensor faults, transmission noise, and environmental interference. To address these issues, anomaly detection methods are widely adopted. Despite their wide use and variety, there is a lack of systematic evaluation that comprehensively compares these techniques. Existing reviews are often constrained by limited scope, minimal comparative synthesis, and insufficient focus on real-time performance and multivariate analysis. Consequently, this systematic literature review (SLR) analyzes 36 peer-reviewed studies published between 2020 and 2025, sourced from eight reputable databases. Unlike prior reviews, this work presents a novel four-dimensional taxonomy covering real-time capability, multivariate support, analysis domain, and detection methods. Moreover, detection methods are further classified into three categories: distance-based, predictive, and image processing. A comparative evaluation of the reviewed detection methods is performed across five key dimensions: robustness, scalability, real-world deployment feasibility, interpretability, and data dependency. Findings reveal that image-processing methods are the most frequently applied (22 studies), providing high detection accuracy but facing scalability challenges due to computational intensity. Predictive models offer a trade-off between interpretability and performance, whereas distance-based methods remain less common due to their sensitivity to dimensionality and environmental factors. Notably, only 11 studies support real-time anomaly detection, and multivariate analysis is often overlooked. Moreover, time-domain signal processing dominates the field, while frequency and time-frequency domain methods remain rare despite their potential. Finally, this review highlights key challenges such as scalability, interpretability, robustness, and practicality of current models. Further research should focus on developing adaptive and interpretable anomaly detection frameworks that are efficient enough for real-world SHM deployment. These models should combine multi-modal strategies, handle uncertainty, and follow standardized evaluation protocols across varied monitoring environments. Full article
(This article belongs to the Special Issue Structural Health Monitoring Through Advanced Artificial Intelligence)
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