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Advances in Structural Health Monitoring with Hybrid Data-Driven and Model-Based...
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Advances in Structural Health Monitoring with Hybrid Data-Driven and Model-Based Approaches for Buildings

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

Deadline for manuscript submissions: 15 October 2026 | Viewed by 1693

Editors

Dr. Gaetano Miraglia

E-Mail Website
Guest Editor
Department of Structural, Geotechnical and Building Engineering (DISEG), Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Turin, Italy
Interests: bayesian methods; computational models; damage identification; earthquake engineering; masonry buildings; peridynamics; satellite InSAR data; structural health monitoring; system identification; structural prognosis
Dr. Stefania Coccimiglio

E-Mail Website
Guest Editor
Department of Structural, Geotechnical and Building Engineering (DISEG), Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Turin, Italy
Interests: structural health monitoring; InSAR data; geophysical satellite data; environmental effects; data integration; structure–soil–environmental interaction

Special Issue Information

Dear Colleagues,

In recent years, buildings have shown an intrinsic vulnerability due to ageing and the progressive approach of their design service life. These criticalities have favoured the development and improvement of Structural Health Monitoring (SHM) techniques. However, research has highlighted that data‑driven or model‑based SHM approaches have limitations when applied independently. For this reason, the integrated use of both approaches is taking on an increasingly significant role in scientific research. This Special Issue will promote interdisciplinary research and foster synergy between SHM approaches. Emphasis is placed on approaches that bridge digitalization, machine learning, and large-scale data analysis to improve decision-making. The main topics of interest include, but are not limited to, the following:

  • Effect of Environmental and Operational Variations (EOVs) on damage detection and localization;
  • Data integration and data fusion;
  • Digital twinning;
  • Remote and contactless solutions;
  • Short- and long-term structural monitoring;
  • Machine learning;
  • Transfer learning;
  • Linear and nonlinear system identification.

Dr. Gaetano Miraglia
Dr. Stefania Coccimiglio
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-anonymized 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

  • data-driven and model-based approaches
  • damage detection and localization
  • data fusion
  • digital twinning
  • environmental and operational variations (EOVs)
  • machine learning
  • structural health monitoring (SHM)

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

Published Papers (2 papers)

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28 pages, 36425 KB  
Article
Multi-Criterion Mode Selection in Stochastic Subspace Identification (SSI): Enhancing Reliability in Noisy Environments
by Gürhan Tokgöz and Eda Avanoğlu Sıcacık
Buildings 2026, 16(10), 1961; https://doi.org/10.3390/buildings16101961 - 15 May 2026
Viewed by 460
Abstract
In the classical Stochastic Subspace Identification (SSI) method, mode selection is primarily based on frequency stability, damping stability, and mode shape similarity using the Modal Assurance Criterion (MAC). However, these criteria are often insufficient for reliable modal identification in high-noise environments. This study [...] Read more.
In the classical Stochastic Subspace Identification (SSI) method, mode selection is primarily based on frequency stability, damping stability, and mode shape similarity using the Modal Assurance Criterion (MAC). However, these criteria are often insufficient for reliable modal identification in high-noise environments. This study advances beyond the classical approach by introducing a multi-criteria optimization framework for mode evaluation. In addition to the conventional frequency and damping assessments utilized in the classical SSI method, the proposed approach incorporates a range of supplementary structural metrics. These include Density, Cosine Similarity Difference (CSD), Damping Stability (DS), Spatial Roughness (SR), Mode Shape Complexity (MSC), Signal Energy Coherence (SEC), and Normalized Modal Difference (NMD). These metrics are computed within specifically optimized windows on the stabilization diagram. By integrating spatial, phase, and energy-based characteristics of mode shapes alongside traditional metrics such as the MAC, the method enables a more comprehensive and robust mode selection process that surpasses the limitations of relying solely on frequency and damping stability. Compared to the classical SSI, the optimized window approach provides a significant advantage by enabling the reliable selection of consistent modes by considering the continuity and multi-criteria coherence of modes across window transitions. As a result, the elimination of noise modes and the reliable separation of structural modes are established on a more systematic basis. To achieve this, a two-stage optimization strategy is implemented: the first stage determines the optimal frequency window width and minimum mode count threshold, while the second stage utilizes a Multi-Criteria Decision Making (MCDM) framework based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm to assign optimized weights to the structural metrics and rank the candidate windows accordingly. As a result, the ideal frequency window is identified based on its TOPSIS score and subsequently validated using the MAC, confirming that the selected window corresponds to reliable structural modes. The framework is validated using long-term in situ measurements from a Roller Compacted Concrete (RCC) dam operating under significant environmental and operational noise. The dataset comprises continuous, high-resolution (200 Hz) vibration recordings collected between 1 July 2023 and 30 October 2024. While the calendar duration is limited to several weeks, the uninterrupted 24 h measurements yield a high-density time-series dataset with substantial information content, enabling a statistically meaningful and robust evaluation of modal identification performance under real-world and noisy conditions. The results reveal that relying solely on traditional selection criteria such as pole density and the MAC can often lead to the identification of spurious modes, particularly in noisy environments. In contrast, the proposed TOPSIS-based multi-criteria decision-making framework incorporates a broader range of structural indicators, balancing frequency, damping, spatial, and energy-related metrics to enhance the consistency and reliability of mode selection. This approach proved effective even under high-noise conditions, successfully distinguishing true structural modes from artificial ones. Application of the TOPSIS method to RCC dam data revealed consistent fundamental frequencies at approximately 5–10 Hz, 10 Hz, and 15 Hz, confirming its robustness and suitability for complex structural monitoring tasks. Full article
(This article belongs to the Special Issue Advances in Structural Health Monitoring with Hybrid Data-Driven and Model-Based Approaches for Buildings)
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33 pages, 6273 KB  
Systematic Review
A Systematic Review of Sensor–AI Integration in Structural Health Monitoring of Civil Buildings
by Cosmina-Mihaela Rosca, Adrian Stancu and Catalin Popescu
Buildings 2026, 16(12), 2299; https://doi.org/10.3390/buildings16122299 - 8 Jun 2026
Viewed by 777
Abstract
Structural health monitoring (SHM) is a component of modern civil engineering. This review analyzes the integration of sensing technologies and artificial-intelligence-based methods for damage detection, localization, classification, prognosis, and anomaly detection in buildings and civil infrastructure. The database search covered Web of Science [...] Read more.
Structural health monitoring (SHM) is a component of modern civil engineering. This review analyzes the integration of sensing technologies and artificial-intelligence-based methods for damage detection, localization, classification, prognosis, and anomaly detection in buildings and civil infrastructure. The database search covered Web of Science (WoS), Scopus, and IEEE Xplore for the period 1 January 2020–31 December 2025. The initial records were 292 in WoS, 311 in Scopus, and 338 in IEEE Xplore; after applying the AI-related search constraint, the corresponding AI-SHM corpora were 71, 79, and 139 records, respectively. The combined screening and eligibility workflow produced 31 open-access studies for detailed qualitative analysis, while the task-specific performance tables synthesize the subset of studies for which the sensor type, AI model, SHM task, validation context, and performance metrics were explicitly reported. The review, therefore, interprets reported performance by SHM task and sensor modality, rather than treating heterogeneous metrics as directly comparable across different datasets and experimental conditions. The results indicate that high values reported for accelerometer-, fiber-optic-, piezoelectric transducer-, and vision-based systems are mainly obtained under controlled, benchmark, simulated, or study-specific validation conditions. Consequently, robustness, transferability to operational structures, uncertainty quantification, sensor-network design, and integration with Physics-Informed Machine Learning and Digital Twin technologies remain central research needs. Full article
(This article belongs to the Special Issue Advances in Structural Health Monitoring with Hybrid Data-Driven and Model-Based Approaches for Buildings)
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