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24 pages, 58207 KB

Open AccessArticle

Multitemporal Geodetic and TLS Survey of the Bridge ‘Ponte della Costituzione’ in Venice for High-Precision Deformation Monitoring

by Massimo Fabris, Andrea Menin and Michele Monego

Appl. Sci. 2026, 16(10), 5096; https://doi.org/10.3390/app16105096 - 20 May 2026

Abstract

Deformation monitoring of bridges is essential to ensure the structural integrity and serviceability of these critical civil infrastructures. In this context, geodetic measurements using total stations and 3D terrestrial laser scanning (TLS) surveys can provide accurate and reliable data. Multitemporal geodetic observations from [...] Read more.

Deformation monitoring of bridges is essential to ensure the structural integrity and serviceability of these critical civil infrastructures. In this context, geodetic measurements using total stations and 3D terrestrial laser scanning (TLS) surveys can provide accurate and reliable data. Multitemporal geodetic observations from total stations enable the tracking of displacements at discrete points, whereas TLS surveys allow for the extension of deformation analysis to entire surfaces. Both techniques can achieve comparable millimeter-level precision. These methods were applied to monitor the deformation of the Ponte della Costituzione (PdC), the most recent pedestrian arch bridge spanning the Grand Canal in Venice (Italy). A total station was used to measure the displacements of six control points installed on structurally significant locations of the bridge. Between 3 October 2023 and 2 February 2026, 28 multitemporal measurement campaigns were conducted. In addition, four TLS surveys, using two different laser scanners, were carried out on 1 August 2025 and 2 February 2026, in order to capture conditions corresponding to maximum annual thermal deformation. The results derived from geodetic measurements reveal a strong correlation among: (i) variations in the distance between the abutments (on the order of 6–7 mm); (ii) vertical displacements of the central upper points of the arch (ranging from 9 to 12 cm); and (iii) fluctuations in ambient temperature. TLS data highlighted a spatially homogeneous deformation pattern extending from the crown of the arch to the abutments, demonstrating that longitudinal displacements affect the entire lateral structure. Mid-term deformation analysis over the two-year period from 6 February 2024 to 2 February 2026 indicates displacement rates of approximately 1.4 mm/year for increasing separation between the abutments and 16.2 mm/year for the decrease in elevation of the central arch point. However, these trends are significantly influenced by environmental temperature variations, as evidenced by an estimated temperature change rate of −3.5 °C/year over the same period. Therefore, continued deformation monitoring of the PdC bridge is recommended in the coming years, particularly in light of ongoing climate change and the associated increase in temperature variability. Full article

(This article belongs to the Special Issue State-of-the-Art Structural Health Monitoring Application)

34 pages, 7180 KB

Open AccessArticle

A Vibration Measurement Data Enhancement Approach Based on Variational Autoencoders for Structural Health Monitoring

by Gianmarco Battista, Stefano Pavoni and Marcello Vanali

Appl. Sci. 2026, 16(10), 4844; https://doi.org/10.3390/app16104844 - 13 May 2026

Viewed by 283

Abstract

Structural Health Monitoring (SHM) increasingly relies on data-driven approaches to detect structural changes under environmental and operational variability, yet the limited availability and imbalance of baseline data remain critical challenges. This study proposes a novel framework for vibration-based SHM that combines Convolutional Neural [...] Read more.

Structural Health Monitoring (SHM) increasingly relies on data-driven approaches to detect structural changes under environmental and operational variability, yet the limited availability and imbalance of baseline data remain critical challenges. This study proposes a novel framework for vibration-based SHM that combines Convolutional Neural Networks and Variational Autoencoders to model structural response in the frequency domain through Cross-Spectral Matrices. The methodology includes a tailored data representation based on Cholesky factorisation, a CNN-VAE architecture with structural constraints to ensure data consistency, and an Enhanced Loss Function designed to improve sensitivity to modal characteristics. The trained model is used both as a generative tool to produce realistic synthetic data and as a feature extractor through latent variable distributions. Validation on an experimental truss structure subject to thermal variability shows that the model accurately reproduces the statistical distribution of natural frequencies and spectral features, while generating plausible synthetic responses. The proposed approach enables baseline enhancement through data balancing and supports effective damage detection using both modal features and latent space indicators. These results demonstrate that the framework can improve the robustness of vibration-based SHM systems and can be integrated with existing frequency domain monitoring techniques, offering a practical data-driven solution for real-world applications. Full article

(This article belongs to the Special Issue State-of-the-Art Structural Health Monitoring Application)

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42 pages, 14790 KB

Open AccessArticle

Machine Learning-Based Classification of Vibration Patterns Under Multiple Excitation Scenarios for Structural Health Monitoring

by Leidy Esperanza Pamplona Berón, Marco Claudio De Simone, Domenico de Falco and Domenico Guida

Appl. Sci. 2026, 16(4), 2107; https://doi.org/10.3390/app16042107 - 21 Feb 2026

Cited by 1 | Viewed by 661

Abstract

Tracking structural behavior is critically important to reduce maintenance and repair costs. Structural Health Monitoring (SHM) aims to evaluate the structural integrity, detect damage or abnormalities, and estimate overall safety. The integration of Machine Learning techniques has significantly advanced SHM by enabling the [...] Read more.

Tracking structural behavior is critically important to reduce maintenance and repair costs. Structural Health Monitoring (SHM) aims to evaluate the structural integrity, detect damage or abnormalities, and estimate overall safety. The integration of Machine Learning techniques has significantly advanced SHM by enabling the identification of deterioration patterns through sensor data analysis. This study focuses on classifying different vibration patterns recorded under various excitation scenarios (ambient, transient, and forced) using sensors installed directly on a 3-DoF structure. The proposed approach used a two-dimensional convolutional neural network (2D-CNN) trained on vibration image patterns generated from vibration signal scalogram images. To address dataset imbalance, stratified 5 × 3 Nested cross-validation and multiple performance metrics were computed to ensure robust evaluation. The proposed method was compared with single-sensor scalogram approaches and baseline models, including Support Vector Machines (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), One-Dimensional Convolutional Neural Network (1D-CNN), and Long Short-Term Memory (LSTM) models, incorporating class-weighting strategies. Additionally, the contribution of the Total Energy Delivered by Sensor (TES) feature was evaluated for SVM, RF, and XGBoost models. The 2D-CNN model achieved superior performance in identifying excitation types associated with structural dynamic behavior, highlighting its effectiveness for structural vibration pattern recognition in SHM applications. Full article

(This article belongs to the Special Issue State-of-the-Art Structural Health Monitoring Application)

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15 pages, 4731 KB

Open AccessArticle

Interlayer Mechanical Behavior in CRTS II Slab Ballastless Tracks Under Vertical Loading

by Xiao Guo, Xiaonan Xie, Xuebing Zhang, Li Wang and Ping Xiang

Appl. Sci. 2025, 15(24), 13058; https://doi.org/10.3390/app152413058 - 11 Dec 2025

Viewed by 619

Abstract

Reliable in situ quantification of interlayer mechanics in CRTS-II ballastless track slabs remains limited by the poor instrumentability of the CA mortar layer. This study implements a quasi-distributed fiber-optic sensing scheme by encapsulating FBGs in PVC conduits and embedding them within the CA [...] Read more.

Reliable in situ quantification of interlayer mechanics in CRTS-II ballastless track slabs remains limited by the poor instrumentability of the CA mortar layer. This study implements a quasi-distributed fiber-optic sensing scheme by encapsulating FBGs in PVC conduits and embedding them within the CA mortar to track strain evolution under vertical loading. Four 1:3 scaled slabs were tested using stepwise load control (200 kN per step) to failure, and fiber measurements were cross-validated against conventional strain gauges on the reinforcement. The two systems showed consistent load–strain trends, while the fiber approach exhibited near-zero baseline offset and higher temporal resolution, enabling detection of small-amplitude strain changes that the gauges missed. The CA mortar displayed a clear tension-to-compression transition with increasing load; with two vertical rebars the ultimate load of the mortar layer reached 1400 kN, representing a 75% improvement over the rebar-free configuration and delaying compressive crushing through enhanced interlayer cooperation. Increasing the rebar diameter further restrained deformation and elevated the load level at which the transition occurred. The results demonstrate a practical interlayer monitoring route for CA mortar and quantify the strengthening role of vertical rebars, offering actionable guidance for design optimization and long-term condition assessment of CRTS-II slab tracks. Full article

(This article belongs to the Special Issue State-of-the-Art Structural Health Monitoring Application)

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26 pages, 3670 KB

Open AccessArticle

A Novel WaveNet Deep Learning Approach for Enhanced Bridge Damage Detection

by Mohab Turkomany, AbdelAziz Ibrahem AbdelLatef and Nasim Uddin

Appl. Sci. 2025, 15(22), 12228; https://doi.org/10.3390/app152212228 - 18 Nov 2025

Cited by 1 | Viewed by 2533

Abstract

Bridges are vital components of global infrastructure, with millions constructed over the years. Many of them face aging and are vulnerable to risks. Traditional bridge inspection methods are costly and time-consuming. They often rely on many manual laborers without providing system-level insights. Moreover, [...] Read more.

Bridges are vital components of global infrastructure, with millions constructed over the years. Many of them face aging and are vulnerable to risks. Traditional bridge inspection methods are costly and time-consuming. They often rely on many manual laborers without providing system-level insights. Moreover, these outdated approaches make it difficult to obtain a clear representation of the current bridge health. This paper introduces a novel framework based on deep learning (DL) for identifying local bridge damage using acceleration data collected by Unmanned Aerial Vehicle (UAV)-mounted sensors. The framework employs WaveNet, which was designed as a generative audio DL model. Its causal dilated convolution deals with long-range temporal correlations without recurrence. Two WaveNet regressors are used to predict the damage location and its severity. The methodology is integrated with an optimized sensor spacing strategy for UAV deployments. The results demonstrate that the severity model achieved an average R² = 0.98, while the location model reached R² = 0.85. Optimal sensor spacing “S” was found at S = 1.0 m for localization and S = 0.5 m for severity. A field-simulated case was accurately identified by the two models, representing the potential of the proposed framework for more reliable bridge health monitoring. Full article

(This article belongs to the Special Issue State-of-the-Art Structural Health Monitoring Application)

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33 pages, 41854 KB

Open AccessArticle

Application of Signal Processing Techniques to the Vibration Analysis of a 3-DoF Structure Under Multiple Excitation Scenarios

by Leidy Esperanza Pamplona Berón, Marco Claudio De Simone and Domenico Guida

Appl. Sci. 2025, 15(15), 8241; https://doi.org/10.3390/app15158241 - 24 Jul 2025

Cited by 2 | Viewed by 2238

Abstract

Structural Health Monitoring (SHM) techniques are crucial for evaluating the condition of structures, enabling early maintenance interventions, and monitoring factors that could compromise structural integrity. Modal analysis studies the dynamic response of structures when subjected to vibrations, evaluating natural frequencies and vibration modes. [...] Read more.

Structural Health Monitoring (SHM) techniques are crucial for evaluating the condition of structures, enabling early maintenance interventions, and monitoring factors that could compromise structural integrity. Modal analysis studies the dynamic response of structures when subjected to vibrations, evaluating natural frequencies and vibration modes. This study focuses on detecting and comparing the natural frequencies of a 3-DoF structure under various excitation scenarios, including ambient vibration (in healthy and damaged conditions), two types of transient excitation, and three harmonic excitation variations. Signal processing techniques, specifically Power Spectral Density (PSD) and Continuous Wavelet Transform (CWT), were employed. Each method provides valuable insights into frequency and time-frequency domain analysis. Under ambient vibration excitation, the damaged condition exhibits spectral differences in amplitude and frequency compared to the undamaged state. For the transient excitations, the scalogram images reveal localized energetic differences in frequency components over time, whereas PSD alone cannot observe these behaviors. For the harmonic excitations, PSD provides higher spectral resolution, while CWT adds insight into temporal energy evolution near resonance bands. This study discusses how these analyses provide sensitive features for damage detection applications, as well as the influence of different excitation types on the natural frequencies of the structure. Full article

(This article belongs to the Special Issue State-of-the-Art Structural Health Monitoring Application)

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22 pages, 6104 KB

Open AccessArticle

An Unsupervised Hybrid Approach for Detection of Damage with Autoencoder and One-Class Support Vector Machine

by Burcu Gunes and Oguz Gunes

Appl. Sci. 2025, 15(8), 4098; https://doi.org/10.3390/app15084098 - 8 Apr 2025

Cited by 5 | Viewed by 1898

Abstract

Progressive deterioration and accumulated damage due to overloading, extreme events, and fatigue necessitate the continuous monitoring of civil infrastructure to ensure serviceability and safety. With advances in sensor technology, data-driven structural health monitoring (SHM) strategies, particularly artificial neural networks (ANNs), have gained prominence [...] Read more.

Progressive deterioration and accumulated damage due to overloading, extreme events, and fatigue necessitate the continuous monitoring of civil infrastructure to ensure serviceability and safety. With advances in sensor technology, data-driven structural health monitoring (SHM) strategies, particularly artificial neural networks (ANNs), have gained prominence for analyzing large datasets and identifying complex patterns. Among these, autoencoders (AEs), a specialized class of ANNs, are well-suited for unsupervised learning tasks, enabling dimensionality reduction and feature extraction. This study employs transmissibility functions (TFs) as training samples for the AE. TFs are directly derived from response measurements without the need to measure input and exhibit local sensitivity to changes in dynamic properties, making them an efficient feature for structural assessment. The reconstruction errors in TFs, quantifying the deviation between the original and AE-reconstructed data, are leveraged as damage-sensitive features for classification using a one-class support vector machine (OC-SVM). The proposed methodology is validated through numerical simulations with noise-contaminated data representing various damage scenarios in a shear-building model, as well as experimental tests on a masonry arch bridge model subjected to progressive damage. Numerical investigations demonstrate improved detection accuracy and robustness of the procedure through the incorporation of nonlinear encoding into the dimensionality reduction process, compared to the classical principal component analysis method.. Experimental results confirm the framework’s effectiveness in detecting and localizing damage using unlabeled field data. Full article

(This article belongs to the Special Issue State-of-the-Art Structural Health Monitoring Application)

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Review

Jump to: Research

20 pages, 1396 KB

Open AccessReview

A Comprehensive Review of Structural Health Monitoring for Steel Bridges: Technologies, Data Analytics, and Future Directions

by Alaa Elsisi, Amal Zamrawi and Shimaa Emad

Appl. Sci. 2025, 15(22), 12090; https://doi.org/10.3390/app152212090 - 14 Nov 2025

Cited by 2 | Viewed by 4624

Abstract

Structural Health Monitoring (SHM) of steel bridges is vital for ensuring the longevity, safety, and reliability of critical transportation infrastructure. This review synthesizes recent advancements in SHM technologies and methodologies for steel bridges, highlighting the shift from traditional vibration-based monitoring to data-driven, intelligent [...] Read more.

Structural Health Monitoring (SHM) of steel bridges is vital for ensuring the longevity, safety, and reliability of critical transportation infrastructure. This review synthesizes recent advancements in SHM technologies and methodologies for steel bridges, highlighting the shift from traditional vibration-based monitoring to data-driven, intelligent systems. It covers core technological themes, including various sensing systems such as wireless sensor networks, fiber optics, and piezoelectric transducers, along with the impact of machine learning, artificial intelligence, and statistical pattern recognition. The paper explores applications for damage detection, such as fatigue life assessment and monitoring of components like expansion joints. Persistent challenges, including deployment costs, data management complexities, and the need for real-world validation, are addressed. The future of SHM lies in integrating diverse sensing technologies with computational analytics, advancing from periodic inspections to continuous, predictive infrastructure management, which enhances bridge safety, resilience, and economic sustainability. Full article

(This article belongs to the Special Issue State-of-the-Art Structural Health Monitoring Application)

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