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Sensing Technologies and Deep Learning Methods for Structural Health Monitoring Systems

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (15 March 2025) | Viewed by 6095

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Special Issue Editors

Prof. Dr. Genda Chen

E-Mail Website1 Website2
Guest Editor

1. Department of Civil, Architectural and Environmental Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA
2. Director of the Center for Intelligent Infrastructure, Missouri University of Science and Technology, Rolla, MO 65401, USA
3. Director of INSPIRE University Transportation Center, Missouri University of Science and Technology, Rolla, MO 65401, USA
4. Associate Director of Mid-America Transportation Center, University of Nebraska, Lincoln, NE 68588, USA
Interests: structural health monitoring; structural control; interface mechanics and deterioration; multihazard mitigation; bridge inspection and maintenance
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Dr. Woubishet Taffese

E-Mail Website
Guest Editor

Center for Intelligent Infrastructure, Missouri University of Science and Technology, Rolla, MO 65409, USA
Interests: predictive maintenance; digital twin; machine learning; deep learning; Internet of Things; computer vision

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue focused on the latest advancements at the intersection of deep learning and structural health monitoring (SHM). Sensors have become instrumental in SHM by providing crucial data for assessing the health and integrity of various structures. Recent innovations in deep learning methodologies present a promising avenue to enhance the analysis and interpretation of sensor-generated data for more accurate and efficient SHM. This Special Issue aims to compile state-of-the-art research exploring the integration of deep learning techniques with sensor data to advance structural health monitoring.

We invite researchers and practitioners to submit original research and review articles related to sensing technologies and deep learning methods for structural health monitoring systems. Topics of interest include, but are not limited to:

Deep learning algorithms for SHM;
Sensor fusion and multimodal data processing for comprehensive structural assessment;
Edge computing and real-time applications for SHM;
Transfer learning and domain adaptation for SHM tasks in various domains;
Uncertainty quantification and model interpretability in SHM systems.

Prof. Dr. Genda Chen
Dr. Woubishet Taffese
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Sensors 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
deep learning
sensor networks
intelligent monitoring techniques
computer vision
damage detection
Internet of Things

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Published Papers (4 papers)

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Research

32 pages, 11570 KiB

Open AccessArticle

Damage Identification Using Measured and Simulated Guided Wave Damage Interaction Coefficients Predicted Ad Hoc by Deep Neural Networks

by Christoph Humer, Simon Höll and Martin Schagerl

Sensors 2025, 25(6), 1681; https://doi.org/10.3390/s25061681 - 8 Mar 2025

Viewed by 550

Abstract

Thin-walled structures are widely used in aeronautical and aerospace engineering due to their light weight and high structural performance. Ensuring their integrity is crucial for safety and reliability, which is why structural health monitoring (SHM) methods, such as guided wave-based techniques, have been developed to detect and characterize damage in such components. This study presents a novel damage identification procedure for guided wave-based SHM using deep neural networks (DNNs) trained with experimental data. This technique employs the so-called wave damage interaction coefficients (WDICs) as highly sensitive damage features that describe the unique scattering pattern around possible damage. The DNNs learn intricate relationships between damage characteristics, e.g., size or orientation, and corresponding WDIC patterns from only a limited number of damage cases. An experimental training data set is used, where the WDICs of a selected damage type are extracted from measurements using a scanning laser Doppler vibrometer. Surface-bonded artificial damages are selected herein for demonstration purposes. It is demonstrated that smart DNN interpolations can replicate WDIC patterns even when trained on noisy measurement data, and their generalization capabilities allow for precise predictions for damages with arbitrary properties within the range of trained damage characteristics. These WDIC predictions are readily available, i.e., ad hoc, and can be compared to measurement data from an unknown damage for damage characterization. Furthermore, the fully trained DNN allows for predicting WDICs specifically for the sensing angles requested during inspection. Additionally, an anglewise principal component analysis is proposed to efficiently reduce the feature dimensionality on average by more than

90 %

while accounting for the angular dependencies of the WDICs. The proposed damage identification methodology is investigated under challenging conditions using experimental data from only three sensors of a damage case not contained in the training data sets. Detailed statistical analyses indicate excellent performance and high recognition accuracy for this experimental data-based approach. This study also analyzes differences between simulated and experimental WDIC patterns. Therefore, an existing DNN trained on simulated data is also employed. The differences between the simulations and experiments affect the identification performance, and the resulting limitations of the simulation-based approach are clearly explained. This highlights the potential of the proposed experimental data-based DNN methodology for practical applications of guided wave-based SHM. Full article

(This article belongs to the Special Issue Sensing Technologies and Deep Learning Methods for Structural Health Monitoring Systems)

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19 pages, 7808 KiB

Open AccessArticle

ANN-Based Bridge Support Fixity Quantification Using Thermal Response Data from Real-Time Wireless Sensing

by Prakash Bhandari, Shinae Jang, Ramesh B. Malla and Song Han

Sensors 2024, 24(16), 5350; https://doi.org/10.3390/s24165350 - 19 Aug 2024

Viewed by 1457

Abstract

Bridges are critical infrastructures that support our economic activities and daily lives. Aging bridges have been a major issue for decades, prompting researchers to improve resilience and performance through structural health monitoring. While most research focuses on superstructure damage, the majority of bridge failures are associated with support or joint damages, indicating the importance of bridge support. Indeed, bridge support affects the performance of both the substructure and superstructure by maintaining the load path and allowing certain movements to mitigate thermal and other stresses. The support deterioration leads to a change in fixity in the superstructure, compromising the bridge’s integrity and safety. Hence, a reliable method to determine support fixity level is essential to detecting bearing health and enhancing the accuracy of the bridge health monitoring system. However, such research is lacking because of its complexity. In this study, we developed a support fixity quantification method based on thermal responses using an Artificial Neural Network (ANN) model. A finite element (FE) model of a representative highway bridge is used to derive thermal displacement data under different bearing stiffnesses, superstructure damage, and thermal loading. The thermal displacement behavior of the bridge under different support fixity conditions is presented, and the model is trained on the simulated response. The performance of the developed FE model and ANN was validated with field monitoring data collected from two in-service bridges in Connecticut using a real-time Wireless Sensor Network (WSN). Finally, the support stiffnesses of both bridges were predicted using the ANN model for validation. Full article

(This article belongs to the Special Issue Sensing Technologies and Deep Learning Methods for Structural Health Monitoring Systems)

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16 pages, 743 KiB

Open AccessArticle

Tiny-Machine-Learning-Based Supply Canal Surface Condition Monitoring

by Chengjie Huang, Xinjuan Sun and Yuxuan Zhang

Sensors 2024, 24(13), 4124; https://doi.org/10.3390/s24134124 - 25 Jun 2024

Cited by 3 | Viewed by 1595

Abstract

The South-to-North Water Diversion Project in China is an extensive inter-basin water transfer project, for which ensuring the safe operation and maintenance of infrastructure poses a fundamental challenge. In this context, structural health monitoring is crucial for the safe and efficient operation of hydraulic infrastructure. Currently, most health monitoring systems for hydraulic infrastructure rely on commercial software or algorithms that only run on desktop computers. This study developed for the first time a lightweight convolutional neural network (CNN) model specifically for early detection of structural damage in water supply canals and deployed it as a tiny machine learning (TinyML) application on a low-power microcontroller unit (MCU). The model uses damage images of the supply canals that we collected as input and the damage types as output. With data augmentation techniques to enhance the training dataset, the deployed model is only 7.57 KB in size and demonstrates an accuracy of 94.17 ± 1.67% and a precision of 94.47 ± 1.46%, outperforming other commonly used CNN models in terms of performance and energy efficiency. Moreover, each inference consumes only 5610.18

μ

J of energy, allowing a standard 225 mAh button cell to run continuously for nearly 11 years and perform approximately 4,945,055 inferences. This research not only confirms the feasibility of deploying real-time supply canal surface condition monitoring on low-power, resource-constrained devices but also provides practical technical solutions for improving infrastructure security. Full article

(This article belongs to the Special Issue Sensing Technologies and Deep Learning Methods for Structural Health Monitoring Systems)

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19 pages, 11574 KiB

Open AccessArticle

Wind-Induced Vibration Monitoring of High-Mast Illumination Poles Using Wireless Smart Sensors

by Mona Shaheen, Jian Li, Caroline Bennett and William Collins

Sensors 2024, 24(8), 2506; https://doi.org/10.3390/s24082506 - 14 Apr 2024

Cited by 1 | Viewed by 1545

Abstract

This paper describes the use of wireless smart sensors for examining the underlying mechanism for the wind-induced vibration of high-mast illumination pole (HMIP) structures. HMIPs are tall, slender structures with low inherent damping. Video recordings of multiple HMIPs showed considerable vibrations of these HMIPs under wind loading in the state of Kansas. The HMIPs experienced cyclic large-amplitude displacements at the top, which can produce high-stress demand and lead to fatigue cracking at the bottom of the pole. In this study, the natural frequencies of the HMIP were assessed using pluck tests and finite element modeling, and the recorded vibration frequencies were obtained through computer vision-based video analysis. Meanwhile, a 30.48 m tall HMIP with three LED luminaires made of galvanized steel located in Wakeeney, Kansas, was selected for long-term vibration monitoring using wireless smart sensors to investigate the underlying mechanism for the excessive wind-induced vibrations. Data analysis with the long-term monitoring data indicates that while vortex-induced vibration occurs frequently at relatively low amplitude, buffeting-induced vibration was the leading cause of the excessive vibrations of the monitored HMIP. The findings provide crucial information to guide the design of vibration mitigation strategies for these HMIP structures. Full article

(This article belongs to the Special Issue Sensing Technologies and Deep Learning Methods for Structural Health Monitoring Systems)

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