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Advanced Sensing Systems for Structural Monitoring and Damage Detection

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

Deadline for manuscript submissions: closed (31 March 2025) | Viewed by 11347

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Special Issue Editors

Dr. Magda Ruiz

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Guest Editor

Department of Mathematics, Universitat Politècnica de Catalunya, Barcelona, Spain
Interests: structural health monitoring; damage detection; damage sensitive

Dr. Luis Eduardo Mujica

E-Mail Website
Guest Editor

Department of Mathematics, Universitat Politècnica de Catalunya, Barcelona, Spain
Interests: structural health monitoring; signal processing

Special Issue Information

Dear Colleagues,

Structural health monitoring uses advanced sensing and data analytics to continuously assess infrastructure condition, detect damage early, and enable timely repairs. New technologies, like fiber optic sensors and AI algorithms, analyze real-time sensor data to identify abnormalities indicative of flaws, providing 24/7 monitoring and actionable information on both local damage and global performance. In this sense, the main objective of this Special Issue is to provide a space to present these advances, which could revolutionize the monitoring of civil infrastructure health.

Dr. Magda Ruiz
Dr. Luis Eduardo Mujica
Guest Editors

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Keywords

structural health monitoring (SHM)
assessing condition and performance
early detection of damage/deterioration
repairs and maintenance of structures
advanced sensing technologies
real-time distributed data
artificial intelligence
statistical learning
machine learning
data science
feature extraction
pattern recognition

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Published Papers (6 papers)

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Research

15 pages, 201 KiB

Open AccessArticle

Data Interpretation in Structural Health Monitoring: Toward a Universal Language

by Magda Ruiz, Óscar Gualdrón, José A. Peral Mondaza and Luis Eduardo Mujica Delgado

Sensors 2025, 25(10), 3054; https://doi.org/10.3390/s25103054 - 12 May 2025

Viewed by 153

Abstract

Structural Health Monitoring (SHM) relies on the effective communication between sensors and diagnostic systems, yet data interpretation remains inconsistent and subjective. This paper introduces a novel perspective, viewing data as a form of language with its own syntax, semantics, and pragmatics. By adopting this linguistic framework, the study emphasizes the need for standardized “grammars” in data collection, processing, and analysis to reduce ambiguity and enhance diagnostic reliability. Using case studies from SHM, the paper illustrates how subjective decisions in variable selection, cluster labels, preprocessing, and modeling introduce biases that affect the outcomes. The findings highlight the potential of context-aware algorithms and integrated data sources to mitigate these biases. This conceptual approach has broader implications for data science, suggesting a universal “language of data” that fosters consistency and collaboration across disciplines. By recognizing the constructed nature of data, this work offers a path toward more accurate, efficient, and reliable structural diagnostics, advancing both SHM practices and data interpretation methodologies. Full article

(This article belongs to the Special Issue Advanced Sensing Systems for Structural Monitoring and Damage Detection)

19 pages, 5509 KiB

Open AccessArticle

A Hybrid Deep Learning Model for Enhanced Structural Damage Detection: Integrating ResNet50, GoogLeNet, and Attention Mechanisms

by Vikash Singh, Anuj Baral, Roshan Kumar, Sudhakar Tummala, Mohammad Noori, Swati Varun Yadav, Shuai Kang and Wei Zhao

Sensors 2024, 24(22), 7249; https://doi.org/10.3390/s24227249 - 13 Nov 2024

Cited by 6 | Viewed by 2129

Abstract

Quick and accurate structural damage detection is essential for maintaining the safety and integrity of infrastructure, especially following natural disasters. Traditional methods of damage assessment, which rely on manual inspections, can be labor-intensive and subject to human error. This paper introduces a hybrid deep learning model that combines the capabilities of ResNet50 and GoogLeNet, further enhanced by a convolutional block attention module (CBAM), proposed to improve both the accuracy and performance in detecting structural damage. For training purposes, a diverse dataset of images depicting both structural damage cases and undamaged cases was used. To further enhance the robustness, data augmentation techniques were also employed. In this research, precision, recall, F1-score, and accuracy were employed to evaluate the effectiveness of the introduced hybrid deep learning model. Our findings indicate that the hybrid deep neural network introduced in this study significantly outperformed standalone architectures such as ResNet50 and GoogLeNet, making it a highly effective solution for applications in disaster response and infrastructure maintenance. Full article

(This article belongs to the Special Issue Advanced Sensing Systems for Structural Monitoring and Damage Detection)

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19 pages, 7912 KiB

Open AccessArticle

Structural Damage Detection Using PZT Transmission Line Circuit Model

by Jozue Vieira Filho, Nicolás E. Cortez, Mario De Oliveira, Luis Paulo M. Lima and Gyuhae Park

Sensors 2024, 24(22), 7113; https://doi.org/10.3390/s24227113 - 5 Nov 2024

Cited by 1 | Viewed by 860

Abstract

Arrangements of piezoelectric transducers, such as PZT (lead zirconate titanate), have been widely used in numerous structural health monitoring (SHM) applications. Usually, when two or more PZT transducers are placed close together, significant interference, namely crosstalk, appears. Such an effect is usually neglected in most SHM applications. However, it can potentially be used as a sensitive parameter to identify structural faults. Accordingly, this work proposes using the crosstalk effect in an arrangement of PZT transducers modeled as a multiconductor transmission line to detect structural damage. This effect is exploited by computing an impedance matrix representing a host structure with PZTs attached to it. The proposed method was assessed in an aluminum beam structure with two PZTs attached to it using finite element modeling in OnScale^® software to simulate both healthy and damaged conditions. Similarly, experimental tests were also carried out. The results, when compared to those obtained using a traditional electromechanical impedance (EMI) method, prove that the new approach significantly improved the sensitivity of EMI-based technique in SHM applications. Full article

(This article belongs to the Special Issue Advanced Sensing Systems for Structural Monitoring and Damage Detection)

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23 pages, 12258 KiB

Open AccessArticle

Leakage Identification of Underground Structures Using Classification Deep Neural Networks and Transfer Learning

by Wenyang Wang, Qingwei Chen, Yongjiang Shen and Zhengliang Xiang

Sensors 2024, 24(17), 5569; https://doi.org/10.3390/s24175569 - 28 Aug 2024

Cited by 1 | Viewed by 940

Abstract

Water leakage defects often occur in underground structures, leading to accelerated structural aging and threatening structural safety. Leakage identification can detect early diseases of underground structures and provide important guidance for reinforcement and maintenance. Deep learning-based computer vision methods have been rapidly developed and widely used in many fields. However, establishing a deep learning model for underground structure leakage identification usually requires a lot of training data on leakage defects, which is very expensive. To overcome the data shortage, a deep neural network method for leakage identification is developed based on transfer learning in this paper. For comparison, four famous classification models, including VGG16, AlexNet, SqueezeNet, and ResNet18, are constructed. To train the classification models, a transfer learning strategy is developed, and a dataset of underground structure leakage is created. Finally, the classification performance on the leakage dataset of different deep learning models is comparatively studied under different sizes of training data. The results showed that the VGG16, AlexNet, and SqueezeNet models with transfer learning can overall provide higher and more stable classification performance on the leakage dataset than those without transfer learning. The ResNet18 model with transfer learning can overall provide a similar value of classification performance on the leakage dataset than that without transfer learning, but its classification performance is more stable than that without transfer learning. In addition, the SqueezeNet model obtains an overall higher and more stable performance than the comparative models on the leakage dataset for all classification metrics. Full article

(This article belongs to the Special Issue Advanced Sensing Systems for Structural Monitoring and Damage Detection)

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20 pages, 7187 KiB

Open AccessArticle

A Discussion of Building a Smart SHM Platform for Long-Span Bridge Monitoring

by Yilin Xie, Xiaolin Meng, Dinh Tung Nguyen, Zejun Xiang, George Ye and Liangliang Hu

Sensors 2024, 24(10), 3163; https://doi.org/10.3390/s24103163 - 16 May 2024

Cited by 4 | Viewed by 4605

Abstract

This paper explores the development of a smart Structural Health Monitoring (SHM) platform tailored for long-span bridge monitoring, using the Forth Road Bridge (FRB) as a case study. It discusses the selection of smart sensors available for real-time monitoring, the formulation of an effective data strategy encompassing the collection, processing, management, analysis, and visualization of monitoring data sets to support decision-making, and the establishment of a cost-effective and intelligent sensor network aligned with the objectives set through comprehensive communication with asset owners. Due to the high data rates and dense sensor installations, conventional processing techniques are inadequate for fulfilling monitoring functionalities and ensuring security. Cloud-computing emerges as a widely adopted solution for processing and storing vast monitoring data sets. Drawing from the authors’ experience in implementing long-span bridge monitoring systems in the UK and China, this paper compares the advantages and limitations of employing cloud- computing for long-span bridge monitoring. Furthermore, it explores strategies for developing a robust data strategy and leveraging artificial intelligence (AI) and digital twin (DT) technologies to extract relevant information or patterns regarding asset health conditions. This information is then visualized through the interaction between physical and virtual worlds, facilitating timely and informed decision-making in managing critical road transport infrastructure. Full article

(This article belongs to the Special Issue Advanced Sensing Systems for Structural Monitoring and Damage Detection)

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20 pages, 11148 KiB

Open AccessArticle

A Multiple Attention Convolutional Neural Networks for Diesel Engine Fault Diagnosis

by Xiao Yang, Fengrong Bi, Jiangang Cheng, Daijie Tang, Pengfei Shen and Xiaoyang Bi

Sensors 2024, 24(9), 2708; https://doi.org/10.3390/s24092708 - 24 Apr 2024

Cited by 8 | Viewed by 1817

Abstract

Fault diagnosis can improve the safety and reliability of diesel engines. An end-to-end method based on a multi-attention convolutional neural network (MACNN) is proposed for accurate and efficient diesel engine fault diagnosis. By optimizing the arrangement and kernel size of the channel and spatial attention modules, the feature extraction capability is improved, and an improved convolutional block attention module (ICBAM) is obtained. Vibration signal features are acquired using a feature extraction model alternating between the convolutional neural network (CNN) and ICBAM. The feature map is recombined to reconstruct the sequence order information. Next, the self-attention mechanism (SAM) is applied to learn the recombined sequence features directly. A Swish activation function is introduced to solve “Dead ReLU” and improve the accuracy. A dynamic learning rate curve is designed to improve the convergence ability of the model. The diesel engine fault simulation experiment is carried out to simulate three kinds of fault types (abnormal valve clearance, abnormal rail pressure, and insufficient fuel supply), and each kind of fault varies in different degrees. The comparison results show that the accuracy of MACNN on the eight-class fault dataset at different speeds is more than 97%. The testing time of the MACNN is much less than the machine running time (for one work cycle). Therefore, the proposed end-to-end fault diagnosis method has a good application prospect. Full article

(This article belongs to the Special Issue Advanced Sensing Systems for Structural Monitoring and Damage Detection)

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