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Advanced Sensing Systems for Structural Monitoring and Damage Identification of Buildings and Bridges

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 3323

Special Issue Editor

Prof. Dr. Costas P. Providakis

E-Mail Website
Guest Editor
Applied Mechanics & Strength of Materials Lab, School of Architecture, Technical University of Crete, 73100 Chania, Greece
Interests: health monitoring (SHM); smart sensors and smart materials; nondestructive testing; mechanics of materials; damage mechanics; earthquake engineering and structural dynamics; structural mechanics and control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The methods implemented to identify and analyze anomalies resulting in damage to a particular structure, especially those that are mechanical- or civil-engineering-related, are referred to as structure health monitoring methods. Structural health monitoring is essential for old and decaying structures, including buildings, bridges, and pipelines. Various sensors have been employed for accurate and real-time monitoring of building and bridge structures, including electromechanical, electrochemical, ultrasonic, fiber-optic, piezoelectric, wireless, and fiber Bragg grating sensors, as well as self-sensing concrete. Aside from the available monitoring new trends allowing for remote measurement of vibration characteristics (e.g., scanning laser vibrometer, digital image correlation, drone image processing etc.) have been gaining traction for qualitative dynamic characterization and damage identification as advanced tools for simple, speedy, and non-intrusive contactless measurement. Moreover, notable improvements in structural monitoring and damage identification seen with the progress in technology have led to more accurate measurements, a reduction in the signal-to-noise ratio and transmission speed, and the deployment of machine learning, deep learning, and artificial intelligence in structure health monitoring and damage identification.

In this Special Issue, ‘Advanced Sensing Systems for Structural Monitoring and Damage Identification of Bridges and Buildings’, we welcome recent experimental research and cases studies on structural health monitoring and damage identification data analysis to detect specific targets. Topics of interest include, but are not limited to:

  • Advanced sensing technologies and networks for structural health monitoring and damage identification.
  • Innovative non-destructive evaluation of civil infrastructures.
  • Remote sensing techniques for structural health monitoring and damage identification.
  • Smart materials for structural dynamics characterization and damage detection.
  • Μonitoring tools and techniques for current and historical structures and infrastructures.
  • Embedded self-sensing systems of structural materials.
  • Cure monitoring and early-age concrete damage identification.
  • Structural health monitoring using artificial intelligence and machine learning.
  • Innovative neural network architecture for structural health monitoring.
  • Information modeling/bridge information modeling integrated with structural health monitoring systems and methodologies.

Prof. Dr. Costas P. Providakis
Guest Editor

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.

Published Papers (4 papers)

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Research

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21 pages, 4511 KiB  
Article
Subspace Identification of Bridge Frequencies Based on the Dimensionless Response of a Two-Axle Vehicle
by Yixin Quan, Qing Zeng, Nan Jin, Yipeng Zhu and Chengyin Liu
Sensors 2024, 24(6), 1946; https://doi.org/10.3390/s24061946 - 18 Mar 2024
Viewed by 490
Abstract
As an essential reference to bridge dynamic characteristics, the identification of bridge frequencies has far-reaching consequences for the health monitoring and damage evaluation of bridges. This study proposes a uniform scheme to identify bridge frequencies with two different subspace-based methodologies, i.e., an improved [...] Read more.
As an essential reference to bridge dynamic characteristics, the identification of bridge frequencies has far-reaching consequences for the health monitoring and damage evaluation of bridges. This study proposes a uniform scheme to identify bridge frequencies with two different subspace-based methodologies, i.e., an improved Short-Time Stochastic Subspace Identification (ST-SSI) method and an improved Multivariable Output Error State Space (MOESP) method, by simply adjusting the signal inputs. One of the key features of the proposed scheme is the dimensionless description of the vehicle–bridge interaction system and the employment of the dimensionless response of a two-axle vehicle as the state input, which enhances the robustness of the vehicle properties and speed. Additionally, it establishes the equation of the vehicle biaxial response difference considering the time shift between the front and the rear wheels, theoretically eliminating the road roughness information in the state equation and output signal effectively. The numerical examples discuss the effects of vehicle speeds, road roughness conditions, and ongoing traffic on the bridge identification. According to the dimensionless speed parameter Sv1 of the vehicle, the ST-SSI (Sv1 < 0.1) or MOESP (Sv1 ≥ 0.1) algorithm is applied to extract the frequencies of a simply supported bridge from the dimensionless response of a two-axle vehicle on a single passage. In addition, the proposed methodology is applied to two types of long-span complex bridges. The results show that the proposed approaches exhibit good performance in identifying multi-order frequencies of the bridges, even considering high vehicle speeds, high levels of road surface roughness, and random traffic flows. Full article
(This article belongs to the Special Issue Advanced Sensing Systems for Structural Monitoring and Damage Identification of Buildings and Bridges)
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21 pages, 3740 KiB  
Article
A Multi-Stage Feature Aggregation and Structure Awareness Network for Concrete Bridge Crack Detection
by Erhu Zhang, Tao Jiang and Jinghong Duan
Sensors 2024, 24(5), 1542; https://doi.org/10.3390/s24051542 - 28 Feb 2024
Viewed by 411
Abstract
One of the most significant problems affecting a concrete bridge’s safety is cracks. However, detecting concrete bridge cracks is still challenging due to their slender nature, low contrast, and background interference. The existing convolutional methods with square kernels struggle to capture crack features [...] Read more.
One of the most significant problems affecting a concrete bridge’s safety is cracks. However, detecting concrete bridge cracks is still challenging due to their slender nature, low contrast, and background interference. The existing convolutional methods with square kernels struggle to capture crack features effectively, fail to perceive the long-range dependencies between crack regions, and have weak suppression ability for background noises, leading to low detection precision of bridge cracks. To address this problem, a multi-stage feature aggregation and structure awareness network (MFSA-Net) for pixel-level concrete bridge crack detection is proposed in this paper. Specifically, in the coding stage, a structure-aware convolution block is proposed by combining square convolution with strip convolution to perceive the linear structure of concrete bridge cracks. Square convolution is used to capture detailed local information. In contrast, strip convolution is employed to interact with the local features to establish the long-range dependence relationship between discrete crack regions. Unlike the self-attention mechanism, strip convolution also suppresses background interference near crack regions. Meanwhile, the feature attention fusion block is presented for fusing features from the encoder and decoder at the same stage, which can sharpen the edges of concrete bridge cracks. In order to fully utilize the shallow detail features and deep semantic features, the features from different stages are aggregated to obtain fine-grained segmentation results. The proposed MFSA-Net was trained and evaluated on the publicly available concrete bridge crack dataset and achieved average results of 73.74%, 77.04%, 75.30%, and 60.48% for precision, recall, F1 score, and IoU, respectively, on three typical sub-datasets, thus showing optimal performance in comparison with other existing methods. MFSA-Net also gained optimal performance on two publicly available concrete pavement crack datasets, thereby indicating its adaptability to crack detection across diverse scenarios. Full article
(This article belongs to the Special Issue Advanced Sensing Systems for Structural Monitoring and Damage Identification of Buildings and Bridges)
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22 pages, 9538 KiB  
Article
A Deep Learning Model with Signal Decomposition and Informer Network for Equipment Vibration Trend Prediction
by Huiyun Wang, Maozu Guo and Le Tian
Sensors 2023, 23(13), 5819; https://doi.org/10.3390/s23135819 - 22 Jun 2023
Viewed by 1147
Abstract
Accurate equipment operation trend prediction plays an important role in ensuring the safe operation of equipment and reducing maintenance costs. Therefore, monitoring the equipment vibration and predicting the time series of the vibration trend is one of the effective means to prevent equipment [...] Read more.
Accurate equipment operation trend prediction plays an important role in ensuring the safe operation of equipment and reducing maintenance costs. Therefore, monitoring the equipment vibration and predicting the time series of the vibration trend is one of the effective means to prevent equipment failures. In order to reduce the error of equipment operation trend prediction, this paper proposes a method for equipment operation trend prediction based on a combination of signal decomposition and an Informer prediction model. Aiming at the problem of high noise in vibration signals, which makes it difficult to obtain intrinsic characteristics when directly using raw data for prediction, the original signal is decomposed once using the variational mode decomposition (VMD) algorithm optimized by the improved sparrow search algorithm (ISSA) to obtain the intrinsic mode function (IMF) for different frequencies and calculate the fuzzy entropy. The improved adaptive white noise complete set empirical mode decomposition (ICEEMDAN) is used to decompose the components with the largest fuzzy entropy to obtain a series of intrinsic mode components, fully combining the advantages of the Informer model in processing long time series, and predict equipment operation trend data. Input all subsequences into the Informer model and reconstruct the results to obtain the predicted results. The experimental results indicate that the proposed method can effectively improve the accuracy of equipment operation trend prediction compared to other models. Full article
(This article belongs to the Special Issue Advanced Sensing Systems for Structural Monitoring and Damage Identification of Buildings and Bridges)
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Review

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28 pages, 3322 KiB  
Review
Reference-Free Vibration-Based Damage Identification Techniques for Bridge Structural Health Monitoring—A Critical Review and Perspective
by Mohammad Moravvej and Mamdouh El-Badry
Sensors 2024, 24(3), 876; https://doi.org/10.3390/s24030876 - 29 Jan 2024
Viewed by 788
Abstract
Bridges are designed and built to be safe against failure and perform satisfactorily over their service life. Bridge structural health monitoring (BSHM) systems are therefore essential to ensure the safety and serviceability of such critical transportation infrastructure. Identification of structural damage at the [...] Read more.
Bridges are designed and built to be safe against failure and perform satisfactorily over their service life. Bridge structural health monitoring (BSHM) systems are therefore essential to ensure the safety and serviceability of such critical transportation infrastructure. Identification of structural damage at the earliest time possible is a major goal of BSHM processes. Among many developed damage identification techniques (DITs), vibration-based techniques have shown great potential to be implemented in BSHM systems. In a vibration-based DIT, the response of a bridge is measured and analyzed in either time or space domain for the purpose of detecting damage-induced changes in the extracted dynamic properties of the bridge. This approach usually requires a comparison between two structural states of the bridge—the current state and a reference (intact/undamaged) state. In most in-situ cases, however, data on the bridge structural response in the reference state are not available. Therefore, researchers have been recently working on the development of DITs that eliminate the need for a prior knowledge of the reference state. This paper thoroughly explains why and how the reference state can be excluded from the damage identification process. It then reviews the state-of-the-art reference-free vibration-based DITs and summarizes their merits and shortcomings to give guidance on their applicability to BSHM systems. Finally, some recommendations are given for further research. Full article
(This article belongs to the Special Issue Advanced Sensing Systems for Structural Monitoring and Damage Identification of Buildings and Bridges)
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