Recent Advances in Structural Health Monitoring, 2nd Volume

Topic Information

Dear Colleagues,

Following the great success of the Topic Proposal "Recent Advances in Structural Health Monitoring", which was closed on March 31, 2023, and in which 72 papers were published, we have decided to launch a second edition, which we hope will be as successful and provide as much insight as the first.

All the theoretical and technological aspects of structural control and health monitoring theory on materials and structures are covered within the concept of Structural Health Monitoring (SHM).

There are currently a number of highly effective, non-destructive evaluation tools available for SHM monitoring. Nondestructive testing (NDT) refers to a group of non-invasive inspection procedures that are used to assess material qualities, components, and complete process units. Damage mechanisms can also be detected, characterized, and measured using these techniques.

The emphasis of this Topical Collection is the crucial field of damage identification and maintenance of modern and historical buildings, as well as for aerospace and mechanical engineering structures and civil infrastructure.

Original contributions using analytical, numerical, and experimental methods are sought in the main areas of monitoring and control of materials and structures.

Topics will include the more classic areas of monitoring, such as data acquisition, signal processing, and sensor technology, by using acoustic emission damage detection or vibration-based identification methods. Furthermore, in the field of mechanics, passive, active, and semi-active schemes and implementations to perform systems control diagnostics are also desired topics.

Other areas of great interest are those of remote data analysis methodologies, such as wireless communications, control of monitoring systems, sensor–logger combinations for mobile applications, and those on multifunctional materials and structures or artificial intelligence tools.

Among others, the methodologies that involve the use of embedded N/MEMS sensors for local damage detection, corrosion sensors, optical fiber sensors, sonic–ultrasonic tests, digital image correlation, tomography techniques, Raman and terahertz spectroscopy, and electromagnetic analysis, which allow for the evaluation of the level of structural damage and its evolution over time, will be incorporated in this Topical Collection.

As a result, the aim of this new initiative is to bring together researchers working in the field of NDT-SHM, both at the material and structure scales. It is our desire to provide novel insights into the application of NDT to a wide variety of materials and structures in the fields of Civil Engineering and Architecture as well as in Mechanical Engineering.

Prof. Dr. Giuseppe Lacidogna
Dr. Alessandro Grazzini
Dr. Gianfranco Piana
Prof. Dr. Sanichiro Yoshida
Dr. Guang-Liang Feng
Prof. Dr. Jie Xu
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	17.8 Days	CHF 2400	Submit
CivilEng civileng	-	2.8	2020	35.5 Days	CHF 1200	Submit
Materials materials	3.1	5.8	2008	15.5 Days	CHF 2600	Submit
Remote Sensing remotesensing	4.2	8.3	2009	24.7 Days	CHF 2700	Submit
Sensors sensors	3.4	7.3	2001	16.8 Days	CHF 2600	Submit

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Published Papers (8 papers)

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All Applied Sciences CivilEng Materials Remote Sensing Sensors

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27 pages, 8948 KiB  

Open AccessArticle

Defect Detection and 3D Reconstruction of Complex Urban Underground Pipeline Scenes for Sewer Robots

by Ruihao Liu, Zhongxi Shao, Qiang Sun and Zhenzhong Yu

Sensors 2024, 24(23), 7557; https://doi.org/10.3390/s24237557 (registering DOI) - 26 Nov 2024

Abstract

Detecting defects in complex urban sewer scenes is crucial for urban underground structure health monitoring. However, most image-based sewer defect detection models are complex, have high resource consumption, and fail to provide detailed damage information. To increase defect detection efficiency, visualize pipelines, and [...] Read more.

Detecting defects in complex urban sewer scenes is crucial for urban underground structure health monitoring. However, most image-based sewer defect detection models are complex, have high resource consumption, and fail to provide detailed damage information. To increase defect detection efficiency, visualize pipelines, and enable deployment on edge devices, this paper proposes a computer vision-based robotic defect detection framework for sewers. The framework encompasses positioning, defect detection, model deployment, 3D reconstruction, and the measurement of realistic pipelines. A lightweight Sewer-YOLO-Slim model is introduced, which reconstructs the YOLOv7-tiny network by adjusting its backbone, neck, and head. Channel pruning is applied to further reduce the model’s complexity. Additionally, a multiview reconstruction technique is employed to build a 3D model of the pipeline from images captured by the sewer robot, allowing for accurate measurements. The Sewer-YOLO-Slim model achieves reductions of 60.2%, 60.0%, and 65.9% in model size, parameters, and floating-point operations (FLOPs), respectively, while improving the mean average precision (mAP) by 1.5%, reaching 93.5%. Notably, the pruned model is only 4.9 MB in size. Comprehensive comparisons and analyses are conducted with 12 mainstream detection algorithms to validate the superiority of the proposed model. The model is deployed on edge devices with the aid of TensorRT for acceleration, and the detection speed reaches 15.3 ms per image. For a real section of the pipeline, the maximum measurement error of the 3D reconstruction model is 0.57 m. These results indicate that the proposed sewer inspection framework is effective, with the detection model exhibiting advanced performance in terms of accuracy, low computational demand, and real-time capability. The 3D modeling approach offers valuable insights for underground pipeline data visualization and defect measurement. Full article

(This article belongs to the Topic Recent Advances in Structural Health Monitoring, 2nd Volume)

15 pages, 9401 KiB  

Open AccessArticle

Sound Sensing: Generative and Discriminant Model-Based Approaches to Bolt Loosening Detection

by Liehai Cheng, Zhenli Zhang, Giuseppe Lacidogna, Xiao Wang, Mutian Jia and Zhitao Liu

Sensors 2024, 24(19), 6447; https://doi.org/10.3390/s24196447 - 5 Oct 2024

Viewed by 699

Abstract

The detection of bolt looseness is crucial to ensure the integrity and safety of bolted connection structures. Percussion-based bolt looseness detection provides a simple and cost-effective approach. However, this method has some inherent shortcomings that limit its application. For example, it highly depends [...] Read more.

The detection of bolt looseness is crucial to ensure the integrity and safety of bolted connection structures. Percussion-based bolt looseness detection provides a simple and cost-effective approach. However, this method has some inherent shortcomings that limit its application. For example, it highly depends on the inspector’s hearing and experience and is more easily affected by ambient noise. In this article, a whole set of signal processing procedures are proposed and a new kind of damage index vector is constructed to strengthen the reliability and robustness of this method. Firstly, a series of audio signal preprocessing algorithms including denoising, segmenting, and smooth filtering are performed in the raw audio signal. Then, the cumulative energy entropy (CEE) and mel frequency cepstrum coefficients (MFCCs) are utilized to extract damage index vectors, which are used as input vectors for generative and discriminative classifier models (Gaussian discriminant analysis and support vector machine), respectively. Finally, multiple repeated experiments are conducted to verify the effectiveness of the proposed method and its ability to detect the bolt looseness in terms of audio signal. The testing accuracy of the trained model approaches 90% and 96.7% under different combinations of torque levels, respectively. Full article

(This article belongs to the Topic Recent Advances in Structural Health Monitoring, 2nd Volume)

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19 pages, 8484 KiB  

Open AccessArticle

Distributed Embedded System for Multiparametric Assessment of Infrastructure Durability Using Electrochemical Techniques

by Javier Monreal-Trigo, José Enrique Ramón, Román Bataller, Miguel Alcañiz, Juan Soto and José Manuel Gandía-Romero

Sensors 2024, 24(18), 5882; https://doi.org/10.3390/s24185882 - 10 Sep 2024

Viewed by 773

Abstract

We present an autonomous system that remotely monitors the state of reinforced concrete structures. This system performs real-time follow-up of the corrosion rate of rebars (i_CORR), along with other relevant parameters such as temperature, corrosion potential (E_CORR), and electrical [...] Read more.

We present an autonomous system that remotely monitors the state of reinforced concrete structures. This system performs real-time follow-up of the corrosion rate of rebars (i_CORR), along with other relevant parameters such as temperature, corrosion potential (E_CORR), and electrical resistance of concrete (R_E), at many of a structure’s control points by using embedded sensors. i_CORR is obtained by applying a novel low-stress electrochemical polarization technique to corrosion sensors. The custom electronic system manages the sensor network, consisting of a measurement board per control point connected to a central single-board computer in charge of processing measurement data and uploading results to a server via 4G connection. In this work, we report the results obtained after implementing the sensor system into a reinforced concrete wall, where two well-differentiated representative areas were monitored. The obtained corrosion parameters showed consistent values. Similar conclusions are obtained with E_CORR recorded in rebars. With the i_CORR follow-up, the corrosion penetration damage diagram is built. This diagram is particularly useful for identifying critical events during the corrosion propagation period and to be able to estimate structures’ service life. Hence, the system is presented as a useful tool for the structural maintenance and service life predictions of new structures. Full article

(This article belongs to the Topic Recent Advances in Structural Health Monitoring, 2nd Volume)

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15 pages, 3022 KiB  

Open AccessArticle

A Rotating Machinery Fault Diagnosis Method Based on Dynamic Graph Convolution Network and Hard Threshold Denoising

by Qiting Zhou, Longxian Xue, Jie He, Sixiang Jia and Yongbo Li

Sensors 2024, 24(15), 4887; https://doi.org/10.3390/s24154887 - 27 Jul 2024

Cited by 1 | Viewed by 927

Abstract

With the development of precision sensing instruments and data storage devices, the fusion of multi-sensor data in gearbox fault diagnosis has attracted much attention. However, existing methods have difficulty in capturing the local temporal dependencies of multi-sensor monitoring information, and the inescapable noise [...] Read more.

With the development of precision sensing instruments and data storage devices, the fusion of multi-sensor data in gearbox fault diagnosis has attracted much attention. However, existing methods have difficulty in capturing the local temporal dependencies of multi-sensor monitoring information, and the inescapable noise severely decreases the accuracy of multi-sensor information fusion diagnosis. To address these issues, this paper proposes a fault diagnosis method based on dynamic graph convolutional neural networks and hard threshold denoising. Firstly, considering that the relationships between monitoring data from different sensors change over time, a dynamic graph structure is adopted to model the temporal dependencies of multi-sensor data, and, further, a graph convolutional neural network is constructed to achieve the interaction and feature extraction of temporal information from multi-sensor data. Secondly, to avoid the influence of noise in practical engineering, a hard threshold denoising strategy is designed, and a learnable hard threshold denoising layer is embedded into the graph neural network. Experimental fault datasets from two typical gearbox fault test benches under environmental noise are used to verify the effectiveness of the proposed method in gearbox fault diagnosis. The experimental results show that the proposed DDGCN method achieves an average diagnostic accuracy of up to 99.7% under different levels of environmental noise, demonstrating good noise resistance. Full article

(This article belongs to the Topic Recent Advances in Structural Health Monitoring, 2nd Volume)

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27 pages, 12923 KiB  

Open AccessArticle

Failure Analysis for Overall Overturning of Concrete Single-Column Pier Bridges Induced by Temperature and Overloaded Vehicles

by Yelu Wang, Yongjun Zhou, Yuxin Xue, Changwei Yao, Kailong Wang and Xuchang Luo

Materials 2024, 17(11), 2650; https://doi.org/10.3390/ma17112650 - 30 May 2024

Cited by 2 | Viewed by 673

Abstract

Several overloaded-induced overturning incidents of girder bridges with single-column piers have occurred in recent years, resulting in significant casualties and economic losses. Temperature, in addition to overloading, may also play a role in exacerbating bridge overturning. To investigate the association between temperature and [...] Read more.

Several overloaded-induced overturning incidents of girder bridges with single-column piers have occurred in recent years, resulting in significant casualties and economic losses. Temperature, in addition to overloading, may also play a role in exacerbating bridge overturning. To investigate the association between temperature and bridge overturning, an explicit finite element model (EFEM) of a three-span concrete curved continuous bridge considering nonlinearities was developed to simulate overall collapse. The effects of uniform and gradient temperatures on the overall overturning stability of curved and straight bridges were evaluated based on the EFEMs. Furthermore, the temperature–bridge coupling model and temperature–vehicle–bridge coupling model were utilized to examine how gradient temperature influences bridge overturning. The results show that the overall overturning collapse of a bridge follows four stages: stabilization, transition, risk and overturning. Variations in uniform temperature from −30 °C to 60 °C had a negligible effect on the ultimate vehicle weight for bridge overturning, with a variation of less than 1%. As the gradient temperature ranged from −30 °C to 60 °C, curved bridges show less than a 2% variation in ultimate vehicle weights, compared to a range of −6.1% to 11.7% for straight bridges. The torsion caused by positive gradient temperature in curved bridges can exacerbate bridge overturning, while negative gradient temperature in straight bridges can lead the girder to ‘upward warping’, facilitating girder separation from bearings. Monitoring the girder rotation angle and vertical reaction force of bearings can serve as important indicators for comparing the stability of bridges. Full article

(This article belongs to the Topic Recent Advances in Structural Health Monitoring, 2nd Volume)

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19 pages, 25026 KiB  

Open AccessArticle

Effect of Mineral Composition and Particle Size on the Failure Characteristics and Mechanisms of Marble in the China Jinping Underground Laboratory

by Hong Xu, Peiqi Jing, Guangliang Feng, Zhen Zhang, Quan Jiang and Jie Yan

Materials 2024, 17(10), 2290; https://doi.org/10.3390/ma17102290 - 12 May 2024

Viewed by 1226

Abstract

In deep underground engineering, the deformation, failure characteristics, and mechanism of surrounding rock under the influence of grain sizes and mineral compositions are not clear. Based on CJPL-II variously colored marbles, the differences in grain size and mineral composition of the marble were [...] Read more.

In deep underground engineering, the deformation, failure characteristics, and mechanism of surrounding rock under the influence of grain sizes and mineral compositions are not clear. Based on CJPL-II variously colored marbles, the differences in grain size and mineral composition of the marble were analyzed by thin-section analysis and XRD tests, and the effect of intermediate principal stress on the mechanical properties of marble was investigated. Both SEM and microfracture analysis were coupled to reveal the failure mechanisms. The results highlight that the crack initiation strength, damage strength, peak strength, and elasticity modulus of Jinping marble exhibit an increasing trend with an increase in intermediate principal stress, while the peak strain initially increases and subsequently decreases. Moreover, this study established negative correlations between marble strength, brittleness characteristics, and fracture angle with grain size, whereas positive correlations were identified with the content of quartz, sodium feldspar, and the magnitude of the intermediate principal stress. The microcrack density in marble was found to increase with larger grain sizes and decrease with elevated quartz and sodium feldspar content, as well as with increasing intermediate principal stress. Notably, as the intermediate principal stress intensifies and grain size diminishes, the transgranular tensile failure of marble becomes more conspicuous. These research findings contribute to the effective implementation of disaster prevention and control strategies. Full article

(This article belongs to the Topic Recent Advances in Structural Health Monitoring, 2nd Volume)

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20 pages, 8234 KiB  

Open AccessArticle

Rock Layer Classification and Identification in Ground-Penetrating Radar via Machine Learning

by Hong Xu, Jie Yan, Guangliang Feng, Zhuo Jia and Peiqi Jing

Remote Sens. 2024, 16(8), 1310; https://doi.org/10.3390/rs16081310 - 9 Apr 2024

Cited by 1 | Viewed by 1961

Abstract

Ground-penetrating radar (GPR) faces complex challenges in identifying underground rock formations and lithological structures. The diversity, intricate shapes, and electromagnetic properties of subsurface rock formations make their accurate detection difficult. Additionally, the heterogeneity of subsurface media, signal scattering, and non-linear propagation effects contribute [...] Read more.

Ground-penetrating radar (GPR) faces complex challenges in identifying underground rock formations and lithological structures. The diversity, intricate shapes, and electromagnetic properties of subsurface rock formations make their accurate detection difficult. Additionally, the heterogeneity of subsurface media, signal scattering, and non-linear propagation effects contribute to the complexity of signal interpretation. To address these challenges, this study fully considers the unique advantages of convolutional neural networks (CNNs) in accurately identifying underground rock formations and lithological structures, particularly their powerful feature extraction capabilities. Deep learning models possess the ability to automatically extract complex signal features from radar data, while also demonstrating excellent generalization performance, enabling them to handle data from various geological conditions. Moreover, deep learning can efficiently process large-scale data, thereby improving the accuracy and efficiency of identification. In our research, we utilized deep neural networks to process GPR signals, using radar images as inputs and generating structure-related information associated with rock formations and lithological structures as outputs. Through training and learning, we successfully established an effective mapping relationship between radar images and lithological label signals. The results from synthetic data indicate a rock block identification success rate exceeding 88%, with a satisfactory continuity identification of lithological structures. Transferring the network to measured data, the trained model exhibits excellent performance in predicting data collected from the field, further enhancing the geological interpretation and analysis. Therefore, through the results obtained from synthetic and measured data, we can demonstrate the effectiveness and feasibility of this research method. Full article

(This article belongs to the Topic Recent Advances in Structural Health Monitoring, 2nd Volume)

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19 pages, 5688 KiB  

Open AccessArticle

Parametric Design and Shape Sensing of Geared Back Frame Shell Structure for Floating Cylindrical Reflector Antenna off the Coast

by Mengmei Mei, He Huang, Yugang Li and Zhe Zheng

Appl. Sci. 2023, 13(20), 11602; https://doi.org/10.3390/app132011602 - 23 Oct 2023

Cited by 1 | Viewed by 1060

Abstract

At present, numerous reflector antennas have been constructed worldwide on land. However, there are few applications of reflector antennas directly set off the coast. To expand the application region of reflector antennas, a floating cylindrical reflector antenna (FCRA) driven by the moving mass [...] Read more.

At present, numerous reflector antennas have been constructed worldwide on land. However, there are few applications of reflector antennas directly set off the coast. To expand the application region of reflector antennas, a floating cylindrical reflector antenna (FCRA) driven by the moving mass was developed to implement the elevation angle adjustment. Firstly, the structure design is introduced in detail. The design parameters are stated and analyzed to obtain the kinematic relationship while considering the water surface constraint. Then, the effects of each variable on the rotation capacity and structural stability are discussed. Further, the feasibility of the elevation angle adjustment process is demonstrated by using a prototype model test and software simulation. Finally, the deformation analyses and shape sensing of the back frame are carried out on the basis of the inverse finite element method (iFEM). We concluded that this new structure is feasible and expected to sit off the coast. In addition, the iFEM algorithm with sub-region reconstruction was proved to be suitable for the shape sensing of the over-constrained FCRA during the angle adjustment process via several quasi-static sampling moments. Full article

(This article belongs to the Topic Recent Advances in Structural Health Monitoring, 2nd Volume)

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