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Smart Sensing Technology and Infrastructure Health Monitoring
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Smart Sensing Technology and Infrastructure Health Monitoring

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

Deadline for manuscript submissions: closed (15 January 2024) | Viewed by 16012

Special Issue Editors

Prof. Dr. Ying-Qing Guo

E-Mail Website
Guest Editor
Mechanical and Electronic Engineering School, Nanjing Forestry University, Nanjing 210037, China
Interests: detection technology and automation devices; control theory and control engineering; machine vision
Dr. Fernando Moreu

E-Mail Website
Guest Editor
Department Civil, Construction, and Environmental Engineering, University of New Mexico, Albuquerque, NM 87131, USA
Interests: structural health monitoring; wireless smart sensor networks; infrastructure management and policies; performance-based monitoring; augmented reality; human–machine interfaces and human cognition
Special Issues, Collections and Topics in MDPI journals
Dr. Hongfang Lu

E-Mail Website
Guest Editor
China-Pakistan Belt and Road Joint Laboratory on Smart Disaster Prevention of Major Infrastructures, Southeast University, Nanjing 210096, China
Interests: pipeline health monitoring; pipeline trenchless repair; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Xinqun Zhu

E-Mail Website
Guest Editor
School of Civil and Environmental Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia
Interests: structural health monitoring; structural dynamics; structural engineering; advanced signal processing and sensor technology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhao-Dong Xu

E-Mail Website
Guest Editor
China-Pakistan Belt and Road Joint Laboratory on Smart Disaster Prevention of Major Infrastructures, School of Civil Engineering, Southeast University, Nanjing 211189, China
Interests: structural vibration control and safety monitoring; anti-earthquake and intelligent disaster prevention for multi-disasters; intelligent materials and structures
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Bridges, roads, buildings, and underground pipe networks are essential types of infrastructure in people's lives. Security incidents frequently occur due to natural disasters, human factors, or performance issues in these types of infrastructure. Once the structure is damaged or fails, it will cause serious economic losses and may even lead to casualties. How to deal with the impact of natural disasters and other unfavorable factors on the life of infrastructure and how to improve its safety and durability in long-term service are urgent research topics. Monitoring infrastructure health makes the real-time diagnosis of the health infrastructure similar to that of a doctor and can take place in an online, dynamic, and automated way. In addition to being able to accurately grasp its operating status, it can effectively predict future health trends. With the development of smart sensing technology, distributed optical fibers, ultrasonic sensors, and other sensors are already widely used. They have great application prospects in the field of infrastructure health monitoring. At the same time, multi-source heterogeneous big data mining and deep learning are also important participants in this process.

For this Special Issue of Sensors, we aim to present a collection of review and original research articles related to the latest technologies in infrastructure health monitoring, including smart sensors, and damage identification based on artificial intelligence, infrastructure disaster deduction, early warnings for infrastructure safety, etc.

Prof. Dr. Ying-Qing Guo
Prof. Dr. Fernando Moreu
Prof. Dr. Hongfang Lu
Dr. Xinqun Zhu
Prof. Dr. Zhao-Dong Xu
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 inspection
  • structural health monitoring
  • smart sensors
  • smart materials
  • disaster deduction
  • structural disaster prevention

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Published Papers (6 papers)

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Research

20 pages, 7956 KiB  
Article
Parameter Identification of Multispan Rigid Frames Using a Stiffness Separation Method
by Feng Xiao, Yu Yan, Xiangwei Meng, Yuxue Mao and Gang S. Chen
Sensors 2024, 24(6), 1884; https://doi.org/10.3390/s24061884 - 15 Mar 2024
Cited by 4 | Viewed by 972
Abstract
Identifying the parameters of multispan rigid frames is challenging because of their complex structures and large computational workloads. This paper presents a stiffness separation method for the static response parameter identification of multispan rigid frames. The stiffness separation method segments the global stiffness [...] Read more.
Identifying the parameters of multispan rigid frames is challenging because of their complex structures and large computational workloads. This paper presents a stiffness separation method for the static response parameter identification of multispan rigid frames. The stiffness separation method segments the global stiffness matrix of the overall structure into the stiffness matrices of its substructures, which are to be computed, thereby reducing the computational workload and improving the efficiency of parameter identification. Loads can be applied individually to each separate substructure, thereby guaranteeing obvious local static responses. The veracity and efficacy of the proposed methodology are substantiated by applying it to three- and eight-span continuous rigid frame structures. The findings indicate that the proposed approach significantly enhances the efficiency of parameter identification for multispan rigid frames. Full article
(This article belongs to the Special Issue Smart Sensing Technology and Infrastructure Health Monitoring)
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20 pages, 16064 KiB  
Article
The Application of High-Resolution, Embedded Fibre Optic (FO) Sensing for Large-Diameter Composite Steel/Plastic Pipeline Performance under Dynamic Transport Loads
by Nigel J. Cassidy, Paul O’Regan, Sha Luo, David N. Chapman and Ian Jefferson
Sensors 2024, 24(4), 1298; https://doi.org/10.3390/s24041298 - 17 Feb 2024
Cited by 1 | Viewed by 1335
Abstract
Distributed optical fibre sensing (DOFS)-based strain measurement systems are now routinely deployed across infrastructure health monitoring applications. However, there are still practical performance and measurement issues associated with the fibre’s attachment method, particularly with thermoplastic pipeline materials (e.g., high-density polyethylene, HDPE) and adhesive [...] Read more.
Distributed optical fibre sensing (DOFS)-based strain measurement systems are now routinely deployed across infrastructure health monitoring applications. However, there are still practical performance and measurement issues associated with the fibre’s attachment method, particularly with thermoplastic pipeline materials (e.g., high-density polyethylene, HDPE) and adhesive affixment methods. In this paper, we introduce a new optical fibre installation method that utilises a hot-weld encapsulation approach that fully embeds the fibre onto the pipeline’s plastic surface. We describe the development, application and benefits of the new embedment approach (as compared to adhesive methods) and illustrate its practical performance via a full-scale, real-world, dynamic loading trial undertaken on a 1.8 m diameter, 6.4 m long stormwater pipeline structure constructed from composite spiral-wound, steel-reinforced, HDPE pipe. The optical frequency domain reflectometry (OFDR)-based strain results show how the new method improves strain transference and dynamic measurement performance and how the data can be easily interpreted, in a practical context, without the need for complex strain transfer functions. Through the different performance tests, based on UK rail-road network transport loading conditions, we also show how centimetre- to metre-scale strain variations can be clearly resolved at the frequencies and levels consistent with transport- and construction-based, buried infrastructure loading scenarios. Full article
(This article belongs to the Special Issue Smart Sensing Technology and Infrastructure Health Monitoring)
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16 pages, 3212 KiB  
Article
Transfer-Learning-Based Temperature Uncertainty Reduction Algorithm for Large Scale Oil Tank Ground Settlement Monitoring
by Tao Liu, Tao Jiang, Gang Liu and Changsen Sun
Sensors 2024, 24(1), 215; https://doi.org/10.3390/s24010215 - 29 Dec 2023
Viewed by 1068
Abstract
Sensors operating in open-air environments can be affected by various environmental factors. Specifically, ground settlement (GS) monitoring sensors installed in oil tanks are susceptible to non-uniform temperature fields caused by uneven sunshine exposure. This disparity in environmental conditions can lead to errors in [...] Read more.
Sensors operating in open-air environments can be affected by various environmental factors. Specifically, ground settlement (GS) monitoring sensors installed in oil tanks are susceptible to non-uniform temperature fields caused by uneven sunshine exposure. This disparity in environmental conditions can lead to errors in sensor readings. To address this issue, this study aimed to analyze the impact of temperature on GS monitoring sensors and establish a mapping relationship between temperature uncertainty (fluctuations of measurement caused by temperature variation) and temperature variation. By collecting the temperature information and inferring the temperature uncertainty being introduced, this interference can be removed. However, it is crucial to note that in real-world complex scenarios, the relationship between temperature uncertainty and temperature variation is not always a constant positive correlation, which limits the data available for certain periods. Moreover, the limited availability of data presents a challenge when analyzing the complex mapping relationship. To overcome these challenges, a transfer-learning-based algorithm was introduced to develop a more accurate model for predicting temperature uncertainty based on temperature variation, even with limited data. Subsequently, a practical test was conducted to validate the proposed algorithm’s performance. The results demonstrated that the algorithm outperformed a simple linear fitting model using the least squares method (LSM), achieving an improvement of up to 21.9%. This outcome highlights the algorithm’s potential for enhancing the performance of GS sensors in daytime monitoring and contributing to the safe operation of oil tank facilities and infrastructure health monitoring. Full article
(This article belongs to the Special Issue Smart Sensing Technology and Infrastructure Health Monitoring)
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22 pages, 9735 KiB  
Article
Multiple Damaged Cables Identification in Cable-Stayed Bridges Using Basis Vector Matrix Method
by Jianying Ren, Xinqun Zhu and Shaohua Li
Sensors 2023, 23(2), 860; https://doi.org/10.3390/s23020860 - 11 Jan 2023
Viewed by 2342
Abstract
A new damaged cable identification method using the basis vector matrix (BVM) is proposed to identify multiple damaged cables in cable-stayed bridges. The relationships between the cable tension stiffness and the girder bending strain of the cable-stayed bridge are established using a force [...] Read more.
A new damaged cable identification method using the basis vector matrix (BVM) is proposed to identify multiple damaged cables in cable-stayed bridges. The relationships between the cable tension stiffness and the girder bending strain of the cable-stayed bridge are established using a force method. The difference between the maximum bending strains of the bridges with intact and damaged cables is used to obtain the damage index vectors (DIXVs). Then, BVM is obtained by the normalized DIXV. Finally, the damage indicator vector (DIV) is obtained by DIXV and BVM to identify the damaged cables. The damage indicator is substituted into the damage severity function to identify the corresponding damage severity. A field cable-stayed bridge is used to verify the proposed method. The three-dimensional finite element model is established using ANSYS, and the model is validated using the field measurements. The validated model is used to simulate the strain response of the bridge with different damage scenarios subject to a moving vehicle load, including one, two, three, and four damaged cables with damage severity of 10%, 20%, and 30%, respectively. The noise effect is also discussed. The results show that the BVM method has good anti-noise capability and robustness. Full article
(This article belongs to the Special Issue Smart Sensing Technology and Infrastructure Health Monitoring)
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16 pages, 5193 KiB  
Article
Vision-Based Detection of Bolt Loosening Using YOLOv5
by Yuhang Sun, Mengxuan Li, Ruiwen Dong, Weiyu Chen and Dong Jiang
Sensors 2022, 22(14), 5184; https://doi.org/10.3390/s22145184 - 11 Jul 2022
Cited by 32 | Viewed by 6718
Abstract
Bolted connections have been widely applied in engineering structures, loosening will happen when bolted connections are subjected to continuous cyclic load, and a significant rotation between the nut and the bolt can be observed. Combining deep learning with machine vision, a bolt loosening [...] Read more.
Bolted connections have been widely applied in engineering structures, loosening will happen when bolted connections are subjected to continuous cyclic load, and a significant rotation between the nut and the bolt can be observed. Combining deep learning with machine vision, a bolt loosening detection method based on the fifth version of You Only Look Once (YOLOv5) is proposed, and the rotation of the nut is identified to detect the bolt loosening. Two different circular markers are added to the bolt and the nut separately, and then YOLOv5 is used to identify the circular markers, and the rotation angle of the nut against the bolt is calculated according to the center coordinate of each predicted box. A bolted connection structure is adopted to illustrate the effectiveness of the method. First, 200 images containing bolts and circular markers are collected to make the dataset, which is divided into a training set, verification set and test set. Second, YOLOv5 is used to train the model; the precision rate and recall rate are respectively 99.8% and 100%. Finally, the robustness of the proposed method in different shooting environments is verified by changing the shooting distance, shooting angle and light condition. When using this method to detect the bolt loosening angle, the minimum identifiable angle is 1°, and the maximum detection error is 5.91% when the camera is tilted 45°. The experimental results show that the proposed method can detect the loosening angle of the bolted connection with high accuracy; especially, the tiny angle of bolt loosening can be identified. Even under some difficult shooting conditions, the method still works. The early stage of bolt loosening can be detected by measuring the rotation angle of the nut against the bolt. Full article
(This article belongs to the Special Issue Smart Sensing Technology and Infrastructure Health Monitoring)
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22 pages, 13271 KiB  
Article
An Approach for Time Synchronization of Wireless Accelerometer Sensors Using Frequency-Squeezing-Based Operational Modal Analysis
by Yi Chen, Xiaoqing Zheng, Yaozhi Luo, Yanbin Shen, Yu Xue and Wenwei Fu
Sensors 2022, 22(13), 4784; https://doi.org/10.3390/s22134784 - 24 Jun 2022
Cited by 9 | Viewed by 2177
Abstract
Wireless sensor networks usually suffer from the issue of time synchronization discrepancy due to environmental effects or clock management collapse. This will result in time delays between the dynamic responses collected by wireless sensors. If non-synchronized dynamic response data are directly used for [...] Read more.
Wireless sensor networks usually suffer from the issue of time synchronization discrepancy due to environmental effects or clock management collapse. This will result in time delays between the dynamic responses collected by wireless sensors. If non-synchronized dynamic response data are directly used for structural modal identification, it leads to the misestimation of modal parameters. To overcome the non-synchronization issue, this study proposes a time synchronization approach to detect and correct asynchronous dynamic responses based on frequency domain decomposition (FDD) with frequency-squeezing processing (FSP). By imposing the expected relationship between modal phase angles extracted from the first-order singular value spectrum, the time lags between different sensors can be estimated, and synchronization can be achieved. The effectiveness of the proposed approach is fully demonstrated by numerical and experimental studies, as well as field measurement of a large-span spatial structure. The results verify that the proposed approach is effective for the time synchronization of wireless accelerometer sensors. Full article
(This article belongs to the Special Issue Smart Sensing Technology and Infrastructure Health Monitoring)
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