Special Issue "Symmetry in Structural Health Monitoring"

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer and Engineering Science and Symmetry/Asymmetry".

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Prof. Dr. Yang Yang
E-Mail Website
Guest Editor
School of Civil Engineering, Chongqing Univerity, Chongqing, China
Interests: bridge and structure inspection and reinforcement; structural health monitoring; structural vibration; seismic evaluation for structure
Prof. Dr. Ying Lei
E-Mail Website
Guest Editor
School of Architecture and Civil Engineering, Xiamen University, Xiamen 365001, China
Interests: structural health monitoring; structural identification and damage detection; structural random vibration
Prof. Dr. Xiaolin Meng
E-Mail Website
Guest Editor
Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, 100 Pingleyuan, Chaoyang District, Beijing 100124, China
Interests: global navigation satellite system (GNSS); integrated algorithms and solutions for precise positioning; structural health monitoring; intelligent mobility; connected and autonomous vehicles; precision agriculture and livestock farming, digital innovation
Special Issues and Collections in MDPI journals
Prof. Dr. Jun Li
E-Mail Website
Guest Editor
School of Civil and Mechanical Engineering, Curtin University, Bentley, WA, Australia
Interests: structural health monitoring; structural identification and model updating; artificial intelligence and vision based methods for SHM

Special Issue Information

Dear Colleagues,

Structural health monitoring refers to the strategy and process of damage diagnosis and characterization of engineering structures. With the development of urbanization, various types of infrastructure and mechanical equipment provide people with convenient life services. It also shows the importance of structural health monitoring where symmetry is widely used. By analyzing the symmetry of the structure and using sensors to collect data, it is possible to study the performance of the structure itself. This is a hot topic in current research. The collected data contains the structural random vibration and environmental noise; good equipment can improve the efficiency of data collection; effective denoising methods with the knowledge of structural dynamics can extract the characteristic parameters of the structure from the data.

In this Special Issue on symmetry, we mainly discuss the application of symmetry in various structural health monitoring. For example, considering the health monitoring of a known structure, by obtaining the static or dynamic response of the structure, using different signal processing methods, including some advanced filtering methods, to remove the influence of environmental noise, and extract structural feature parameters to determine the safety of the structure. These damage diagnosis methods can also be effectively applied to various types of infrastructure and mechanical equipment. For this reason, the vibration control of various structures and the knowledge of random structure dynamics should be considered, which will promote the rapid development of the structural health monitoring. Among them, signal extraction and evaluation methods are also worthy of study. The improvement of signal acquisition instruments and acquisition methods improves the accuracy of data. A good evaluation method will help to correctly understand the performance with different types of infrastructure and mechanical equipment.

Prof. Dr. Yang Yang
Prof. Dr. Ying Lei
Prof. Dr. Xiaolin Meng
Prof. Dr. Jun Li
Guest Editors

Manuscript Submission Information

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Keywords

  • Symmetry
  • Structural health monitoring
  • Structural diagnosis
  • Structural vibration
  • Information processing

Published Papers (9 papers)

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Research

Article
Review on Vibration-Based Structural Health Monitoring Techniques and Technical Codes
Symmetry 2021, 13(11), 1998; https://0-doi-org.brum.beds.ac.uk/10.3390/sym13111998 (registering DOI) - 22 Oct 2021
Viewed by 193
Abstract
Structural damages occur in modern structures during operations due to environmental and human factors. The damages accumulating with time may lead to a significant decrease in structure performance or even destruction; natural symmetry is broken, resulting in an unexpected life and economic loss. [...] Read more.
Structural damages occur in modern structures during operations due to environmental and human factors. The damages accumulating with time may lead to a significant decrease in structure performance or even destruction; natural symmetry is broken, resulting in an unexpected life and economic loss. Therefore, it is necessary to monitor the structural response to detect the damage in an early stage, evaluate the health condition of structures, and ensure the operation safety of structures. In fact, the structure and the evaluation can be considered as a special symmetry. Among several SHM methods, vibration-based SHM techniques have been widely adopted recently. Hence, this paper reviews the vibration-based SHM methods in terms of the vibrational parameters used. In addition, the technical codes on vibration based SHM system have also been reviewed, since they are more important in engineering applications. Several related ISO standards and national codes have been developed and implemented, while more specific technical codes are still required to provide more detailed guidelines in practice to maintain structure safety and natural symmetry. Full article
(This article belongs to the Special Issue Symmetry in Structural Health Monitoring)
Article
Intelligent Safety Assessment of Prestressed Steel Structures Based on Digital Twins
Symmetry 2021, 13(10), 1927; https://0-doi-org.brum.beds.ac.uk/10.3390/sym13101927 - 14 Oct 2021
Viewed by 261
Abstract
In the development process of intelligent construction, the safety assessment of prestressed steel structures as an important research direction has become more and more attractive in academia. Digital twins (DTs) is the key technology to realize intelligent construction. The virtual and real interaction [...] Read more.
In the development process of intelligent construction, the safety assessment of prestressed steel structures as an important research direction has become more and more attractive in academia. Digital twins (DTs) is the key technology to realize intelligent construction. The virtual and real interaction of the DTs can provide an efficient management and control mechanism for the construction process. This research proposes an intelligent safety assessment method of prestressed steel structures based on DTs. In this research method, the structural safety assessment is divided into two aspects: performance analysis and maintenance. By analyzing the characteristics of the construction safety assessment, a DTs framework for construction safety assessment is built. Driven by the DTs framework, a physical space model and a virtual space model are constructed. On the basis of virtual and actual interaction, multidimensional information fusion of time and space is carried out to realize the analysis of structural safety performance. On this basis, the paper establishes a Bow-tie model for the maintenance modeling of unsafe construction events. Moreover, the theoretical method formed is applied to the construction of a symmetrical structure (wheel–spoke cable truss). The validity of the method is verified by comparing the cable force calculated by the theoretical method and measured on site. The assessment method driven by the DTs ensures the structural safety and improves the intelligence level of safety management and control of the structure construction. Full article
(This article belongs to the Special Issue Symmetry in Structural Health Monitoring)
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Article
Substructure Shake Table Testing of Frame Structure–Damper System Using Model-Based Integration Algorithms and Finite Element Method: Numerical Study
Symmetry 2021, 13(9), 1739; https://0-doi-org.brum.beds.ac.uk/10.3390/sym13091739 - 18 Sep 2021
Viewed by 278
Abstract
Substructure shake table testing (SSTT) is an advanced experimental technique that is suitable for investigating the vibration control of secondary structure-type dampers such as tuned mass dampers (TMDs). The primary structure and damper are considered as analytical and experimental substructures, respectively. The analytical [...] Read more.
Substructure shake table testing (SSTT) is an advanced experimental technique that is suitable for investigating the vibration control of secondary structure-type dampers such as tuned mass dampers (TMDs). The primary structure and damper are considered as analytical and experimental substructures, respectively. The analytical substructures of existing SSTTs have mostly been simplified as SDOF structures or shear-type structures, which is not realistic. A common trend is to simulate the analytical substructure via the finite element (FE) method. In this study, the control effects of four dampers, i.e., TMD, tuned liquid damper (TLD), particle damper (PD) and particle-tuned mass damper (PTMD), on a frame were examined by conducting virtual SSTTs. The frame was modeled through stiffness-based beam-column elements with fiber sections and was solved by a family of model-based integration algorithms. The influences of the auxiliary mass ratio, integration parameters, time step, and time delay on SSTT were investigated. The results indicate that the TLD had the best performance. In addition, SSTT using model-based integration algorithms can provide satisfactory results, even when the time step is relatively large. The effects of integration parameters and time delay are not significant. Full article
(This article belongs to the Special Issue Symmetry in Structural Health Monitoring)
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Article
A Real-Time Detection Method for Concrete Surface Cracks Based on Improved YOLOv4
Symmetry 2021, 13(9), 1716; https://0-doi-org.brum.beds.ac.uk/10.3390/sym13091716 - 16 Sep 2021
Viewed by 418
Abstract
Many structures in civil engineering are symmetrical. Crack detection is a critical task in the monitoring and inspection of civil engineering structures. This study implements a lightweight neural network based on the YOLOv4 algorithm to detect concrete surface cracks. In the extraction of [...] Read more.
Many structures in civil engineering are symmetrical. Crack detection is a critical task in the monitoring and inspection of civil engineering structures. This study implements a lightweight neural network based on the YOLOv4 algorithm to detect concrete surface cracks. In the extraction of backbone and the design of neck and head, the symmetry concept is adopted. The model modules are improved to reduce the depth and complexity of the overall network structure. Meanwhile, the separable convolution is used to realize spatial convolution, and the SPP and PANet modules are improved to reduce the model parameters. The convolutional layer and batch normalization layer are merged to improve the model inference speed. In addition, using the focal loss function for reference, the loss function of object detection network is improved to balance the proportion of the cracks and the background samples. To comprehensively evaluate the performance of the improved method, 10,000 images (256 × 256 pixels in size) of cracks on concrete surfaces are collected to build the database. The improved YOLOv4 model achieves an mAP of 94.09% with 8.04 M and 0.64 GMacs. The results show that the improved model is satisfactory in mAP, and the model size and calculation amount are greatly reduced. This performs better in terms of real-time detection on concrete surface cracks. Full article
(This article belongs to the Special Issue Symmetry in Structural Health Monitoring)
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Article
Post-Processing of High Formwork Monitoring Data Based on the Back Propagation Neural Networks Model and the Autoregressive—Moving-Average Model
Symmetry 2021, 13(8), 1543; https://0-doi-org.brum.beds.ac.uk/10.3390/sym13081543 - 23 Aug 2021
Viewed by 391
Abstract
Many high formwork systems are currently equipped with health monitoring systems, and the analysis of the data obtained can determine whether high formwork is a hazard. Therefore, the post-processing of monitoring data has become an issue of widespread concern. In this paper, we [...] Read more.
Many high formwork systems are currently equipped with health monitoring systems, and the analysis of the data obtained can determine whether high formwork is a hazard. Therefore, the post-processing of monitoring data has become an issue of widespread concern. In this paper, we discussed the fitting effect of the symmetrical high formwork monitoring data using the autoregressive–moving-average (ARMA) model and the back propagation neural networks (BPNN) combined model to process. In the actual project, the symmetry of the high formwork system allows the analysis of local monitoring results to be well extended to the whole. For the establishment of the ARMA model, the accurate judgment of the model order has a significant impact. In this paper, back propagation neural networks (BPNN) are used to simulate the ARMA process. The order of the ARMA model is estimated by determining the optimal neural network structure, which is suitable for linear or nonlinear sequences. We validated this approach from the ARMA model data simulated in Monte Carlo and compared it with the Akaike information criterion (AIC) and Bayesian information criterion (BIC). The length of the sequence, the coefficients and the order of the ARMA model are considered as factors that influence the judgment effect. Under different conditions, the BPNN always shows an accuracy rate of more than 90%, while the BIC only has a higher accuracy rate when the model order is low and the judgment efficiency of the AIC is below 50%. Finally, the proposed method successfully modeled the stress sequence and obtained the stress change trend. Compared with AIC and BIC, the efficiency of the processing time series is increased by about 50% when an order is obtained by BPNN. Full article
(This article belongs to the Special Issue Symmetry in Structural Health Monitoring)
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Article
Force Analysis of Self-Anchored Suspension Bridges after Cable Clamp Slippage
Symmetry 2021, 13(8), 1514; https://0-doi-org.brum.beds.ac.uk/10.3390/sym13081514 - 18 Aug 2021
Viewed by 516
Abstract
The slippage of cable clamps during the long-term operation of suspension bridges is a common and detrimental phenomenon. From an experimental point of view, the cable clamp slippage of a suspension bridge was investigated to reveal the effect of this sliding on the [...] Read more.
The slippage of cable clamps during the long-term operation of suspension bridges is a common and detrimental phenomenon. From an experimental point of view, the cable clamp slippage of a suspension bridge was investigated to reveal the effect of this sliding on the force acting on the full bridge. The forces acting on the bridge before and after the slippage were analyzed using a finite element model. The calculation results showed that the cable clamp slippage directly affects the cable forces of the hangers. The hanger cable force decreased by 19.2% when the slippage reached 10.2 cm, while the maximum increase in the cable force of adjacent hangers was 147.7 kN, an increase of 7.25%. The variation of forces in the hanger cable disrupted the force balance of the main girder, thereby producing a torque effect at the corresponding position in the girder, i.e., increased torque. Meanwhile, the slippage affected the axial tension in the main cable and the main girder. The impact of the tower internal force was less than 1%. Hence, the study concluded that the effect of cable clamp slippage is better understood, ensuring the safety of the suspension bridge. Full article
(This article belongs to the Special Issue Symmetry in Structural Health Monitoring)
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Article
Time–Frequency Extraction Model Based on Variational Mode Decomposition and Hilbert–Huang Transform for Offshore Oil Platforms Using MIMU Data
Symmetry 2021, 13(8), 1443; https://0-doi-org.brum.beds.ac.uk/10.3390/sym13081443 - 06 Aug 2021
Viewed by 432
Abstract
Time–frequency extraction is a key issue to understand structural symmetry of dynamic responses of offshore oil platforms for early warning during drilling operations. Current popular methods for signal characteristics extraction can only obtain the attributes with a single dimension or poor precision. To [...] Read more.
Time–frequency extraction is a key issue to understand structural symmetry of dynamic responses of offshore oil platforms for early warning during drilling operations. Current popular methods for signal characteristics extraction can only obtain the attributes with a single dimension or poor precision. To solve this, a combined Hilbert–Huang transform (HHT) and variational mode decomposition (VMD) method is proposed to extract multidimensional dynamic response characteristics of time, frequency, and energy of offshore oil platforms. Based on the extracted time–frequency–energy information, the frequency-domain integration approach (FDIA) can be applied to calculate the displacement using accelerometer in the micro inertial measurement unit (MIMU). A complementary filtering algorithm was designed to measure the torsion angle of platforms using six degrees of freedom data from the MIMU to obtain the torsion angle information. The performance of the proposed method was validated using a series of simulation shaking-table tests and a field test conducted on an offshore oil platform at Dongying City, Shandong Province, China. During the field test, seven out of eight collisions were detected in the frequency range 5 Hz to 12 Hz. The intensity of the fifth collision was the highest, and the maximum displacement obtained by the accelerometer was 6 mm. In addition, the results show a correlation between the axes of the accelerometer and gyroscope, and their combination can measure a torsion angle up to 1.1°. Full article
(This article belongs to the Special Issue Symmetry in Structural Health Monitoring)
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Article
Data Anomaly Detection of Bridge Structures Using Convolutional Neural Network Based on Structural Vibration Signals
Symmetry 2021, 13(7), 1186; https://0-doi-org.brum.beds.ac.uk/10.3390/sym13071186 - 30 Jun 2021
Cited by 2 | Viewed by 459
Abstract
Structural monitoring provides valuable information on the state of structural health, which is helpful for structural damage detection and structural state assessment. However, when the sensors are exposed to harsh environmental conditions, various anomalies caused by sensor failure or damage lead to abnormalities [...] Read more.
Structural monitoring provides valuable information on the state of structural health, which is helpful for structural damage detection and structural state assessment. However, when the sensors are exposed to harsh environmental conditions, various anomalies caused by sensor failure or damage lead to abnormalities of the monitoring data. It is inefficient to remove abnormal data by manual elimination because of the massive number of data obtained by monitoring systems. In this paper, a data anomaly detection method based on structural vibration signals and a convolutional neural network (CNN) is proposed, which can automatically identify and eliminate abnormal data. First, the anomaly detection problem is modeled as a time series classification problem. Data preprocessing and data augmentation, including data expansion and down-sampling to construct new samples, are employed to process the original time series. For a small number of samples in the data set, randomly increase outliers, symmetrical flipping, and noise addition methods are used for data expansion, and samples with the same label are added without increasing the original samples. The down-sampling method of symmetrically extracting the maximum value and the minimum value at the same time can effectively reduce the dimensionality of the input sample, while retaining the characteristics of the data to the greatest extent. Using hyperparameter tuning of the classification weights, CNN is more effective in dealing with unbalanced training sets. Finally, the effectiveness of the proposed method is proved by the anomaly detection of acceleration data on a long-span bridge. For the anomaly detection problem modeled as a time series classification problem, the proposed method can effectively identify various abnormal patterns. Full article
(This article belongs to the Special Issue Symmetry in Structural Health Monitoring)
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Article
Mixed Sensitivity-Based Robust H Control Method for Real-Time Hybrid Simulation
Symmetry 2021, 13(5), 840; https://0-doi-org.brum.beds.ac.uk/10.3390/sym13050840 - 10 May 2021
Viewed by 390
Abstract
Real-time hybrid simulation (RTHS), dividing the emulated structure into numerical substructures (NS) and physical substructures (PS), is a powerful technique to obtain responses and then to assess the seismic performance of civil engineering structures. A transfer system, a servo-hydraulic actuator or shaking table, [...] Read more.
Real-time hybrid simulation (RTHS), dividing the emulated structure into numerical substructures (NS) and physical substructures (PS), is a powerful technique to obtain responses and then to assess the seismic performance of civil engineering structures. A transfer system, a servo-hydraulic actuator or shaking table, is used to apply boundary conditions between the two substructures. However, the servo-hydraulic actuator is inherently a complex system with nonlinearities and may introduce time delays into the RTHS, which will decrease the accuracy and stability of the RTHS. Moreover, there are various uncertainties in RTHS. An accurate and robust actuator control strategy is necessary to guarantee reliable simulation results. Therefore, a mixed sensitivity-based H control method was proposed for RTHS. In H control, the dynamics and robustness of the closed-loop transfer system are realized by performance weighting functions. A form of weighting function was given considering the requirement in RTHS. The influence of the weighting functions on the dynamics was investigated. Numerical simulations and actual RTHSs were carried out under symmetric and asymmetric dynamic loads, namely sinusoidal and earthquake excitation, respectively. Results indicated that the H control method used for RTHS is feasible, and it exhibits an excellent tracking performance and robustness. Full article
(This article belongs to the Special Issue Symmetry in Structural Health Monitoring)
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