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Buildings
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Recent Developments in Structural Health Monitoring
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Recent Developments in Structural Health Monitoring

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Structures".

Deadline for manuscript submissions: 15 January 2025 | Viewed by 7626

Special Issue Editors

Dr. Huaping Wang

E-Mail Website
Guest Editor
School of Civil Engineering and Mechanics, Lanzhou University, Lanzhou, China
Interests: structural health monitoring; optical fiber sensor; strain transfer analysis; smart composite structures; damage identification; performance assessment
Special Issues, Collections and Topics in MDPI journals
Dr. Pengfei Cao

E-Mail Website
Guest Editor
School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
Interests: optical fiber sensing technology; vibration sensor; smart monitoring; wireless energy transmission technology; artificial intelligent algorithm
Prof. Dr. Ping Xiang

E-Mail Website
Guest Editor
School of Civil Engineering, Central South University, Changsha 410082, China
Interests: computing in civil engineering; solid mechanics; structural mechanics; bridge engineering; structural materials
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue “Recent Developments in Structural Health Monitoring” aims to collect recent advances and developments in the structural health monitoring of buildings, bridges, dams, oil tanks, pipes and aerospace equipment. The safe operation of these important structures has always been a significant scientific problem. How to protect these structures from the disasters and maintain their regular function requires advanced sensing technology and smart structural health monitoring (SHM) systems. Parameterical reflection analysis based on the measured data and data-motivated model updating are also critically significant. It determines the accuracy and reliability of the monitoring technique, which also influences the management scheme and rehabilitation measures. For this reason, this Special Issue intends to present contributions in advanced monitoring technologies (i.e., optical fiber sensing technology), feasible parametric reflection methods, time and frequency domain analysis, data-motivated model updating, damage identification and performance assessment methods. This Special Issue aims to cover original or review articles exploring innovations in SHM. Themes of interest include, but are not limited to:

  • Smart sensing technology and SHM systems of structures;
  • Self-sensing structures to measure the parameters, such as stress (or force), strain (or deformation), crack, damage, temperature and pressure;
  • Optical fiber sensors and components in engineering;
  • Smart materials and structures with both self-sensing and self-healing functions;
  • Vibration testing based structural damage identification;
  • Dynamic analysis and modal parameter recognition;
  • Monitoring data motivated model updating;
  • Structural performance assessment;
  • Smart operation and management.

Dr. Huaping Wang
Dr. Pengfei Cao
Prof. Dr. Ping Xiang
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. Buildings is an international peer-reviewed open access monthly 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 health monitoring
  • smart sensors and structures
  • damage identification
  • dynamic analysis
  • data-motivated model updating
  • performance assessment
  • buildings, bridges and dams
  • optical fiber sensing technology

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (7 papers)

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Research

21 pages, 5545 KiB  
Article
A Novel Long Short-Term Memory Seq2Seq Model with Chaos-Based Optimization and Attention Mechanism for Enhanced Dam Deformation Prediction
by Lei Wang, Jiajun Wang, Dawei Tong and Xiaoling Wang
Buildings 2024, 14(11), 3675; https://doi.org/10.3390/buildings14113675 - 19 Nov 2024
Viewed by 281
Abstract
The accurate prediction of dam deformation is essential for ensuring safe and efficient dam operation and risk management. However, the nonlinear relationships between deformation and time-varying environmental factors pose significant challenges, often limiting the accuracy of conventional and deep learning models. To address [...] Read more.
The accurate prediction of dam deformation is essential for ensuring safe and efficient dam operation and risk management. However, the nonlinear relationships between deformation and time-varying environmental factors pose significant challenges, often limiting the accuracy of conventional and deep learning models. To address these issues, this study aimed to improve the predictive accuracy and interpretability in dam deformation modeling by proposing a novel LSTM seq2seq model that integrates a chaos-based arithmetic optimization algorithm (AOA) and an attention mechanism. The AOA optimizes the model’s learnable parameters by utilizing the distribution patterns of four mathematical operators, further enhanced by logistic and cubic mappings, to avoid local optima. The attention mechanism, placed between the encoder and decoder networks, dynamically quantifies the impact of influencing factors on deformation, enabling the model to focus on the most relevant information. This approach was applied to an earth-rock dam, achieving superior predictive performance with RMSE, MAE, and MAPE values of 0.695 mm, 0.301 mm, and 0.156%, respectively, outperforming conventional machine learning and deep learning models. The attention weights provide insights into the contributions of each factor, enhancing interpretability. This model holds potential for real-time deformation monitoring and predictive maintenance, contributing to the safety and resilience of dam infrastructure. Full article
(This article belongs to the Special Issue Recent Developments in Structural Health Monitoring)
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19 pages, 4398 KiB  
Article
Seismic Energy Dissipation and Hysteresis Performances of Distinctly Shaped Steel-Reinforced Concrete Column–Beam Joints under Cyclic Loading
by Junquan Duan, Delei Yang, Xiaochun Liu and Ping Xiang
Buildings 2024, 14(9), 2777; https://doi.org/10.3390/buildings14092777 - 4 Sep 2024
Viewed by 821
Abstract
The distinctly shaped steel-reinforced concrete (SRC) column–beam framing system offers an innovative and tailored structural solution that combines load-bearing capabilities with architectural esthetics. This study introduces an innovative joint design methodology, focusing on examining the seismic responsiveness of the uniquely designed SRC columns [...] Read more.
The distinctly shaped steel-reinforced concrete (SRC) column–beam framing system offers an innovative and tailored structural solution that combines load-bearing capabilities with architectural esthetics. This study introduces an innovative joint design methodology, focusing on examining the seismic responsiveness of the uniquely designed SRC columns when interconnected with reinforced concrete (RC) beams, subjected to bidirectional low cycle loading patterns through precisely calibrated pseudo-static evaluations with varied stirrup spacing. A comparative assessment was undertaken, comparing the joints of SRC test specimens with their RC counterparts, ensuring equivalency in steel and reinforcement area to maintain fairness. The evaluation encompassed a thorough examination of hysteresis loop backbone curves, as well as load–strain hysteresis patterns. It was found that the specimens incorporating structural steel and tubes demonstrated enhanced energy dissipation capabilities, surpassing other specimens in this critical performance aspect. An in-depth analysis was also conducted by comparing the ductility coefficient and the equivalent viscous damping coefficient to evaluate the joints’ performance in dissipating energy, coupled with a thorough examination of their stiffness deterioration behavior. The conclusion is that the energy dissipation capacity and stiffness degradation of distinctly shaped SRC column joints are superior to those of conventional, distinctly shaped concrete column joints, indicating promising application prospects. Full article
(This article belongs to the Special Issue Recent Developments in Structural Health Monitoring)
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27 pages, 22071 KiB  
Article
FBG Sensing Data Motivated Dynamic Feature Assessment of the Complicated CFRP Antenna Beam under Various Vibration Modes
by Cong Chen, Chao Zhang, Jie Ma, Shi-Zhong He, Jian Chen, Liang Sun and Hua-Ping Wang
Buildings 2024, 14(7), 2249; https://doi.org/10.3390/buildings14072249 - 22 Jul 2024
Viewed by 796
Abstract
Carbon fiber-reinforced polymer (CFRP) components were extensively used and current studies mainly refer to CFRP laminates. The dynamic performance of the complicated CFRP antenna beams is yet to be explored. Therefore, a sensor layout based on fiber Bragg gratings (FBGs) in series was [...] Read more.
Carbon fiber-reinforced polymer (CFRP) components were extensively used and current studies mainly refer to CFRP laminates. The dynamic performance of the complicated CFRP antenna beams is yet to be explored. Therefore, a sensor layout based on fiber Bragg gratings (FBGs) in series was designed to measure the dynamic response of the CFRP antenna beam, and various vibration tests (sweep frequency test, simulated long-life vibration test, shock vibration test, functional vibration test, and constant frequency vibration test) were conducted. The time and frequency domain analysis on FBG sensing signals was performed to check the vibration performance and assess the health condition of this novel CFRP structure. The results indicate that strain values reach a maximum of only 300 µε under different test conditions. The antenna beam exhibited clear vibration patterns, with the first four intrinsic frequencies identified at 44, 94.87, 107.1, and 193.45 Hz. It shows that strain distribution and vibration modes of the antenna beam can be characterized from the sensing data, and the dynamic feature can be much more accurately assessed. The FBG sensors attached on the surface of CFRP antenna beam can accurately and stably measure the dynamic response, which validates that the interfaces between optical fiber sensing elements and CFRP material have excellent interfacial bonding characteristics. The novel CFRP antenna beam exhibits the excellent dynamic performance and stability, offering the replacement of traditional steel antenna beams. The study can finally instruct the development of self-sensing CFRP antenna beams assembled with FBGs in series. Full article
(This article belongs to the Special Issue Recent Developments in Structural Health Monitoring)
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15 pages, 5443 KiB  
Article
Adaptive Vibration Monitoring of Railway Track Structures Using the UWFBG by the Identification of Train-Load Patterns
by Jiahui Chen, Qiuyi Li, Shijie Zhang, Chao Lin and Shiyin Wei
Buildings 2024, 14(5), 1239; https://doi.org/10.3390/buildings14051239 - 26 Apr 2024
Cited by 1 | Viewed by 941
Abstract
Due to the capability of multiplexing thousands of sensors on a single optical cable, ultra-weak fiber Bragg grating (UWFBG) vibration sensing technology has been utilized in monitoring the vibration response of large-scale infrastructures, particularly urban railway tracks, and the volume of the collected [...] Read more.
Due to the capability of multiplexing thousands of sensors on a single optical cable, ultra-weak fiber Bragg grating (UWFBG) vibration sensing technology has been utilized in monitoring the vibration response of large-scale infrastructures, particularly urban railway tracks, and the volume of the collected monitoring data can be huge with the great number of sensors. Even though the train-induced vibration responses of urban railway tracks constitute the most informative and crucial component, they comprised less than 7% of the total operational period. This is mainly attributed to the temporal sparsity of commuting trains. Consequently, the majority of the stored data consisted of low-informative environmental noise and interference excitation data, leading to an inefficient structural health monitoring (SHM) system. To address this issue, this paper introduced an adaptive monitoring strategy for railway track structures, which is capable of identifying train-load patterns by leveraging deep learning techniques. Inspired by image semantic segmentation, a U-net model with one-dimensional convolution layers (U-net-1D) was developed for the pointwise classification of vibration monitoring data. The proposed model was trained and validated using a dataset obtained from an actual urban railway track in China. Results indicated that the proposed method outperforms the traditional dual-threshold method, achieving an Intersection over Union (IoU) of 94.27% on the segmentation task of the test dataset. Full article
(This article belongs to the Special Issue Recent Developments in Structural Health Monitoring)
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20 pages, 7914 KiB  
Article
Damage Detection of Gantry Crane with a Moving Mass Using Artificial Neural Network
by Mohammad Safaei, Mahsa Hejazian, Siamak Pedrammehr, Sajjad Pakzad, Mir Mohammad Ettefagh and Mohammad Fotouhi
Buildings 2024, 14(2), 458; https://doi.org/10.3390/buildings14020458 - 7 Feb 2024
Cited by 2 | Viewed by 1688
Abstract
Gantry cranes play a pivotal role in various industrial applications, and their reliable operation is paramount. While routine inspections are standard practice, certain defects, particularly in less accessible components, remain challenging to detect early. In this study, first a finite element model is [...] Read more.
Gantry cranes play a pivotal role in various industrial applications, and their reliable operation is paramount. While routine inspections are standard practice, certain defects, particularly in less accessible components, remain challenging to detect early. In this study, first a finite element model is presented, and the damage is introduced using random changes in the stiffness of different parts of the structure. Contrary to the assumption of inherent reliability, undetected defects in crucial structural elements can lead to catastrophic failures. Then, the vibration equations of healthy and damaged models are analyzed to find the displacement, velocity, and acceleration of the different crane parts. The learning vector quantization neural network is used to train and detect the defects. The output is the location of the damage and the damage severity. Noisy data are then used to evaluate the network performance robustness. This research also addresses the limitations of traditional inspection methods, providing early detection and classification of defects in gantry cranes. The study’s relevance lies in the need for a comprehensive and efficient damage detection method, especially for components not easily accessible during routine inspections. Full article
(This article belongs to the Special Issue Recent Developments in Structural Health Monitoring)
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17 pages, 9631 KiB  
Article
An Output-Only, Energy-Based, Damage Detection Method Using the Trend Lines of the Structural Acceleration Response
by Hadi Kordestani, Chunwei Zhang and Ali Arab
Buildings 2023, 13(12), 3007; https://doi.org/10.3390/buildings13123007 - 1 Dec 2023
Cited by 1 | Viewed by 1025
Abstract
Using the trendlines of an acceleration response as a tool to decompose a structural response is a new topic that was proposed by authors in 2020. This paper provides a numerical/experimental investigation of using a Savitzky–Golay filter (SGF) in a method to calculate [...] Read more.
Using the trendlines of an acceleration response as a tool to decompose a structural response is a new topic that was proposed by authors in 2020. This paper provides a numerical/experimental investigation of using a Savitzky–Golay filter (SGF) in a method to calculate the trendline and decompose building acceleration responses when subjected to a seismic load. Hence, this paper proposes an output-only, energy-based, damage detection method in which the trend lines of a building’s structural acceleration responses are used to locate the damage. For this purpose, an adjusted SGF is utilized to calculate an especial trend line for each floor’s acceleration response of the building structural model. The energy of these trend lines is then calculated and normalized. Two damage indices are used, of which, the second one is being proposed for the first time in this paper. The accuracy of the proposed method is numerically and experimentally investigated using a five-floor building structural model subjected to white noise excitation through a shake table. The results prove that the proposed method is capable of accurately locating and quantifying structural damages with a severity of more than 10% in a noisy environment. In view that the proposed method locates the damage with no need of determining the structural modal properties or parameters, it can be categorized as an online and quick structural damage detection method. Full article
(This article belongs to the Special Issue Recent Developments in Structural Health Monitoring)
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23 pages, 15814 KiB  
Article
Dynamic Feature Identification of Carbon-Fiber-Reinforced Polymer Laminates Based on Fiber Bragg Grating Sensing Technology
by Cong Chen, Hua-Ping Wang, Jie Ma and Maihemuti Wusiman
Buildings 2023, 13(9), 2292; https://doi.org/10.3390/buildings13092292 - 8 Sep 2023
Cited by 2 | Viewed by 1205
Abstract
Carbon-fiber-reinforced polymer (CFRP) composites have many advantages, and have been widely used in aerospace structures, buildings, bridges, etc. The analysis of dynamic response characteristics of CFRP composite structures is of great significance for promoting the development of smart composite structures. For this reason, [...] Read more.
Carbon-fiber-reinforced polymer (CFRP) composites have many advantages, and have been widely used in aerospace structures, buildings, bridges, etc. The analysis of dynamic response characteristics of CFRP composite structures is of great significance for promoting the development of smart composite structures. For this reason, vibration experiments of CFRP laminates with surface-attached fiber Bragg grating (FBG) sensors under various dynamic loading conditions were carried out. Time- and frequency-domain analyses were conducted on the FBG testing signals to check the dynamic characteristics of the CFRP structure and the sensing performance of the installed sensors. The results show that the FBG sensors attached to the surface of the CFRP laminates can accurately measure the dynamic response and determine the excited position of the CFRP laminates, as well as invert the strain distribution of the CFRP laminates through the FBG sensors at different positions. By performing Fourier transform, short-time Fourier transform, and frequency domain decomposition (FDD) on the FBG sensing signals, the time–frequency information and the first eight modal frequencies of the excited CFRP structure can be obtained. The modal frequencies obtained by different excitation types are similar, which can be used for structural damage identification. The research in this paper clarifies the effectiveness and accuracy of FBG sensors in sensing the dynamic characteristics of CFRP structures, which can be used for performance evaluation of CFRP structures and will effectively promote the design and development of intelligent composite material structures. Full article
(This article belongs to the Special Issue Recent Developments in Structural Health Monitoring)
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