Structural Health Monitoring and Non-destructive Testing for Large-Scale Structures (2nd Edition)

Topic Information

Dear Colleagues,

This Topic is a continuation of the previous successful Topic “Structural Health Monitoring and Non-destructive Testing for Large-Scale Structures”, which was closed on 31 May 2023, and in which 44 papers were published. Structural health monitoring (SHM) and non-destructive testing (NDT) are of significant importance to civil, mechanical, aerospace, and offshore structures. Nowadays, we can find SHM and NDT applications being used on various structures with very different requirements. The SHM-NDT field involves a wide range of transdisciplinary areas, including smart materials, embedded sensors and actuators, damage diagnosis and prognosis, signal and image processing, wireless sensor networks, data interpretation, machine learning, data fusion, energy harvesting, etc. Since the 1970s, there has been a large and increasing volume of research on SHM and NDT; a great deal of effort has been focused on developing cost-effective, automatic, and reliable damage detection technologies. However, few industrial and commercial applications can be found in the literature. The practical implementation of strategies for the detection of structural damage to real structures outside of laboratory conditions is always one of the most demanding tasks for engineers. One reason for the rare transfer of research outcomes into industrial practice is that most of the methods that have been developed have been tested on simple beam and plate structures in the laboratory, while many practical problems only manifest themselves in complex structures. Another reason is the influence of environmental and operational variations (EOVs) on damage-sensitive features. Thus, for the successful development of SHM and NDT for large structures, techniques should be enhanced to toward having the capability of dealing with the influence of EOVs. In addition, signal/data processing plays an important role in the implementation of SHM and NDT technologies. The processing and interpretation of the massive amount of data generated through the long-term monitoring of large and complex structures (e.g., bridges, buildings, ships, aircrafts, wind turbines, pipes, etc.) has become an emerging challenge that needs to be addressed by the community. This topical collection brings together the most established as well as newly emerging SHM and NDT techniques that can be used for the detection and evaluation of defects and damage development in large-scale or full-scale structures. We cordially invite you to submit your cutting-edge research for consideration. Suitable topics include the following:

• SHM and NDT for aerospace, civil, mechanical, and offshore infrastructures
• Global monitoring of large structures
• Large-area monitoring for a part/region of a larger structure
• Localised monitoring and damage detection
• SHM and NDT for composite, steel, and concrete structures
• SHM and NDT of bridges, buildings, ships, aircrafts, wind turbines, pipes, and industrial machines
• Novel algorithms for SHM and NDT
• Strategies for the removal of EOVs for SHM and NDT
• Advanced signal processing for SHM and NDT
• Artificial intelligence and machine learning for SHM and NDT
• Time series analysis and statistical approaches for SHM and NDT
• Damage detection, diagnosis, and prognosis

Dr. Phong B. Dao
Prof. Dr. Lei Qiu
Dr. Liang Yu
Prof. Dr. Tadeusz Uhl
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Aerospace aerospace	2.6	3.0	2014	22.3 Days	CHF 2400	Submit
Applied Sciences applsci	2.7	4.5	2011	16.9 Days	CHF 2400	Submit
Energies energies	3.2	5.5	2008	16.1 Days	CHF 2600	Submit
Materials materials	3.4	5.2	2008	13.9 Days	CHF 2600	Submit
Sensors sensors	3.9	6.8	2001	17 Days	CHF 2600	Submit

MDPI Topics is cooperating with Preprints.org and has built a direct connection between MDPI journals and Preprints.org. Authors are encouraged to enjoy the benefits by posting a preprint at Preprints.org prior to publication:

1. Immediately share your ideas ahead of publication and establish your research priority;
2. Protect your idea from being stolen with this time-stamped preprint article;
3. Enhance the exposure and impact of your research;
4. Receive feedback from your peers in advance;
5. Have it indexed in Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Related Topic

Published Papers (9 papers)

Order results

Content type Publication Date

Result details

Normal Extended Compact

Journals

All Aerospace Applied Sciences Energies Materials Sensors

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

Error

Oops... you haven't selected anything for export.

Ok

clear

26 pages, 5878 KiB  

Open AccessArticle

Application of Response Surface-Corrected Finite Element Model and Bayesian Neural Networks to Predict the Dynamic Response of Forth Road Bridges under Strong Winds

by Yan Liu, Xiaolin Meng, Liangliang Hu, Yan Bao and Craig Hancock

Sensors 2024, 24(7), 2091; https://doi.org/10.3390/s24072091 - 25 Mar 2024

Viewed by 580

Abstract

With the rapid development of big data, the Internet of Things (IoT), and other technological advancements, digital twin (DT) technology is increasingly being applied to the field of bridge structural health monitoring. Achieving the precise implementation of DT relies significantly on a dual-drive [...] Read more.

With the rapid development of big data, the Internet of Things (IoT), and other technological advancements, digital twin (DT) technology is increasingly being applied to the field of bridge structural health monitoring. Achieving the precise implementation of DT relies significantly on a dual-drive approach, combining the influence of both physical model-driven and data-driven methodologies. In this paper, two methods are proposed to predict the displacement and dynamic response of structures under strong winds, namely, a Bayesian Neural Network (BNN) model based on Bayesian inference and a finite element model (FEM) method modified based on genetic algorithms (GAs) and multi-objective optimization (MOO) using response surface methodology (RSM). The characteristics of these approaches in predicting the dynamic response of large-span bridges are explored, and a comparative analysis is conducted to evaluate their differences in computational accuracy, efficiency, model complexity, interpretability, and comprehensiveness. The characteristics of the two methods were evaluated using data collected on the Forth Road Bridge (FRB) as an example under unusual weather conditions with strong wind action. This work proposes a dual-driven approach, integrating machine learning and FEM with GNSS and Earth Observation for Structural Health Monitoring (GeoSHM), to bridge the gap in the limited application of dual-driven methods primarily applied for small- and medium-sized bridges to large-span bridge structures. The research results show that the BNN model achieved higher $R^2$ values for predicting the Y and Z displacements (0.9073 and 0.7969, respectively) compared to the FEM model (0.6167 and 0.6283). The BNN model exhibited significantly faster computation, taking only 20 s, while the FEM model required 5 h. However, the physical model provided higher interpretability and the ability to predict the dynamic response of the entire structure. These findings help to promote the further integration of these two approaches to obtain an accurate and comprehensive dual-driven approach for predicting the structural dynamic response of large-span bridge structures affected by strong wind loading. Full article

(This article belongs to the Topic Structural Health Monitoring and Non-destructive Testing for Large-Scale Structures (2nd Edition))

►▼ Show Figures

Figure 1

31 pages, 15424 KiB  

Open AccessArticle

Displacement Reconstruction Based on Physics-Informed DeepONet Regularizing Geometric Differential Equations of Beam or Plate

by Zifeng Zhao, Xuesong Yang, Ding Ding, Qiangyong Wang, Feiran Zhang, Zhicheng Hu, Kaikai Xu and Xuelin Wang

Appl. Sci. 2024, 14(6), 2615; https://doi.org/10.3390/app14062615 - 20 Mar 2024

Viewed by 535

Abstract

Physics-informed DeepONet (PI_DeepONet) is utilized for the reconstruction task of structural displacement based on measured strain. For beam and plate structures, the PI_DeepONet is built by regularizing the strain–displacement relation and boundary conditions, referred to as geometric differential equations (GDEs) in this paper, [...] Read more.

Physics-informed DeepONet (PI_DeepONet) is utilized for the reconstruction task of structural displacement based on measured strain. For beam and plate structures, the PI_DeepONet is built by regularizing the strain–displacement relation and boundary conditions, referred to as geometric differential equations (GDEs) in this paper, and the training datasets are constructed by modeling strain functions with mean-zero Gaussian random fields. For the GDEs with more than one Neumann boundary condition, an algorithm is proposed to balance the interplay between different loss terms. The algorithm updates the weight of each loss term adaptively using the back-propagated gradient statistics during the training process. The trained network essentially serves as a solution operator of GDEs, which directly maps the strain function to the displacement function. We demonstrate the application of the proposed method in the displacement reconstruction of Euler–Bernoulli beams and Kirchhoff plates, without any paired strain–displacement observations. The PI_DeepONet exhibits remarkable precision in the displacement reconstruction, with the reconstructed results achieving a close proximity, surpassing 99%, to the finite element calculations. Full article

(This article belongs to the Topic Structural Health Monitoring and Non-destructive Testing for Large-Scale Structures (2nd Edition))

►▼ Show Figures

Figure 1

16 pages, 23635 KiB  

Open AccessArticle

Damage Detection in Glass Fibre Composites Using Cointegrated Hyperspectral Images

by Jan Długosz, Phong B. Dao, Wiesław J. Staszewski and Tadeusz Uhl

Sensors 2024, 24(6), 1980; https://doi.org/10.3390/s24061980 - 20 Mar 2024

Viewed by 517

Abstract

Hyperspectral imaging (HSI) is a remote sensing technique that has been successfully applied for the task of damage detection in glass fibre-reinforced plastic (GFRP) materials. Similarly to other vision-based detection methods, one of the drawbacks of HSI is its susceptibility to the lighting [...] Read more.

Hyperspectral imaging (HSI) is a remote sensing technique that has been successfully applied for the task of damage detection in glass fibre-reinforced plastic (GFRP) materials. Similarly to other vision-based detection methods, one of the drawbacks of HSI is its susceptibility to the lighting conditions during the imaging, which is a serious issue for gathering hyperspectral data in real-life scenarios. In this study, a data conditioning procedure is proposed for improving the results of damage detection with various classifiers. The developed procedure is based on the concept of signal stationarity and cointegration analysis, and achieves its goal by performing the detection and removal of the non-stationary trends in hyperspectral images caused by imperfect lighting. To evaluate the effectiveness of the proposed method, two damage detection tests have been performed on a damaged GFRP specimen: one using the proposed method, and one using an established damage detection workflow, based on the works of other authors. Application of the proposed procedure in the processing of a hyperspectral image of a damaged GFRP specimen resulted in significantly improved accuracy, sensitivity, and F-score, independently of the type of classifier used. Full article

(This article belongs to the Topic Structural Health Monitoring and Non-destructive Testing for Large-Scale Structures (2nd Edition))

►▼ Show Figures

Figure 1

21 pages, 6408 KiB  

Open AccessArticle

Quantifying the Impact of Environment Loads on Displacements in a Suspension Bridge with a Data-Driven Approach

by Jiaojiao Li, Xiaolin Meng, Liangliang Hu and Yan Bao

Sensors 2024, 24(6), 1877; https://doi.org/10.3390/s24061877 - 14 Mar 2024

Viewed by 656

Abstract

Long-span bridges are susceptible to damage, aging, and deformation in harsh environments for a long time. Therefore, structural health monitoring (SHM) systems need to be used for reasonable monitoring and maintenance. Among various indicators, bridge displacement is a crucial parameter reflecting the bridge’s [...] Read more.

Long-span bridges are susceptible to damage, aging, and deformation in harsh environments for a long time. Therefore, structural health monitoring (SHM) systems need to be used for reasonable monitoring and maintenance. Among various indicators, bridge displacement is a crucial parameter reflecting the bridge’s health condition. Due to the simultaneous bearing of multiple environmental loads on suspension bridges, determining the impact of different loads on displacement is beneficial for the better understanding of the health conditions of the bridges. Considering the fact that extreme gradient boosting (XGBoost) has higher prediction performance and robustness, the authors of this paper have developed a data-driven approach based on the XGBoost model to quantify the impact between different environmental loads and the displacement of a suspension bridge. Simultaneously, this study combined wavelet threshold (WT) denoising and the variational mode decomposition (VMD) method to conduct a modal decomposition of three-dimensional (3D) displacement, further investigating the interrelationships between different loads and bridge displacements. This model links wind speed, temperature, air pressure, and humidity with the 3D displacement response of the span using the bridge monitoring data provided by the GNSS and Earth Observation for Structural Health Monitoring (GeoSHM) system of the Forth Road Bridge (FRB) in the United Kingdom (UK), thus eliminating the temperature time-lag effect on displacement data. The effects of the different loads on the displacement are quantified individually with partial dependence plots (PDPs). Employing testing, it was found that the XGBoost model has a high predictive effect on the target variable of displacement. The analysis of quantification and correlation reveals that lateral displacement is primarily affected by same-direction wind, showing a clear positive correlation, and vertical displacement is mainly influenced by temperature and exhibits a negative correlation. Longitudinal displacement is jointly influenced by various environmental loads, showing a positive correlation with atmospheric pressure, temperature, and vertical wind and a negative correlation with longitudinal wind, lateral wind, and humidity. The results can guide bridge structural health monitoring in extreme weather to avoid accidents. Full article

(This article belongs to the Topic Structural Health Monitoring and Non-destructive Testing for Large-Scale Structures (2nd Edition))

►▼ Show Figures

Figure 1

14 pages, 2803 KiB  

Open AccessArticle

Vibration Signal Evaluation Based on K-Means Clustering as a Pre-Stage of Operational Modal Analysis for Structural Health Monitoring of Rotating Machines

by Nathali Rolon Dreher, Gustavo Chaves Storti and Tiago Henrique Machado

Energies 2023, 16(23), 7848; https://doi.org/10.3390/en16237848 - 30 Nov 2023

Cited by 1 | Viewed by 726

Abstract

Rotating machines are key components in energy generation processes, and faults can lead to shutdowns or catastrophes encompassing economic and social losses. Structural Health Monitoring (SHM) of structures in operation is successfully performed via Operational Modal Analysis (OMA), which has advantages over traditional [...] Read more.

Rotating machines are key components in energy generation processes, and faults can lead to shutdowns or catastrophes encompassing economic and social losses. Structural Health Monitoring (SHM) of structures in operation is successfully performed via Operational Modal Analysis (OMA), which has advantages over traditional methods. In OMA, white noise inputs lead to the accurate extraction of modal parameters without taking the system out of operation. However, this excitation condition is not easy to attain for rotating machines used in power generation, and OMA can provide inaccurate information. This research investigates the applicability of machine learning as a pre-stage of OMA to differentiate adequate from inadequate excitations and prevent inaccurate extraction of modal parameters. Data from a rotor system was collected under different conditions and OMA was applied. In a training stage, measurements were characterized by statistical features and K-means was used to determine which features provided information about the excitation condition, that is, which excitation was adequate to extract the rotor’s modal parameters via OMA. In a testing stage, data were successfully classified as adequate or not adequate for OMA, achieving 100% accuracy and revealing the technique’s potential to support SHM of rotating machines. The technique is extendable to other monitoring systems based on OMA. Full article

(This article belongs to the Topic Structural Health Monitoring and Non-destructive Testing for Large-Scale Structures (2nd Edition))

►▼ Show Figures

Figure 1

23 pages, 7397 KiB  

Open AccessArticle

Development of a Simulation Model for Digital Twin of an Oscillating Water Column Wave Power Generator Structure with Ocean Environmental Effect

by Byungmo Kim, Jaewon Oh and Cheonhong Min

Sensors 2023, 23(23), 9472; https://doi.org/10.3390/s23239472 - 28 Nov 2023

Cited by 1 | Viewed by 616

Abstract

This research article focuses on developing a baseline digital twin model for a wave power generator structure located in Yongsu-ri, Jeju-do, South Korea. First, this study performs a cause analysis on the discrepancy of the dynamic properties from the real structure and an [...] Read more.

This research article focuses on developing a baseline digital twin model for a wave power generator structure located in Yongsu-ri, Jeju-do, South Korea. First, this study performs a cause analysis on the discrepancy of the dynamic properties from the real structure and an existing simulation model and finds the necessity of modeling the non-structural masses and the environmental factors. The large amounts of the ballast are modeled in the finite element model to enhance the accuracy of the digital twin. Considering the influence of environmental factors such as tide level and wave direction, the added mass effect of structural members, one of the hydrodynamic effects, depending on the change of the ocean environments is calculated based on the rule of Det Norske Veritas and applied. The results indicate that non-structural mass components significantly impact the dynamic characteristics of the structure. Additionally, environmental factors have a greater effect on the dynamic behavior of the box-type structure compared to lightweight offshore structures. Full article

(This article belongs to the Topic Structural Health Monitoring and Non-destructive Testing for Large-Scale Structures (2nd Edition))

►▼ Show Figures

Graphical abstract

15 pages, 2100 KiB  

Open AccessArticle

Lamb Wave-Based Structural Damage Detection: A Time Series Approach Using Cointegration

by Phong B. Dao

Materials 2023, 16(21), 6894; https://doi.org/10.3390/ma16216894 - 27 Oct 2023

Viewed by 605

Abstract

Although Lamb waves have found extensive use in structural damage detection, their practical applications remain limited. This limitation primarily arises from the intricate nature of Lamb wave propagation modes and the effect of temperature variations. Therefore, rather than directly inspecting and interpreting Lamb [...] Read more.

Although Lamb waves have found extensive use in structural damage detection, their practical applications remain limited. This limitation primarily arises from the intricate nature of Lamb wave propagation modes and the effect of temperature variations. Therefore, rather than directly inspecting and interpreting Lamb wave responses for insights into the structural health, this study proposes a novel approach, based on a two-step cointegration-based computation procedure, for structural damage evaluation using Lamb wave data represented as time series that exhibit some common trends. The first step involves the composition of Lamb wave series sharing a common upward (or downward) trend of temperature. In the second step, the cointegration analysis is applied for each group of Lamb wave series, which represents a certain condition of damage. So, a cointegration analysis model of Lamb wave series is created for each damage condition. The geometrical and statistical features of Lamb wave series and cointegration residual series are used for detecting and distinguishing damage conditions. These features include the shape, peak-to-peak amplitude, and variance of the series. The validity of this method is confirmed through its application to the Lamb wave data collected from both undamaged and damaged aluminium plates subjected to temperature fluctuations. The proposed approach can find its application not only in Lamb wave-based damage detection, but also in other structural health monitoring (SHM) systems where the data can be arranged in the form of sharing common environmental and/or operational trends. Full article

(This article belongs to the Topic Structural Health Monitoring and Non-destructive Testing for Large-Scale Structures (2nd Edition))

►▼ Show Figures

Figure 1

18 pages, 8284 KiB  

Open AccessArticle

An Online Fatigue Damage Evaluation Method for Gas Turbine Hot Components

by Hongxin Zhu, Shun Dai, Xiaoyi Zhang, Jian Chen, Mingyu Luo and Weiguang Huang

Energies 2023, 16(19), 6785; https://doi.org/10.3390/en16196785 - 23 Sep 2023

Cited by 1 | Viewed by 892

Abstract

The failure of gas turbines’ hot components due to fatigue significantly affects their efficient and stable operation. Conducting online damage assessment of components subjected to complex cyclic loads based on the working conditions of gas turbines can provide real-time reflection of component fatigue [...] Read more.

The failure of gas turbines’ hot components due to fatigue significantly affects their efficient and stable operation. Conducting online damage assessment of components subjected to complex cyclic loads based on the working conditions of gas turbines can provide real-time reflection of component fatigue damage and achieve the purpose of predictive maintenance. In this study, we propose an online cycle counting method that considers temperature fluctuations during the cycle process. Our method is based on the four-point online rainflow counting method by coupling the counting variable with time, introducing the concept of the duration time for full cycles and half cycles, and incorporating a characteristic temperature that better represents the temperature information during the cycle process. With reference to the characteristic temperature, our proposed method comprehensively considers the form and parameters of subsequent life assessment models. This paper provides a detailed explanation of the proposed method and applies it to the fatigue damage assessment of turbine vanes in a micro gas turbine, thereby verifying its accuracy and applicability. Full article

(This article belongs to the Topic Structural Health Monitoring and Non-destructive Testing for Large-Scale Structures (2nd Edition))

►▼ Show Figures

Figure 1

16 pages, 4335 KiB  

Open AccessArticle

Bibliometric Analysis of Engine Vibration Detection

by Mai Xin, Zhifeng Ye, Tong Zhang and Xiong Pan

Aerospace 2023, 10(9), 819; https://doi.org/10.3390/aerospace10090819 - 20 Sep 2023

Viewed by 880

Abstract

After many years of development, the technology of analyzing the working condition of power units based on vibration signals has received relatively stable applications, but the accuracy and the degree of automation and intelligence for fault diagnosis are still inadequate due to the [...] Read more.

After many years of development, the technology of analyzing the working condition of power units based on vibration signals has received relatively stable applications, but the accuracy and the degree of automation and intelligence for fault diagnosis are still inadequate due to the limitations in the ongoing development of key technologies. With the development of big data and artificial intelligence technology, the involvement of new technologies will be an important boost to the development of this field. In this study, in order to support subsequent research, bibliometrics is used as a tool to sort the development of the technology in this field at the macro level. At the micro level, key publications in the literature are studied to better understand the development status at the technical level and prepare for the selection of entry points to facilitate in-depth innovation in the future. Full article

(This article belongs to the Topic Structural Health Monitoring and Non-destructive Testing for Large-Scale Structures (2nd Edition))

►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

 

Error

Oops... you haven't selected anything for export.

Ok

clear

Displaying articles 1-9

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.