Structural Health Monitoring Technology Research Articles

Basic study of structural health monitoring for layered structures subjected to vibration from a semi-active dynamic vibration absorbers

Vibration suppression of structures and structural health monitoring (SHM) are important technologies for realizing a sustainable society, and as stated in SDGs No. 11 "Sustainable cities and communities,"their importance is widely known. Semi-active dynamic vibration absorbers (SADVA) are used in vibration suppression technology, in addition to passive and active dynamic vibration absorbers. Basically, the physical properties of SADVA, which are passive elements such as the spring constant and viscous damping coefficient, can be changed externally. These dynamic vibration absorbers generally operate only when an earthquake occurs. Here, to achieve SDG No. 12 "Responsible consumption, production," we verify whether it is possible to use some of the SADVA mentioned above for SHM. The vibration data of structures is sometimes used in SHM technology. Specifically, an abnormality is diagnosed by measuring the frequency response with a sensor installed in a structure and focusing on the change in the frequency response between normal and abnormal conditions. In this study, we propose a method to identify the location and level of abnormalities in layered structures using a part of SADVA as a shaker.

An unsupervised learning framework for health diagnosis by incorporating multiscale data

Integrating real-time evaluations of health states with Structural Health Monitoring (SHM) data and environmental observations is crucial, particularly under large datasets. Not only is the volume of real-time data analysis substantial, but traditional algorithms like wavelet analysis often require extensive prior work and significant manual input, limiting the application of SHM and big data technologies in structural analysis. To overcome this limitation, this study introduces an unsupervised analytical framework coupling a Denoising Sparse Wavelet Network (DeSpaWN) and a dynamic-inner Principal Component Analysis (DiPCA) algorithm. The DeSpaWN is designed to extract sparse representation from multiscale sensor signals, and the features are subsequently analyzed using the DiPCA to assess health diagnoses. The DeSpaWN is specifically tailored for monitoring high-frequency time series dynamically and adaptively, enabling reconstruction and extraction of signals across various frequency domains. Additionally, the DiPCA effectively handles large-scale time-series data, providing precise evaluations by accounting for dynamic latent correlations. To validate the method, a 5-Degree of Freedom (DoF) structure model is established, equipped with five virtual sensors at each DoF and an anemometer at the top DoF. The sensor signals, obtained under different health states and subjected to artificial excitations, are input into the proposed framework. Validation results demonstrate the effectiveness in synthesizing multi-sensor data and assessing structural integrity. The framework's practical utility is showcased through a real-world case study that assesses the structural integrity of a wind turbine. By integrating real and virtual sensor data collected over a year and cross-referencing this with the fault log, the study validates the algorithm's efficacy in processing multisource heterogeneous data and its precision in structural health diagnosis. The study substantiates real-time tracking of health states for SHM-equipped structures, underscoring its potential to enhance safety and reliability in civil infrastructure.

A multi-scale deep residual network-based guided wave imaging evaluation method for fatigue crack quantification

Abstract As a promising structural health monitoring (SHM) technology, guided wave (GW) imaging is gaining increasing attention for crack monitoring of aircraft structures. However, actual fatigue crack propagation is a complex dynamically evolving process affected by various variabilities. It is still challenging to accurately track and quantify the dynamic fatigue crack propagation with GW imaging methods. Therefore, in order to achieve more accurate fatigue crack quantification, this paper proposes a multi-scale deep residual network-based GW imaging evaluation method. A convolutional neural network (CNN) is utilized to evaluate the entire pixel distribution of GW imaging maps to fuse damage-related information from multiple GW monitoring paths. By designing multi-scale convolutional kernels and deep residual learning, a robust quantitative image feature extraction is ensured with the dynamic evolution process of fatigue crack growth and the performance degradation is avoided as the CNN goes deeper, thereby improving the quantification accuracy. The method is validated on a fatigue test of landing gear beams, which are important load-carrying aircraft structural components. The results demonstrate that the proposed method can extract multi-scale crack length-related features and accurately track fatigue crack propagations. For batch specimens, the maximum quantification error is reduced from the original 6.1mm to 1.6mm, marking a significant improvement.

Research on the influence of impact damage on force identification for composite material

The invisible damage caused by low-velocity impacts are safety threats to engineering structures. Thus, impact force identification is crucial in the context of composite structures for both structure health monitoring (SHM) and composite structure design. This paper investigates the process of identifying impacts on composite structures subjected to low-velocity impact. Considering the damage evolution in the composite structure during impact, this paper explores the influence of impact damage on the accuracy of force identification. Impact experiments on carbon fiber reinforced polymer (CFRP) laminates were conducted to obtain impact force peaks and displacement responses. Furthermore, a validated finite element model (FEM) is established for more elaborate analysis on the mechanism. The findings reveal that the structural damage can lead to significant deviations in force identification if the damage is not considered. Finally, a neural network is employed to predict the force history taking impact damage into consideration. This research provides a reference for the composite structures design and health monitoring of engineering structures considering impact damage.

Sensing Skin Technology for Fatigue Crack Monitoring of Steel Bridges: Laboratory Development, Field Validation, and Future Directions

A significant number of steel bridges are vulnerable to fatigue cracks, and the timely discovery of damage is critical in ensuring safety and continuous operations. Despite various crack monitoring methods available in the field of structural health monitoring (SHM) to empower real-time feedback, few commercially-available technologies are applicable to the task of discovering new cracks because of the highly localized nature of the sensors. The authors have developed a sensing skin constituted from soft elastomeric capacitors (SECs). An SEC is a large-area strain gauge that transduces strain into a measurable change in capacitance. It can be easily deployed over large surfaces, and thus can be used to discover new fatigue cracks. The technology has been developed and characterized in a laboratory environment over the last decade. It has been recently deployed in the field on a bridge located in Kansas, USA. The aim of this paper is to present and discuss technological updates that were necessary to enable field deployment, with the objective of supporting the field deployment of the cSEC and other SHM technologies. In particular, it reviews the following SEC technology modifications: 1) corrugation of the surface of the dielectric, creating a corrugated SEC or cSEC, to improve sensing performance; 2) development of a dedicated wireless data acquisition system; and 3) improvement of the data fusion algorithm to account for higher signal contamination in the field. After this review, challenges in conducting field deployment are examined. Lastly, a discussion is provided on a path to commercialization.

Best reconstruction frequency for time-reversal process of Lamb waves and its determination from a single measurement

This article presents a theoretical framework for the concept of the best reconstruction frequency (BRF) of Lamb waves in elastic plates and its significance in the context of structural health monitoring (SHM). The time-reversal process (TRP) of Lamb waves has been widely explored for baseline-free damage detection in thin-walled structures. Previous studies have indicated that the accuracy of damage detection can be significantly improved by exciting Lamb waves at a frequency where the time-reversibility in the undamaged state is maximum within a desired frequency range. This frequency, termed the BRF, has been determined phenomenologically in prior research but lacked a rigorous theoretical foundation. In this study, the BRF is derived as the frequency at which the amplitude dispersion of the main mode of the reconstructed signal after the TRP is minimized. Accordingly, a method is put forth to determine this frequency from a single measurement using a broadband excitation eliminating the need to compute the similarity between the reconstructed and input waveforms at different excitation frequencies. The developed theory is validated with experiments conducted on an aluminium plate with Lamb waves generated and sensed by surface-bonded piezoelectric patch transducers. Notably, the study establishes that the BRF is a system property specific to a given transducer-plate-adhesive system and is independent of the distance between transducers. This characteristic makes it suitable for identifying damage locations when employing multiple sensing paths. The findings constitute an important milestone in understanding the TRP of Lamb waves and developing robust SHM technologies based on it.

Review of AI in Civil Engineering

This paper reviews the transformative impact of Artificial Intelligence (AI) on civil engineering. It explores AI's fundamental concepts and its applications across structural analysis, construction management, transportation, geotechnical engineering, and sustainability. The review highlights AI's role in automating tasks, predicting outcomes, and optimizing designs throughout project lifecycles. Recent advancements in AI-driven technologies for structural health monitoring, predictive maintenance, and risk assessment are discussed, along with challenges like data quality and model interpretability. Future trends such as autonomous construction and digital twins are examined, emphasizing the need for continued research and interdisciplinary collaboration. In conclusion, this paper offers insights for leveraging AI to address evolving challenges and opportunities in civil engineering, fostering innovation, sustainability, and resilience in infrastructure development.

Automated modal parameter tracking with neural network based normalization of environmental perturbations

Automated modal parameter tracking is an essential part of vibration-based structural health monitoring of engineering structures, e.g., bridges, high-rise building, wind turbines, amongst others. In this work, we conceive a robust algorithm for accomplishing this task. First, the modal parameters are extracted with by greedy clustering the stabilization diagram, which is based on covariance-driven subspace identification and the related uncertainty quantification. After the selection of a set of relevant modal parameters to be monitored, the tracking is performed by an active search of stable modes close to the reference ones, again using statistical criteria and automated clustering. The applied strategies allow for a robust method, allowing also for relatively strong variations in the natural frequencies or the MAC without losing track of the monitored modes. Finally, when EOVs (Environmental and Operational Variables) are measured and training and testing periods are defined, a neural network is trained based on the tracked frequencies and the EOVs, with the goal of correcting the environmental influence on the estimated frequencies based on the model predictions. In doing so, a distance metric is defined for damage detection, which is robust to environmental perturbations under the measured EOVs. The whole framework is showcased on monitoring data of a full-scale bridge.

On-line Updated Guided Wave-Based Gaussian Process Method for Damage Evaluation of Aircraft Structure

Accurate damage evaluation for an aircraft by using guided wave-based structural health monitoring (SHM) technology is in critical demand. An aircraft experiences various uncertainties during its service life, such as the time-varying environment, operational conditions, and different flight missions. Additionally, the actual aircraft structure involves complex structural forms such as varied thickness, stiffeners, ribs, and large cutouts, leading to a strong uncertainty of damage initiation and propagation. Consequently, when applied to an individual aircraft structure, the diagnosis model trained by prior data inevitably introduces errors, and the errors accumulate as the damage propagates, which brings challenges in engineering application. To achieve individual aircraft structure’s more accurate damage evaluation, an on-line updated guided wave-based Gaussian process (GP) damage evaluation method is investigated in this paper. Multi-source data from the design, service, and maintenance stages are utilized to continuously update and evolve the GP model, enabling its accurate evaluation throughout the long-term service life.

The damage localization method in the composite panel by multifrequency guided wave signal using parallel network

Fiber-reinforced composite materials find extensive application in the realm of aeronautics; nonetheless, during operational phases, structural integrity may be compromised due to external impacts or extreme environmental conditions. Consequently, the imperative is the necessity of vigilantly monitoring the structural state. The structural health monitoring (SHM) technology based on ultrasonic guided waves can achieve long-distance and wide-ranging damage detection utilizing a minimal number of cost-effective piezoelectric transducers. However, traditional damage identification methods necessitate the analysis of the multi-modal characteristics of guided waves and the deployment of dense sensor arrays. In this paper, we propose a network model that leverages multi-frequency guided-wave signals to achieve spatiotemporal parallelism, incorporating residual blocks and convolutional attention mechanisms (GCRA). This model accurately estimates damage locations with an $R^2$ value of 0.9147. The average error for the six randomly selected damage locations is 0.4275 cm. The results demonstrate that the GCRA facilitates the identification of damaged positions with a high degree of confidence.

POD methodologies for SHM application in civil engineering

Structural Health Monitoring (SHM) technologies can significantly enhance safety through continuous monitoring. The successful deployment of real-time SHM systems rely mainly on reliability evaluations. While numerous standards exist for reliability assessments or Probability of Detection (POD) of NDT techniques, there are no specific standards guiding reliability procedures for SHM data. Consequently, NDT POD methods are often applied to SHM data, despite the time and statistically dependent nature of SHM data. Especially in the field of civil engineering, there is the need for a SHM specific POD approach able to perform a meaningful reliability evaluation. Hence, in the current work, different SHM POD models including random intercept model and length at detection (LaD) model are implemented for a vibration-based monitoring data relevant for civil engineering. Unlike the linear response observed in NDT inspection data concerning damage, SHM data can exhibit non-linear characteristics in certain cases. In those cases, an advanced LaD model suitable for non-linear data is also developed in the current study. In addition, a comparison between the results obtained using NDT POD models and SHM POD models is demonstrated using an open-source GUI (TU1402) based on FE model. Moreover, the results obtained by the application of modified LaD POD model to a real test bridge data is demonstrated. Keywords: SHM, reliability, POD, civil engineering.

Smart Clay Bricks enabling Diffuse Strain Monitoring in Masonry Buildings: Progresses at Full-scale

Strain monitoring in masonry buildings can provide useful information regarding the onset of structural damage during operating conditions. This is because when cracks develop on masonry components, i.e., clay bricks and mortar layers, induce discontinuities and redistributions in their original stress/strain configurations. Although the application of local strain measurements for Structural Health Monitoring (SHM) of masonry buildings can be effective in detecting anomalies and modifications in their structural response caused by crack development, achieving diffuse strain monitoring remains a challenge in practical SHM applications, nowadays. This is mainly because strain sensors currently available on the market present several practical limitations, including scalability issues, which often make their large-scale application to masonry buildings difficult. In recent years, for overcoming the drawbacks of traditional sensor systems, innovative smart clay bricks have been developed, able to measure deformation when incorporated into load-bearing masonry structures. In that sense, this paper presents the latest advancements and applications of this brand-new sensing technology for SHM of real-scale masonry buildings.

Review and Prospect of Research on Structural Health Monitoring Technology for Bridges

As a crucial infrastructure in the transport system, the safe operation of bridges is directly related to all aspects of people’s daily lives. The development of bridge structural health monitoring technology and its application play an important role in ensuring the safety and extending the service life of bridges. This paper carries out in-depth research and analysis on the related technology of bridge structural health monitoring. Firstly, the existing monitoring technologies at home and abroad are sorted out, and the advantages and problems of various methods are compared and analyzed, including nondestructive testing, stress measurement, vibration characteristic identification, and other commonly used monitoring technologies. Secondly, the key technologies and equipment in the bridge health monitoring system, such as sensor technology, data acquisition, and processing technology, are introduced in detail. Finally, the development trend in the field of bridge health monitoring is prospected from both theoretical research and technical application. In the future, with the development of emerging technologies such as big data, cloud computing, and the Internet of Things, it is expected that bridge health monitoring with intelligent and systematic features will be more widely applied to provide a stronger guarantee for the safe and efficient operation of bridges.

Implantable sensing technology for civil engineering structures

Providing crack location and stiffness variation information using practical structural health monitoring technology is highly significant for in-service structures. Inspired by the fast development of implantable devices, this paper introduced an implantable sensing technology (IST) for health monitoring of a typical concrete member known as shear wall undergoing loading progression. Two collaborative monitoring steps were developed to acquire targeted damage information at different loading stages, the one was, a cylindrical concrete implantable module (CCIM) sensor-enabled IST was applied for monitoring and imaging the early cracks of the shear wall at the initial loading stage; and the other was, a concrete implantable bar (CIB) sensor-enabled IST was employed for monitoring and assessing the damage evolution at the mid-late loading stage. Experimental results showed that these two collaborative ISTs can accurately identify the early cracking regions of the shear wall and reveal its stiffness degradation processes throughout the loading.

A scientometric analysis of drone-based structural health monitoring and new technologies

Critical global challenges, such as climate change and the insufficient availability of resources, mean that it is a pivotal time to make cities more intelligent, efficient, and sustainable in a drive towards a net-zero carbon future. This requires intelligent, interactive, and responsive structural health monitoring (SHM) to assure the longevity and safety of ageing infrastructure. Drones have the potential to revolutionise SHM. Drone-based SHM (as a potential fly-by technique) involves equipping drones with various sensors, or using inbuilt sensors, to capture data and images of structures from different angles and perspectives. The data is then processed and analysed to facilitate accurate assessment of the structure’s health and early diagnosis of damage. Although the use of fly-by is relatively new, the speedy advances in various technologies that could be integrated with it, such as computer vision with artificial intelligence, deep learning, and links to digital twins, put these systems on the verge of a potential breakthrough. This paper provides an overview of fly-by SHM technique using both scientometric and qualitative systematic literature review processes, in order to provide a distinct understanding of the state of the art of research. As an original contribution, our research identified four main clusters of research within the field of fly-by SHM: (1) the application of UAV-enabled vision-based monitoring; (2) the integration of drones, advanced sensor technologies, and artificial intelligence; (3) drone-based SHM integrating modal analysis, energy harvesting, and deep learning; and (4) automation and robotics in drone-based SHM. The paper highlights the integration of new technologies such as artificial intelligence, machine learning, and sensors with the fly-by technique for SHM, identifies the gaps in current fly-by SHM research, and suggests new directions for research.

Smartphone Prospects in Bridge Structural Health Monitoring, a Literature Review.

Bridges are critical components of transportation networks, and their conditions have effects on societal well-being, the economy, and the environment. Automation needs in inspections and maintenance have made structural health monitoring (SHM) systems a key research pillar to assess bridge safety/health. The last decade brought a boom in innovative bridge SHM applications with the rise in next-generation smart and mobile technologies. A key advancement within this direction is smartphones with their sensory usage as SHM devices. This focused review reports recent advances in bridge SHM backed by smartphone sensor technologies and provides case studies on bridge SHM applications. The review includes model-based and data-driven SHM prospects utilizing smartphones as the sensing and acquisition portal and conveys three distinct messages in terms of the technological domain and level of mobility: (i) vibration-based dynamic identification and damage-detection approaches; (ii) deformation and condition monitoring empowered by computer vision-based measurement capabilities; (iii) drive-by or pedestrianized bridge monitoring approaches, and miscellaneous SHM applications with unconventional/emerging technological features and new research domains. The review is intended to bring together bridge engineering, SHM, and sensor technology audiences with decade-long multidisciplinary experience observed within the smartphone-based SHM theme and presents exemplary cases referring to a variety of levels of mobility.

Investigation of temperature effects on wide steel box girder of suspension bridge based on long-term monitoring data

The time-varying temperature distributions on bridge structures may remarkably change structural performance, which may result in differential strain/stress responses on structural members compared with the design conditions. Therefore, it is crucial to have a comprehensive understanding of temperature distributions and its effects on bridges. In this study, taking advantage of structural health monitoring technology, 1-year field monitoring data collected from a long-span suspension bridge were used to investigate the temperature distributions and their effects on the steel box girder. Specifically, the distributions and probability statistics of temperatures on the top and bottom plates were firstly analyzed. Based on which, the transverse and vertical temperature differences on the box girder were further examined, moreover, the representative values of temperature differences for various return periods were calculated by exceedance probability method. At end, a temperature prediction method was proposed to simulated the temperature field distributions during bridge life cycle, to provide substantial temperature data for estimating future operation condition. The results of this study were beneficial to structural evaluation of in-service bridges to ensure their serviceability and integrity in the life cycle.

Review of Image-Processing-Based Technology for Structural Health Monitoring of Civil Infrastructures.

The continuous monitoring of civil infrastructures is crucial for ensuring public safety and extending the lifespan of structures. In recent years, image-processing-based technologies have emerged as powerful tools for the structural health monitoring (SHM) of civil infrastructures. This review provides a comprehensive overview of the advancements, applications, and challenges associated with image processing in the field of SHM. The discussion encompasses various imaging techniques such as satellite imagery, Light Detection and Ranging (LiDAR), optical cameras, and other non-destructive testing methods. Key topics include the use of image processing for damage detection, crack identification, deformation monitoring, and overall structural assessment. This review explores the integration of artificial intelligence and machine learning techniques with image processing for enhanced automation and accuracy in SHM. By consolidating the current state of image-processing-based technology for SHM, this review aims to show the full potential of image-based approaches for researchers, engineers, and professionals involved in civil engineering, SHM, image processing, and related fields.

Internet of things (IoT)-based structural health monitoring of laboratory-scale civil engineering structures

Rapid advances in the Internet of Things (IoT) domain have made it a crucial technology for the real-time structural health monitoring (SHM) of civil engineering infrastructures. The availability of quick and accurate vibration data is essential for SHM, and such data can be obtained through IoT devices mounted on the structures. This study proposes a real-time damage prediction and localization approach using a low-cost "do-it-yourself" wireless sensor node with IoT capabilities for SHM. The proposed sensor node comprised a microcontroller (NODE MCU ESP8266) and a 6-axis accelerometer (MPU6050). The IoT devices track the real-time frequency of the laboratory-scale structure indirectly via measurement of acceleration-time history, and their results are compared with conventional industry-standard accelerometers. Promising results, with a <6% average difference from the conventional accelerometer (difference ranging from 1.3 to 14.3%), provided an innovative SHM for vibration-based real-time SHM using the IoT paradigm. The performance of the proposed methodology was validated numerically and experimentally on two laboratory-scale structures, and the potential of IoT technology for enhancing the efficiency of SHM was demonstrated. The proposed method thus can enable the early detection of damages in infrastructures such as buildings and bridges and thus can reduce the likelihood of accidents via continuous SHM.

Robust transport and topological valley refraction of fundamental symmetric lamb waves in perforated phononic crystal plates

Lamb waves can be divided into antisymmetric Lamb waves (type A) and symmetric Lamb waves (type S), which are widely used in health monitoring of engineering structures and nondestructive evaluation of materials. However, due to the fact that each mode of Lamb wave has the setting group velocity in a certain bandwidth range, it is easy to cause the aliasing of multi-mode Lamb waves, partly limiting the application scenarios of Lamb wave. In this paper, a hexagonal lattice meta-structure is designed and the zero-order symmetric (S0) Lamb wave in a finite thickness plate is successfully separated. By breaking the spatial C 3v symmetry, a valley topological phononic crystal (PnC) plate for the pure S0 mode is constructed, and its robust topological transports are demonstrated. Firstly, the valley topological phase transition of S0 mode is realized by changing the angle of scatterer in PnC plate and the valley edge state of S0 mode is obtained. Furthermore, by introducing structural mismatch, it is verified that the edge state of S0 mode is robust to various bends and defect. Finally, by changing the material property of the terminal, the valley refraction with different directionality is realized. The results offer a route to individually study the symmetric modes of Lamb waves, and have potential application prospects in rapid and accurate ultrasonic nondestructive testing based on Lamb waves.