Structural Health Monitoring Technology Research Articles

From data to insight, enhancing structural health monitoring using physics-informed machine learning and advanced data collection methods

Abstract Owing to recent advancements in sensor technology, data mining, Machine Learning (ML) and cloud computation, Structural Health Monitoring (SHM) based on a data-driven approach has gained more popularity and interest. The data-driven methodology has proved to be more efficient and robust compared with traditional physics-based methods. The past decade has witnessed remarkable progress in ML, especially in the field of Deep Learning (DL) which are effective in many tasks and has achieved state-of-the-art results in various engineering domains. In the same manner, DL has also revolutionized SHM technology by improving the effectiveness and efficiency of models, as well as enhancing safety and reliability. To some extent, it has also paved the way for implementing SHM in real-world complex civil and mechanical infrastructures. However, despite all the success, DL has intrinsic limitations such as its massive-labelled data Requirement, inability to generate consistent results and lack of generalizability to out-of-sample scenarios. Conversely, in SHM, the lack of data corresponding to a different state of the structure is still a challenging task. Recent development in physics-informed ML methods has provided an opportunity to resolve these challenges in which limited-noisy data and mathematical models are integrated through ML algorithms. This method automatically satisfies physical invariants providing better accuracy and improved generalization. This manuscript presents the sate-of-the-art review of prevailing ML methods for efficient damage inspection, discuss their limitations, and explains the diverse applications and benefits of physics-informed ML in the SHM setting. Moreover, the latest data extraction strategy and the internet of things (IoT) that support the present data-driven methods and SHM are also briefly discussed in the last section.

Piezoelectric Glass‐Fiber‐Reinforced Polymer Sensor for Structural Health Monitoring and Stiffness Sensing

Strengthening and repairing infrastructure with fiber‐reinforced polymers while achieving low‐cost, real‐time monitoring of cracks in engineering structures is highly challenging. Herein, a piezoelectric glass‐fiber‐reinforced polymer (piezo‐GFRP) sensor is used to monitor the mechanical behavior of cement beams with cracks under cyclic bending. The output voltage and frequency of the sensor are shown to be effective feedback signals for determining the mechanical behavior. The thin piezo‐GFRP sensor shows sufficient piezoelectric sensitivity to monitor the slight variations in the mechanical behavior of the cement beams during the early stages of cracking. It is indicated in this result that such sensors have the potential for the multifunctional structural health monitoring of engineering structures.

Development of Intelligent Technologies in SHM on the Innovative Diagnosis in Civil Engineering—A Comprehensive Review

This comprehensive review focuses on the integration of intelligent technologies, such as the Internet of Things (IoT), Artificial intelligence (AI), and Nondestructive Testing (NDT), in the Structural Health Monitoring (SHM) field of civil engineering. The article discusses intelligent technologies in SHM for residential, commercial, industrial, historical, and special buildings, such as nuclear power plants (NPPs). With the incorporation of intelligent technologies, there have been remarkable advancements in SHM, a crucial aspect of infrastructure safety, reliability, and durability. The combination of SHM and intelligent technologies provides a cost-effective and efficient building monitoring approach, significantly contributing to energy and resource conservation. This article explores using electronic instruments, such as sensors, microcontrollers, and embedded systems, to measure displacement, force, strain, and temperature, which are crucial for detecting structural damage. Implementing intelligent technologies in SHM reduces the reliance on manual and hazardous inspection practices, simplifying and reducing the cost of building monitoring. The article highlights the social, economic, and environmental benefits of adopting intelligent technologies in SHM by presenting key findings from existing research. This review aims to increase the reader’s understanding of the significance of these technologies in enhancing the efficiency of SHM in civil engineering by illuminating their advancements and applications.

Reconstructing the Global Stress of Marine Structures Based on Artificial-Intelligence-Generated Content

This paper proposes an approach that utilizes Artificial-Intelligence-Generated Content (AIGC) to overcome the constraints of Structural Health Monitoring (SHM) devices in capturing global stress with limited sensors. Feature elements are selected based on correlation analysis among finite elements and used as stress-measured points. An Artificial Neural Network (ANN) is used to establish the relationship between the feature and correlation elements. The proposed method is applied to the connector structure of an offshore platform, and an optimal ANN is established to optimize its performance by considering factors such as the number of sensors, the neural network framework, and the convergence criteria. The generalization performance of the ANN is validated through a real-scale model test, with deviations below 10% and an average deviation of less than 4% in multiple conditions, verifying its accuracy. This technology represents a significant advancement, enhancing the practicality of the SHM technology from “point monitoring” to “field monitoring”.

Novel Approach-Based Sparsity for Damage Localization in Functionally Graded Material

Model-based approaches have been widely employed in damage detection and localization studies. However, alternative techniques, such as built-in online detection methods, hold promise for future advancements in structural health monitoring technologies. In this research paper, we present a dynamic algorithm specifically designed for accurate damage localization in functionally graded plates. The suggested method involves the creation of a grid matrix that captures the dynamic response of the structure over time. Subsequently, an optimization process is performed using a linear equation that incorporates the information contained within the grid, enabling the precise localization of damage. To address the inherent sparsity of the localization nature, we utilize the FISTA (fast iterative shrinkage-thresholding algorithm) as a problem solver. The effectiveness of our approach is evaluated through experimental tests on a functionally graded plate with clamped free boundary conditions. Multiple damage scenarios are investigated, including cases with damage signals on and off-the-grid. The results demonstrate that our proposed approach is capable of accurately predicting the position of damage, indicating its suitability for application in low-size data systems.

A Roadmap for an Integrated Assessment Approach to the Adaptation of Concrete Bridges to Climate Change

Bridges play a crucial role in modern societies, regardless of their culture, geographical location, or economic development. The safest, most economical, and most resilient bridges are those that are well managed and maintained. Recently, climate change has been posed as one of the greatest concerns for the health of bridges. Although the uncertainty associated with the magnitude of the change is large, the fact that our climate is changing is unequivocal. As a result, making bridges resilient to climate change is a priority for the authorities. A well-planned early intervention may save lives and money. Until now, the focus of scientific research has mostly been on the climate science, but any practical plan for the adaptation of bridges has to be rooted in other disciplines, including physics, chemistry, engineering, economics, and finance. Therefore, the goal of this paper is to review the work already done from climate change to bridges and set a roadmap for an integrated assessment approach to the adaptation of bridges to climate change. This approach is grounded in a probabilistic- and physics-based framework able to prioritize bridge adaptation measures as a function of bridge location, climate scenario, impact, vulnerability, risk, and cost in order to assist the authorities in the decision-making process. Because adaptation to climate change is highly context-specific, this approach is mainly focused on concrete bridges. Structural health monitoring technology is proposed as a mechanism for assessing and continuously evaluating the structural condition of bridges and for triggering adaptation measures as a function of the predicted severity of climate change.

Towards high-precision data modeling of SHM measurements using an improved sparse Bayesian learning scheme with strong generalization ability

Central to structural health monitoring (SHM) is data modeling, manipulation, and interpretation on the basis of a sophisticated SHM system. Despite continuous evolution of SHM technology, the precise modeling and forecasting of SHM measurements under various uncertainties to extract structural condition-relevant knowledge remains a challenge. Aiming to resolve this problem, a novel application of a fully probabilistic and high-precision data modeling method was proposed in the context of an improved Sparse Bayesian Learning (iSBL) scheme. The proposed iSBL data modeling framework features the following merits. It can remove the need to specify the number of terms in the data-fitting function, and automatize sparsity of the Bayesian model based on the features of SHM monitoring data, which will enhance the generalization ability and then improve the data prediction accuracy. Embedded in a Bayesian framework which exhibits built-in protection against over-fitting problems, the proposed iSBL scheme has high robustness to data noise, especially for data forecasting. The model is verified to be effective on SHM vibration field monitoring data collected from a real-world large-scale cable-stayed bridge. The recorded acceleration data with two different vibration patterns, that is, stationary ambient vibration data and non-stationary decay vibration data, are investigated, returning accurate probabilistic predictions in both the time and frequency domains.

Fiber Optic Sensing Technology and Vision Sensing Technology for Structural Health Monitoring.

Structural health monitoring is currently a crucial measure for the analysis of structural safety. As a structural asset management approach, it can provide a cost-effective measure and has been used successfully in a variety of structures. In recent years, the development of fiber optic sensing technology and vision sensing technology has led to further advances in structural health monitoring. This paper focuses on the basic principles, recent advances, and current status of applications of these two sensing technologies. It provides the reader with a broad review of the literature. It introduces the advantages, limitations, and future directions of these two sensing technologies. In addition, the main contribution of this paper is that the integration of fiber optic sensing technology and vision sensing technology is discussed. This paper demonstrates the feasibility and application potential of this integration by citing numerous examples. The conclusions show that this new integrated sensing technology can effectively utilize the advantages of both fields.

Guided Wave Tomography of Surface Defects Based on the Method of Moments

AbstractThe detection and evaluation of corrosion, cracks, flaws, and other imperfections in aged structures are critical for enhancing safety and evaluating residual service life in structural health monitoring (SHM) and nondestructive testing (NDT). A guided wave tomography based on the method of moments (MOM) for exact detection of the surface defects in plates, including their profile, size, and position is proposed in this paper. The mathematical relationship between the plate thickness and the objective function is derived theoretically for the A0 mode. Based on the obtained relationship, a plate thickness imaging technique is proposed. The efficiency of the proposed technique is confirmed by the results of numerical simulation and experimental data. Center and off‐center surface defects are considered. 64 probes are evenly distributed in a circle outside the defect to capture omnidirectional wave signals. Both numerical and experimental results demonstrate that the guided wave tomography proposed can effectively reconstruct the spatial plate thickness distribution, enabling the precise identification of the defect's shape, depth, and position. The qualitative analysis and quantitative data provide effective guidance for NDT or SHM technologies in engineering.

An improved probabilistic diagnosis imaging algorithm for quantifying Hole-edge crack growth

Probabilistic damage imaging (PDI) algorithm based on Lamb wave is one of the most promising structural health monitoring technologies for quantifying the hole-edge crack. However, it is still a challenge how to use a limited number of sensors to quantify the hole-edge crack. This paper proposed an improved probabilistic damage imaging algorithm (IPDI) to track the hole-edge crack growth. A simulation has been conducted to deeply study the interaction of cracks and Lamb waves. A damage index calculated by the frequency-domain energy has been found to have a good capacity for monotonically characterizing crack propagation. The further study derives a unifying relationship between damage index and the ellipse size control factor from the crack to transmitter–receiver paths with different lengths and angles. The unifying relation is used to define a new dynamic elliptical control parameter in PDI algorithm to improve the capacity of crack quantification. The improved PDI algorithm is verified by both of simulation and experiments with different exciting frequencies, whose results show that the crack imaging error of the IPDI algorithm is below 1 mm, and the accuracy is much higher than that of the traditional PDI algorithm. The proposed IPDI algorithm can effectively quantify the hole-edge crack growth.

In-situ integration and performance verification of large-scale PZT network for composite aerospace structure

Piezoelectric transducer (PZT) based structural health monitoring (SHM) technology has been proved to be effective in increasing safety and reliability of composite aircraft structures. However, the attachment of PZT network to the host structure is considered as a weak link when facing the long term durability requirement of aerospace SHM, which should be overcome for aerospace SHM. In this paper, a surface-mounted co-curing method is creatively proposed to realize in-situ integration of large-scale PZT network and composite structure. By jointly controlling curing temperature and pressure, the proposed method can realize integration of large-scale PZT network and structural surface with high reliability and performance consistency. Compared with the conventional integration methods, the proposed method does not affect the manufacturing process or reduce the mechanical property of structure, and can be easily implemented whenever the host structure is available. To evaluate the effectiveness of the proposed integration method, a large-scale PZT network with an overall dimension of 1100 mm × 600 mm is integrated with a carbon fiber composite panel of wing box. The integration caused influence on functional integrity is first assessed by performing electro-mechanical impedance based theoretical analysis and experimental investigation. Then guided wave signals of the integrated large-scale PZT network are also acquired and analyzed, which proves the good signal repeatability and consistency of the network. At last, the SHM performance of the network is verified by conducting impact damages on the composite panel and performing damage monitoring, experimental results show that accurate monitoring is realized.

State of the art in structural health monitoring of offshore and marine structures

This paper deals with state of the art in structural health monitoring (SHM) methods in offshore and marine structures. Most SHM methods have been developed for onshore infrastructures. Few studies are available to implement SHM technologies in offshore and marine structures. This paper aims to fill this gap and highlight the challenges in implementing SHM methods in offshore and marine structures. The present work categorises the available techniques for establishing SHM models in oil rigs, offshore wind turbine structures, subsea systems, vessels, pipelines and so on. Additionally, the capabilities of proposed ideas in recent publications are classified into three main categories: model-based methods, vibration-based methods and digital twin methods. Recently developed novel signal processing and machine learning algorithms are reviewed and their abilities are discussed. Developed methods in vision-based and population-based approaches are also presented and discussed. The aim of this paper is to provide guidelines for selecting and establishing SHM in offshore and marine structures.

Investigation of electrically isolated capacitive sensing skins on concrete to reduce structure/sensor capacitive coupling

Damage to bridges can result in partial or complete structural failures, with fatal consequences. Cracks develop in concrete infrastructure from fatigue loading, vibrations, corrosion, or unforeseen structural displacement. Effective long-term monitoring of civil infrastructure can reduce the risk of structural failures and potentially reduce the cost and frequency of inspections. However, deploying structural health monitoring technologies for crack detection on bridges is expensive, especially long-term, due to the density of sensors required to detect, localize, and quantify cracks. Previous research on soft elastomeric capacitors (SECs) has shown their viability for low-cost monitoring of cracks in transportation infrastructure. However, when deployed on concrete for strain monitoring, a structure/sensor capacitive coupling exists that may cause a significant amplification in the signal collected from the SEC sensor. This work provides a detailed experimental study of electrically isolating capacitive sensing skins for concrete structures to reduce the structure/sensor capacitive coupling of an electrically grounded sensor. The study illustrates that the use of rubber isolators effectively decreases the capacitive coupling between concrete, which inherently has capacitive properties, and sensors such as the SEC that utilize capacitance measurements. In addition, the required thickness of isolation for accurate strain monitoring using the SEC with geometry described in the paper is investigated and better strain correlation is observed between the rubber of isolation thickness 0.30 mm and 0.64 mm with rubber of isolation of approximately 0.40 mm having the best response. Tests were conducted on small-scale concrete beams, and results were validated on full-scale reinforced concrete bridge decks recently taken out of service. This study demonstrates that with proper isolation material, the SEC can accurately transduce strain from concrete within a 10 error for strain levels beyond 25 .

A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring.

This paper reviews recent advances in sensor technologies for non-destructive testing (NDT) and structural health monitoring (SHM) of civil structures. The article is motivated by the rapid developments in sensor technologies and data analytics leading to ever-advancing systems for assessing and monitoring structures. Conventional and advanced sensor technologies are systematically reviewed and evaluated in the context of providing input parameters for NDT and SHM systems and for their suitability to determine the health state of structures. The presented sensing technologies and monitoring systems are selected based on their capabilities, reliability, maturity, affordability, popularity, ease of use, resilience, and innovation. A significant focus is placed on evaluating the selected technologies and associated data analytics, highlighting limitations, advantages, and disadvantages. The paper presents sensing techniques such as fiber optics, laser vibrometry, acoustic emission, ultrasonics, thermography, drones, microelectromechanical systems (MEMS), magnetostrictive sensors, and next-generation technologies.

Salud estructural en edificaciones utilizando tecnología BIM: Revisión literaria

In the science of monitoring and health maintenance have facilitated damage and defects in buildings by expanding technology through the use of wireless sensors, 3D model, among others, visualize and document results that have encouraged researchers to use powerful tools such as BIM technology, aiming to gain importance due to driving tools that are widely used in the architecture, engineering and construction sector, due to the combination of the results of structural health monitoring (SHM). Eighty articles indexed in Scopus, Ebsco, Scielo and Science Direct databases were reviewed, from the years 2016 - 2021, the following keywords were used to search for information: structural health and BIM technology, Structural Health in building and Bim, Properties of recycled concrete, Health and buildings, Building and BIM technology, Structural health in buildings, structural health of buildings through BIM. Subject area filters were used such as: Engineering, Engineering and Building. Concluding that the union of BIM and SHM technology is the support to establish the situation of the structure, specifying the range of the structural damage and estimating the duration of the structure.

Piezoelectric Materials and Sensors for Structural Health Monitoring: Fundamental Aspects, Current Status, and Future Perspectives.

Structural health monitoring technology can assess the status and integrity of structures in real time by advanced sensors, evaluate the remaining life of structure, and make the maintenance decisions on the structures. Piezoelectric materials, which can yield electrical output in response to mechanical strain/stress, are at the heart of structural health monitoring. Here, we present an overview of the recent progress in piezoelectric materials and sensors for structural health monitoring. The article commences with a brief introduction of the fundamental physical science of piezoelectric effect. Emphases are placed on the piezoelectric materials engineered by various strategies and the applications of piezoelectric sensors for structural health monitoring. Finally, challenges along with opportunities for future research and development of high-performance piezoelectric materials and sensors for structural health monitoring are highlighted.

Exceptional Points Induced by Time-Varying Mass to Enhance the Sensitivity of Defect Detection

The exceptional point (EP), a non-Hermitian degeneracy at which eigenvalues and eigenvectors coalesce simultaneously, is investigated in mechanical oscillators involving a time-varying mass. A dynamic modulation mechanism is employed to create the time-varying mass, which can potentially develop an EP by acting as an energy source and drain. To fully demonstrate the existence of EPs, the eigenvalue problem for the modulated system is theoretically formulated through two different schemes, based on the state-space and harmonic balance methods. A second-order EP is confirmed by both methods and exists as a result of the coalescence of fundamental and harmonic eigenmodes. As a salient property of this second-order EP, the square-root behavior of the eigenfrequency subject to small perturbations of the system parameters is theoretically demonstrated, and utilized to devise a proof-of-concept model for mechanical sensors with high sensitivity to void defects in elastic solids. The EP behavior induced by a time-varying mass may find potential applications in damage assessment and health monitoring of engineering structures.

Sensory methods and machine learning based damage identification of fibre-reinforced composite structures: An introductory review

Fibre-reinforced composite materials are extensively used for manufacturing critical engineering components in diverse applications, which demands intelligent and reliable structural health monitoring (SHM) schemes to prevent catastrophic failures associated with composite structures. Composite materials have complex failure mechanisms, and it is essential to employ reliable SHM methods with high accuracy to detect damages at the incipient stage. Although there are several SHM technologies available, no single strategy is impeccable for tackling all damage types due to the incredibly complex failure mechanisms of the composite materials. Machine learning (ML) methods are frequently integrated to compensate for the limitations of the traditional SHM methods. This paper presents the state-of-the-art sensory methods and deep learning (DL) techniques while emphasizing the future directions for the engineering and scientific community interested in developing novel SHM systems for fibre-reinforced polymer composite structures intended for civil, aerospace, automotive, marine, oil and gas exploration industries.

Beam-focused SH wave transducer based on YSr3(PO4)3 piezoelectric crystal for high temperature structural health monitoring

Guided-wave-based structural health monitoring (SHM) technology is of great importance for real-time inspection of high-temperature structures. The fundamental shear horizontal (SH0) wave is believed to be an ideal wave mode for developing SHM systems due to its nondispersive characteristics. However, currently very limited SH0 wave transducers can be used for SHM of high-temperature structures due to the limitation of materials. Herein, a novel YSr3(PO4)3 (YSP) piezoelectric crystal in the space group I4¯3d was grown. Experiments show that the face-shear piezoelectric coefficient d14 (d14 = d25 = d36) is 9.7 pC/N and varies little from 25 to 800 °C. Then a beam-focused SH0 wave piezoelectric transducer is developed based on face-shear-mode YSP wafers. Both finite element simulations and experimental results indicate that the YSP-based transducer can excite pure SH0 wave and focus the wave energy along two opposite main directions. Especially, the obtained SH0 wave beam is highly concentrated with a small divergence angle of less than 30°, originating from the high working frequency range from 300 to 400 kHz. The excellent temperature stability of the as-grown YSP crystal makes the proposed SH0 wave piezoelectric transducer very suitable for SHM of high-temperature structures.

Electrically conductive and 3D-printable copolymer/MWCNT nanocomposites for strain sensing

Structural health monitoring (SHM) of safety-critical composite components is essential to ensure mechanical stasis and detect local damage before it can produce global failure. SHM technologies must also adapt to ever-evolving materials and geometries, but traditional piezoresistive strain sensors lack the ability for end-user customization and modifications throughout service-life. Additive manufacturing via fused filament fabrication (FFF) provides a practical pathway to overcome such sensor shortcomings. However, the electrical conductivity of conventional polymer feedstock is not sufficient for accurate strain measurements without compromise to melt viscosity and thus printability. Here we report the development of a new 3D-printable and electrically responsive nanocomposite by melt-mixing poly(ethylene-co-methacrylic acid) (EMAA) with multi-walled carbon nanotubes (MWCNT). Bulk electrical conductivity of 43.9 S m−1 is achieved at only 5 wt% loading – higher than comparable materials – where the nano-dispersion heterogeneity of MWCNT in EMAA is linked to the favorable conductivity while retaining molten flowability. FFF is employed to print thin (150μm) serpentine strain sensors onto the surface of a glass fiber-reinforced polymer composite, which exhibit strong adhesion and accurate piezoresistive sensing under cyclic flexural loading. Twenty consecutive cycles with converged sensor readings (i.e., < 1% variation in measured resistance) demonstrates reliable performance across a relevant service strain range (0.4 – 0.8 %) for such fiber-composites. This rapid fabrication and transferable sensing strategy, suitable for new and existing structures, thus provides a crosscutting SHM solution.