Real-time Structural Health Monitoring Research Articles

High-Temperature and Flexible Piezoelectric Sensors for Lamb-Wave-Based Structural Health Monitoring.

Piezoelectric sensors can be utilized in Lamb-wave-based structural health monitoring (SHM), which is an effective method for aircraft structural damage detection. However, due to the inherent stiffness, brittleness, weight, and thickness of piezoelectric ceramics, their applications in aircraft structures with complex curved surfaces are seriously restricted. Herein, we report a flexible, light-weight, and high-performance BaTiO3:Sm2O3/SrRuO3/SrTiO3/mica film sensor that can be used in high-temperature SHM of aircraft. Enhanced ferroelectric Curie temperature (487 °C) and piezoelectric coefficient d33 (120-130 pm/V) are achieved in BaTiO3, which can be attributed to the tensile strain developed by stiff Sm2O3 nanopillars. Stable ferroelectricity and piezoelectricity are retained up to 150 °C. The flexible BaTiO3:Sm2O3/SrRuO3/SrTiO3/mica film is validated as an ultrasonic sensor with high sensitivity and stability for damage monitoring on aircraft structures with the curved surface ranging from 25 to 150 °C. Our work demonstrates that flexible and light-weight BaTiO3:Sm2O3/SrRuO3/SrTiO3/mica film sensors can be employed as high-temperature piezoelectric sensors for real-time SHM of aircraft structures with complex curved surfaces.

Auto-detection of acoustic emission signals from cracking of concrete structures using convolutional neural networks: Upscaling from specimen

Acoustic emission (AE) monitoring has gained significant interest as a promising method for monitoring of changes in structural integrity and durability. Long-term AE monitoring needs to detect and distinguish crack signals from ambient noise (or dummy) signals; however, it is still a daunting task which currently limits field implementation of the AE method. Herein, we explore the feasibility of using convolutional neural network (CNN) models to detect AE crack signals from ambient signals. The trained models are validated both with noise-embedded synthesized signals and with upscaled physical model experiments simulating earthquake loading to a scaled model foundation by using a large-scale shaking table. The 2D CNN model trained the laboratory-synthesized signal sets effectively captured the crack and crack-free signals in all cases including the upscaled physical model experiments. This study presents a simple but robust CNN model for pre-filtering of crack signals and a novel training method for enhanced accuracy, which can be applied for real-time structural health monitoring of concrete-based structures.

Estimation of Damage Evolution within an In Situ Rock Mass Using the Acoustic Emissions Technique under Incremental Cyclic Loading

Abstract The acoustic emissions (AE) technique has been regarded as a promising method for real-time structural health monitoring of a waste repository and is widely used to evaluate the damage mechanism. However, relatively little effort has been made to use AE monitoring to evaluate the quantitative damage assessment of a rock mass. The objective of this study was to identify the progressive damage evolution of an in situ rock mass based on AE monitoring under a dynamic loading condition, which could serve as primary information for quantifying rock-mass damage. The shape of the damage evolution curve in granite was first determined from a uniaxial compression test with incremental cyclic loading. Based on the results of a laboratory test, the in situ damage evolution curve of a rock mass was subsequently estimated from in situ tests using a Goodman jack and simultaneous AE monitoring in the Underground Research Tunnel of the Korea Atomic Energy Research Institute. With regard to in situ estimation of damage evolution, numerical simulation was also applied to infer the localized strength of a rock mass, in which elastic constants were measured using in situ tests and applied as the basic input parameters. The method suggested in this study could contribute to quantification of rock mass damage from AE observations.

Real-time structural health monitoring system based on streaming data

In this paper, a novel real-time structural health monitoring (SHM) system based on streaming data is proposed. In contrast to a traditional SHM system, the proposed system implements a series of optimizations for data transmission and processing to reduce the latency and better satisfy the real-time requirement. The concept of the watermark in the streaming system is adopted to address the problem of when to trigger the time window calculation under the real-time requirement. Moreover, a well-designed parallel mechanism is used to satisfy the multistage computation requirement in the parallel data stream. A case study in which the proposed system is applied to the Shanghai Tower is presented. The peak picking method is used as an example in the test environment to track the latency of each main operation under different parallelism schemes. The results show that computing in parallel effectively reduces the latency and provides a reference for integrating the random decrement technique (RDT), stochastic subspace identification (SSI), or other more complex but effective algorithms in parallel into the system in the future. The total latency under the test environment from data generation to data transmission to the web server is approximately only 200-400 ms, which indicates the excellent real-time performance of the system.

A simplified framework for prediction of sensor network coverage in real-time structural health monitoring of plate-like structures

In this article, the coverage area prediction of piezoelectric sensor network for detecting a specific type of under-surface crack in plate-like structures is addressed. In particular, this article proposes a simplified framework to estimate the coverage of any given sensor network arrangement when a critical defect is known. Based on numerical results from finite element methods (FEM), a simplified framework to estimate coverage area of any given network arrangement is developed. Using such a simplified framework, one can avoid time-consuming procedure of evaluating numerous FEM models in estimating sensor network coverage. Back-scatter fields of partial cracks are estimated using a proposed function, whose parameters are estimated from the results of a limited number of FEM simulations. The proposed function efficiently predicts the back-scattered field of any combination of transmitters and receivers for a given crack geometry. Superposition is used to estimate the coverage area of an arbitrary piezoelectric (e.g., PZT) sensor network. It is shown that the coverage area of a sensor network depends on both sensor network geometry and defect properties (e.g., crack inclination) and it is not necessarily a linear function of the number of sensors. Furthermore, it is shown that the network arrangement has an important effect on the geometry of the coverage area. Experimental results of a network of 14 PZTs in two clusters confirm the accuracy of the method.

Recent Advancements in the Development of Sensors for the Structural Health Monitoring (SHM) at High-Temperature Environment: A Review

With Industry 4.0 becoming increasingly pervasive, the importance and usage of sensors has increased several folds. Industry 4.0 refers to a new phase in the industrial revolution that mainly focuses on interconnectivity, automation, machine learning, and real-time data. Real-time structural health monitoring (SHM) of components in the industrial process is one of the crucial and important component of Industry 4.0. SHM of components exposed to high-temperature ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 650^{\circ }\text{C}$ </tex-math></inline-formula> ) is becoming increasingly important nowadays. However, harsh and high temperature environments impose a great challenge towards their implementation. This review is an attempt to demonstrate the development, application, limitations and recent advancement of the existing sensors used for SHM. Some sensors such as eddy current (EC) sensors and fiber Bragg grating (FBG) sensors have been discussed in detail. A phenomenological study of the electromagnetic sensor for the SHM of engineering components that are exposed to high temperature has been addressed. State-of-the-art fabrication methodologies such as low temperature co-fired ceramic (LTCC) technology for such type of sensors for high temperature SHM applications have been elucidated. Future challenges and opportunities for SHM applications of high temperature sensors have been highlighted.

A multi-scale model order reduction scheme for transient modelling of periodic structures

In this paper, a Floquet Model Order Reduction (MOR) method is proposed for the modelling of finite periodic structures subjected to harmonic or transient loads. This Floquet MOR generates frequency-independent projection matrices of the full-scale structure using a Floquet expansion of the local state-vectors computed on a single unit-cell with the Forced Wave Finite Element framework. This two-step MOR method can handle large multi-scale waveguides regardless of the scale and the number of unit-cells. A comparison with a standard sub-structuring approach based on unit-cell wave-mode reduction demonstrates that Floquet MOR is able to create reduced models of finite periodic structures with an unequalled computational efficiency. The method’s ability to produce small and accurate models suitable for time-domain simulations presents a high potential for virtual sensing, real-time Structural Health Monitoring (SHM) and digital twins for large periodic structures and metamaterials.

Structural health monitoring for TLP-FOWT (floating offshore wind turbine) tendon using sensors

In this paper, a new algorithm for the real-time structural health monitoring of TLP (tension-leg platform) tendons is presented. The algorithm is based on bi-axial sensors installed along the tendon and top tension-meter. Then, the tension and bending moment along the line can be estimated by using the developed algorithm. For bending moment, a generalized-coordinate-based FEA (Finite Element Analysis) formulas with cubic interpolation are derived. For tension, analytical formulas with small angle assumption are derived. Hull-mooring-riser fully coupled software is used as a simulation tool to generate the corresponding numerical-sensor signals. To validate the developed algorithms, several case studies are conducted, considering variations in sensor spacing, line-failure detection, different boundary conditions, and ocean-storm conditions. The results show that the proposed algorithms can monitor the actual variations of tensions and bending moments (or axial and bending stresses) along the tendon with high accuracy, which means that the real-time monitoring of accumulated fatigue damage at hot-spots is also possible. The developed algorithm can also detect various line-failure scenarios by observing the change of stress signals. Its capability of real-time stress tracing in 10-yr and 100-yr storm conditions is also demonstrated.

Torsional control of asymmetric buildings using online 3‐D damage detection and adaptive stiffness devices

Developing a smart civil structure, in which the structure instantaneously identifies damages during vibration and makes decisions to compensate or reduce them, is one of the new issues in the seismic control of structures. One of the most important challenges in asymmetric buildings is the torsion due to the damage occurrence in the structure during severe excitation. The aim of this study is to try to reduce the torsional effects and dynamic responses of the structure by combining two strategies, including real time structural health monitoring (SHM) and semi-active control. Hence, a new damage detection method based on identified Markov parameters of the system is proposed to identify the damage in 3-D shear model structures. Also, in order to create a smart structure, a new controller is designed to regulate mechanical characteristics of the installed adaptive stiffness devices based on the detected damages and eccentricity variations in the structure. The performance of the proposed controller and damage detection method is investigated through numerical examples under the influence of various earthquake records. The obtained results reveal that the proposed method estimates the location, amount, and type of damages with acceptable accuracy even with noise contamination of the measured data. Moreover, the presented controller can effectively reduce the increase in the torsional response of the structure owing to the damage occurrence during strong ground motions.

Structural Health Monitoring Method of Pantograph–Catenary System Based on Strain Response Inversion

Pantograph-catenary system provides electric energy for the subway lines; its health status is essential to the serviceability of the vehicle. In this study, a real-time structural health monitoring method based on strain response inversion is proposed to calculate the magnitude and position of the dynamic contact force between the catenary and pantograph. The measurement principle, calibration, and installation detail of the fiber Bragg grating (FBG) sensors are also presented in this article. Putting this monitoring system in use, an application example of a subway with a rigid overhead catenary is given to demonstrate its performance. The pantograph was monitored and analyzed, running underground at a maximum speed of 80 km/h. The results show that the strain response inversion method has high measurement accuracy, good data consistency, and flexibility on sensor installation. It can accurately calculate the magnitude and location of the contact force exerted on the pantograph.

Modeling lamb wave propagation in visco-elastic composite plates using a fifth-order plate theory

A new extension of the shear deformation theory to fifth order in order to calculate the spectrum of Lamb waves in orthotropic media over a wide frequency range is developed and analyzed. The aspiration of the proposed method is to create an alternative framework to exhaustive 3D elasticity based solutions by increasing computational efficiency without losing accuracy, nor robustness. A new computational framework is introduced which allows to estimate the dispersion curves for the first nine symmetric and nine anti-symmetric Lamb modes. Analytically calculated dispersion curves using 5-SDT for different propagation directions and polar plots for selected frequency of different materials are compared with the results from both the semi analytical finite element method, and lower order shear deformation theories. Careful analysis for individual laminae and for symmetric composite laminates exhibits a good agreement between the new higher order plate theory and the semi analytical finite element method over an extensive frequency range. In addition, attenuation plots show that the proposed method can also be used for visco-elastic materials (or highly damped materials). The advantage of the new higher order plate theory and its numerical implementation is that it is much more computationally efficient compared to comprehensive methods as Lamb wave polar plots of composite plates as function of incidence angle, polar angle and frequency can be calculated in less than a second on a standard laptop. Consequently, the use of this framework in inversion routines opens up the possibility of quasi real-time Structural Health Monitoring for visco-elastic composites covering a sufficiently wide frequency range.

Lamb wave based damage detection in metallic plates using multi-headed 1-dimensional convolutional neural network

Lamb wave based damage diagnosis holds potential for real-time structural health monitoring; however, analysing the Lamb wave response possess challenge due to its complex physics. Data-driven machine learning (ML) algorithms are often more effective in identifying the damage-related features from these complex responses. However, in analysing such complex responses the ML algorithms requires extensive data pre-processing and are often not suitable for real-time damage detection. This paper presents a deep learning multi-headed 1-dimensional convolutional neural network (1D-CNN) architecture capable to operate directly on raw discrete time-domain Lamb wave signals recorded from a thin metallic plate. The multi-headed configuration consisting of two parallel 1D-CNN layers is capable to learn higher order damage-related features and enhances robustness of overall classification performance. To train the adopted 1D-CNN algorithm a diverse database is also constructed consisting 216 numerically and 24 experimentally generated responses of a thin 1.6 mm Al-5052 plate structure. The diversification of training database is achieved by varying parameters like scanning length, scanning frequency and adding different levels of white noises to the captured responses. Later, the trained 1D-CNN architecture is tested against two separated unseen test-databases. The first test database consist of experimentally generated 12 samples with notch-like damage and 12 samples of pristine condition. The proposed 1D-CNN classifier generalizes well on the unseen samples and decisively predicts the outcome for 23 out of 24 samples of first test database. The second test database consists of 108 unseen FE simulated samples capturing additional damage scenarios. In the second test phase, the model has correctly predicted the condition of all the 108 samples.

Structural monitoring and remaining fatigue life estimation of typical welded crack details in the Manhattan Bridge

Manhattan Bridge is a suspension structure that connects Manhattan to Downtown Brooklyn. This bridge is over 110 years old and subjected to daily repeated axles of transit trains and other vehicular traffic. Visual inspections of the bridge revealed manifestation of many cracks in the welded details due to the out-of-plane forces at the connections between transit and floor beams. The research reported herein, pertains to the fatigue life evaluation and reliability assessment of typical cracked welded details. The bridge was instrumented with a real-time structural health monitoring system based on FBG sensors for remote monitoring of the cracked connections. Both deterministic and probabilistic approaches were employed for prediction of the remaining fatigue life of typical cracked details. For the same fatigue reliability index of 2.3, the deterministic and probability based methods yielded remaining fatigue life of 369 and 120 years, respectively, for the typical welded connections details of the bridge.

An Investigation on the Effects of Wind Flow on Real-Time Structural Health Monitoring With Lamb Wave-Based Technique

In this paper, the effects of wind flow on real-time structural health monitoring (SHM) with Lamb wave-based technique is studied with the dedicated experimental platform. Firstly, the environmental signal coming from the wind-induced vibration is observed at experimental conditions with different wind speeds by using a couple (two) of piezoelectric transducers. In order to optimize the received signal for real-time monitoring, the shunt resistor, working as the filter for the signal processing, is introduced to suppress the low-frequency noise from the wind-induced vibration. Both experimental and modeling work have been conducted to cross verify the results. Moreover, the specimen with the shunt resistor has been tested at different wind speeds condition with proper results. Furthermore, in order to verify the feasibility for practical real-time SHM, the test with only one piece of piezoelectric transducer operated as sensor and actuator simultaneously has also been conducted, and the environmental signal could be filtered as expected. The work studies the effects of wind flow on real-time SHM with Lamb wave-based technique and the signal processing method is proposed to eliminate the resultant effects, which could be applied into the real-time aircraft health monitoring.

The value of structural health monitoring in seismic emergency management of bridges

The management of civil infrastructures in the aftermath of a seismic event is a concern for decision makers, which have to choose quickly among alternative actions with limited knowledge on the actual structural conditions. The availability of real time Structural Health Monitoring (SHM) data on the asset might be particularly useful. However, SHM data are not collected for free, and the cost of the SHM system should be compared with its associated benefit. A powerful tool to estimate such benefit is the Value of Information (VoI) from Bayesian decision theory. This paper provides a methodology to compute the VoI of SHM for seismic emergency management of roadway bridges. This methodology can be used by decision makers before the installation of a seismic SHM system to quantify the cost benefit of doing so and thus optimize the allocation of economic resources. Results show that the VoI is high when the expected costs of the decision alternatives (such as ‘keep the bridge open’ or ‘close the bridge’) evaluated without the SHM information are comparable. In this condition, which is highly dependent on the seismic hazard and on emergency management costs at stake, the SHM information provides the maximum support to decision making.

Real-time structural health monitoring of nonlinear hysteretic structures using a fast and slow dynamics separation method

Structural health monitoring (SHM) is the process of identification, localisation, and quantification of structural damage due to external loads, such as an earthquake. SHM results simplify and automate typical visual or localised experimental approaches and enable more informed structural safety assessment and post-event retrofit. On-line SHM is of particular interest for rapid safety assessment by owners and civil defence authorities, and can also inform active control systems to further avoid or mitigate damage during an event. This significant advantage over off-line SHM has drawn many researchers to on-line or real-time methods. This paper presents an on-line SHM algorithm for model-based identification and monitoring of non-linear hysteretic structures. It separates fast and slow dynamics and uses robust piecewise least squares fitting over different time intervals to identify key structural parameters over time, including stiffness, damping, and the well-accepted Bouc–Wen hysteretic model parameters, which are directly related to well-recognised damage metrics. Proof-of-method simulations of a realistic nonlinear case study structure, subjected to a suite of 20 different ground motions, show the algorithm accurately identifies structural parameters to within 2.7% and 4.3% of the actual as-modelled values for stiffness and damping, respectively. Results for a range of major changes (damage) in structural parameters show the algorithm performs well in tracking these changes in real-time. Overall, the results present an accurate, real-time, model-based SHM method for highly nonlinear structures.

Structural health monitoring of polymer-matrix composite using embedded piezoelectric ceramic transducers during several four-points bending tests

This article investigates the interest of a promising methodology, dealing with the use of an in-situ piezoelectric (PZT) disk to perform real-time Structural Health Monitoring (SHM) of glass fiber-reinforced polymer (GFRP) composites submitted to various four-points bending loadings: monotonic, load-unload and incremental load until failure. The real-time in-situ SHM is conducted acquiring the electrical signature (capacitance) variation of the embedded PZT transducer. To establish the link between the PZT capacitance response and the occurring physical phenomena, especially damage, a multi-instrumentation composed of external Non-Destructive Testing techniques (Acoustic Emission (AE) and Digital Image Correlation (DIC)) was implemented on the tested specimens, so that it was possible to make multi-physical couplings between the various obtained measurements and the PZT capacitance curves. It was shown that the PZT capacitance is very sensitive to damage initiation and progression inside the polymer-matrix composite material until its failure, mainly due to the specimens Neutral Fiber (NF) vertical offset which changes the piezoelectric coupling between the host composite and the transducer. Consequently, the SHM potential of such a PZT disk is highlighted, opening the way for its use in real technical structures submitted to bending loadings, such as aircraft wings.

Automatic modal parameter identification of high arch dams: feasibility verification

Modal parameters, including fundamental frequencies, damping ratios, and mode shapes, could be used to evaluate the health condition of structures. Automatic modal parameter identification, which plays an essential role in realtime structural health monitoring, has become a popular topic in recent years. In this study, an automatic modal parameter identification procedure for high arch dams is proposed. The proposed procedure is implemented by combining the density-based spatial clustering of applications with noise (DBSCAN) algorithm and the stochastic subspace identification (SSI). The 210-m-high Dagangshan Dam is investigated as an example to verify the feasibility of the procedure. The results show that the DBSCAN algorithm is robust enough to interpret the stabilization diagram from SSI and may avoid outline modes. This leads to the proposed procedure obtaining a better performance than the partitioned clustering and hierarchical clustering algorithms. In addition, the errors of the identified frequencies of the arch dam are within 4%, and the identified mode shapes are in agreement with those obtained from the finite element model, which implies that the proposed procedure is accurate enough to use in modal parameter identification. The procedure is feasible for online modal parameter identification and modal tracking of arch dams.

Integration of piezoelectric transducers (PZT and PVDF) within polymer-matrix composites for structural health monitoring applications: new success and challenges

ABSTRACT This article investigates the interest of using in-situ piezoelectric (PZT and PVDF) disks to perform real-time Structural Health Monitoring (SHM) of glass fiber-reinforced polymer composites submitted to various tensile loadings. The goal is to evaluate the working range and SHM potential of such embedded transducers for relatively simple mechanical loadings, with the long-term aim of using them to monitor complete 3D structures submitted to more complex loadings. SHM is performed acquiring the electrical capacitance variation of the embedded transducers. To study the potential links between the in-situ capacitance signal and the global response of the loaded host specimens, a multi-instrumentation composed of external Nondestructive Testing techniques was implemented on the surfaces of the specimens to search for multi-physical couplings between these external measurements and the capacitance curves. Results confirmed the non-intrusiveness of the embedded transducers, and allowed estimating their working domain. PZT capacitance signal follows well the mechanical loadings, but the piezoceramic transducer gets damaged after a determined relatively low strain level due to its brittleness. The limits of this working domain are extended by using a stretchable PolyVinylidene Fluoride (PVDF) polymer transducer, allowing this one to perform in-situ and real-time SHM of its host tensile specimens until failure.

Metamodel-based pattern recognition approach for real-time identification of earthquake-induced damage in historic masonry structures

Damage localization/quantification through vibration-based Structural Health Monitoring (SHM) is commonly performed by inverse calibration of a numerical model. Nevertheless, the numerous simulations required in the associated optimization problem pose a daunting obstacle when applied to real-time SHM. Particularly critical are heritage buildings, whose complex geometries often require computationally intensive modellings. In this light, this paper presents a novel earthquake-induced damage identification approach for historic masonry structures. This relies upon the use of a computationally efficient meta-model suited for real-time system identification. The optimization problem is formulated accounting for discrepancies between numerical and experimental resonant frequencies and mode shapes. Damage localization/quantification is enabled by multivariate analyses of continuously identified model parameters. A real medieval tower is presented as a case study, and several damage scenarios are simulated and used for validation. The reported results pave the way for the development of next-generation long-term vibration-based SHM systems with real-time damage identification capabilities.