Structural Health Monitoring Techniques Research Articles

Economic and Ecological Benefits of SHM Techniques in Chemical Process Industry

High-performance machine components are involved in a wide range of industrial production processes. They play an important role in the provision of chemical products, pharmaceuticals, and energy resources. They require monitoring because they can fail during production due to corrosion and/or wear, which often means an increased risk to people and the environment. Periodic inspections with on-site test personnel often require a shutdown of the chemical plant. Ideally, an ultrasonic structural health monitoring (SHM) system could be used to provide continuous data on the structural integrity of these components while reducing or eliminating plant downtime. A plant shutdown is not only a high economic or supply risk but can also be energy intensive. In our presentation, we would like to give an overview of the technical requirements for an SHM system to be used in the chemical industry. Finally, the presentation will highlight the economic benefits of SHM as well as its contribution to energy savings and reduction of CO2 emissions per ton of chemical product produced.

Development of a Bayesian Framework for Kinematic Data Fusion

Structural health monitoring (SHM) is widely used for assessing the condition of bridges at risk. Traditional SHM techniques rely on point-wise information provided by individual sensors placed at strategic locations. However, a more comprehensive assessment of the bridge state can be achieved through data fusion, integrating information from different sensors. This article presents a Bayesian framework data fusion method that combines information from various measurements to improve the knowledge of the structural deformation state. The proposed framework identifies key deformation parameters by exploiting a simplified model that describes the system deformation state and uses an extensive set of data, including prisms, extensometers, tiltmeters, and beyond. Moreover, this approach provides a continuous knowledge of the deformation state, and reduces the uncertainties associated with individual sensor measurements. The framework developed is initially applied to a simulated case study of a simply supported beam, and then to the Colle Isarco viaduct, a highway bridge equipped with an extensive monitoring system.

Indirect prediction of graphene nanoplatelets-reinforced cementitious composites compressive strength by using machine learning approaches

Graphene nanoplatelets (GrNs) emerge as promising conductive fillers to significantly enhance the electrical conductivity and strength of cementitious composites, contributing to the development of highly efficient composites and the advancement of non-destructive structural health monitoring techniques. However, the complexities involved in these nanoscale cementitious composites are markedly intricate. Conventional regression models encounter limitations in fully understanding these intricate compositions. Thus, the current study employed four machine learning (ML) methods such as decision tree (DT), categorical boosting machine (CatBoost), adaptive neuro-fuzzy inference system (ANFIS), and light gradient boosting machine (LightGBM) to establish strong prediction models for compressive strength (CS) of graphene nanoplatelets-based materials. An extensive dataset containing 172 data points was gathered from published literature for model development. The majority portion (70%) of the database was utilized for training the model while 30% was used for validating the model efficacy on unseen data. Different metrics were employed to assess the performance of the established ML models. In addition, SHapley Additve explanation (SHAP) for model interpretability. The DT, CatBoost, LightGBM, and ANFIS models exhibited excellent prediction efficacy with R-values of 0.8708, 0.9999, 0.9043, and 0.8662, respectively. While all the suggested models demonstrated acceptable accuracy in predicting compressive strength, the CatBoost model exhibited exceptional prediction efficiency. Furthermore, the SHAP analysis provided that the thickness of GrN plays a pivotal role in GrNCC, significantly influencing CS and consequently exhibiting the highest SHAP value of + 9.39. The diameter of GrN, curing age, and w/c ratio are also prominent features in estimating the strength of graphene nanoplatelets-based cementitious materials. This research underscores the efficacy of ML methods in accurately forecasting the characteristics of concrete reinforced with graphene nanoplatelets, providing a swift and economical substitute for laborious experimental procedures. It is suggested that to improve the generalization of the study, more inputs with increased datasets should be considered in future studies.

Damage identification technique by model enrichment for structural elastodynamic problems

Structural Health Monitoring (SHM) techniques are key to monitor the health state of engineering structures, where damage type, location and severity are to be estimated by applying sophisticated techniques to signals measured by sensors. However, very localized damage detection algorithms applied to dynamics problems when dealing with rigid structures at low-frequency range remains still a big challenge. The last due to the low influence of very localized damage on the overall response of the structure (Saint-Venant principle). In this context, in the present work, we propose a methodology for locally correcting the models from collected data for elastodynamics problems at low-frequency range which is able to predict very localized damage. The proposed technique consists in enriching the structural model in a sparse way and solving the identification problem in the frequency domain, where the influence of damage over a large frequency band is exploited to improve the prediction of the damage location. The advantages and potential of the proposed technique are illustrated for the damage detection in a plate problem, demonstrating the advantages of the method in detecting very localized damage. The proposed technique is limited to a methodological description, and further developments should be considered to approach its applicability in an industrial scenario.

Recent Advances in Detection of Failure in Laminated Glass Using Non-Destructive Testing and Structural Health Monitoring Techniques (Review Paper)

Recent Advances in Detection of Failure in Laminated Glass Using Non-Destructive Testing and Structural Health Monitoring Techniques (Review Paper)

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.

Monitoring damage growth and topographical changes in plate structures using sideband peak count-index and topological acoustic sensing techniques

Some topographies in plate structures can hide cracks and make it difficult to monitor damage growth. This is because topographical features convert homogeneous structures to heterogeneous one and complicate the wave propagation through such structures. At certain points destructive interference between incident, reflected and transmitted elastic waves can make those points insensitive to the damage growth when adopting acoustics based structural health monitoring (SHM) techniques. A newly developed nonlinear ultrasonic (NLU) technique called sideband peak count – index (or SPC-I) has shown its effectiveness and superiority compared to other techniques for nondestructive testing (NDT) and SHM applications and is adopted in this work for monitoring damage growth in plate structures with topographical features. The performance of SPC-I technique in heterogeneous specimens having different topographies is investigated using nonlocal peridynamics based peri-ultrasound modeling. Three types of topographies – “X” topography, “Y” topography and “XY” topography are investigated. It is observed that “X” and “XY” topographies can help to hide the crack growth, thus making cracks undetectable when the SPC-I based monitoring technique is adopted. In addition to the SPC-I technique, we also investigate the effectiveness of an emerging sensing technique based on topological acoustic sensing. This method monitors the changes in the geometric phase; a measure of the changes in the acoustic wave’s spatial behavior. The computed results show that changes in the geometric phase can be exploited to monitor the damage growth in plate structures for all three topographies considered here. The significant changes in geometric phase can be related to the crack growth even when these cracks remain hidden for some topographies during the SPC-I based single point inspection. Sensitivities of both the SPC-I and the topological acoustic sensing techniques are also investigated for sensing the topographical changes in the plate structures.

Comprehensive assessment and monitoring of bond deterioration in GFRP-reinforced concrete beams using guided wave and acoustic emission techniques

The debonding between glass fiber-reinforced polymer (GFRP) rebars and concrete significantly reduces the bond strength and jeopardizes the safety and durability of GFRP-reinforced concrete (GFRP-RC) structures. As a result, it is imperative to monitor the structural integrity of infrastructures being built using these composite materials in structural elements during their service lives. Previous studies have primarily focused on artificially induced debonding scenarios for guided wave (GW)-based monitoring, which has resulted in an inability to capture debonding events that arise naturally due to external loads. Furthermore, flexural bond test investigations simulating real loading scenarios in GFRP-RC structures have not been linked with acoustic emission (AE) and GW monitoring. The present study addresses these research gaps by comprehensively examining the flexural bond integrity in GFRP-RC structural elements subjected to realistic loading conditions using a hybrid approach combining GW and AE as active and passive structural health monitoring techniques, respectively. A series of bond tests, conforming to RILEM specifications, have been performed to investigate various parameters (including rebar surface characteristics, embedment length, and confinement conditions) that affect the flexural bond degradation response of GFRP-RC members. Damage indices based on GW and AE signal parameters have been developed to evaluate the characteristics of confined and unconfined specimens distinctly. It has been observed from the test results that the L (0,3) GW mode is more sensitive to debonding than other modes and that a threshold of 25% has been established for the GW damage index to denote significant bond damage. Furthermore, AE damage indices have provided valuable insights into concrete damage, including splitting and damage at the rebar-concrete interface. Thus, a thorough understanding of bond deterioration in GFRP-RC structures has been developed by combining GW and AE health monitoring techniques, with implications for enhancing structural safety and durability.

Delamination Depth Detection in Composite Plates Using the Lamb Wave Technique Based on Convolutional Neural Networks.

Delamination represents one of the most significant and dangerous damages in composite plates. Recently, many papers have presented the capability of structural health monitoring (SHM) techniques for the investigation of structural delamination with various shapes and thickness depths. However, few studies have been conducted regarding the utilization of convolutional neural network (CNN) methods for automating the non-destructive testing (NDT) techniques database to identify the delamination size and depth. In this paper, an automated system qualified for distinguishing between pristine and damaged structures and classifying three classes of delamination with various depths is presented. This system includes a proposed CNN model and the Lamb wave technique. In this work, a unidirectional composite plate with three samples of delamination inserted at different depths was prepared for numerical and experimental investigations. In the numerical part, the guided wave propagation and interaction with three samples of delamination were studied to observe how the delamination depth can affect the scattered and trapped waves over the delamination region. This numerical study was validated experimentally using an efficient ultrasonic guided waves technique. This technique involved piezoelectric wafer active sensors (PWASs) and a scanning laser Doppler vibrometer (SLDV). Both numerical and experimental studies demonstrate that the delamination depth has a direct effect on the trapped waves' energy and distribution. Three different datasets were collected from the numerical and experimental studies, involving the numerical wavefield image dataset, experimental wavefield image dataset, and experimental wavenumber spectrum image dataset. These three datasets were used independently with the proposed CNN model to develop a system that can automatically classify four classes (pristine class and three different delamination classes). The results of all three datasets show the capability of the proposed CNN model for predicting the delamination depth with high accuracy. The proposed CNN model results of the three different datasets were validated using the GoogLeNet CNN. The results of both methods show an excellent agreement. The results proved the capability of the wavefield image and wavenumber spectrum datasets to be used as input data to the CNN for the detection of delamination depth.

Barely visible impact damage detection in CFRP composite using vibrothermography with narrowband sweep excitation

The extensive use of carbon fiber-reinforced plastic (CFRP) composite materials in various industries has posed new challenges for conducting effective structural health monitoring and nondestructive evaluation techniques. Based on local defect resonance (LDR), the vibrothermography with narrowband sweep excitation is proposed to detect BVIDs in the CFRP composite. The response spectrum of displacement at the impact crater was collected by a laser Doppler vibrometer, which was used to determine the frequency range of narrowband sweep excitation. The frequency range was further validated by the analysis of local defect resonance for thermal responses, and the sweep duration was also optimized. Subsequently, the proposed vibrothermography was employed to detect BVIDs with varying severity. The impact damage could be efficiently activated and a significant temperature rise was generated in the defective areas, which enhances the temperature contrast of the thermal images and facilitates the quantification of defects.

Lamb wave-based damage assessment for composite laminates using a deep learning approach

With the increasing utilization of composite materials due to their superior properties, the need for efficient structural health monitoring techniques rises rapidly to ensure the integrity and reliability of composite structures. Deep learning approaches have great potential applications for Lamb wave-based damage detection. However, it remains challenging to quantitatively detect and characterize damage such as delamination in multi-layered structures. These deep learning architectures still lack a certain degree of physical interpretability. In this study, a convolutional sparse coding-based UNet (CSCUNet) is proposed for ultrasonic Lamb wave-based damage assessment in composite laminates. A low-resolution image is generated using delay-and-sum algorithm based on Lamb waves acquired by transducer array. The encoder-decoder framework in the proposed CSCUNet enables the transformation of low-resolution input image to high-resolution damage image. In addition, the multi-layer convolutional sparse coding block is introduced into encoder of the CSCUNet to improve both performance and interpretability of the model. The proposed method is tested on both numerical and experimental data acquired on the surface of composite specimen. The results demonstrate its effectiveness in identifying the delamination location, size, and shape. The network has powerful feature extraction capability and enhanced interpretability, enabling high-resolution imaging and contour evaluation of composite material damage.

Acoustic emission onset time detection for structural monitoring with U-Net neural network architecture

Acoustic Emission (AE) is a non-destructive structural health monitoring technique, which studies elastic waves emitted during crack formation. Utilizing piezoelectric sensors, these waves are converted into electrical signals for subsequent analysis, offering insights into crack propagation and structural durability. This study focuses on the identification of AE signal onset times, crucial for determining crack locations. Conventional methods often encounter challenges with background noise, prompting the need for innovative approaches. Leveraging a U-Net neural network, specialized in segmentation tasks, onset time identification is approached as a one-dimensional segmentation challenge. Through training and testing on Pencil Lead Break (PLB) test data, commonly used in AE evaluations, the effectiveness of the method is demonstrated even with continuous signals, suggesting potential applicability in real-time monitoring.

Structural health monitoring of aircraft through prediction of delamination using machine learning.

Structural health monitoring (SHM) is a regular procedure of monitoring and recognizing changes in the material and geometric qualities of aircraft structures, bridges, buildings, and so on. The structural health of an airplane is more important in aerospace manufacturing and design. Inadequate structural health monitoring causes catastrophic breakdowns, and the resulting damage is costly. There is a need for an automated SHM technique that monitors and reports structural health effectively. The dataset utilized in our suggested study achieved a 0.95 R2 score earlier. The suggested work employs support vector machine (SVM) + extra tree + gradient boost + AdaBoost + decision tree approaches in an effort to improve performance in the delamination prediction process in aircraft construction. The stacking ensemble method outperformed all the technique with 0.975 R2 and 0.023 RMSE for old coupon and 0.928 R2 and 0.053 RMSE for new coupon. It shown the increase in R2 and decrease in root mean square error (RMSE).

Thermal deformation monitoring of a highway bridge: combined analysis of geodetic and satellite-based InSAR measurements with structural simulations

Structural Health Monitoring (SHM) of civil engineering structures is experiencing an increasing progress in the last decades. The present work focuses on the static behavior of a highway bridge due to environmental temperature effects. The goal of the present study was to test the applicability of the satellite-based synthetic aperture radar interferometry (InSAR) for deformation monitoring of a large, curved highway bridge and to compare the obtained results with alternative measurement techniques like classical geodesy surveying and with an advanced computer simulation. Such a comparison is quite rare and provides an important insight into the accuracy, efficiency and limitations of the InSAR technique in the context of SHM. Especially interesting was the question whether the InSAR technique is suitable for blind monitoring of a cluster of bridges in the region of interest. The present study shows that a pre-knowledge about each structure can be very important for a reliable interpretation of the InSAR measurement results. The second challenge of the study was to overcome several objective difficulties of combining and comparing quite different monitoring techniques that result from different sampling rates, measurement points and other specific features and sensitivities. Nevertheless, a suitable approach has been developed and implemented in the present study for the InSAR and total station measurements, providing new results and important knowledge about novel SHM techniques.

Acoustic Emission Denoising Based on Bio-inspired Antlion Optimization: A Novel Technique for Structural Health Monitoring

Acoustic Emission Denoising Based on Bio-inspired Antlion Optimization: A Novel Technique for Structural Health Monitoring

Determination of plastic deformation with variable ultrasonic excitation positions

For highly stressed mechanical components, information regarding the beginning of plastic deformation is of enormous importance. Therefore, nondestructive testing and structural health monitoring techniques are essential to examine said components and to make statements concerning the mechanical integrity. In this work, dislocation theory was extended and combined with ultrasonic wave excitation to obtain dislocations in isotropic materials. In the analytical derivation, the ultrasonic longitudinal waves were sent in different propagation directions relative to the uniaxial tensile load. The derived nonlinearity coefficients enabled the investigation as a function of these angles. The evaluated coefficient behaviour allows for statements about the position of the ultrasonic excitation relative to the tensile load to be made. Furthermore, a signature could be derived from which the degree of plasticity can be determined if a reference measurement is available. The newly derived coefficients and their behaviour make it possible to efficiently detect the beginning of plastic deformation and to predict the position of the applied load vector.

Augmented Reality-Based Real-Time Visualization for Structural Modal Identification.

In the era of aging civil infrastructure and growing concerns about rapid structural deterioration due to climate change, the demand for real-time structural health monitoring (SHM) techniques has been predominant worldwide. Traditional SHM methods face challenges, including delays in processing acquired data from large structures, time-intensive dense instrumentation, and visualization of real-time structural information. To address these issues, this paper develops a novel real-time visualization method using Augmented Reality (AR) to enhance vibration-based onsite structural inspections. The proposed approach presents a visualization system designed for real-time fieldwork, enabling detailed multi-sensor analyses within the immersive environment of AR. Leveraging the remote connectivity of the AR device, real-time communication is established with an external database and Python library through a web server, expanding the analytical capabilities of data acquisition, and data processing, such as modal identification, and the resulting visualization of SHM information. The proposed system allows live visualization of time-domain, frequency-domain, and system identification information through AR. This paper provides an overview of the proposed technology and presents the results of a lab-scale experimental model. It is concluded that the proposed approach yields accurate processing of real-time data and visualization of system identification information by highlighting its potential to enhance efficiency and safety in SHM by integrating AR technology with real-world fieldwork.

Acoustic Emission, A Non-Destructive Technique - An Overview

Abstract: With increasing number of structures approaching their design life, it became very important for the designers and operators to develop innovative Structural Health Monitoring (SHM) techniques. Structural Health Monitoring (SHM) is the process of monitoring or assessing the condition of a structure in order to gather information on its current state by tracking variables like vibration, strain, stress and other physical phenomena, responses and conditions. It seeks to assist in nondestructive evaluations aimed to detect location and extent of damage, calculate the remaining life of an asset and predict upcoming accidents. Acoustic Emission technique is a passive monitoring approach based on the detection of elastic waves in structural components generated by damages, such as the initiation and propagation of cracks, the failure of steel wires, and the failure of bonds. Its primary goal is to detect, locate, and assess the intensity of damage in a non-invasive way, both when the structure is in-service and during load tests. Its application in SHM (Structural Health Monitoring) started much later compared to other fields, such as the aerospace industry. The interest has increased because elastic waves generated by damages propagate throughout the structure; therefore, it is possible to remotely detect damages in areas that are not easily accessible to visual inspections and direct measurements

Enhancing Seismic Resilience: Evaluating Buildings with Passive Energy Dissipation Strategies

Structures are recommended to be designed and constructed with the integration of structural health monitoring techniques to ensure that they can dissipate a large amount of energy without considerable damage when subjected to earthquakes. Hysteretic (H), friction (F), viscous (V), and viscoelastic (VE) dampers were employed in this study to observe the response of buildings using the commercially available software ETABS. The effect of different dampers along with configurations on three prototype concrete buildings (3, 5, and 10-storey) was studied by performing a time history analysis. Initially, the response of the buildings was observed in terms of storey drifts, base shear, and displacement without using dampers, while gradually increasing the damping ratio from 0 to 40%. Subsequently, the response of the buildings was evaluated in terms of displacements and base shear using various types of dampers with different configurations. The analysis results demonstrated that the effectiveness of viscous and viscoelastic dampers is higher for 3 and 5-storey buildings, while friction and hysteresis dampers are more suitable for 10-storey buildings. This information enables informed decisions regarding the performance and maintenance of dampers, contributing to the overall resilience and durability of structures in seismic events.

Optimal tuning of three deep learning methods with signal processing and anomaly detection for multi-class damage detection of a large-scale bridge

Long-span bridges play a crucial role in urbanization, connecting communities across vast obstacles. Structural health monitoring techniques have been deployed on these bridges, generate large amounts of data through sensor measurements, requiring data-driven approaches like deep learning (DL) for effective analysis. However, feature extraction from time-domain vibration response signals poses challenges for DL methods. To address this, the study proposes utilizing signal processing techniques such as the multivariate empirical mode decomposition (MEMD) and Wavelet transform (WT) to extract essential features for damage classification. The incorporation of MEMD and WT aims to overcome limitations and process nonstationary and nonlinear signals effectively. Three DL techniques, long-short-term memory (LSTM), one dimensional convolutional neural network (1D-CNN), multi-layer perceptron (MLP) are tuned and applied to Structural Health Monitoring of Tianjin Yonghe Bridge (located in China) as a real-world case study, in order to detect its condition by Deep signal anomaly detection and identify types of the damage. A powerful meta-heuristic algorithm called Observer-Teacher-Learner-Based Optimization, is used to optimize both hyperparameters and architecture of each DL models. The results demonstrate that the optimally tuned DLs are successful in identifying types of damage, as well as the condition of the structure, for the Tianjin Yonghe Bridge. The average accuracy values are obtained as 98.13, 97.96, and 97.79 for 1D-CNN, LSTM, and MLP, respectively. Such optimally tuned DLs are evaluated as effective solutions for detecting damage on large-scale bridges by extracting statistical time-domain and time–frequency domain features using the WT and MEMD.