Structural Health Monitoring Technology Research Articles

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.

Autonomous Industrial IoT for Civil Engineering Structural Health Monitoring

Autonomous Industrial IoT for Civil Engineering Structural Health Monitoring

Hole-edge crack monitoring in attachment lug with large bolt hole based on guided wave and circular piezoelectric sensor array

The crack damage monitoring of aircraft structures is very significant for ensuring aircraft safety, reducing maintenance costs and extending service life. Due to the extreme service environment, the attachment lug is prone to initiate crack damage at the hole edge, which leads to crack propagation and fracture failure. Structural health monitoring technology based on piezoelectric guided wave has been widely studied, promoting the development of crack monitoring. However, at present, research on hole-edge crack damage monitoring of attachment lugs still needs to be further carried out. It is difficult to monitor small cracks at the initial stage of crack propagation, and the accuracy of crack monitoring needs to be improved. By focusing on the accuracy of the crack monitoring in the attachment lug, a crack damage monitoring method based on the circular piezoelectric sensor array is proposed in this paper. Combined with damage alarming and localization imaging, this method comprehensively evaluates the hole-edge crack damage monitoring situation and improves the monitoring effect. The method is verified by an experiment in attachment lug, and this verification includes small crack monitoring and crack propagation monitoring. The experimental results demonstrate that this method can achieve correct damage alarming results, and the maximum localization error of crack damage is only 3.02 mm, which provides a research idea for the accurate monitoring of crack damage at the hole edge.

Effect of bonding adhesive thickness on temperature characteristics and strain characteristics of SAW strain sensors

Accurate measurement of physical parameters based on sensing technology is an important basis for equipment structural health monitoring technology. In harsh environments, strain measurement techniques based on SAW sensors have attracted much attention. The bonding adhesive is a key step in the strain measurement process, and its effect on the accuracy of the measurement results cannot be ignored. In this paper, the one-port resonant SAW strain sensor is prepared using LiNbO3 as a piezoelectric substrate, Pt as an electrode and SiO2 as a protective layer. The strain characteristics and temperature characteristics of SAW strain sensors at the thickness of the bonding adhesive (Model: DOUBLE-BOND CHEMICAL®DB5016) are explored. The frequency–temperature curves of the SAW sensors show a quasi-linear decreasing trend with increasing temperature, and the temperature–frequency characteristics are similar for different bonding adhesive thicknesses. The strain sensitivity increases and then decreases with increasing temperature, and reaches a maximum at 150 °C. An increase in the thickness of the bonding adhesive leads to a decrease in the temperature linearity and an increase in the temperature sensitivity of the SAW strain sensor, which is maximized at a bonding adhesive thickness of 0.35 mm. The increase in the thickness of the bonding adhesive leads to a decrease in the strain linearity and strain sensitivity of the SAW sensor. The relationship between the strain transfer efficiency of the SAW sensor and the thickness of the bonding layer, the shear modulus of the bonding layer, the length of the sensor, the thickness of the sensor substrate and the shear modulus of the sensor is established through theoretical derivation. A theoretical foundation is provided for the accurate measurement of strain based on SAW technology.

Spatial-Vibration-Modulation-Assisted Blade Damage Localization for Industrial Quadrotor UAVs

The rapid development of unmanned aerial vehicle (UAV) market has triggered the demand for UAV structural health monitoring technology, and blade damage has received extensive attention as a common mechanical fault. In this study, the spatial vibration modulation method (SVMM) is proposed to assist in blade damage localization for an industrial quadrotor UAV. The key to SVMM is that, by arranging silicone-made ring-shaped vibrators with randomly selected structural parameters on the beams of the UAV, the frequency transfer functions of these beams with low cross-correlation are achieved. With SVMM, high-accuracy blade damage localization capability can be realized under single sensor strategies on the symmetrical UAV. The study considers the situations of one and two damage locations, and the recognition accuracies are both 97%, while those without SVMM are 90% and 86%, respectively. The results show that SVMM can effectively enhance the localization capability for blade damage. Due to the customized frequency transfer functions, the technique proposed in this study shows potential in the fields of UAVs or other mechanical equipment for structural health monitoring and damage assessment.

A Structural Health Monitoring System for Bond Line Flaws Detection on a Full-Scale Wingbox Section Demonstrator

In recent years, there has been a significant increase in the use of structural health monitoring (SHM) technologies as systems for monitoring the integrity of aircraft’s structures. The use of compact and embeddable sensor networks, like the ones based on fibre optics (FO), is particularly attractive from the perspective of releasing an integrated structural system with intrinsic sensing capacity. Usually, an SHM system architecture is completed by a dedicated algorithm that processes the data gathered from the sensors to elaborate on the level of damage currently suffered by the structure, with the further possibility of providing information to the relevant specialists involved with its supervision. One of the main SHM applications that is attracting major interest is related to the inspection and detection of anomalies in bonded joints, which is extremely relevant in many composite realizations. Aeronautical regulations allow the use of bonded joints on an aircraft’s primary structure but require the implementation of a means to ensure their absolute safety, such as the introduction of further mechanical links aimed at stopping the propagation of a possible flaw or the availability of Non-Destructive Inspection (NDI) systems to prove the absence of relevant damaged areas. Generally, the main typical defects occurring during the manufacturing of bonded joints include adhesive curing, kissing bonds, poor porosity, and poor surface preparation. The current NDI systems more widely used and available to detect defects are still inaccurate due to the lack of standard procedures for the creation of representative defects in a controlled manner, which would allow for the development of reliable methodologies and tools able to ensure the safety of a bonded joint, as required by safety regulations. This paper shows the results relative to the implementation of an SHM system developed by the Italian Aerospace Research Centre (CIRA) aimed at monitoring the bonding lines between spar caps and panels of a typical composite wingbox section and detecting faults in location and length. The work was performed during typical ground static tests by using a fibre optical sensing network embedded within relevant adhesive paste layers during the manufacturing process of the structure. In the reported investigation, the SHM system assumed the function of an NDI system tool. The results show that the developed SHM system has good reliability for the detection of both the position and size of damage areas that were artificially inserted within the test article during the bonding phase, showing its potential as a candidate to be used as a tool to verify the conditions of a bonded joint, as required by aviation authorities’ regulations.

A whole service time SHM damage quantification model hierarchical evolution mechanism

Accurate damage quantification for an individual aircraft by using structural health monitoring (SHM) technology poses a persistent challenge in practical engineering. Existing damage quantification models typically rely on prior data acquired from ground design and tests. However, an individual aircraft is subject to diverse uncertainties throughout its whole service time, such as time-varying environmental and operational conditions, different flight missions, and different damage morphologies. Consequently, employing a prior trained model for damage quantification inevitably introduces errors, thereby limiting the engineering applicability of SHM. To improve the damage quantification accuracy for individual aircraft under the influence of uncertainty, a new whole lifetime data-based damage quantification model hierarchical evolution mechanism is proposed in this paper. Multi-source data from the design, service, and maintenance stages of individual aircraft are adopted to continuously evolve the probabilistic damage diagnosis model, enabling it to characterize the specific data distribution and track the damage propagation. The proposed method is validated through fatigue tests of a landing gear beam structure, which is an important aircraft load-carrying component, with guided wave monitoring based hidden Markov model quantifying its crack propagation. The validation result demonstrates the effectiveness of the proposed method in ensuring the long-term reliability of the model and achieving more accurate damage quantification for an individual aircraft.

Why Bridges Need our Attention

Kistler Group’s weigh-in-motion and structural health monitoring technology can help protect sensitive infrastructure, such as bridges, extending their service life and, in a worst-case scenario, preventing their collapse

Self-sensing concrete masonry structures with intrinsic abilities of strain monitoring and damage detection

Self-sensing cementitious materials are emerging technologies for Structural Health Monitoring (SHM) of civil structures. Their application for SHM of concrete masonry elements was not investigated in previous studies. The effects of triaxial strain/stress states of masonry joints and units on their self-sensing response are still unknown. Therefore, this work aimed to investigate mechanical and self-sensing properties of masonry elements produced with self-sensing mortar joints, solid concrete bricks and/or hollow concrete blocks fabricated with carbon black nanoparticles. The intrinsic abilities of strain monitoring and damage detection were evaluated in piezoresistive tests of masonry prisms, based on variations in the type of unit, mortar bedding approach, bonding arrangement, relative strength of mortar and units, joint thickness and location of self-sensing regions. These variations did not affect significantly the gauge factor of cementitious sensors embedded into the prism units. In contrast, the gauge factor of self-sensing horizontal or vertical joints was statistically affected by most of these variations. Stages of balance and abrupt increases in fractional changes in electrical resistivity (FCRs) during the plastic regime of the prisms provided an anticipated warming associated with the propagation of vertical and diagonal cracks associated with tensile splitting and crushing failure of units. The strong non-linearity on the electrical output of self-sensing joints provided an evidence of the mortar pore collapse. In conclusion, the self-sensing units and mortar joints were found to be promising alternatives for strain monitoring and damage detection in smart concrete masonry structures.

Advancing Bridge Structural Health Monitoring: Insights into Knowledge-Driven and Data-Driven Approaches

Structural health monitoring (SHM) is increasingly being used in the field of bridge engineering, and the technology for monitoring bridges has undergone a radical change. It has evolved from the initial local monitoring and assessment, which relied mainly on manual work, to the current all-round and full-time intelligent assessment provided by intelligent monitoring systems. This paper reviews the development of structural health monitoring technology in the civil engineering field and examines two current AI methods in bridge structural health monitoring, namely knowledge-driven and data-driven approaches. The advantages and disadvantages of these two AI methods are analyzed, and future development trends are also discussed. The overview results reveal that knowledge-driven methods have the advantages of interpretability and stability. However, their current application is limited, and significant technical bottlenecks remain. On the other hand, the data-driven approach demonstrates higher efficiency and accuracy. Nevertheless, it is characterized by instability and insecurity due to its "black-box" nature, which hinders its ability to explain the internal operation mechanism. Given these findings, the hybrid knowledge-data-driven approach emerges as a potential solution. This approach can effectively integrate the advantages of both knowledge-driven and data-driven methods while avoiding their respective disadvantages. Consequently, the hybrid approach proves to be more stable, safe, and efficient in practical applications.

A study on fiber Bragg grating (FBG) strain sensor based on symmetrical lever structure.

Thin-walled structure deformation detection technology is one of the key technologies for structural health monitoring and fault diagnosis of high-end mechanical equipment. Aiming at the problem that the existing Fiber Bragg Grating (FBG) strain sensor is difficult to effectively measure the deformation of thin-walled structures, an FBG strain sensor based on a symmetrical lever structure is proposed. The sensitivity of the sensor is analyzed theoretically, and the sensor is simulated and analyzed by the SOLIDWORKS and Abaqus software, and then, the structural parameters are optimized. According to the simulation results, the sensor is developed and a strain testing system is set up to test the performance of the sensor. The results indicate that the sensor sensitivity is ∼6.6 pm/με, which is about 5.5 times that of bare FBG. Its strain measurement sensitivity and stability are much higher than those of bare FBG, thus meeting the strain detection requirements of thin-walled structural parts during deformation. Moreover, the linearity is more than 99%, which enables the accurate measurement of tiny strains caused by the deformation and reconstruction of the thin-walled structure by the strain sensor. The results of this study provide a reference for the development of like sensors and a further improvement in the sensitivity of the optic-fiber strain sensor.

Bolt looseness identification using a knocking acoustic signal

As a common failure form of the bolted joint, bolt looseness can lead to relative slip between the connected surfaces. Under the action of shear external forces, the bolt experiences fatigue failure and fracture, which can affect the service performance and connection strength of the equipment, and even cause major safety accidents. At present, good results have been achieved in detecting the tightness of bolt connections through Structural Health Monitoring (SHM) technology. However, most of them are still carried out under laboratory conditions and cannot be applied in engineering. Therefore, this article proposes looseness recognition of bolts by knocking acoustic signals. This method converts the acoustic time series signal into a Mel-Frequency Cepstral Coefficient (MFCC) frequency spectrum and then it recognizes the bolt looseness status through the Mobilenetv3 deep neural network. The results show this method can identify loose bolts. Meanwhile, it has a strong engineering application background.

Interpretation of magnetostrictive sensor data in Mode 1 and Mode 2 of carbon fibre reinforced polymers

Structural health monitoring (SHM) of carbon fibre reinforced polymers (CFRP) provides a valuable way to assess the condition of these materials, which possess desirable traits, but exhibit little elastic failure. Further complications arise due to their multi component nature, which means they can fail in a variety of ways. High-fidelity magnetostrictive SHM technology exhibits higher cost when utilising top-end materials that contain rare-earth elements. At the expense of fidelity, cost can be reduced by using Fe-based ribbons and cheap transducers. This reduction in accuracy can be overcome by coupling a detection array with the development of specific detection schemes for each failure type to reduce signal complexity. In this work, we investigate the failure of CFRP coupons in Mode 1 and 2 failure, using FeSiB ribbons as the actuator either bonded between the CFRP ply or co-cured on the surface. During the measurement, a set of 4 pancake coils are used to measure the change in inductance, as the CFRP fails, thus demonstrating that this magnetostrictive-coil set-up can be used to measure cracks forming and propagating within the CFRP, hence it is a promising SHM system. For the first time, it is demonstrated that there is a correlation between the induction signal and the elastic energy stored within the CFRP coupon.

Crack monitoring on concrete structures with distributed fiber optic sensors—Toward automated data evaluation and assessment

AbstractThe ability to measure strains quasi‐continuously with high spatial resolution makes distributed fiber optic sensing a promising technology for structural health monitoring as it allows to locate and measure damages in concrete structures, such as cracks. Depending on whether the distributed fiber optic sensor (DFOS) is embedded into the concrete matrix or bonded to the reinforcement, different approaches for crack width calculation exist. The high spatial resolution of DFOS quickly leads to a large amount of data, especially with time continuous monitoring. Scalable, automated analysis approaches are required to handle such big data and to derive a gain in knowledge from the measurements. Thus, in a first step, the Python framework fosanalysis is presented and made available to other researchers or monitoring specialists as free software. The most important input parameters for crack width calculation are discussed for concrete strain and steel strain DFOS. Accurate crack monitoring for a 4 m long reinforced concrete beam is demonstrated by using fosanalysis. The calculated crack widths are in good agreement with digital image correlation measurements.

Characterization of a polyvinylidene fluoride (PVDF) nanofiber film coated with carbon nanotubes (CNTs) on a polyimide substrate by integrated experimental measurements and computational analysis

In this work, a strain sensor was fabricated using conductive electrospun polyvinylidene fluoride (PVDF) nanofiber film coated with carbon nanotubes (CNTs) on a polyimide (PI) substrate. An investigation of the sensor’s properties during stretching and relaxation under varying experimental conditions was undertaken. The morphologies of the sensing films were examined to gain an understanding of their structure. The experimental design was complemented by simulation to optimize the shape and size of the substrate to ensure uniform and consistent straining under loading conditions. The insight derived from the computational analysis played an instrumental role in selecting the appropriate substrate design and determining the ideal location for placing the PVDF/CNTs sensing film. The difference in the deformation value of the substrate with the selected design during the computational and experimental analysis was 17.5%. An integrated approach, combining both experimental and computational methodologies, was used to significantly enhance the study’s value and facilitate the development of an efficient and effective strain sensor. The sensor displayed a gauge factor (GF) of 1.83 and 1.19 under the incremental loads of 0.98 and 1.96 N, respectively. In conclusion, this study contributes to an improved understanding of the strain sensor’s characteristics and potential applications in structural health monitoring technologies.

Deep Learning for Structural Health Monitoring: Data, Algorithms, Applications, Challenges, and Trends

Environmental effects may lead to cracking, stiffness loss, brace damage, and other damages in bridges, frame structures, buildings, etc. Structural Health Monitoring (SHM) technology could prevent catastrophic events by detecting damage early. In recent years, Deep Learning (DL) has developed rapidly and has been applied to SHM to detect, localize, and evaluate diverse damages through efficient feature extraction. This paper analyzes 337 articles through a systematic literature review to investigate the application of DL for SHM in the operation and maintenance phase of facilities from three perspectives: data, DL algorithms, and applications. Firstly, the data types in SHM and the corresponding collection methods are summarized and analyzed. The most common data types are vibration signals and images, accounting for 80% of the literature studied. Secondly, the popular DL algorithm types and application areas are reviewed, of which CNN accounts for 60%. Then, this article carefully analyzes the specific functions of DL application for SHM based on the facility’s characteristics. The most scrutinized study focused on cracks, accounting for 30 percent of research papers. Finally, challenges and trends in applying DL for SHM are discussed. Among the trends, the Structural Health Monitoring Digital Twin (SHMDT) model framework is suggested in response to the trend of strong coupling between SHM technology and Digital Twin (DT), which can advance the digitalization, visualization, and intelligent management of SHM.

Anti-interference damage localization in composite overwrapped pressure vessels using machine learning and ultrasonic guided waves

This paper describes a structural health monitoring (SHM) technique for composite overwrapped pressure vessels (COPVs), which utilizes machine learning (ML) algorithms to achieve anti-interference damage localization based on ultrasonic guided waves. Firstly, this study developed a multi-channel guided wave signal automatic acquisition system and established a comprehensive dataset comprising multi-region damage/health signals from composite pressure vessels. Subsequently, based on the interaction between guided wave features and damage, a feature extraction-ML based anti-interference damage localization scheme was proposed. This scheme achieved robust damage localization through steps such as data cleaning, feature extraction, feature selection, and ML-based damage localization. Furthermore, this study introduced the guided wave-temporal convolutional network (GW-TCN) model based on deep learning method. The GW-TCN model integrates the temporal correlation and the multi-channel physical properties of guided waves, while leveraging the advantages of deep learning's adaptive feature extraction. It effectively mitigates the vibration and sensor failure interferences that mimic real operational conditions, enabling precise damage localization. Finally, through validation on multiple data samples, it was demonstrated that the model exhibits good generalization ability. The research findings will contribute to the advancement of composite pressure vessel SHM technology, paving the way for improved safety and reliability in industrial applications.