Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring

Potential of Non-Contact Dynamic Response Measurements for Predicting Small Size or Hidden Damages in Highly Damped Structures

Vibration-based structural health monitoring (SHM) is essential for evaluating structural integrity. Traditional methods using contact vibration sensors like accelerometers have limitations in accessibility, coverage, and impact on structural dynamics. Recent digital advancements offer new solutions through high-speed camera-based measurements. This study explores how camera settings (speed and resolution) influence the accuracy of dynamic response measurements for detecting small cracks in damped cantilever beams. Different beam thicknesses affect damping, altering dynamic response parameters such as frequency and amplitude, which are crucial for damage quantification. Experiments were conducted on 3D-printed Acrylonitrile Butadiene Styrene (ABS) cantilever beams with varying crack depth ratios from 0% to 60% of the beam thickness. The study utilised the Canny edge detection technique and Fast Fourier Transform to analyse vibration behaviour captured by cameras at different settings. The results show an optimal set of camera resolutions and frame rates for accurately capturing dynamic responses. Empirical models based on four image resolutions were validated against experimental data, achieving over 98% accuracy for predicting the natural frequency and around 90% for resonance amplitude. The optimal frame rate for measuring natural frequency and amplitude was found to be 2.4 times the beam's natural frequency. The findings provide a method for damage assessment by establishing a relationship between crack depth, beam thickness, and damping ratio.

Non-Destructive Testing for Documenting Properties of Structural Concrete for Reuse in New Buildings: A Review

Reuse in new buildings of structural concrete components from demolitions holds the potential for avoiding the use of raw materials to produce new components, including cement for new castings. Reuse rates are high in the circular economy; however, reusing structural components requires documentation of the properties to equate the safety of using reused and new components. Yet, there is no structured or recognized way to perform the documentation. This paper discusses a framework for the documentation requirements for structural concrete, stating the need for documenting the mechanical properties, concrete heterogeneity, and corrosion status of the reinforcement. The possibility is explored for documenting the required properties while the components are in the donor building by use of non-destructive test (NDT) methods. Such use of NDT methods is new. A comprehensive literature survey on the indirect literature, where NDT methods are used to demonstrate similar concrete properties though related to other purposes, is conducted. The overall conclusion is that the use of NDT methods has the potential to document the requested properties before reuse. The next steps towards implementation of NDT for documenting the properties of structural concrete components for reuse involve research in combined NDT methods and the development of AI systems for data interpretation.

Advancements in and Research on Coplanar Capacitive Sensing Techniques for Non-Destructive Testing and Evaluation: A State-of-the-Art Review

In contrast to conventional non-destructive testing (NDT) and non-destructive evaluation (NDE) methodologies, including radiography, ultrasound, and eddy current analysis, coplanar capacitive sensing technique emerges as a novel and promising avenue within the field. This paper endeavors to elucidate the efficacy of coplanar capacitive sensing, also referred to as capacitive imaging (CI), within the realm of NDT. Leveraging extant scholarly discourse, this review offers a comprehensive and methodical examination of the coplanar capacitive technique, encompassing its fundamental principles, factors influencing sensor efficacy, and diverse applications for defect identification across various NDT domains. Furthermore, this review deliberates on extant challenges and anticipates future trajectories for the technique. The manifold advantages inherent to coplanar capacitive sensing vis-à-vis traditional NDT methodologies not only afford its versatility in application but also underscore its potential for pioneering advancements in forthcoming applications.

A Health Monitoring Model for Circulation Water Pumps in a Nuclear Power Plant Based on Graph Neural Network Observer

The health monitoring of CRF (circulation water) pumps is essential for prognostics and management in nuclear power plants. However, the operational status of CRF pumps can vary due to environmental factors and human intervention, and the interrelationships between monitoring parameters are often complex. Consequently, the existing methods face challenges in effectively assessing the health status of CRF pumps. In this study, we propose a health monitoring model for CRF pumps utilizing a meta graph transformer (MGT) observer. Initially, the meta graph transformer, a temporal-spatial graph learning model, is employed to predict trends across the various monitoring parameters of the CRF pump. Subsequently, a fault observer is constructed to generate early warnings of potential faults. The proposed model was validated using real data from CRF pumps in a nuclear power plant. The results demonstrate that the average Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) of normal predictions were reduced to 1.2385, 0.5614, and 2.6554, respectively. These findings indicate that our model achieves higher prediction accuracy compared to the existing methods and can provide fault warnings at least one week in advance.

Graph Feature Refinement and Fusion in Transformer for Structural Damage Detection

Structural damage detection is of significance for maintaining the structural health. Currently, data-driven deep learning approaches have emerged as a highly promising research field. However, little progress has been made in studying the relationship between the global and local information of structural response data. In this paper, we have presented an innovative Convolutional Enhancement and Graph Features Fusion in Transformer (CGsformer) network for structural damage detection. The proposed CGsformer network introduces an innovative approach for hierarchical learning from global to local information to extract acceleration response signal features for structural damage representation. The key advantage of this network is the integration of a graph convolutional network in the learning process, which enables the construction of a graph structure for global features. By incorporating node learning, the graph convolutional network filters out noise in the global features, thereby facilitating the extraction to more effective local features. In the verification based on the experimental data of four-story steel frame model experiment data and IASC-ASCE benchmark structure simulated data, the CGsformer network achieved damage identification accuracies of 92.44% and 96.71%, respectively. It surpassed the existing traditional damage detection methods based on deep learning. Notably, the model demonstrates good robustness under noisy conditions.

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.

Advanced Image Stitching Method for Dual-Sensor Inspection

Efficient image stitching plays a vital role in the Non-Destructive Evaluation (NDE) of infrastructures. An essential challenge in the NDE of infrastructures is precisely visualizing defects within large structures. The existing literature predominantly relies on high-resolution close-distance images to detect surface or subsurface defects. While the automatic detection of all defect types represents a significant advancement, understanding the location and continuity of defects is imperative. It is worth noting that some defects may be too small to capture from a considerable distance. Consequently, multiple image sequences are captured and processed using image stitching techniques. Additionally, visible and infrared data fusion strategies prove essential for acquiring comprehensive information to detect defects across vast structures. Hence, there is a need for an effective image stitching method appropriate for infrared and visible images of structures and industrial assets, facilitating enhanced visualization and automated inspection for structural maintenance. This paper proposes an advanced image stitching method appropriate for dual-sensor inspections. The proposed image stitching technique employs self-supervised feature detection to enhance the quality and quantity of feature detection. Subsequently, a graph neural network is employed for robust feature matching. Ultimately, the proposed method results in image stitching that effectively eliminates perspective distortion in both infrared and visible images, a prerequisite for subsequent multi-modal fusion strategies. Our results substantially enhance the visualization capabilities for infrastructure inspection. Comparative analysis with popular state-of-the-art methods confirms the effectiveness of the proposed approach.

A Review of Wearable Optical Fiber Sensors for Rehabilitation Monitoring

As the global aging population increases, the demand for rehabilitation of elderly hand conditions has attracted increased attention in the field of wearable sensors. Owing to their distinctive anti-electromagnetic interference properties, high sensitivity, and excellent biocompatibility, optical fiber sensors exhibit substantial potential for applications in monitoring finger movements, physiological parameters, and tactile responses during rehabilitation. This review provides a brief introduction to the principles and technologies of various fiber sensors, including the Fiber Bragg Grating sensor, self-luminescent stretchable optical fiber sensor, and optic fiber Fabry-Perot sensor. In addition, specific applications are discussed within the rehabilitation field. Furthermore, challenges inherent to current optical fiber sensing technology, such as enhancing the sensitivity and flexibility of the sensors, reducing their cost, and refining system integration, are also addressed. Due to technological developments and greater efforts by researchers, it is likely that wearable optical fiber sensors will become commercially available and extensively utilized for rehabilitation.

Damage Severity Assessment of Multi-Layer Complex Structures Based on a Damage Information Extraction Method with Ladder Feature Mining

Multi-layer complex structures are widely used in large-scale engineering structures because of their diverse combinations of properties and excellent overall performance. However, multi-layer complex structures are prone to interlaminar debonding damage during use. Therefore, it is necessary to monitor debonding damage in engineering applications to determine structural integrity. In this paper, a damage information extraction method with ladder feature mining for Lamb waves is proposed. The method is able to optimize and screen effective damage information through ladder-type damage extraction. It is suitable for evaluating the severity of debonding damage in aluminum-foamed silicone rubber, a novel multi-layer complex structure. The proposed method contains ladder feature mining stages of damage information selection and damage feature fusion, realizing a multi-level damage information extraction process from coarse to fine. The results show that the accuracy of damage severity assessment by the damage information extraction method with ladder feature mining is improved by more than 5% compared to other methods. The effectiveness and accuracy of the method in assessing the damage severity of multi-layer complex structures are demonstrated, providing a new perspective and solution for damage monitoring of multi-layer complex structures.

Nanocarbon-based sensors for the structural health monitoring of smart biocomposites

Structural health monitoring (SHM) is a critical aspect of ensuring the safety and durability of smart biocomposite materials used as multifunctional materials. Smart biocomposites are composed of renewable or biodegradable materials and have emerged as eco-friendly alternatives of traditional non-biodegradable glass fiber-based composite materials. Although biocomposites exhibit fascinating properties and many desirable traits, real-time and early stage SHM is the most challenging issue to enable their long-term use. Smart biocomposites are integrated with sensors for in situ identification of the progress of damage and composite failure. The sensitivity of such smart biocomposites is a key functionality, which can be tuned by the introduction of an appropriate filler. In particular, nanocarbons hold promising potential to be incorporated in SHM applications of biocomposites. This review focused on the potential applications of nanocarbons in SHM of biocomposites. The aspects related to fabrication techniques and working mechanism of sensors are comprehensively discussed. Furthermore, their unique mechanical and electrical properties and sustainable nature ensure seamless integration into biocomposites, allowing for real-time monitoring without compromising the material's properties. These sensors offer multi-parameter sensing capabilities, such as strain, pressure, humidity, temperature, and chemical exposure, allowing a comprehensive assessment of biocomposite health. Additionally, their durability and longevity in harsh conditions, along with wireless connectivity options, provide cost-effective and sustainable SHM solutions. As research in this field advances, ongoing efforts seek to enhance the sensitivity and selectivity of these sensors, optimizing their performance for real-world applications. This review highlights the significant advances, ongoing efforts to enhance the sensitivity and selectivity, and performance optimization of nanocarbon-based sensors along with their working mechanism in the field of SHM for smart biocomposites. The key challenges and future research perspectives facing the conversion of nanocarbons to smart biocomposites are also displayed.

A Review on Concrete Structural Properties and Damage Evolution Monitoring Techniques

Concrete structures have emerged as some of the most extensively utilized materials in the construction industry due to their inherent plasticity and high-strength characteristics. However, due to the temperature fluctuations, humidity, and damage caused by human activities, challenges such as crack propagation and structural failures pose threats to the safety of people's lives and property. Meanwhile, conventional non-destructive testing methods are limited to defect detection and lack the capability to provide real-time monitoring and evaluating of concrete structural stability. Consequently, there is a growing emphasis on the development of effective techniques for monitoring the health of concrete structures, facilitating prompt repairs and mitigation of potential instabilities. This paper comprehensively presents traditional and novel methods for concrete structural properties and damage evolution monitoring, including emission techniques, electrical resistivity monitoring, electromagnetic radiation method, piezoelectric transducers, ultrasonic techniques, and the infrared thermography approach. Moreover, the fundamental principles, advantages, limitations, similarities and differences of each monitoring technique are extensively discussed, along with future research directions. Each method has its suitable monitoring scenarios, and in practical applications, several methods are often combined to achieve better monitoring results. The outcomes of this research provide valuable technical insights for future studies and advancements in the field of concrete structural health monitoring.

Characterization of Fatigue Damage in Hadfield Steel Using Acoustic Emission and Machine Learning-Based Methods

Structural health monitoring (SHM) of fatigue cracks is essential for ensuring the safe operation of engineering equipment. The acoustic emission (AE) technique is one of the SHM techniques that is capable of monitoring fatigue-crack growth (FCG) in real time. In this study, fatigue-damage evolution of Hadfield steel was characterized using acoustic emission (AE) and machine learning-based methods. The AE signals generated from the entire fatigue-load process were acquired and correlated with fatigue-damage evolution. The AE-source mechanisms were discussed based on waveform characteristics and bispectrum analysis. Moreover, multiple machine learning algorithms were used to classify fatigue sub-stages, and the results show the effectiveness of classification of fatigue sub-stages using machine learning algorithms. The novelty of this research lies in the use of machine learning algorithms for the classification of fatigue sub-stages, unlike the existing methodology, which requires prior knowledge of AE-loading history and calculation of ∆K.

Study of Damage Prediction of Carbon Fiber Tows Using Eddy Current Measurement

When manufacturing fiber-reinforced composites, it is possible to improve the quality of fiber steel fire and reduce the number of cracks in the finished product if it is possible to quickly identify the defects of the fiber tow. Therefore, in this study, we developed a method to identify the condition of carbon fiber tow using eddy current test (ECT), which is used to improve the quality of composite materials. Using the eddy current detection sensor, we checked the impedance results according to the condition of the CF tow. We found that the materials of the workbench used in the experiment greatly affected the ECT results, so it is necessary to use a material with a non-conductive and smooth surface. We evaluated the impedance results of the carbon fiber at 2 mm intervals using the ECT sensor and summarized the impedance results according to the fiber width direction, presenting the condition of the section as a constant of variation (CV). If the condition of the carbon fiber tow was unstable, the deviation of the CV per section was large. In particular, the deviation of the CV per section was more than 0.15 when the arrangement of the fibers was changed, foreign substances were formed on the surface of the fibers, and damage occurred in the direction of the fiber width of more than 4 mm, so it was easy to evaluate the quality on CF tow.

Printed Eddy Current Testing Sensors: Toward Structural Health Monitoring Applications

Reliable measurements in structural health monitoring mean for the instrumentation to be set in perfect reproducible conditions. The solution described in this study consists of printing the sensors directly on the parts to be controlled. This method solves the reproducibility issue, limits human error, and can be used in confined or hazardous environments. This work was limited to eddy current testing, but the settings and conclusions are transposable to any non-destructive testing methods (ultrasounds, etc.). The first salve of tests was run to establish the best dielectric and conductive ink combination. The Dupont ink combination gave the best performances. Then, the dispenser- and the screen-printing methods were carried out to print flat spiral coils on flexible substrates. The resulting sensors were compared to flex-printed circuit boards (PCB-flex) using copper for the electrical circuit. The conductive ink methods were revealed to be just as efficient. The last stage of this work consisted of printing sensors on solid parts. For this, 20-turn spiral coils were printed on 3 mm thick stainless-steel plates. The permanent sensors showed good sensibility in the same range as the portative ones, demonstrating the method's feasibility.

LiDAR Point Cloud Data Combined Structural Analysis Based on Strong Form Meshless Method Using Essential Boundary Condition Capturing

This study proposes a novel hybrid simulation technique for analyzing structural deformation and stress using light detection and ranging (LiDAR)-scanned point cloud data (PCD) and polynomial regression processing. The method estimates the edge and corner points of the deformed structure from the PCD. It transforms into a Dirichlet boundary condition for the numerical simulation using the particle difference method (PDM), which utilizes nodes only based on the strong formulation, and it is advantageous for handling essential boundaries and nodal rearrangement, including node generation and deletion between analysis steps. Unlike previous studies, which relied on digital images with attached targets, this research uses PCD acquired through LiDAR scanning during the loading process without any target. Essential boundary condition implementation naturally builds a boundary value problem for the PDM simulation. The developed hybrid simulation technique was validated through an elastic beam problem and a three-point bending test on a rubber beam. The results were compared with those of ANSYS analysis, showing that the technique accurately approximates the deformed edge shape leading to accurate stress calculations. The accuracy improved when using a linear strain model and increasing the number of PDM model nodes. Additionally, the error that occurred during PCD processing and edge point extraction was affected by the order of polynomial regression equation. The simulation technique offers advantages in cases where linking numerical analysis with digital images is challenging and when direct mechanical gauge measurement is difficult. In addition, it has potential applications in structural health monitoring and smart construction involving machine leading techniques.

Ultrasonic defect detection of high-density polyethylene pipe materials using FIR filtering and block-wise singular value decomposition

Condition monitoring of high-density polyethylene (HDPE) pipes used for fluid and gas transfer is important for the safety of energy conservation and the environment. Ultrasonic phased array imaging methods provide a solution to detect and assess defects in HDPE pipes. However, ultrasonic bulk waves propagating in these viscoelastic media are strongly attenuated, resulting in reduced signal amplitude. In this study, a linear-phase Finite Impulse Response (FIR) filter is used to remove unwanted frequency components from the measured ultrasonic signals to improve the signal-to-noise ratio before applying the imaging algorithm of the total focusing method (TFM). Building upon this, a block-wise singular value decomposition (SVD) technique, which can adaptively determine the singular value cutoff threshold based on each block in the whole TFM image, is used to enhance the obtained TFM image quality. The performance of the combination of FIR filtering and block-wise SVD technique is validated by experimental data of HDPE pipe materials. Results demonstrate that the proposed approach generates good images to provide the detection and characterization of side-drilled holes in HDPE pipe materials.

A Survey on the Security Challenges of Low-Power Wireless Communication Protocols for Communicating Concrete in Civil Engineerings

With the increase in low-power wireless communication solutions, the deployment of Wireless Sensor Networks is becoming usual, especially to implement Cyber-Physical Systems. These latter can be used for Structural Health Monitoring applications in critical environments. To ensure a long-term deployment, battery-free and energy-autonomous wireless sensors are designed and can be powered by ambient energy harvesting or Wireless Power Transfer. Because of the criticality of the applications and the limited resources of the nodes, the security is generally relegated to the background, which leads to vulnerabilities in the entire system. In this paper, a security analysis based on an example: the implementation of a communicating reinforced concrete using a network of battery-free nodes; is presented. First, the employed wireless communication protocols are presented in regard of their native security features, main vulnerabilities, and most usual attacks. Then, the security analysis is carried out for the targeted implementation, especially by defining the main hypothesis of the attack and its consequences. Finally, solutions to secure the data and the network are compared. From a global point-of-view, this security analysis must be initiated from the project definition and must be continued throughout the deployment to allow the use of adapted, updatable and upgradable solutions.

Ultrasonic Phased Array Imaging Approach Using Omni-Directional Velocity Correction for Quantitative Evaluation of Delamination in Composite Structure

The ultrasonic detectability of buried defects within composite materials is dependent on the anisotropy of the composite material by which the propagation property of acoustic wave in each direction is variably affected. In this study, the characteristics of acoustic waves propagating in different directions for composite materials are explored based on the full matrix capture (FMC) data using an ultrasonic phased array. The elastic constant of multidirectional carbon fiber reinforced plastic (CFRP) laminate is first derived based on the genetic algorithm. The characteristics of transmitted and reflected waves in higher angles are predicted by implementing the Christoffel equation, and the focal law used in post-processing of FMC data can be optimized accordingly. The imaging results of the total focusing method (TFM) using the improved focal law are compared with the results of the conventional TFM. The results suggest that the optimized TFM can effectively characterize the defect by reducing the background noise. Furthermore, since it is impractical to theoretically correct angle-dependent velocity for in situ inspection, a linear extrapolation method based on the experimentally measurable velocity at low angles is proposed to estimate the velocity profile at higher angles. The imaging results using the fast extrapolated velocity profile is then compared with the theoretical, and it has been demonstrated that while the difference between the images using the theoretical focal law and the linearly extrapolated one is barely visible, the later one is overwhelmingly advantageous to be realiszd for engineering practices.

An Overview of the Measurement of Permeability of Composite Reinforcements

Liquid composite molding (LCM) is a class of fast and cheap processes suitable for the fabrication of large parts with good geometrical and mechanical properties. One of the main steps in an LCM process is represented by the filling stage, during which a reinforcing fiber preform is impregnated with a low-viscosity resin. Darcy's permeability is the key property for the filling stage, not usually available and depending on several factors. Permeability is also essential in computational modeling to reduce costly trial-and-error procedures during composite manufacturing. This review aims to present the most used and recent methods for permeability measurement. Several solutions, introduced to monitor resin flow within the preform and to calculate the in-plane and out-of-plane permeability, will be presented. Finally, the new trends toward reliable methods based mainly on non-invasive and possibly integrated sensors will be described.

A Sensitive Frequency Range Method Based on Laser Ultrasounds for Micro-Crack Depth Determination

The laser ultrasonic method using the characteristics of transmitted Rayleigh waves in the frequency domain to determine micro-crack depth is proposed. A low-pass filter model based on the interaction between Rayleigh waves and surface cracks is built and shows that the stop band, called the sensitive frequency range, is sensitive to the depth of surface cracks. The sum of transmission coefficients in the sensitive frequency range is defined as an evaluated parameter to determine crack depth. Moreover, the effects of the sensitive frequency range and measured distance on the evaluated results are analyzed by the finite-element method to validate the robustness of this depth-evaluating method. The estimated results of surface cracks with depths ranging from 0.08 mm to ~0.5 mm on the FEM models and aluminum-alloy samples demonstrate that the laser ultrasounds using the characteristics of Rayleigh waves in the frequency domain do work for quantitative crack depth.

Carbon Microfiber-Doped Smart Concrete Sensors for Strain Monitoring in Reinforced Concrete Structures: An Experimental Study at Various Scales

Concrete constructions need widespread monitoring for the control of their state of integrity during their service life. In particular, after critical events such as earthquakes, this type of structure may experience the formation and development of cracks and damage. A quick and affordable assessment of structural behavior is indicated to identify conditions of danger for users and the incipient collapse of structural elements. This work presents investigations on multifunctional concretes with self-sensing capabilities to carry out static and dynamic monitoring. The materials were produced by the addition of conductive carbon microfibers to the concrete matrix. Electrical and sensing tests were carried out on samples with small-, medium-, and full-scale dimensions. The tests demonstrated the good electrical and electromechanical properties of the proposed smart concrete sensors, which appear promising for their use in civil elements or structures. In particular, tests on real-scale beams demonstrated the capability of the material to monitor the dynamic behavior of full-scale structural elements.

Active Ultrasonic Structural Health Monitoring Enabled by Piezoelectric Direct-Write Transducers and Edge Computing Process

While the active ultrasonic method is an attractive structural health monitoring (SHM) technology, many practical issues such as weight of transducers and cables, energy consumption, reliability and cost of implementation are restraining its application. To overcome these challenges, an active ultrasonic SHM technology enabled by a direct-write transducer (DWT) array and edge computing process is proposed in this work. The operation feasibility of the monitoring function is demonstrated with Lamb wave excited and detected by a linear DWT array fabricated in situ from piezoelectric P(VDF-TrFE) polymer coating on an aluminum alloy plate with a simulated defect. The DWT array features lightweight, small profile, high conformability, and implementation scalability, whilst the edge-computing circuit dedicatedly designed for the active ultrasonic SHM is able to perform signal processing at the sensor nodes before wirelessly transmitting the data to a remote host device. The successful implementation of edge-computing processes is able to greatly decrease the amount of data to be transferred by 331 times and decrease the total energy consumption for the wireless module by 224 times. The results and analyses show that the combination of the piezoelectric DWT and edge-computing process provides a promising technical solution for realizing practical wireless active ultrasonic SHM system.

Performance Evaluation of Blind Modal Identification in Large-Scale Civil Infrastructure

The monitoring and maintenance of existing civil infrastructure has recently received worldwide attention. Several structural health monitoring methods have been developed, including time-, frequency-, and time–frequency domain methods of modal identification and damage detection to estimate the structural and modal parameters of large-scale structures. However, there are several implementation challenges of these modal identification methods, depending on the size of the structures, measurement noise, number of available sensors, and their operational loads. In this paper, two modal identification methods, Second-Order Blind Identification (SOBI) and Time-Varying Filtering Empirical Mode Decomposition (TVF-EMD), are evaluated and compared for large-scale structures including a footbridge and a wind turbine blade with a wide range of dynamic characteristics. The results show that TVF-EMD results in better accuracy in modal identification compared to SOBI for both structures. However, when the number of sensors is equal to or more than the number of target modes of the structure, SOBI results in better computational efficiencies compared to TVF-EMD.

Experimental Research of the Structure Condition Using Geodetic Methods and Crackmeter

The article presents an approach to monitoring the structure’s condition with two measurement methods: the SHM-X crackmeter and the classic geodetic method of determining displacements, supplemented with additional information on the condition of the external environment obtained from thermal images. The study aimed to propose an approach combining geodetic and non-geodetic methods of assessing the condition of a structure and its effectiveness in practical application. The research facility is a public utility building of the Bydgoszcz University of Technology with a reinforced concrete structure. Objects of this type require periodic tests of their constancy. Interpreting the test results and identifying possible dangerous states that may indicate the risk of a construction failure is extremely important. The results presented in the article are an extension of the previous ones, in which several factors that could have a destructive effect on the structure were excluded. Observation of the object showed that only the reinforced construction plate is deformed. The only factor influencing the change in structure geometry is thermal changes. As part of the tests in places where cracks were noticed, the SHM-X crackmeter was used to measure the cracks’ opening. In the geodetic research, measurements of the measurement and control network displacement were carried out, in which the TDRA6000 laser station measurement technology was used. The control points were also placed in places where the width of the cracks was directly observed. The proposed approach, with the applied calculation scheme and supplementing the information with the temperature measurement with thermal images, showed the submillimeter accuracy of the determined 3D displacements of the controlled points. Additionally, the parallel application of these methods gives a complete picture of changes in the structure elements, in which signs of destruction appear under the influence of stress.

Inspection of Steel Bars Corrosion in Reinforced Concrete Structures by Nondestructive Ground Penetrating Radar

In this study, the degree of corrosion of steel reinforcement is compared to the reflected voltage of electromagnetic waves and the ASTM C876 specification. This study conducted some tests on steel bars with different degrees of corrosion by transmitting electromagnetic waves in the reinforced concrete. In the test, a corrosion potentiometer was used to analyze and compare the experimental results. The electromagnetic wave reflection signals from the image generated by the ground penetrating radar were used to capture the different media in the concrete components. The reflection coefficient method was used to analyze the calculated relative dielectric constant of the media and to obtain the reflection energy and phase changes on the medium interface. We compared and discussed the corrosion potential and the reflected voltage from the corroded steel bars in the reinforced concrete. The reflected voltage analysis of the ground penetrating radar showed that the average corrosion potential on the corroded plate on the 3rd floor was −280 m V (Area C). According to ASTM C876, the steel bars in the concrete were moderately corroded. The reflected voltage analysis of the electromagnetic waves concluded that areas A and B were moderately corroded, which is consistent with the conclusion from ASTM C876. Using the innovative method, this study has established a reference standard for the degree of corrosion of steel bars based on ASTM C876 and has calculated the quantitative state of corroded steel bars. The variations of the electromagnetic wave reflected voltage of the ground penetrating radar can mark the changes in the corrosion interface of concrete and steel reinforcement with different degrees of corrosion and different thicknesses of the protective layer.

Research on an Improved SOM Model for Damage Identification of Concrete Structures

In order to solve the problem of intelligent detection of damage of modern concrete structures under complex constraints, an improved self-organizing mapping (SOM) neural network model algorithm was proposed to construct an accurate identification model of concrete structure damage. Based on the structure and algorithm of the SOM network model, the whole process of the core construction of the concrete structure damage identification network model is summarized. Combined with the damage texture characteristics of concrete structures, through the self-developed 3D laser scanning system, an improved method based on a small number of samples to effectively improve the effectiveness of network input samples is proposed. Based on the principle of network topology map analysis and its image characteristics, a SOM model improvement method that can effectively improve the accuracy of the network identification model is studied. In addition, based on the reactive powder concrete bending fatigue loading test, the feasibility and accuracy of the improved method are verified. The results show that the improved SOM concrete structure damage identification model can effectively identify unknown neuron categories in a limited sample space, and the identification accuracy of the SOM network model is improved by 4.69%. The proposed improved SOM model method fully combines the network topology and its unique image features and can accurately identify structural damage. This research contributes to the realization of high-precision intelligent health monitoring of damage to modern concrete structures. In addition, it is of great significance for the timely detection, identification and localization of early damage to structures.

Performance Evaluation of a Long-Span Cable-Stayed Bridge Using Non-Destructive Field Loading Tests

As an important part of the transportation network, the reliability of bridge structures is of great significance to people’s personal safety, as well as to the national economy. In order to evaluate the performance of complex bridge structures, their mechanical behavior and fundamental characteristics need to be studied. Structural health monitoring (SHM) has been introduced into bridge engineering, and the structural response assessment, load effects monitoring, and reliability evaluation have been developed based on the collected SHM information. In this study, a performance evaluation method for complex bridge structures based on non-destructive field loading tests is proposed. The cable-stayed bridge in Guangxi with the largest span (Pingnan Xiangsizhou Bridge) was selected as the research object, and loading on the main girder was transferred to the piers and tower through the stay cables, whose structural responses are critical in the process of bridge operation. Therefore, the field loading tests—including deflection and strain testing of the main girder, as well as cable force tests—were also conducted for Pingnan Xiangsizhou Bridge by using non-destructive measurement techniques (multifunctional static strain test system, radar interferometric deformation measurement technology, etc.). Based on the numerically simulated results of a finite element model for Pingnan Xiangsizhou Bridge, reasonable field loading test conditions and loading arrangement were determined. Non-destructive field loading test results showed that the quality of the bridge’s construction is up to standard, due to a good agreement between the calculated and measured frequencies of the bridge. In addition, the calibration coefficients of displacement and strain were less than 1, indicating that Pingnan Xiangsizhou Bridge has satisfactory stiffness and strength.

The multiplex PCR assay detection of Staphylococcus sciuri antibiotic resistance, mecA gene, and the inhibitory effect of root exudate of Nigella sativa (black seeds) treated with magnetized water

121 bacterial samples isolated from wounds from both sexes and all age groups were collected from Salahadin General Hospital, Salahadin provenance, Iraq. Only 8 Staphylococcus sciuri (S. sciuri) isolates were identified. The bacterial isolation showed the highest sensitivity to Amoxicillin/Clavulanic acid, Cefotaxime, Methicillin, Streptomycin, and Vancomycin and resistance to all other antibiotics. The root exudates of black seeds were used for 10 and 20 days for both treatments with and without magnetized water, and the exudates were superior when using magnetized water for 20 days. Antibiotic resistance and the mecA gene were investigated, and a multiplex PCR assay was used to detect the mecA gene in S. sciuri. Optimized conditions were used to amplify mecA fragments that encode methicillin resistance.

A Novel Life Prediction Model Based on Monitoring Electrical Properties of Self-Sensing Cement-Based Materials

Assessing the damage level in concrete infrastructures over time is a critical issue to plan their timely maintenance with proper actions. Self-sensing concretes offer new opportunities for damage assessment by monitoring their electrical properties and relating their variations to damage levels. In this research, fatigue tests were conducted to study the response of a self-sensing concrete under high-cycle dynamic loading. The concept of G-value was defined as the slope of the voltage response baseline of the self-sensing concrete over time that reflects the damage created under the fatigue-loading test. Based on this definition, log (G)–log (N) curves were obtained using a linear regression approach, with N representing the number of cycles during the fatigue tests. While traditional fatigue curves S-log (N) are used to estimate the remaining life under fatigue loading, log (G)–log (N) diagrams can be used to determine the damage level based on the voltage response of the self-sensing concrete as a function of the loading history. This finding can be useful for the estimation of the lifetime and remaining life of self-sensing concrete structures and infrastructure, eventually helping to optimize the related maintenance operations.

An Acoustic Emission Method for Assessing the Degree of Degradation of Mechanical Properties and Residual Life of Metal Structures under Complex Dynamic Deformation Stresses

An acoustic emission method for assessing the degree of degradation of mechanical properties under conditions of complex dynamic deformation stresses is proposed. It has been shown that changing the operating conditions of metal structures, peak loads, external collisions, and thermally changing loads, which cannot be taken into account, leads to uncertainty and unpredictable structural changes in the material. This in turn makes it difficult to identify the state of the structure material to ensure trouble-free operation of the equipment. Changes in the mechanical properties under difficult loading conditions are identified by polynomial approximation of the results of AE measurements and the construction of boundary curves separating the operability region from the fracture region. This is achieved by approximating the experimental dependences of the acoustic parameters for various types of loading. This approach significantly expands the capabilities of the technical means of identification systems of metal structures, and in particular, allows the current state of the equipment and its suitability for further operation to be assessed without stopping the equipment in real time. It is of interest not only to fix the damage, but also to diagnose the processes of reducing the mechanical properties during the operation of the equipment.