Structural Health Monitoring Methods Research Articles

Intelligent health indicator construction for prognostics of composite structures utilizing a semi-supervised deep neural network and SHM data

A health indicator (HI) is a valuable index demonstrating the health level of an engineering system or structure, which is a direct intermediate connection between raw signals collected by structural health monitoring (SHM) methods and prognostic models for remaining useful life estimation. An appropriate HI should conform to prognostic criteria, i.e., monotonicity, trendability, and prognosability, that are commonly utilized to measure the HI’s quality. However, constructing such a HI is challenging, particularly for composite structures due to their vulnerability to complex damage scenarios. Data-driven models and deep learning are powerful mathematical tools that can be employed to achieve this purpose. Yet the availability of a large dataset with labels plays a crucial role in these fields, and the data collected by SHM methods can only be labeled after the structure fails. In this respect, semi-supervised learning can incorporate unlabeled data monitored from structures that have not yet failed. In the present work, a semi-supervised deep neural network is proposed to construct HI by SHM data fusion. For the first time, the prognostic criteria are used as targets of the network rather than employing them only as a measurement tool of HI’s quality. In this regard, the acoustic emission method was used to monitor composite panels during fatigue loading, and extracted features were used to construct an intelligent HI. Finally, the proposed roadmap is evaluated by the holdout method, which shows a 77.3% improvement in the HI’s quality, and the leave-one-out cross-validation method, which indicates the generalized model has at least an 81.77% score on the prognostic criteria. This study demonstrates that even when the true HI labels are unknown but the qualified HI pattern (according to the prognostic criteria) can be recognized, a model can still be built that provides HIs aligning with the desired degradation behavior.

STRUCTURAL HEALTH MONITORING METHODS OF BRIDGESTRUCTURES: AREVIEWOFEXISTING METHODS WITH RECOMMENDATIONS

Bridges are a crucial part of the transport infrastructure and represent an important asset of a society. One of the crucial parts of that asset goes to monitoring and maintenance since the bridge damage could yield to severe consequences in human lives and material damage. Periodic inspection keeps these structures operational and increases bridge integrity. Different methods have been proposed by different researchers for bridge damage detection based on different theories and methodologies. Various review papers on bridge damage detection are published in the literature in the last few decades. In this paper, a concise review of literature on damage detection in bridge structures based on various methodologies is carried out. The purpose of this paper is to critically examine various methods that have been proposed for bridge damage identification along with some recommendations to improve these methods. The gaps, limitations, and superiority of the existing methods have been identified, and future applications which are important for bridge inspection are covered.

Efficacy and Damage Diagnosis of Reinforced Concrete Columns and Joints Strengthened with FRP Ropes Using Piezoelectric Transducers.

Recent research has indicated that the implantation of a network of piezoelectric transducer patches in element regions of potential damage development, such as the beam-column joint (BCJ) area, substantially increases the efficacy and accuracy of the structural health monitoring (SHM) methods to identify damage level, providing a reliable diagnosis. The use of piezoelectric lead zirconate titanate (PZT) transducers for the examination of the efficiency of an innovative strengthening technique of reinforced concrete (RC) columns and BCJs is presented and commented on. Two real-scale RC BCJ subassemblages were constructed for this investigation. The columns and the joint panel of the second subassemblage were externally strengthened with carbon fiber-reinforced polymer (C-FRP) ropes. To examine the efficiency of this strengthening technique we used the following transducers: (a) PZT sensors on the ropes and the concrete; (b) tSring linear variable displacement transducers (SLVDTs), diagonally installed on the BCJ, to measure the shear deformations of the BCJ panel; (c) Strain gauges on the internal steel bars. From the experimental results, it became apparent that the PZT transducers successfully diagnosed the loading step at which the primary damage occurred in the first BCJ subassemblage and the damage state of the strengthened BCJ during the loading procedure. Further, data acquired from the diagonal SLVDTs and the strain gauges provided insight into the damage state of the two tested specimens at each step of the loading procedure and confirmed the diagnosis provided by the PZT transducers. Furthermore, data acquired by the PZT transducers, SLVDTs and strain gauges proved the effectiveness of the applied strengthening technique with C-FRP ropes externally mounted on the column and the conjunction area of the examined BCJ subassemblages.

A Review of the Synthesis, Performance, and Mechanism of Strontium Based Mechanoluminescence Material

Various types of sensors are used for structural health monitoring systems, some of these systems are strain sensors, vibration sensors, etc. which are based on the resistance of material whose change can be recorded via a microcontroller and plotted to understand the real-time behavior of a structure and the effects of various loads acting on it. But these sensors require expensive equipment and skilled personnel, external power, as well as the acquired data needs to be processed such that some sense can be made out of it. Thus, a new method of Structural Health Monitoring using Mechanoluminescence materials that don't require external power seems quite promising, but being in the initial stage of research and development it comes with its own set of challenges. This study tries to understand the synthesis methods available to synthesize Strontium-based Mechanoluminescence sensors, their working, the embedment processes, and their performance when subjected to loading.

Novelty detection approach for the monitoring of structural vibrations using vision-based mean frequency maps

Damage detection is an important part of modern-day engineering. Early damage detection is facilitated by Structural Health Monitoring methods, which may be employed using numerous modalities, such as vibration, guided waves, thermography, or computer vision. These methods produce information that can then be interpreted to detect and localize damage or quantify its extent. Novelty Detection (ND) is a data interpretation approach that enables damage detection without prior knowledge of damage-related influences on gathered data. ND can be conveniently performed using computer vision methods, which allow continuous, non-contact monitoring of large structures with the possibility of relying on such quantifiers as deflection, vibration, or strains.In this work, we present an ND method for monitoring the structural changes in rotary machinery equipment using vision-based data. The proposed technique detects changes in the characteristic frequencies of vibrations due to damage. Using the optical flow calculated for the videos acquired using a high-speed camera, the maps of mean frequency can be estimated and used for evaluating the differences between the reference data set and the data obtained during the monitoring. The Optimal Baseline Selection is used to compensate for the varying operational conditions under which the structure is monitored. The approach was tested on the air compressor working under variable pressure, and the damage introduced to the structure was successfully detected.

Acoustic emission monitoring of wood materials and timber structures: A critical review

• Different NDE methods applied to wood and timber structures were explained. • Concept of AE and its experimentation was introduced. • Impact of wood characteristics on AE wave (velocity, attenuation) was discussed. • Applications of AE in monitoring of wood materials were critically reviewed. • Opportunities, challenges, and outline of future research were discussed. The growing interest in timber construction and using more wood for civil engineering applications has given highlighted importance of developing non-destructive evaluation (NDE) methods for structural health monitoring and quality control of wooden construction. This study, critically reviews the acoustic emission (AE) method and its applications in the wood and timber industry. Various other NDE methods for wood monitoring such as infrared spectroscopy, stress wave, guided wave propagation, X-ray computed tomography and thermography are also included. The concept and experimentation of AE are explained, and the impact of wood properties on AE signal velocity and energy attenuation is discussed. The state-of-the-art AE monitoring of wood and timber structures is organized into six applications: (1) wood machining monitoring; (2) wood drying; (3) wood fracture; (4) timber structural health monitoring; (5) termite infestation monitoring; and (6) quality control. For each application, the opportunities that the AE method offers for in-situ monitoring or smart assessment of wood-based materials are discussed, and the challenges and direction for future research are critically outlined. Overall, compared with structural health monitoring of other materials, less attention has been paid to data-driven methods and machine learning applied to AE monitoring of wood and timber. In addition, most studies have focused on extracting simple time-domain features, whereas there is a gap in using sophisticated signal processing and feature engineering techniques. Future research should explore the sensor fusion for monitoring full-scale timber buildings and structures and focus on applying AE to large-size structures containing defects. Moreover, the effectiveness of AE methods used for wood composites and mass timber structures should be further studied.

Smartphone based structural health monitoring using deep neural networks

In order to create a low-cost and easy-to-implement structural health monitoring method, this paper uses smartphones to collect data on the movement of a building to determine whether it has suffered any damage. Smartphones have easy handling, sensors embedded, and data processing properties. The smartphone-based structural health monitoring system have many advantage over the classical methods. Recurrent fuzzy neural network of long- and short-term memory is applied to determine the damage of the structure. Comparisons with classical neural networks and fuzzy systems are given to show the proposed deep neural networks have better performances. Finally, several experiments are applied to evaluate the proposed methods for the determination of structural damage. • A smartphone-based building structural health monitoring system is developed. • This system uses fuzzy long and short term memory (LSTM) to deal the data obtained from the smartphone. • This system is implemented in a prototype of structural health monitoring.

Analysis of Force Sensing Accuracy by Using SHM Methods on Conventionally Manufactured and Additively Manufactured Small Polymer Parts.

Fabricating complex parts using additive manufacturing is becoming more popular in diverse engineering sectors. Structural Health Monitoring (SHM) methods can be implemented to reduce inspection costs and ensure structural integrity and safety in these parts. In this study, the Surface Response to Excitation (SuRE) method was used to investigate the wave propagation characteristics and load sensing capability in conventionally and additively manufactured ABS parts. For the first set of the test specimens, one conventionally manufactured and three additively manufactured rectangular bar-shaped specimens were prepared. Moreover, four additional parts were also additively manufactured with 30% and 60% infill ratios and 1 mm and 2 mm top surface thicknesses. The external geometry of all parts was the same. Ultrasonic surface waves were generated using three different signals via a piezoelectric actuator bonded to one end of the part. At the other end of each part, a piezoelectric disk was bonded to monitor the response to excitation. It was found that hollow sections inside the 3D printed part slowed down the wave travel. The Continuous Wavelet Transform (CWT) and Short-Time Fourier Transform (STFT) were implemented for converting the recorded sensory data into time–frequency images. These image datasets were fed into a convolutional neural network for the estimation of the compressive loading when the load was applied at the center of specimens at five different levels (0 N, 50 N, 100 N, 150 N, and 200 N). The results showed that the classification accuracy was improved when the CWT scalograms were used.

Mechanic-Electric-Thermal Directly Coupling Simulation Method of Lamb Wave under Temperature Effect.

Lamb Wave (LW)-based structural health monitoring method is promising, but its main obstacle is damage assessment in varying environments. LW simulation based on piezoelectric transducers (referred to as PZTs) is an efficient and low-cost method. This paper proposes a multiphysics simulation method of LW propagation with the PZTs under temperature effect. The effect of temperature on LW propagation is considered from two aspects. On the one hand, temperature affects the material parameters of the structure, the adhesive layers and the PZTs. On the other hand, it is considered that the thermal stress caused by the inconsistency of thermal expansion coefficients among the structure, the adhesive layers, and the PZTs affect the piezoelectric constant of the PZTs. Based on the COMSOL Multiphysics, the mechanic–electric–thermal directly coupling simulation model under temperature effect is established. The simulation model consists of two steps. In the first step, the thermal-mechanic coupling is carried out to calculate the thermal stress, and the thermal stress effect is introduced into the piezoelectric constant model. In the second step, mechanic–electric coupling is carried out to simulate LW propagation, which considers the piezoelectric effect of the PZTs for the LW excitation and reception. The simulation results at −20 °C to 60 °C are obtained and compared to the experiment. The results show that the A0 and S0 mode of simulation signals match well with the experimental measurements. Additionally, the effect of temperature on LW propagation is consistent between simulation and experiment; that is, the amplitude increases, and the phase velocity decreases with the increment of temperature.

Preliminary results on acoustic emission and vibration-based monitoring of locally corroded reinforced concrete beams

The current tendency to increase the lifetime of reinforced concrete (RC) structures creates concerns about their serviceability and safety. Reinforcement corrosion is the most common deterioration process in RC, which can severely decrease the structural capacity. Corrosion causes a reduction of the rebar’s cross section, cracking and spalling of the concrete cover due to the expansion of the corrosion product and a decrease of the bond strength at the reinforcement-concrete interface. Assessing the structural capacity of corroded existing structures remains an important issue to maintain good safety and to achieve more targeted repairs. On-site quantification of structural reliability requires advanced non-destructive techniques to evaluate the damage and efficient methods for structural health monitoring. This paper presents the initial results of an experimental campaign on corroding RC beams. Beams with a length of 3 m are subjected to local accelerated corrosion while being monitored with the acoustic emission (AE) technique and dynamic vibration testing (DVT). AE investigates the damage progress and underlying causes of the cracks. When cracks are formed, the stress redistribution in the material causes elastic waves, which can be recorded by piezoelectric transducers placed on the surface of the beam. Examination of the waveforms allows for a localization and characterization of the damage source. Additionally, DVT observes the change in modal parameters of the beams, measured by accelerometers. These changes can be related to a loss of stiffness in the structure, resulting in a perception of the decrease in structural capacity. The combination of local (AE) and global (DVT) monitoring techniques improves the damage inspection as both the damage process and the loss of stiffness are observed. The results of AE provides information when the cracks have not yet reached the surface, while DVT is most relevant when severe damage already occurred. After corrosion, a four-point bending test is performed to evaluate the remaining structural capacity.

Structural health monitoring of railway bridges using innovative sensing technologies and machine learning algorithms: a concise review

Abstract Railway bridges are a vital element of railway infrastructures, and their safety can directly affect the regional economy and commuter transportation. However, railway bridges are often subjected to severe loading and working conditions, caused by rising traffic levels and heavier vehicles, and increases in train running speeds makes the bridges extremely susceptible to degradation and failure. One of the promising tools for evaluating the overall safety and reliability of railway bridges is the bridge structural health monitoring (SHM) system, which not only monitors the structural conditions of bridges and maintains the safety of train operations, but also helps to expand the lifespan of bridges by enhancing their durability and reliability. While a multitude of review papers on SHM and vibration-based structural damage detection methods have been published in the past two decades, there is a paucity of literature that provides a review or overview on the SHM of railway bridges. Some of the review papers have become obsolete and do not reflect the state-of-the-art research. Therefore, the main goal of this article is to summarize state-of-the-art SHM techniques and methods that have been widely used and popular in recent years. First, two state-of-the-art SHM sensing technologies (i.e. fiber optic sensing (FOS) technology and computer vision-based (CV) technology) are reviewed, including the working principles of various sensors and their practical applications for railway bridge monitoring. Second, two state-of-the-art machine learning algorithms (i.e. convolutional neural networks (CNN) and transfer learning (TL)) and their applications for railway bridge structural condition assessment are exemplified. Third, the principle of digital twin (DT) and its applications for railway bridge monitoring are presented. Finally, issues related to the future direction and challenges of the monitoring technologies and condition assessment methods of railway bridges are highlighted.

Critical Evaluation of Reinforced Concrete Beams Using Digital Image Correlation Technique

Digital Image Correlation technique (DIC) is a non-contact optical method for rapid structural health monitoring of critical infrastructure. DIC is used to extract Moment (M) – Curvature (κ) relationships using random speckle patterns. The random speckle pattern is evaluated for 2D DIC measurements and shows an improved correlation with conventional measurements. Characteristics of random speckle patterns are quantified and their applicability to DIC is explored. The ultimate moment-curvature values computed from the random speckled pattern are found to be in good agreement with conventional measurements.

Investigation into the mechanical behavior of a polymer optical fiber embedded in a structural adhesive

To guaranty a safe load transfer through adhesively bonded joints, Structural Health Monitoring (SHM) methods that allow for permanent monitoring of the structural bond condition, are becoming more and more important. To meet this increasing demand, a novel approach for SHM of adhesively bonded joints by use of integrated Polymer Optical Fibers (POF) has been demonstrated in previous work. The sensor operating principle is based on strain transfer from the structural adhesive to the integrated POF. This strain transfer leads to a deformation of the POF cross-sectional shape and in that way affects the optical light propagation in the POF. In the current work, numerical and experimental results are presented into the mechanical behavior of embedded POF in adhesive bulk specimens subjected to tensile loading. Local deformations of both the POF (Asahi Kasei DB 500) and the structural adhesive (3 M Scotch Weld DP410) are evaluated using Digital Image Correlation (DIC). In the loading direction, a linear correlation is shown between strain inside the POF core and strain of the surrounding adhesive. Tests results, as well as fracture analysis, show a highly ductile behavior and failure of the POF cladding material and the interface between the POF core and annular cladding.

Real-time bridge monitoring using ultrasonic techniques combined with six-component (6-C) measurements

This study aims to develop a real time structural health monitoring method by ultrasonic tests combined with advanced six component (6C) translation and rotation measurements. Conventionally, the investigation of the velocity and acceleration response in the translation direction is used to obtain the eigenfrequencies of structures. Recently the measurement of rotation has been considered to fully characterize the dynamic behavior of structures. This research undertakes the evaluation of a novel 6C sensor (IMU50-iXblue) with components originally developed for navigation for the purpose of bridge monitoring. However, as for all vibration recordings, there is a certain influence of environmental conditions (mainly temperature) which may affect evaluation and the results of structural assessment. We propose applying the cross-correlation function to the 6C ambient vibration signals to reconstruct wave propagation and using coda wave interferometry (CWI) to obtain internal velocity variation from waveforms. A field experiment on a large-scale prestressed concrete bridge model is presented. To verify that we are able to identify the pre-stress loss even in presence of temperature effects, we perform measurements in two different scales: the ultrasonic and output-only, vibration measurements. The change in the structural properties due to the pre-stress loss should be detected by the pulse velocity change. The results reveal both the performance and advantages of ultrasonic techniques and the capabilities of 6C sensors. To conclude, the application of CWI to wave signals contributes to a comprehensive assessment for bridge monitoring.

Temperature and external strain sensing with metal-embedded optical fiber sensors for structural health monitoring.

An optical fiber with both temperature and strain fiber Bragg grating sensors were embedded into an aluminum cast structure during the casting process. Temperature and strain calibrations were carried out respectively for the metal-embedded sensors. Temperature and external strain decoupling was further demonstrated in a temperature range from 25 to 80 °C and an external strain range from 0 to ∼110 µɛ. With the interpolated temperature measured by two temperature sensors at different positions, the external strain could be decoupled from temperature and thermal strain at the strain sensor. The temperature and external strain values obtained from our embedded optical fiber sensors agreed well with reference values, revealing the good performance of the metal-embedded optical fiber sensors. The difference between the measured values and the reference values are within ±5 µɛ for external strain and ±1 °C for temperature. With only a single fiber, the in-situ temperature and external strain information in the aluminum structure can be monitored in real time, representing an important step towards fiber-optic smart casts. Our investigation demonstrates that embedded optical fiber sensors can be a promising method for structural health monitoring of metallic structures.

An Intelligent Health Monitoring Model Based on Fuzzy Deep Neural Network.

An intelligent health detection model is a new technology developed under an artificial intelligence environment, which is of great significance to the care of the elderly and other people who cannot take care of themselves. This paper comprehensively reviews the structural health monitoring method based on an intelligent algorithm, introduces the application model of neural networks in structural health monitoring in detail, and points out the shortcomings of using neural network technology alone. On the basis of previous work, the genetic algorithm and fuzzy theory were introduced as optimization tools, and a new neural network training algorithm was constructed by combining genetic algorithm, fuzzy theory, and neural network technology for structural health monitoring research. Aimed at the shortcoming of insufficient samples for training neural networks based on experimental data, this paper proposes to use the finite element method to construct a genetic fuzzy RBF neural network after corresponding processing of the first six-order bending modal frequencies of the structure, so as to realize the localization and detection of delamination damage of composite beams. Injury Assessment. The experimental results of this paper show that the finite element method proposed in this paper can effectively carry out damage localization and damage assessment; compared with the traditional algorithm, the localization accuracy of this algorithm is improved by 20%, and the damage assessment performance is improved by 10%.

A new open‐database benchmark structure for vibration‐based Structural Health Monitoring

Vibration-based Structural Health Monitoring is an ongoing field of research in many engineering disciplines. As for civil engineering, plenty of experimental structures have been erected in the past decades, both under laboratory and real-life conditions. Some of these facilities became a benchmark for different kinds of methods associated with Structural Health Monitoring such as damage analysis and Operational Modal Analysis, which led to fruitful developments in the global research community. When it comes to the continuous monitoring and assessment of the structural integrity of mechanical systems exposed to environmental and operational variability, the robustness and adaptability of the applied methods is of utmost importance. Such properties cannot be fully evaluated under laboratory conditions, which highlights the necessity of outdoor measurement campaigns. To this end, we introduce a test facility for Structural Health Monitoring comprising a lattice tower exposed to realistic conditions and featuring multiple reversible damage mechanisms. The structure located near Hanover in Northern Germany is densely equipped with sensors to capture the structural dynamics. The environmental conditions are monitored in parallel. The obtained continuous measurement data can be accessed online in an open repository. That is the foundation for benchmarks, consisting of a growing data set that enables the development, evaluation, and comparison of Structural Health Monitoring strategies and methods. In this article, we offer a documentation of the test facility and the data acquisition system. Lastly, we characterize the structural dynamics with the help of a finite element model and by analyzing several month of data.

Detection of defect in ballastless track based on impact echo method combined with improved SAFT algorithm

Impact echo method is a superior method for structural health monitoring in concrete, but it has the disadvantages of low accuracy in detecting multi-layer structures. To overcome this limitation, an improved synthetic aperture focusing technique (SAFT) algorithm was proposed and combined with impact echo method for the detection of ballastless track. The improved SAFT algorithm makes full use of time domain echo signals containing multiple conversion elastic waves to image ballastless track defect. Simulation analysis based on finite difference time-domain (FDTD) method and a laboratory experiment were conducted to verify the effectiveness of improved SAFT algorithm combined with impact echo method for the identification of ballastless track defect. The results show that the decrease of sensor spacing and contact time in impact echo method contributes to the visual identification of defects by the improved SAFT algorithm, the visualization effect of defects can be improved by adjusting the minimum threshold level of the color scale of reconstructed image. Except for defect near the boundary, impact echo method combined with the improved SAFT algorithm has good applicability for the detection of ballastless track defects with random spatial location and number distribution.

Particle Swarm Optimization Algorithm for Guided Waves Based Damage Localization Using Fiber Bragg Grating Sensors in Remote Configuration

Structural health monitoring (SHM) systems may allow a reduction in maintenance costs and extend the lifetime of the structure. As a result, they are of interest to the research community. Ideally, the SHM methods should be low cost, while being able to detect and localize small levels of damage reliably and accurately. The fiber Bragg grating (FBG) sensors are light in weight, insensitive to electric and magnetic fields, and can be embedded. The edge filtering configuration for transduction allows the use of FBG for guided wave (GW) sensing. This sensitivity may be further enhanced through their application in the remote bonded configuration. This paper provides a proof-of-concept for the use of remotely bonded FBG for damage localization. In order to improve the computational efficiency, a particle swarm optimization (PSO) based algorithm is developed. The PSO allows a significant improvement in the computation time which makes it better suited for real-time damage localization. The proposed objective function is based on the exponential elliptical approach. First, the suitability of the PSO for damage localization is shown. Then the performance of the chosen objective function is compared with the brute-force algorithm as well as other objective functions found in the literature. The methodology is employed on a simple aluminum plate. The results indicate that indeed the objective function along with the PSO is suitable for damage localization. Also as the objective function is developed taking into consideration the specific challenges with the use of FBG sensors, performs better than the other objective functions as well as the brute force algorithm.

A structural health monitoring method proposal based on optical scanning and computational models

This article proposes a method for continuous bridge displacement monitoring combining the dynamic triangulation scanning and load estimation by vehicle image recognition. The vehicle–bridge interaction is a non-stationary dynamic process parameter of high relevance to understanding the static instability behavior of bridges. The knowledge of the load on a bridge in the specific time when its structural spatial coordinates are measured allows correlating the bridge displacement with the effect of vehicle–bridge interaction. The evaluation of such correlation is mandatory in order to verify if the observed bridge displacement is due to the nature of its operation or due to it is presenting structural damage. The proposed method is continuous structural health monitoring method, based on the combination of three approaches evaluated at laboratory environment: (1) a three-dimensional optical scanning system for displacement measurement, (2) a load measurement system for vehicle–bridge interaction assessment, and (3) a two-measurement systems data correlation; to be implemented in bridges at a real environment to collect their historical behavior. Overall, for each approach, the measurement systems’ principles, the laboratory experimental methodology followed, and results obtained are presented.