Assessment technologies of rail systems' structural adequacy - A review from mechanical engineering perspectives

The structural adequacy challenges of railroad track structures have received considerable attention globally. Track defects and failures due to weak strength and buckling effect account for one-third of all railroad accidents. The current paper provides a comprehensive study of the recent work on the structural adequacy/bearing capacity of rail systems from mechanical engineering perspectives; existing techniques for track stiffness/modulus evaluations, including standstill and continuous methods. Further, this paper demonstrates the current techniques for track stiffness/modulus evaluation. Prevailing track modulus techniques, while accurate but time-taking, effortful, requires a track closure and provides only single-point information. Also, this review provides a suggestion on the non-destructive and non-invasive technologies for example Ground-penetrating radar (GPR) and evaluation of the substructures of tracks as they have great potential for image subsurface features.

Improving Prediction Accuracy and Extraction Precision of Frequency Shift from Low-SNR Brillouin Gain Spectra in Distributed Structural Health Monitoring

In this paper, we studied the possibility of increasing the Brillouin frequency shift (BFS) detection accuracy in distributed fibre-optic sensors by the separate and joint use of different algorithms for finding the spectral maximum: Lorentzian curve fitting (LCF, including the Levenberg–Marquardt (LM) method), the backward correlation technique (BWC) and a machine learning algorithm, the generalized linear model (GLM). The study was carried out on real spectra subjected to the subsequent addition of extreme digital noise. The precision and accuracy of the LM and BWC methods were studied by varying the signal-to-noise ratios (SNRs) and by incorporating the GLM method into the processing steps. It was found that the use of methods in sequence gives a gain in the accuracy of determining the sensor temperature from tenths to several degrees Celsius (or MHz in BFS scale), which is manifested for signal-to-noise ratios within 0 to 20 dB. We have found out that the double processing (BWC + GLM) is more effective for positive SNR values (in dB): it gives a gain in BFS measurement precision near 0.4 °C (428 kHz or 9.3 με); for BWC + GLM, the difference of precisions between single and double processing for SNRs below 2.6 dB is about 1.5 °C (1.6 MHz or 35 με). In this case, double processing is more effective for all SNRs. The described technique’s potential application in structural health monitoring (SHM) of concrete objects and different areas in metrology and sensing were also discussed.

Machine Learning Meets Compressed Sensing in Vibration-Based Monitoring

Artificial Intelligence applied to Structural Health Monitoring (SHM) has provided considerable advantages in the accuracy and quality of the estimated structural integrity. Nevertheless, several challenges still need to be tackled in the SHM field, which extended the monitoring process beyond the mere data analytics and structural assessment task. Besides, one of the open problems in the field relates to the communication layer of the sensor networks since the continuous collection of long time series from multiple sensing units rapidly consumes the available memory resources, and requires complicated protocol to avoid network congestion. In this scenario, the present work presents a comprehensive framework for vibration-based diagnostics, in which data compression techniques are firstly introduced as a means to shrink the dimension of the data to be managed through the system. Then, neural network models solving binary classification problems were implemented for the sake of damage detection, also encompassing the influence of environmental factors in the evaluation of the structural status. Moreover, the potential degradation induced by the usage of low cost sensors on the adopted framework was evaluated: Additional analyses were performed in which experimental data were corrupted with the noise characterizing MEMS sensors. The proposed solutions were tested with experimental data from the Z24 bridge use case, proving that the amalgam of data compression, optimized (i.e., low complexity) machine learning architectures and environmental information allows to attain high classification scores, i.e., accuracy and precision greater than 96% and 95%, respectively.

Combined Use of Cointegration Analysis and Robust Outlier Statistics to Improve Damage Detection in Real-World Structures

Due to the need for controlling many ageing and complex structures, structural health monitoring (SHM) has become increasingly common over the past few decades. However, one of the main limitations for the implementation of continuous monitoring systems in real-world structures is the effect that benign influences, such as environmental and operational variations (EOVs), have on damage sensitive features. These fluctuations may mask malign changes caused by structural damages, resulting in false structural condition assessment. When damage identification is implemented as novelty detection due to the lack of known damage states, outliers may be part of the data set as the result of the benign and malign factors mentioned above. Thanks to the developments in the field of robust outlier detection, the current paper presents a new data fusion method based on the use of cointegration and minimum covariance determinant estimator (MCD), which allows us to visualize and to classify outliers in SHM data, depending on their origin. To validate the effectiveness of this technique, the recent case study of the KW51 bridge has been considered, whose natural frequencies are subjected to variations due to both EOVs and a real structural change.

Development and Validation of a Framework for Smart Wireless Strain and Acceleration Sensing

Civil infrastructure worldwide is subject to factors such as aging and deterioration. Structural health monitoring (SHM) can be used to assess the impact of these processes on structural performance. SHM demands have evolved from routine monitoring to real-time and autonomous assessment. One of the frontiers in achieving effective SHM systems has been the use of wireless smart sensors (WSSs), which are attractive compared to wired sensors, due to their flexibility of use, lower costs, and ease of long-term deployment. Most WSSs use accelerometers to collect global dynamic vibration data. However, obtaining local behaviors in a structure using measurands such as strain may also be desirable. While wireless strain sensors have previously been developed by some researchers, there is still a need for a high sensitivity wireless strain sensor that fully meets the general demands for monitoring large-scale civil infrastructure. In this paper, a framework for synchronized wireless high-fidelity acceleration and strain sensing, which is commonly termed multimetric sensing in the literature, is proposed. The framework is implemented on the Xnode, a next-generation wireless smart sensor platform, and integrates with the strain sensor for strain acquisition. An application of the multimetric sensing framework is illustrated for total displacement estimation. Finally, the potential of the proposed framework integrated with vision-based measurement systems for multi-point displacement estimation with camera-motion compensation is demonstrated. The proposed approach is verified experimentally, showing the potential of the developed framework for various SHM applications.

RC Medium-Rise Building Damage Sensitivity with SSI Effect

Global vibration-based methods in the field of structural health monitoring are intended to capture structural stiffness changes of buildings or other civil engineering structures. Natural frequencies of buildings or bridges are commonly used parameters to monitor these stiffness changes. Therefore, it is essential to clarify the limit at which this method is no longer sensitive enough to be useful for structural health monitoring purposes. This paper numerically investigates the effect of structural damage and soil–structure interaction on cellular-type reinforced concrete buildings’ natural frequencies. These buildings are a common housing stock of Eastern Europe but are rarely investigated in this context. Comparisons with a reinforced concrete frame and infill structure building are made. Finite element models representing three structural system types of nine-story reinforced concrete buildings were used for the numerical simulations. Furthermore, a five-story finite element model was used for a damage sensitivity comparison. It is established that, for cellular-type structure buildings to detect damage comparable to that investigated in the paper, structural health (fixed base model frequency) should be monitored directly. Then, a statistical significance level for frequency changes of no more than 0.1% should be adopted. Conversely, the rocking frequency is a very sensitive parameter to monitor building base condition changes. These changes are often a cause of the cracking of building elements.

Experimental Investigation of Vibration Analysis on Implant Stability for a Novel Implant Design

Osseointegrated prostheses are widely used following transfemoral amputation. However, this technique requires sufficient implant stability before and during the rehabilitation period to mitigate the risk of implant breakage and loosening. Hence, reliable assessment methods for the osseointegration process are essential to ensure initial and long–term implant stability. This paper researches the feasibility of a vibration analysis technique for the osseointegration (OI) process by investigating the change in the dynamic response of the residual femur with a novel implant design during a simulated OI process. The paper also proposes a concept of an energy index (the E–index), which is formulated based on the normalized magnitude. To illustrate the potential of the E–index, this paper reports on changes in the vibrational behaviors of a 133 mm long amputated artificial femur model and implant system, with epoxy adhesives applied at the interface to simulate the OI process. The results show a significant variation in the magnitude of the colormap against curing time. The study also shows that the E–index was sensitive to the interface stiffness change, especially during the early curing process. These findings highlight the feasibility of using the vibration analysis technique and the E–index to quantitatively monitor the osseointegration process for future improvement on the efficiency of human health monitoring and patient rehabilitation.

Non-Destructive Techniques for the Condition and Structural Health Monitoring of Wind Turbines: A Literature Review of the Last 20 Years

A complete surveillance strategy for wind turbines requires both the condition monitoring (CM) of their mechanical components and the structural health monitoring (SHM) of their load-bearing structural elements (foundations, tower, and blades). Therefore, it spans both the civil and mechanical engineering fields. Several traditional and advanced non-destructive techniques (NDTs) have been proposed for both areas of application throughout the last years. These include visual inspection (VI), acoustic emissions (AEs), ultrasonic testing (UT), infrared thermography (IRT), radiographic testing (RT), electromagnetic testing (ET), oil monitoring, and many other methods. These NDTs can be performed by human personnel, robots, or unmanned aerial vehicles (UAVs); they can also be applied both for isolated wind turbines or systematically for whole onshore or offshore wind farms. These non-destructive approaches have been extensively reviewed here; more than 300 scientific articles, technical reports, and other documents are included in this review, encompassing all the main aspects of these survey strategies. Particular attention was dedicated to the latest developments in the last two decades (2000-2021). Highly influential research works, which received major attention from the scientific community, are highlighted and commented upon. Furthermore, for each strategy, a selection of relevant applications is reported by way of example, including newer and less developed strategies as well.

Evaluation of Ultrasonic Stress Wave Transmission in Cylindrical Roller Bearings for Acoustic Emission Condition Monitoring

In the condition monitoring of bearings using acoustic emission (AE), the restriction to solely instrument one of the two rings is generally considered a limitation for detecting signals originating from defects on the opposing non-instrumented ring or its interface with the rollers due to the signal energy loss. This paper presents an approach to evaluate transmission in low-speed roller bearings for application in passive ultrasound monitoring. An analytical framework to describe the propagation and transmission of ultrasonic waves through the geometry and interfaces of a bearing is presented. This framework has been used to evaluate the transmission of simulated damage signals in an experiment with a static bearing. The results suggest that low- to mid-frequency signals (<200 kHz), when passing through the rollers and their interfaces from one raceway to the other, can retain enough energy to be potentially detected. An average transmission loss in the range of 10–15 dB per interface was experimentally observed.

Damage Classification Using Supervised Self-Organizing Maps in Structural Health Monitoring

Improvements in computing capacity have allowed computers today to execute increasingly complex tasks. One of the main benefits of these improvements is the possibility of developing machine learning algorithms, of which the fields of application are extensive and varied. However, an area in which this type of algorithms acquires an increasing relevance is structural health monitoring (SHM), where inspection strategies and guided wave-based approaches make the evaluation of the structural conditions of an aircraft, vessel or building among others possible, by detecting and classifying existing damages. The use of sensors, data acquisition systems (DAQ) and computation has also allowed these damage detection and classification tasks to be carried out automatically. Despite today's advances, it is still necessary to continue with the development of more robust, reliable, and low-cost structural health monitoring systems. For this reason, this work contemplates three key points: (i) the configuration of a data acquisition system for signal gathering from an an active piezoelectric (PZT) sensor network; (ii) the development of a damage classification methodology based on signal processing techniques (normalization and PCA), from which the models that describe the structural conditions of the plate are built; and (iii) the use of machine learning algorithms, more specifically, three variants of the self-organizing maps called CPANN (counterpropagation artificial neural network), SKN (supervised Kohonen) and XYF (X-Y fused Kohonen). The data obtained allowed one to carry out an experimental validation of the damage classification methodology, to determine the presence of damages in two aluminum plates of different sizes, where masses were added to change the vibrational responses captured by the sensor network and a composite (CFRP) plate with real damages, such as delamination and cracks. This classification methodology allowed one to obtain excellent results by validating the usefulness of the SKN and XYF networks in damage classification tasks, showing overall accuracies of 73.75% and 72.5%, respectively, according to the cross-validation process. These percentages are higher than those obtained in comparison with other neural networks such as: kNN, discriminant analysis, classification trees, partial least square discriminant analysis, and backpropagation neural networks, when the cross-validation process was applied.

Damage Detection in Largely Unobserved Structures under Varying Environmental Conditions: An AutoRegressive Spectrum and Multi-Level Machine Learning Methodology

Vibration-based damage detection in civil structures using data-driven methods requires sufficient vibration responses acquired with a sensor network. Due to technical and economic reasons, it is not always possible to deploy a large number of sensors. This limitation may lead to partial information being handled for damage detection purposes, under environmental variability. To address this challenge, this article proposes an innovative multi-level machine learning method by employing the autoregressive spectrum as the main damage-sensitive feature. The proposed method consists of three levels: (i) distance calculation by the log-spectral distance, to increase damage detectability and generate distance-based training and test samples; (ii) feature normalization by an improved factor analysis, to remove environmental variations; and (iii) decision-making for damage localization by means of the Jensen-Shannon divergence. The major contributions of this research are represented by the development of the aforementioned multi-level machine learning method, and by the proposal of the new factor analysis for feature normalization. Limited vibration datasets relevant to a truss structure and consisting of acceleration time histories induced by shaker excitation in a passive system, have been used to validate the proposed method and to compare it with alternate, state-of-the-art strategies.

Automatic Detection of Real Damage in Operating Tie-Rods

Many researchers have proposed vibration-based damage-detection approaches for continuous structural health monitoring. Translation to real applications is not always straightforward because the proposed methods have mostly been developed and validated in controlled environments, and they have not proven to be effective in detecting real damage when considering real scenarios in which environmental and operational variations are not controlled. This work was aimed to develop a fully-automated strategy to detect damage in operating tie-rods that only requires one sensor and that can be carried out without knowledge of physical variables, e.g., the axial load. This strategy was created by defining a damage feature based on tie-rod eigenfrequencies and developing a data-cleansing strategy that could significantly improve performance of outlier detection based on the Mahalanobis squared distance in real applications. Additionally, the majority of damage-detection algorithms presented in the literature related to structural health monitoring were validated in controlled environments considering simulated damage conditions. On the contrary, the approach proposed in this paper was shown to allow for the early detection of real damage associated with a corrosion attack under the effects of an intentionally uncontrolled environment.

Bridge Digital Twinning Using an Output-Only Bayesian Model Updating Method and Recorded Seismic Measurements

Rapid post-earthquake damage diagnosis of bridges can guide decision-making for emergency response management and recovery. This can be facilitated using digital technologies to remove the barriers of manual post-event inspections. Prior mechanics-based Finite Element (FE) models can be used for post-event response simulation using the measured ground motions at nearby stations; however, the damage assessment outcomes would suffer from uncertainties in structural and soil material properties, input excitations, etc. For instrumented bridges, these uncertainties can be reduced by integrating sensory data with prior models through a model updating approach. This study presents a sequential Bayesian model updating technique, through which a linear/nonlinear FE model, including soil-structure interaction effects, and the foundation input motions are jointly identified from measured acceleration responses. The efficacy of the presented model updating technique is first examined through a numerical verification study. Then, seismic data recorded from the San Rogue Canyon Bridge in California are used for a real-world case study. Comparison between the free-field and the foundation input motions reveals valuable information regarding the soil-structure interaction effects at the bridge site. Moreover, the reasonable agreement between the recorded and estimated bridge responses shows the potentials of the presented model updating technique for real-world applications. The described process is a practice of digital twinning and the updated FE model is considered as the digital twin of the bridge and can be used to analyze the bridge and monitor the structural response at element, section, and fiber levels to diagnose the location and severity of any potential damage mechanism.

Distributed Strain Monitoring Using Nanocomposite Paint Sensing Meshes

Strain measurements are vital for monitoring the load-bearing capacity and safety of structures. A common approach is to affix strain gages onto structural surfaces. On the other hand, most aerospace, automotive, civil, and mechanical structures are painted and coated, often with many layers, prior to their deployment. There is an opportunity to design smart and multifunctional paints that can be directly pre-applied onto structural surfaces to serve as a sensing layer among their other layers of functional paints. Therefore, the objective of this study was to design a strain-sensitive paint that can be used for structural monitoring. Carbon nanotubes (CNT) were dispersed in paint by high-speed shear mixing, while paint thinner was employed for adjusting the formulation's viscosity and nanomaterial concentration. The study started with the design and fabrication of the CNT-based paint. Then, the nanocomposite paint's electromechanical properties and its sensitivity to applied strains were characterized. Third, the nanocomposite paint was spray-coated onto patterned substrates to form "Sensing Meshes" for distributed strain monitoring. An electrical resistance tomography (ERT) measurement strategy and algorithm were utilized for reconstructing the conductivity distribution of the Sensing Meshes, where the magnitude of conductivity (or resistivity) corresponded to the magnitude of strain, while strain directionality was determined based on the strut direction in the mesh.

Optimal Array Design and Directive Sensors for Guided Waves DoA Estimation

The estimation of Direction of Arrival (DoA) of guided ultrasonic waves is an important task in many Structural Health Monitoring (SHM) applications. The aim is to locate sources of elastic waves which can be generated by impacts or defects in the inspected structures. In this paper, the array geometry and the shape of the piezo-sensors are designed to optimize the DoA estimation on a pre-defined angular sector, from acquisitions affected by noise and interference. In the proposed approach, the DoA of a wave generated by a single source is considered as a random variable that is uniformly distributed in a given range. The wave velocity is assumed to be unknown and the DoA estimation is performed by measuring the Differences in Time of Arrival (DToAs) of wavefronts impinging on the sensors. The optimization procedure of sensors positioning is based on the computation of the DoA and wave velocity parameters Cramér-Rao Matrix Bound (CRMB) with a Bayesian approach. An efficient DoA estimator is found based on the DToAs Gauss-Markov estimator for a three sensors array. Moreover, a novel directive sensor for guided waves is introduced to cancel out undesired Acoustic Sources impinging from DoAs out of the given angles range. Numerical results show the capability to filter directional interference of the novel sensor and a considerably improved DoA estimation performance provided by the optimized sensor cluster in the pre-defined angular sector, as compared to conventional approaches.

Investigation on Applicability and Limitation of Cosine Similarity-Based Structural Condition Monitoring for Gageocho Offshore Structure

The key to coping with global warming is reconstructing energy governance from carbon-based to sustainable resources. Offshore energy sources, such as offshore wind turbines, are promising alternatives. However, the abnormal climate is a potential threat to the safety of offshore structures because construction guidelines cannot embrace climate outliers. A cosine similarity-based maintenance strategy may be a possible solution for managing and mitigating these risks. However, a study reporting its application to an actual field structure has not yet been reported. Thus, as an initial study, this study investigated whether the technique is applicable or whether it has limitations in the real field using an actual example, the Gageocho Ocean Research Station. Consequently, it was found that damage can only be detected correctly if the damage states are very similar to the comparison target database. Therefore, the high accuracy of natural frequencies, including environmental effects, should be ensured. Specifically, damage scenarios must be carefully designed, and an alternative is to devise more efficient techniques that can compensate for the present procedure.

Learning Damage Representations with Sequence-to-Sequence Models

Natural hazards have caused damages to structures and economic losses worldwide. Post-hazard responses require accurate and fast damage detection and assessment. In many studies, the development of data-driven damage detection within the research community of structural health monitoring has emerged due to the advances in deep learning models. Most data-driven models for damage detection focus on classifying different damage states and hence damage states cannot be effectively quantified. To address such a deficiency in data-driven damage detection, we propose a sequence-to-sequence (Seq2Seq) model to quantify a probability of damage. The model was trained to learn damage representations with only undamaged signals and then quantify the probability of damage by feeding damaged signals into models. We tested the validity of our proposed Seq2Seq model with a signal dataset which was collected from a two-story timber building subjected to shake table tests. Our results show that our Seq2Seq model has a strong capability of distinguishing damage representations and quantifying the probability of damage in terms of highlighting the regions of interest.

Towards Interpretable Machine Learning for Automated Damage Detection Based on Ultrasonic Guided Waves

Data-driven analysis for damage assessment has a large potential in structural health monitoring (SHM) systems, where sensors are permanently attached to the structure, enabling continuous and frequent measurements. In this contribution, we propose a machine learning (ML) approach for automated damage detection, based on an ML toolbox for industrial condition monitoring. The toolbox combines multiple complementary algorithms for feature extraction and selection and automatically chooses the best combination of methods for the dataset at hand. Here, this toolbox is applied to a guided wave-based SHM dataset for varying temperatures and damage locations, which is freely available on the Open Guided Waves platform. A classification rate of 96.2% is achieved, demonstrating reliable and automated damage detection. Moreover, the ability of the ML model to identify a damaged structure at untrained damage locations and temperatures is demonstrated.

Structural Health Monitoring in Composite Structures: A Comprehensive Review

This study presents a comprehensive review of the history of research and development of different damage-detection methods in the realm of composite structures. Different fields of engineering, such as mechanical, architectural, civil, and aerospace engineering, benefit excellent mechanical properties of composite materials. Due to their heterogeneous nature, composite materials can suffer from several complex nonlinear damage modes, including impact damage, delamination, matrix crack, fiber breakage, and voids. Therefore, early damage detection of composite structures can help avoid catastrophic events and tragic consequences, such as airplane crashes, further demanding the development of robust structural health monitoring (SHM) algorithms. This study first reviews different non-destructive damage testing techniques, then investigates vibration-based damage-detection methods along with their respective pros and cons, and concludes with a thorough discussion of a nonlinear hybrid method termed the Vibro-Acoustic Modulation technique. Advanced signal processing, machine learning, and deep learning have been widely employed for solving damage-detection problems of composite structures. Therefore, all of these methods have been fully studied. Considering the wide use of a new generation of smart composites in different applications, a section is dedicated to these materials. At the end of this paper, some final remarks and suggestions for future work are presented.

Real-Time Life-Cycle Monitoring of Composite Structures Using Piezoelectric-Fiber Hybrid Sensor Network

In this paper, an in situ piezoelectric-fiber hybrid sensor network was developed to monitor the life-cycle of carbon fiber-reinforced plastics (CFRPs), from the manufacturing phase to the life in service. The piezoelectric lead-zirconate titanate (PZT) sensors were inserted inside the composite structures during the manufacturing process to monitor important curing parameters, including the storage modulus of resin and the progress of the reaction (POR). The strain that is related to the storage modulus and the state of resin was measured by embedded fiber Bragg grating (FBG) sensors, and the gelation moment identified by the FBG sensors was very close to those determined by dynamic mechanical analysis (DMA) and POR. After curing, experiments were conducted on the fabricated CFRP specimen to investigate the damage identification capability of the embedded piezoelectric sensor network. Furthermore, a modified probability diagnostic imaging (PDI) algorithm with a dynamically adaptive shape factor and fusion frequency was proposed to indicate the damage location in the tested sample and to greatly improve the position precision. The experimental results demonstrated that the average relative distance error (RDE) of the modified PDI method was 68.48% and 46.97% lower than those of the conventional PDI method and the PDI method, respectively, with an averaged shape factor and fusion frequency, indicating the effectiveness and applicability of the proposed damage imaging method. It is obvious that the whole life-cycle of CFRPs can be effectively monitored by the piezoelectric-fiber hybrid sensor network.

Model-Assisted Guided-Wave-Based Approach for Disbond Detection and Size Estimation in Honeycomb Sandwich Composites

One of the axioms of structural health monitoring states that the severity of damage assessment can only be done in a learning mode under the supervision of an expert. Therefore, a numerical analysis was conducted to gain knowledge regarding the influence of the damage size on the propagation of elastic waves in a honeycomb sandwich composite panel. Core-skin debonding was considered as damage. For this purpose, a panel was modelled taking into account the real geometry of the honeycomb core using the time-domain spectral element method and two-dimensional elements. The presented model was compared with the homogenized model of the honeycomb core and validated in the experimental investigation. The result of the parametric study is a function of the influence of damage on the amplitude and energy of propagating waves.

Development of a Wireless Corrosion Detection System for Steel-Framed Structures Using Pulsed Eddy Currents

Structural health monitoring (SHM) can be more efficient with the application of a wireless sensor network (WSN). However, the hardware that makes up this system should have sufficient performance to sample the data collected from the sensor in real-time situations. High-performance hardware can be used for this purpose, but is not suitable in this application because of its relatively high power consumption, high cost, large size, and so on. In this paper, an optimal remote monitoring system platform for SHM is proposed based on pulsed eddy current (PEC) that is utilized for measuring the corrosion of a steel-framed construction. A circuit to delay the PEC response based on the resistance-inductance-capacitance (RLC) combination was designed for data sampling to utilize the conventional hardware of WSN for SHM, and this approach was verified by simulations and experiments. Especially, the importance of configuring sensing modules and the WSN for remote monitoring were studied, and the PEC responses caused by the corrosion of a specimen made with steel were able to be sampled remotely using the proposed system. Therefore, we present a remote SHM system platform for diagnosing the corrosion condition of a building with a steel structure, and proving its viability with experiments.

Stainless-Steel Antenna on Conductive Substrate for an SHM Sensor System with High Power Demand

This paper presents the novel concept of structuring a planar coil antenna structured into the outermost stainless-steel layer of a fiber metal laminate (FML) and investigating its performance. Furthermore, the antenna is modified to sufficiently work on inhomogeneous conductive substrates such as carbon-fiber-reinforced polymers (CFRP) independent from their application-dependent layer configuration, since the influence on antenna performance was expected to be configuration-dependent. The effects of different stack-ups on antenna characteristics and strategies to cope with these influences are investigated. The purpose was to create a wireless self-sustained sensor node for an embedded structural health monitoring (SHM) system inside the monitored material itself. The requirements of such a system are investigated, and measurements on the amount of wireless power that can be harvested are conducted. Mechanical investigations are performed to identify the antenna shape that produces the least wound to the material, and electrical investigations are executed to prove the on-conductor optimization concept. Furthermore, a suitable process to fabricate such antennas is introduced. First measurements fulfilled the expectations: the measured antenna structure prototype could provide up to 11 mW to a sensor node inside the FML component.

Lamb Wave Based Structural Damage Detection Using Stationarity Tests

Lamb waves have been widely used for structural damage detection. However, practical applications of this technique are still limited. One of the main reasons is due to the complexity of Lamb wave propagation modes. Therefore, instead of directly analysing and interpreting Lamb wave propagation modes for information about health conditions of the structure, this study has proposed another approach that is based on statistical analyses of the stationarity of Lamb waves. The method is validated by using Lamb wave data from intact and damaged aluminium plates exposed to temperature variations. Four popular unit root testing methods, including Augmented Dickey-Fuller (ADF) test, Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, Phillips-Perron (PP) test, and Leybourne-McCabe (LM) test, have been investigated and compared in order to understand and make statistical inference about the stationarity of Lamb wave data before and after hole damages are introduced to the aluminium plate. The separation between t-statistic features, obtained from the unit root tests on Lamb wave data, is used for damage detection. The results show that both ADF test and KPSS test can detect damage, while both PP and LM tests were not significant for identifying damage. Moreover, the ADF test was more stable with respect to temperature changes than the KPSS test. However, the KPSS test can detect damage better than the ADF test. Moreover, both KPSS and ADF tests can consistently detect damages in conditions where temperatures vary below 60 °C. However, their t-statistics fluctuate more (or less homogeneous) for temperatures higher than 65 °C. This suggests that both ADF and KPSS tests should be used together for Lamb wave based structural damage detection. The proposed stationarity-based approach is motivated by its simplicity and efficiency. Since the method is based on the concept of stationarity of a time series, it can find applications not only in Lamb wave based SHM but also in condition monitoring and fault diagnosis of industrial systems.

Global Wave Velocity Change Measurement of Rock Material by Full-Waveform Correlation

Measuring accurate wave velocity change is a crucial step in damage assessment of building materials such as rock and concrete. The anisotropy caused by the generation of cracks in the damage process and the uncertainty of the damage level of these building materials make it difficult to obtain accurate wave velocity change. We propose a new method to measure the wave velocity change of anisotropic media at any damage level by full-waveform correlation. In this method, the anisotropy caused by the generation of cracks in the damage process is considered. The accuracy of the improved method is verified by numerical simulation and compared with the existing methods. Finally, the proposed method is applied to measure the wave velocity change in the damage process of rock under uniaxial compression. We monitor the failure process of rock by acoustic emission (AE) monitoring system. Compared with the AE ringing count, the result of damage evaluation obtained by the proposed method is more accurate than the other two methods in the stage of increasing rock heterogeneity. These results show that the proposed method is feasible in damage assessment of building materials such as rock and concrete.

Optical Fiber Sensors for Ultrasonic Structural Health Monitoring: A Review

Guided waves (GW) and acoustic emission (AE) -based structural health monitoring (SHM) have widespread applications in structures, as the monitoring of an entire structure is possible with a limited number of sensors. Optical fiber-based sensors offer several advantages, such as their low weight, small size, ability to be embedded, and immunity to electro-magnetic interference. Therefore, they have long been regarded as an ideal sensing solution for SHM. In this review, the different optical fiber technologies used for ultrasonic sensing are discussed in detail. Special attention has been given to the new developments in the use of FBG sensors for ultrasonic measurements, as they are the most promising and widely used of the sensors. The paper highlights the physics of the wave coupling to the optical fiber and explains the different phenomena such as directional sensitivity and directional coupling of the wave. Applications of the different sensors in real SHM applications have also been discussed. Finally, the review identifies the encouraging trends and future areas where the field is expected to develop.

Study of Building Safety Monitoring by Using Cost-Effective MEMS Accelerometers for Rapid After-Earthquake Assessment with Missing Data

Suffering from structural deterioration and natural disasters, the resilience of civil structures in the face of extreme loadings inevitably drops, which may lead to catastrophic structural failure and presents great threats to public safety. Earthquake-induced extreme loading is one of the major reasons behind the structural failure of buildings. However, many buildings in earthquake-prone areas of China lack safety monitoring, and prevalent structural health monitoring systems are generally very expensive and complicated for extensive applications. To facilitate cost-effective building-safety monitoring, this study investigates a method using cost-effective MEMS accelerometers for buildings’ rapid after-earthquake assessment. First, a parameter analysis of a cost-effective MEMS sensor is conducted to confirm its suitability for building-safety monitoring. Second, different from the existing investigations that tend to use a simplified building model or small-scaled frame structure excited by strong motions in laboratories, this study selects an in-service public building located in a typical earthquake-prone area after an analysis of earthquake risk in China. The building is instrumented with the selected cost-effective MEMS accelerometers, characterized by a low noise level and the capability to capture low-frequency small-amplitude dynamic responses. Furthermore, a rapid after-earthquake assessment scheme is proposed, which systematically includes fast missing data reconstruction, displacement response estimation based on an acceleration response integral, and safety assessment based on the maximum displacement and maximum inter-story drift ratio. Finally, the proposed method is successfully applied to a building-safety assessment by using earthquake-induced building responses suffering from missing data. This study is conducive to the extensive engineering application of MEMS-based cost-effective building monitoring and rapid after-earthquake assessment.

Application of Operational Load Monitoring System for Fatigue Estimation of Main Landing Gear Attachment Frame of an Aircraft

In this paper, we present an approach to fatigue estimation of a Main Landing Gear (MLG) attachment frame due to vertical landing forces based on Operational Loads Monitoring (OLM) system records. In particular, the impact of different phases of landing and on ground operations and fatigue wear of the MLG frame is analyzed. The main functionality of the developed OLM system is the individual assessment of fatigue of the main landing gear node structure for Su-22UM3K aircraft due to standard and Touch-And-Go (T&G) landings. Furthermore, the system allows for assessment of stress cumulation in the main landing gear node structure during touchdown and allows for detection of hard landings. Determination of selected stages of flight, classification of different types of load cycles of the structure recorded by strain gauge sensors during standard full stop landings and taxiing are also implemented in the developed system. Based on those capabilities, it is possible to monitor and compare equivalents of landing fatigue wear between airplanes and landing fatigue wear across all flights of a given airplane, which can be incorporated into fleet management paradigms for the purpose of optimal maintenance of aircraft. In this article, a detailed description of the system and algorithms used for landing gear node fatigue assessment is provided, and the results obtained during the 3-year period of system operation for the fleet of six aircraft are delivered and discussed.

Reducing the Training Samples for Damage Detection of Existing Buildings through Self-Space Approximation Techniques

Data-driven methodologies are among the most effective tools for damage detection of complex existing buildings, such as heritage structures. Indeed, the historical evolution and actual behaviour of these assets are often unknown, no physical models are available, and the assessment must be performed only based on the tracking of a set of damage-sensitive features. Selecting the most representative state indicators to monitor and sampling them with an adequate number of records are therefore essential tasks to guarantee the successful performance of the damage detection strategy. Despite their relevance, these aspects have been frequently taken for granted and little attention has been paid to them by the scientific community working in the field of Structural Health Monitoring. The present paper aims to fill this gap by proposing a multistep strategy to drive the selection of meaningful pairs of correlated features in order to support the damage detection as a one-class classification problem. Numerical methods to reduce the number of necessary acquisitions and estimate the performance of approximation techniques are also provided. The analyses carried out to test and validate the proposed strategy exploit a dense dataset collected during the long-term monitoring of an outstanding heritage structure, i.e., the Church of ‘Santa Maria de Belém’ in Lisbon.

Integration with 3D Visualization and IoT-Based Sensors for Real-Time Structural Health Monitoring

Real-time monitoring on displacement and acceleration of a structure provides vital information for people in different applications such as active control and damage warning systems. Recent developments of the Internet of Things (IoT) and client-side web technologies enable a wireless microcontroller board with sensors to process structural-related data in real-time and to interact with servers so that end-users can view the final processed results of the servers through a browser in a computer or a mobile phone. Unlike traditional structural health monitoring (SHM) systems that deliver warnings based on peak acceleration of earthquake, we built a real-time SHM system that converts raw sensor results into movements and rotations on the monitored structure’s three-dimensional (3D) model. This unique approach displays the overall structural dynamic movements directly from measured displacement data, rather than using force analysis, such as finite element analysis, to predict the displacement statically. As an application to our research outcomes, patterns of movements related to its structure type can be collected for further cross-validating the results derived from the traditional stress-strain analysis. In this work, we overcome several challenges that exist in displaying the 3D effects in real-time. From our proposed algorithm that converts the global displacements into element’s local movements, our system can calculate each element’s (e.g., column’s, beam’s, and floor’s) rotation and displacement at its local coordinate while the sensor’s monitoring result only provides displacements at the global coordinate. While we consider minimizing the overall sensor usage costs and displaying the essential 3D movements at the same time, a sensor deployment method is suggested. To achieve the need of processing the enormous amount of sensor data in real-time, we designed a novel structure for saving sensor data, where relationships among multiple sensor devices and sensor’s spatial and unique identifier can be presented. Moreover, we built a sensor device that can send the monitoring data via wireless network to the local server or cloud so that the SHM web can integrate what we develop altogether to show the real-time 3D movements. In this paper, a 3D model is created according to a two-story structure to demonstrate the SHM system functionality and validate our proposed algorithm.

Changes in Frequency and Mode Shapes Due to Damage in Steel-Concrete Composite Beam

This study presents an analysis of changes in the vibration frequency and mode of vibration of a composite beam due to damage. A steel–concrete composite beam was considered, for which numerical analysis (RFE model) and experimental tests were conducted. Two levels of damage were introduced to the beam. To determine the changes in the mode of vibration before and after the damage, the modal assurance criterion (MAC) and its partial variation (PMAC) were applied. The curvature damage factor (CDF) was used to determine the changes in the modal curvature. The natural frequencies were sensitive to the introduced damage. The results show that the MAC is not effective in determining the location of damage in the connection plane. Two different coefficients were introduced to locate the damage. The PMAC was used for sections of subsequent modes of vibration and allowed effectively locating the damage. The CDF considered simultaneous changes in the curvatures of all vibration modes and was effective in locating the damage in the connection plane.

Acoustic Emission Monitoring of Carbon Fibre Reinforced Composites with Embedded Sensors for In-Situ Damage Identification

Piezoelectric sensors can be embedded in carbon fibre-reinforced plastics (CFRP) for continuous measurement of acoustic emissions (AE) without the sensor being exposed or disrupting hydro- or aerodynamics. Insights into the sensitivity of the embedded sensor are essential for accurate identification of AE sources. Embedded sensors are considered to evoke additional modes of degradation into the composite laminate, accompanied by additional AE. Hence, to monitor CFRPs with embedded sensors, identification of this type of AE is of interest. This study (i) assesses experimentally the performance of embedded sensors for AE measurements, and (ii) investigates AE that emanates from embedded sensor-related degradation. CFRP specimens have been manufactured with and without embedded sensors and tested under four-point bending. AE signals have been recorded by the embedded sensor and two reference surface-bonded sensors. Sensitivity of the embedded sensor has been assessed by comparing centroid frequencies of AE measured using two sizes of embedded sensors. For identification of embedded sensor-induced AE, a hierarchical clustering approach has been implemented based on waveform similarity. It has been confirmed that both types of embedded sensors (7 mm and 20 mm diameter) can measure AE during specimen degradation and final failure. The 7 mm sensor showed higher sensitivity in the 350–450 kHz frequency range. The 20 mm sensor and the reference surface-bounded sensors predominately featured high sensitivity in ranges of 200–300 kHz and 150–350 kHz, respectively. The clustering procedure revealed a type of AE that seems unique to the region of the embedded sensor when under combined in-plane tension and out-of-plane shear stress.

Temperature Prediction Using Multivariate Time Series Deep Learning in the Lining of an Electric Arc Furnace for Ferronickel Production

The analysis of data from sensors in structures subjected to extreme conditions such as the ones used in smelting processes is a great decision tool that allows knowing the behavior of the structure under different operational conditions. In this industry, the furnaces and the different elements are fully instrumented, including sensors to measure variables such as temperature, pressure, level, flow, power, electrode positions, among others. From the point of view of engineering and data analytics, this quantity of data presents an opportunity to understand the operation of the system under normal conditions or to explore new ways of operation by using information from models provided by using deep learning approaches. Although some approaches have been developed with application to this industry, it is still an open research area. As a contribution, this paper presents an applied deep learning temperature prediction model for a 75 MW electric arc furnace, which is used for ferronickel production. In general, the methodology proposed considers two steps: first, a data cleaning process to increase the quality of the data, eliminating both redundant information as well as atypical and unusual data, and second, a multivariate time series deep learning model to predict the temperatures in the furnace lining. The developed deep learning model is a sequential one based on GRU (gated recurrent unit) layer plus a dense layer. The GRU + Dense model achieved an average root mean square error (RMSE) of 1.19 °C in the test set of 16 different thermocouples radially distributed on the furnace.

Two-Step Approach to Processing Raw Strain Monitoring Data for Damage Detection of Structures under Operational Conditions

Strain data of structural health monitoring is a prospective to be made full use of, because it reflects the stress peak and fatigue, especially sensitive to local stress redistribution, which is the probably damage in the vicinity of the sensor. For decoupling structural damage and masking effects caused by operational conditions to eliminate the adverse impacts on strain-based damage detection, small time-scale structural events, i.e., the short-term dynamic strain responses, are analyzed in this paper by employing unsupervised modeling. A two-step approach to successively processing the raw strain monitoring data in the sliding time window is presented, consisting of the wavelet-based initial feature extraction step and the decoupling step to draw damage indicators. The principal component analysis and a low-rank property-based subspace projection method are adopted as two alternative decoupling methodologies. The approach’s feasibility and robustness are substantiated by analyzing the strain monitoring data from a customized truss experiment to successfully remove the masking effects of operating loads and identify local damages even concerning accommodating situations of missing data and limited measuring points. This work also sheds light on the merit of a low-rank property to separate structural damages from masking effects by comparing the performances of the two optional decoupling methods of the distinct rationales.

Comparison of DOFS Attachment Methods for Time-Dependent Strain Sensing

Structural health monitoring (SHM) is a challenge for many industries. Over the last decade, novel strain monitoring methods using optical fibers have been implemented for SHM in aerospace, energy storage, marine, and civil engineering structures. However, the practical attachment of optical fibers (OFs) to the component is still problematic. While monitoring, the amount of substrate strain lost by the OF attachment is often unclear, and difficult to predict under long-term loads. This investigation clarifies how different attachment methods perform under time-dependent loading. Optical fibers are attached on metal, thermoset composite, and thermoplastic substrates for distributed strain sensing. Strains along distributed optical fiber sensors (DOFS) are measured by optical backscatter reflectometry (OBR) and compared to contact extensometer strains under tensile creep loading. The quality of the bondline and its influence on the strain transfer is analyzed. Residual strains and strain fluctuations along the sensor fiber are correlated to the fiber attachment method. Results show that a machine-controlled attachment process (such as in situ 3-D printing) holds great promise for the future as it achieves a highly uniform bondline and provides accurate strain measurements.

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

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

Structural Response Prediction for Damage Identification Using Wavelet Spectra in Convolutional Neural Network

If damage to a building caused by an earthquake is not detected immediately, the opportunity to decide on quick action, such as evacuating the building, is lost. For this reason, it is necessary to develop modern technologies that can quickly obtain the structural safety condition of buildings after an earthquake in order to resume economic and social activities and mitigate future damage by aftershocks. A methodology for the prediction of damage identification is proposed in this study. Using the wavelet spectrum of the absolute acceleration record measured by a single accelerometer located on the upper floor of a building as input data, a CNN model is trained to predict the damage information of the building. The maximum ductility factor, inter-story drift ratio, and maximum response acceleration of each floor are predicted as the damage information, and their accuracy is verified by comparing with the results of seismic response analysis using actual earthquakes. Finally, when an earthquake occurs, the proposed methodology enables immediate action by revealing the damage status of the building from the accelerometer observation records.

Edge Structural Health Monitoring (E-SHM) Using Low-Power Wireless Sensing

Effective Structural Health Monitoring (SHM) often requires continuous monitoring to capture changes of features of interest in structures, which are often located far from power sources. A key challenge lies in continuous low-power data transmission from sensors. Despite significant developments in long-range, low-power telecommunication (e.g., LoRa NB-IoT), there are inadequate demonstrative benchmarks for low-power SHM. Damage detection is often based on monitoring features computed from acceleration signals where data are extensive due to the frequency of sampling (~100-500 Hz). Low-power, long-range telecommunications are restricted in both the size and frequency of data packets. However, microcontrollers are becoming more efficient, enabling local computing of damage-sensitive features. This paper demonstrates the implementation of an Edge-SHM framework through low-power, long-range, wireless, low-cost and off-the-shelf components. A bespoke setup is developed with a low-power MEM accelerometer and a microcontroller where frequency and time domain features are computed over set time intervals before sending them to a cloud platform. A cantilever beam excited by an electrodynamic shaker is monitored, where damage is introduced through the controlled loosening of bolts at the fixed boundary, thereby introducing rotation at its fixed end. The results demonstrate how an IoT-driven edge platform can benefit continuous monitoring.

Development of Low-Cost Wireless Sensing System for Smart Ultra-High Performance Concrete

This study proposes the development of a wireless sensor system integrated with smart ultra-high performance concrete (UHPC) for sensing and transmitting changes in stress and damage occurrence in real-time. The smart UHPC, which has the self-sensing ability, comprises steel fibers, fine steel slag aggregates (FSSAs), and multiwall carbon nanotubes (MWCNTs) as functional fillers. The proposed wireless sensing system used a low-cost microcontroller unit (MCU) and two-probe resistance sensing circuit to capture change in electrical resistance of self-sensing UHPC due to external stress. For wireless transmission, the developed wireless sensing system used Bluetooth low energy (BLE) beacon for low-power and multi-channel data transmission. For experimental validation of the proposed smart UHPC, two types of specimens for tensile and compression tests were fabricated. In the laboratory test, using a universal testing machine, the change in electrical resistivity was measured and compared with a reference DC resistance meter. The proposed wireless sensing system showed decreased electrical resistance under compressive and tensile load. The fractional change in resistivity (FCR) was monitored at 39.2% under the maximum compressive stress and 12.35% per crack under the maximum compressive stress tension. The electrical resistance changes in both compression and tension showed similar behavior, measured by a DC meter and validated the developed integration of wireless sensing system and smart UHPC.

Optimisation of Through-Thickness Embedding Location of Fibre Bragg Grating Sensor in CFRP for Impact Damage Detection

Aerospace composites are susceptible to barely visible impact damage (BVID) produced by low-velocity-impact (LVI) events. Fibre Bragg grating (FBG) sensors can detect BVID, but often FBG sensors are embedded in the mid-plan, where residual strains produced by impact damage are lower, leading to an undervaluation of the damage severity. This study compares the residual strains produced by LVI events measured by FBG embedded at the mid-plan and other through-thickness locations of carbon fibre reinforced polymer (CFRP) composites. The instrumented laminates were subjected to multiple low-velocity impacts while the FBG signals were acquired. The FBG sensor measurements allowed not only for the residual strain after damage to be measured, but also for a strain peak at the time of impact to be detected, which is an important feature to identify the nature and presence of BVID in real-life applications. The results allowed an adequate optical fibre (OF) embedding location to be selected for BVID detection. The effect of small- and large-diameter OF on the impact resistance of the CFRP was compared.

Self-Sensing Cementitious Composites: Review and Perspective

Self-sensing concrete (SSC) has been vastly studied for its possibility to provide a cost-effective solution for structural health monitoring of concrete structures, rendering it very attractive in real-life applications. In this review paper, comprehensive information about the components of self-sensing concrete, dispersion methods and mix design, as well as the recent progress in the field of self-sensing concrete, has been provided. The information and recent research findings about self-sensing materials for smart composites, their properties, measurement of self-sensing signal and the behavior of self-sensing concrete under different loading conditions are included. Factors influencing the electrical resistance of self-sensitive concrete such as dry-wet cycle, ice formation and freeze thaw cycle and current frequency, etc., which were not covered by previous review papers on self-sensing concrete, are discussed in detail. Finally, major emphasis is placed on the application of self-sensing technology in existing and new structures.

Recent Advancements in Carbon Nano-Infused Cementitious Composites

A rising demand for efficient functional materials brings forth research challenges regarding improvements in existing materials. Carbon infused cementitious composites, regardless of being an important research topic worldwide, still present many questions concerning their functionality and properties. The paper aims to highlight the most important materials used for cementitious composites, their properties, and their uses while also including the most relevant of the latest research in that area.

Damage Detection at a Reinforced Concrete Specimen with Coda Wave Interferometry

Reinforced concrete is a widely used construction material in the building industry. With the increasing age of structures and higher loads there is an immense demand for structural health monitoring of built infrastructure. Coda wave interferometry is a possible candidate for damage detection in concrete whose applicability is demonstrated in this study. The technology is based on a correlation evaluation of two ultrasonic signals. In this study, two ways of processing the correlation data for damage detection are compared. The coda wave measurement data are obtained from a four-point bending test at a reinforced concrete specimen that is also instrumented with fibre optic strain measurements. The used ultrasonic signals have a central frequency of 60 kHz which is a significant difference to previous studies. The experiment shows that the coda wave interferometry has a high sensitivity for developing cracks and by solving an inverse problem even multiple cracks can be distinguished. A further specialty of this study is the use of finite elements for solving a diffusion problem which is needed to state the previously mentioned inverse problem for damage localization.

Damage Localization in Composite Plates Using Wavelet Transform and 2-D Convolutional Neural Networks

Nondestructive evaluation of carbon fiber reinforced material structures has received special attention in the last decades. Usage of Ultrasonic Guided Waves (UGW), particularly Lamb waves, has become one of the most popular techniques for damage location, due to their sensitivity to defects, large range of inspection, and good propagation in several material types. However, extracting meaningful physical features from the response signals is challenging due to several factors, such as the multimodal nature of UGW, boundary conditions and the geometric shape of the structure, possible material anisotropies, and their environmental dependency. Neural networks (NN) are becoming a practical and accurate approach to analyzing the acquired data using data-driven methods. In this paper, a Convolutional-Neural-Network (CNN) is proposed to predict the distance-to-damage values from the signals corresponding to a transmitter-receiver path of transducers. The NN input is a 2D image (time-frequency) obtained as the Wavelet transform of the acquired experimental signals. The distances obtained with the NN are the input of a novel damage location algorithm which outputs a bidimensional image of the structure's surface showing the estimated damage locations with a deviation of the actual position lower than 15 mm.

A Combined Experimental-Numerical Framework for Assessing the Load-Bearing Capacity of Existing PC Bridge Decks Accounting for Corrosion of Prestressing Strands

Bridges constitute important elements of the transportation network. A vast part of the Italian existing infrastructural system dates to around 60 years ago, which implies that the related bridge structures were constructed according to past design guidelines and underwent a probable state of material deterioration (e.g., steel corrosion, concrete degradation), especially in those cases in which proper maintenance plans have not been periodically performed over the structural lifetime. Consequently, elaborating rapid yet effective safety assessment strategies for existing bridge structures represents a topical research line. This contribution presents a systematic experimental-numerical approach for assessing the load-bearing capacity of existing prestressed concrete (PC) bridge decks. This methodology is applied to the Longano PC viaduct (southern Italy) as a case study. Initially, natural frequencies and mode shapes of the bridge deck are experimentally identified from vibration data collected in situ through Operational Modal Analysis (OMA), based on which a numerical finite element (FE) model is developed and calibrated. In situ static load tests are then carried out to investigate the static deflections under maximum allowed serviceability loads, which are compared to values provided by the FE model for further validation. Since prestressing strands appear corroded in some portions of the main girders, numerical static nonlinear analysis with a concentrated plasticity approach is finally conducted to quantify the effects of various corrosion scenarios on the resulting load-bearing capacity of the bridge at ultimate limit states. The proposed methodology, encompassing both serviceability and ultimate conditions, can be used to identify critical parts of a large infrastructure network prior to performing widespread and expensive material test campaigns, to gain preliminary insight on the structural health of existing bridges and to plan a priority list of possible repairing actions in a reasonable, safe, and costly effective manner.

Advanced Structural Health Monitoring Method by Integrated Isogeometric Analysis and Distributed Fiber Optic Sensing

Attempts in digital management of structures are among the most popular topics in the trend of Information of Things (IoT). However, the implementation lags behind. This work recognized that Computer Aided Design (CAD) comprises the core of modern engineering; thus, most digital information can be available if CAD is used not only in design but also for life cycle structural health monitoring (SHM). Based on this concept, the newly designed method utilizes the isogeometric analysis (IGA) tool to include the Distributed Fiber Optic Sensing (DFOS) information by proposing a fiber mesh model. The IGA model can be obtained directly from CAD, and the boundary conditions can be provided directly or indirectly from DFOS in real time and remotely. Hence a practical method of SHM is able to achieve highly efficient and accurate numerical model creation, which can even accommodate non-linear constitutive property of materials. The proposed method was applied to a pipe deformation model as an example. The inverse analysis method is also shown to determine the contact force for loading on the pipe, which shows the potential for many engineering applications.

Analysis of the Impact of Sustained Load and Temperature on the Performance of the Electromechanical Impedance Technique through Multilevel Machine Learning and FBG Sensors

The electro-mechanical impedance (EMI) technique has been applied successfully to detect minor damage in engineering structures including reinforced concrete (RC). However, in the presence of temperature variations, it can cause false alarms in structural health monitoring (SHM) applications. This paper has developed an innovative approach that integrates the EMI methodology with multilevel hierarchical machine learning techniques and the use of fiber Bragg grating (FBG) temperature and strain sensors to evaluate the mechanical performance of RC beams strengthened with near surface mounted (NSM)-fiber reinforced polymer (FRP) under sustained load and varied temperatures. This problem is a real challenge since the bond behavior at the concrete–FRP interface plays a key role in the performance of this type of structure, and additionally, its failure occurs in a brittle and sudden way. The method was validated in a specimen tested over a period of 1.5 years under different conditions of sustained load and temperature. The analysis of the experimental results in an especially complex problem with the proposed approach demonstrated its effectiveness as an SHM method in a combined EMI–FBG framework.

Two-Dimensional Mechano-thermoelectric Heterojunctions for Self-Powered Strain Sensors

We here demonstrate the multifunctional properties of atomically thin heterojunctions that are enabled by their strong interfacial interactions and their application toward self-powered sensors with unprecedented performance. Bonding between tin diselenide and graphene produces thermoelectric and mechanoelectric properties beyond the ability of either component. A record-breaking ZT of 2.43 originated from the synergistic combination of graphene's high carrier conductivity and SnSe₂-mediated thermal conductivity lowering. Moreover, spatially varying interaction at the SnSe₂/graphene interface produces stress localization that results in a novel 2D-crack-assisted strain sensing mechanism whose sensitivity (GF = 450) is superior to all other 2D materials. Finally, a graphene-assisted growth process permits the formation of high-quality heterojunctions directly on polymeric substrates for flexible and transparent sensors that achieve self-powered strain sensing from a small temperature gradient. Our work enhances the fundamental understanding of multifunctionality at the atomic scale and provides a route toward structural health monitoring through ubiquitous and smart devices.

Health Monitoring of Aerospace Structures Utilizing Novel Health Indicators Extracted from Complex Strain and Acoustic Emission Data

The development of health indicators (HI) of diagnostic and prognostic potential from generally uninformative raw sensor data is both a challenge and an essential feature for data-driven diagnostics and prognostics of composite structures. In this study, new damage-sensitive features, developed from strains acquired with Fiber Bragg Grating (FBG) and acoustic emission (AE) data, were investigated for their suitability as HIs. Two original fatigue test campaigns (constant and variable amplitude) were conducted on single-stringer composite panels using appropriate sensors. After an initial damage introduction in the form of either impact damage or artificial disbond, the panels were subjected to constant and variable amplitude compression–compression fatigue tests. Strain sensing using FBGs and AE was employed to monitor the damage growth, which was further verified by phased array ultrasound. Several FBGs were incorporated in special SMARTapes^TM, which were bonded along the stiffener’s feet to measure the strain field, whereas the AE sensors were strategically placed on the panels’ skin to record the acoustic emission activity. HIs were developed from FBG and AE raw data with promising behaviors for health monitoring of composite structures during service. A correlation with actual damage was attempted by leveraging the measurements from a phased array camera at several time instances throughout the experiments. The developed HIs displayed highly monotonic behaviors while damage accumulated on the composite panel, with moderate prognosability.

A Multichannel Strain Measurement Technique for Nanomodified Smart Cement-Based Sensors in Reinforced Concrete Structures

Nanomodified smart cement-based sensors are an emerging self-sensing technology for the structural health monitoring (SHM) of reinforced concrete (RC) structures. To date, several literature works demonstrated their strain-sensing capabilities, which make them suited for damage detection and localization. Despite the most recent technological improvements, a tailored measurement technique allowing feasible field implementations of smart cement-based sensors to concrete structures is still missing. In this regard, this paper proposes a multichannel measurement technique for retrieving strains from smart cement-based sensors embedded in RC structures using a distributed biphasic input. The experiments performed for its validation include the investigation on an RC beam with seven embedded sensors subjected to different types of static loading and a long-term monitoring application on an RC plate. Results demonstrate that the proposed technique is effective for retrieving time-stable simultaneous strain measurements from smart cement-based sensors, as well as for aiding the identification of the changes in their electrical outputs due to the influence of environmental effects variable over time. Accordingly, the proposed multichannel strain measurement technique represents a promising approach for performing feasible field implementations of smart cement-based sensors to concrete structures.