Future Structural Health Monitoring Research Articles

Research on Full-Field Dynamic Deflection Measurement of Beams Based on Dense Feature Matching and Mismatch Removal Method

To solve the problems of measurement errors led by mismatches of dense feature matching in machine vision structural deflection measurement, this paper proposes a dense feature extraction, matching, and dual-step mismatch-removal-based full-field structural dynamic deflection measurement method. First, the of dense feature detection and matching theory is introduced to extract the SIFT feature points on a structural surface in an image sequence and matched by FLANN to trace the structure movement, and the mechanisms and causes of mismatches are analyzed. Then, a dual-step mismatch removal method combining RANSAC and Structural Displacement Continuity Restriction (SDCR) is introduced to achieve full-field dynamic beam deflection measurement. The proposed method is validated through indoor cantilever beam experiments, and results show that the method can effectively eliminate a large number of SIFT feature mismatches (accounting for approximately 55% of the total matched feature points). The full-field dynamic displacement field of the beam can be measured with the correctly matched dense feature points by converting dense feature point displacements into continuous and uniform spatiotemporal deflections of the structure. A comparison with the GOM Correlate Professional DIC measurement system was conducted, and the maximum measurement error of the cantilever beam dynamic displacement of the proposed method is between 0.024 and 0.053 mm, the root mean squared error of displacement is approximately 0.01 mm, and the correlation coefficient between two deflection–time curves reaches 0.9964. The proposed algorithm is proven to be effective in full-field displacement measurement and has great potential in future structural health monitoring of bridges.

Data interpretation and forecasting of SHM heteroscedastic measurements under typhoon conditions enabled by an enhanced Hierarchical sparse Bayesian Learning model with high robustness

Rapid advances in Structural Health Monitoring (SHM) have led to a new era of Big Data in the past decade. However, collected data may include a huge amount of heteroscedastic data, particularly under extreme events (e.g., typhoons), which makes robust decisions remain a challenging problem. To this end, this article proposed an enhanced Hierarchical Sparse Bayesian Learning (eHSBL) model for SHM field data interpretation and forecasting, uncertainty analysis, as well as correlation analysis. The proposed eHSBL model incorporates the Gaussian kernel function and the Hierarchical Bayesian model for an operational large-scale suspension bridge, i.e., Tsing Ma Bridge (TMB) in Hong Kong, under extreme events exhibiting heterogeneities, strong volatilities, and high uncertainties. The Gaussian kernel function in the eHSBL model is capable of mapping SHM heteroscedastic data into high-dimensional space, and the Hierarchical Bayesian model can be iteratively updated by introducing hyperparameters to achieve the sparse expression of the model, so the model has strong generalization ability and high robustness. Through the incorporation of the extrapolation forecasting algorithm, the eHSBL can carry out the forecasting analysis for the future SHM data. Results show that the eHSBL regression and forecasting results under extreme events can well reflect the strain responses of the structure. The uncertainty and correlation analysis reveal that the eHSBL model error variation is influenced by temperature, traffic loads, and wind speeds, and the corresponding error variation curves and change rules are also provided and summarized, which will shed light on the performance evaluation and early warning for in-service long-span bridges.

Acoustic Emission Source Characterisation during Fatigue Crack Growth in Al 2024-T3 Specimens.

While acoustic emission (AE) testing can be used as a valuable technique in structural health monitoring and non-destructive testing, little research has been conducted to establish its sources, particularly in 2024-T3 aluminium alloys. The major contribution of this work is that it provides a method to obtain a better linear relationship of count rate with crack growth rate based on waveform. This paper aims to characterise AE sources by synchronising the AE waveforms with load levels and then to propose possible dominant frequency ranges. The AE waveforms during fatigue crack growth in edge-notched 2024-T3 aluminium specimens, from an initial crack length of 10 mm to 70 mm, were collected at two different load ratios R = 0.125 and 0.5. At the same time, the crack growth rate was determined using thermal imaging and associated control software. The AE waveforms obtained were processed using the fast Fourier transform. It was shown that a significantly higher AE count rate was recorded at R = 0.125 compared to R = 0.5 when the maximum load was kept the same. This means that the R-ratio would affect the total amount of AE activities collected. It was also found that the dominant frequency range of the AE waveforms directly related to crack growth was 152-487 kHz, and the ranges due to crack closure were likely to be 310 kHz-316 kHz and 500-700 kHz. Based on the proposed frequency ranges, waveform selection was conducted and a better linear relationship between count rate and crack growth rate was observed. This study provides a better understanding of the AE sources and waveforms for future structural health monitoring applications.

A metasurface radar for steering ultrasonic guided waves

This article presents a new metasurface made of shape memory alloy (SMA) unit cells for steering ultrasonic guided waves in desired emitting directions. The philosophy behind the controllable wave steering performance of the metamaterial system resides in the tunability of its stopband. Such a tunable feature originates from the reversible dramatic variation of the elastic modulus of the shape memory element under the thermal loads. The research starts with the investigation of the adjustable bandgap behavior of the metastructure at different statuses from the scrutiny of the wave dispersive characteristics. Additionally, a systematic parametric study is carried out to unfold the bandgap evolution principle by changing the SMA stub height. Subsequently, numerical modeling of the wave steering behavior is performed by shifting the stopband of one sector within the unit-cell-ring away from the excitation frequency. The wavefield images associated with the wave directionality plots demonstrate that the thermally-activated metasurface area can open up a transmission path for the ultrasonic waves, while the rest portion will not allow guided waves to penetrate. By rotating the designated sector, the metamaterial structure could work like a wave emission radar, realizing the steerable unidirectional wave radiation from a single piezoelectric transducer. In the end, the targeting phenomena are validated by the Scanning Laser Doppler Vibrometry (SLDV) experimental tests. The proposed active metasurface system with the controllable ultrasonic wave interrogating capability may find its application for directional guided wavefield generation in future Structural Health Monitoring (SHM) and Nondestructive Evaluation (NDE) systems.

Dynamic Response and Seismic Vulnerabilities of the Historic Tophane-i Amire: Dynamic and Seismic Analysis of a Historic Masonry Building

This study deals with the dynamic characterization of the historic Tophane-i Amire building, located in Istanbul. The domed structure was constructed in the 18th century and is known as the first industrial building in the Ottoman times. It was an arsenal building used for the production of cannons and cannon balls. An ambient vibration survey was conducted with the Enhanced Frequency Domain Decomposition method to identify the dynamic characteristics of the structure. Later, a finite element model of the structure was constructed and numerical dynamic identification was pursued. The obtained experimental and numerical results have been compared and the finite element model of the structure has been tuned to obtain a validated tool to represent the real dynamic behavior of the structure. While the tuned model of the structure is intended to contribute to understanding its structural behavior and evaluate the mechanical properties of masonry, it has also been used for the evaluation of its seismic vulnerabilities. For this purpose, the structure has been analyzed under maximum considered earthquake response spectrum, and stress concentration regions have been determined. The results also serve as data for future structural health monitoring studies related to the structure.

Metamaterial-enhanced coda wave interferometry with customized artificial frequency-space boundaries for the detection of weak structural damage

Coda wave interferometry (CWI), based on the accumulated scattered and reflected waves from scatterers and structural boundaries, has been shown to offer remarkable sensitivity to weak structural damage. However, coda waves highly rely on wave reflections from structural boundaries, which compromises the efficiency of the CWI for the monitoring of large structures. In addition, coda waves usually contain global damage information, thus hampering their use for damage localization. Targeting these problems, this work proposes a meta-device design to enhance the performance of the CWI. The deployment of the meta-device generates artificial frequency-space boundaries, which allow for an effective manipulation and customization of wave propagation in a selective manner in terms of both frequency content and inspection area. As a result, coda wave energy, alongside their interaction with structural damage, is enhanced, conducive to the detection of weak structural damage. Finite elements are carried out to evaluate the feasibility of the concept and to further explore the mechanisms and characteristics of the enhanced CWI. By synthesizing and combining multiple meta-devices with different bandgaps, a zone-by-zone damage detection and localization strategy is proposed. The proposed meta-device and damage detection strategy are finally validated through experiments. Results confirm that the proposed meta-devices significantly enhance the sensitivity of the CWI to weak damage and their potential for damage localization. The concept of metamaterial-assisted CWI shows great promise for future structural health monitoring applications.

Wave propagation in a doubly tapered shear beam: Model and application to a pyramid‐shaped skyscraper

AbstractOne dimensional wave propagation is studied in a cantilever conical structure deforming in shear. The structure has rectangular cross‐section both dimensions of which decrease linearly along the polar axis (hence, doubly tapered). The equation motion is derived and solved in terms of spherical Bessel functions, which can be expressed exactly as finite algebraic expressions in terms of powers and sine and cosine functions of the polar coordinate. The transfer‐matrix for a truncated pyramid element is then derived and generalized to a chain of elements. The model is used to represent a 48‐story pyramid‐shaped steel‐frame skyscraper, the Transamerica Tower in San Francisco, California, in which the Loma Prieta, 1989 earthquake (, epicentral distance, km) was recorded by an array of accelerometers. The building is modeled by homogeneous and layered truncated pyramids. Wave propagation through the building is studied by analysis of impulse response functions computed from the observed earthquake accelerations. In addition, its equivalent homogeneous beam shear‐wave velocity is identified solely from its geometry and observed fundamental frequency of vibration. Further, the variation of wave velocity along is height is identified by least squares fit of a layered pyramid model in the observed impulse response functions. The results reveal equivalent homogeneous pyramid wave velocity of about 150–160 m/s in both directions, which is similar to other steel‐frame structures. The identified wave velocity profiles are consistent with the structural design. The identified parameters can be used as reference in future structural health monitoring of this structure.

Piezoresistive performance of hydrophobic cement-based sensors under moisture and chloride-rich environments

Silicone hydrophobic powder (SHP) and crystalline waterproofing admixture (CWA) were used to improve the impermeability of carbon black (CB)/cement-based sensors. The mechanical, electrical and piezoresistive properties, waterproofing and chloride resistance of CB/cementitious composites were investigated in this study. The piezoresistivity before or after different durations of immersion in freshwater and 3% sodium chloride solution and the stability in freshwater and marine environment were studied and compared. The results show that compressive strength increased with the additions of CWA and SHP, while the tensile strength slightly decreased with CWA, due to the formation of crystalline. Moreover, cementitious composites with SHP exhibited the best water impermeability, while the counterpart containing CWA presented the optimal chloride resistance. Although cementitious composites with SHP exhibited the highest electrical resistivity, the most stable piezoresistivity occurred after 90 days of immersion in freshwater. On the other hand, cementitious composites incorporating CWA presented the lowest electrical resistivity, but the piezoresistivity continually decreased with the immersion duration. Because of the free ions, piezoresistivity increased as a result of the immersion in sodium chloride solution. The related results will provide an insight into the piezoresistivity of hydrophobic cement-based sensors under moisture and chloride environments for future structural health monitoring.

Merging advanced sensing techniques and simulation tools for future structural health monitoring technologies

Merging advanced sensing techniques and simulation tools for future structural health monitoring technologies Project DREAM-ON aims to design smart autonomous mechanics structures, able to perform online control of their health and take anticipated actions during service for increased reliability and performance. Its innovative concept is a synergistic, real- time, and robust dialogue between advanced sensing techniques and powerful simulation tools to make all knowledge on the physical system available and perform early damage detection, precise diagnosis, and safe decision making.

Modal experiment and model updating for Yingzhou Bridge

To facilitate effective structural analyses of long-span bridges, numerical models of high fidelity are indispensable. Model updating can guarantee the fidelities of the numerical models, and is therefore embraced in many numerical simulation processes of long-span bridges. However, two issues current exist which hinder the effective model updating of bridge models. First, the reference information employed for model updating generally are structural modal parameters measured on the bridges’ locations, but most field modal test methods demand excessive sensing resources, and are therefore hard to be undertaken. Second, the traditional model updating based on sensitivity analyses of uncertain parameters cannot be applied to the detailed numerical models of bridges with regard to the high computational costs. To this end, under the engineering background of Yingzhou Bridge (a special-shaped arch bridge with a main span of 120 m), the applicability of the moving sensor modal test of low sensing resource demand is validated, and the practice of replacing the detailed models with simplified models for model updating is also proved to be effective in this article. With these findings, the detailed numerical model of Yingzhou Bridge is established and validated which will facilitate the future structural health monitoring, structural safety assessment and structural behavior prediction of the arch bridge.

Physicochemical and Piezoresistive properties of smart cementitious composites with graphene nanoplates and graphite plates

Graphene nanoplate (GNP) and graphite plate (GP) are promising functional nanofillers for smart self-sensing cementitious composites. The effects of GNP and GP on physicochemical, mechanical and piezoresistive properties of cementitious composite were investigated in this paper. The results show that cement hydration was accelerated with the increased amounts of GNP and GP because of nucleation effect. The electrical resistivity of GNP-cementitious composites was always lower than the counterpart with GP with the same concentration. On the other hand, percolation occurred for the GNP/cementitious composites at the dosages from 2 to 3% (by weight), while it never happens for the GP/cementitious composites. Moreover, the GNP/cementitious composites reached the maximum mechanical strength when the GNP content was 1.0%, while for the GP/cementitious composites, only minor strength improvement was obtained with a dosage of 0.5% GP. As for the piezoresistivity, the cementitious composites with GNP exhibited higher fractional changes of resistivity. Irreversible resistivity happened for 2–3% GP/cementitious composites subjected to cyclic compression, due to the poor and loose microstructures. The outcomes are expected to provide an insight into the application of GNP/cementitious and GP/cement composites as cement-based sensors for the future structural health monitoring.

Application of Laser Vibrometry to Assess Defects in Ship Hull's Welded Joints' Technical Condition.

The paper presents the measurement process and test results for six thin-walled plates with different dynamic characteristics caused by different defects of welded joints. The tests were carried out using non-destructive testing (NDT). The authors made an attempt to determine the validity of the use and degree of effectiveness of the tests based on laser vibrometry in detecting defects in welded joints. The tests of welded plates were carried out using displacement laser sensors and piezoelectric accelerometers, while the source of vibration extortion was a modal hammer. In the adopted measurement methodology, the application of accelerometers was to obtain the reference data, which allowed for comparison with the measurement data obtained from the laser vibrometer. The analysis of the obtained measurement data, in the fields of time and frequency, made it possible to verify the correctness of the data obtained by means of laser vibrometry and to determine the requirements which are necessary for the correct performance of NDT tests and in the future structural health monitoring (SHM) system of welded joints with the use of a laser vibrometer. The mathematical model developed in the MSC software Pastran-Nastran was also used in the work. The model was developed for the purpose of mutual verification of the measurement and calculation tests. At the present stage of work, it can be stated that the results obtained by laser vibrometry methods should be treated as a supplement to the research conducted with traditional piezoelectric accelerometers. In certain situations, they can be used as an alternative to accelerometers, due to the fact that laser sensors do not require direct contact with the examined object. Where the object under test may be in a strong electromagnetic field, optical sensors are better suited than contact sensors.

Structural health monitoring of a 32‐storey steel‐frame building using 50 years of seismic monitoring data

AbstractThe case study, Los Angeles 32‐storey residential building, has an exceptionally long seismic observation history, spanning 50 years (1971‐2020), during which it has experienced many earthquakes, some causing extensive damage in the metropolitan area. Records of 13 earthquakes are analyzed, including San Fernando of 1971, to find out if permanent loss of stiffness occurred in the structure since 1971 and to describe statistically its nonlinear elastic behavior, not related to damage. It is a rare example of an instrumented pre‐San Fernando earthquake steel‐frame building, constructed with some on‐site welded column‐beam connections, type of construction that exhibited weaknesses after the Northridge, 1994 earthquake. The identification method consists of fitting equivalent uniform and four‐layer Timoshenko beams models by waveform inversion of bandpass filtered impulse responses, which results in piecewise constant profiles of shear wave velocity (the damage sensitive parameter) and the elastic moduli ratio, reflecting the distribution of structural stiffness along the height. The nonlinear elastic behavior derived from the smaller amplitude data constitutes strain‐dependent baseline for future structural health monitoring of this building. This behavior reveals more prominent strain dependency in the longitudinal direction and stronger variation with strain toward the base of the structure.

Quantifying local stiffness loss in beams using rotation rate sensors

The application of rotation rate sensors in vibration-based damage detection is investigated. For this purpose, simulation, FEM analyses of a damaged cantilever plexiglass beam under kinematic excitations are carried out and verified experimentally. Three types of rotation rate sensors are examined. Damage index based on the maximum difference of signals on both sides of the damaged location is compared with averaged damage indices in time and frequency domain. It is demonstrated that the rotation velocities of the beam axis are less sensitive to measurement noise than the transversal accelerations. A simple damage index based on peak response and two, integral damage indices were tested using numerical simulations and laboratory experiments. The integral time-domain damage index appeared to be equally useful in damage detection, like the index based on the transmissibility concept. The results of the experiments show positive prospects for constructing Structural Health Monitoring (SHM) networks using rotation rate sensors. This is particularly important for future SHM systems to be commonly implemented in civil engineering.

Structural Health Monitoring using deep learning with optimal finite element model generated data

Identifying damage through Structural Health Monitoring (SHM) methods is increasingly attracting attention due to multiple maintenance and failure prevention applications. In order to create reliable SHM systems for structural damage identification (DI) tasks, access to large amounts of data containing measured structural responses is usually necessary. The data acquisition is mostly based on direct experimental responses up to now and requires time consuming measurements in various working and ambient conditions of the structure. In the present work, a novel SHM method is tested where all data is solely derived from FE calculated responses, after an initial experimental cost for FE model updating on the healthy structure state. The proposed method can be especially applied in cases where specific damage types are expected or anomalies are adequately defined so they can be effectively simulated by FE models. Origin of such models may then be the healthy experimental status. To test the proposed SHM system, the optimal FE model of an experimental benchmark linear beam structure is constructed, simulating an undamaged condition. In order to check the robustness of the proposed method the damage magnitudes imposed on the benchmark are kept small and combined with random excitations. Next, the optimal FE model is used for generating labeled SHM vibration data through a repetitive load case scheme which also includes uncertainties simulation. The data derived from the optimal FE model is finally used to train a Deep Learning (DL) Convolutional Neural Network (CNN) classifier which is after experimentally validated on the benchmark structure. The optimal FE generated data proves to be able to train an accurate CNN that can predict adequately the experimental benchmark states. A comparison is also given with a CNN trained by the corresponding nominal FE model data which is found not reliable on the experimental validations. The presented combination of optimal FE and DL is a potential solution for future SHM tools and further investigation is encouraged.

Lessons learned from six different structural health monitoring systems on highway bridges

Structural health monitoring has been utilized in numerous ways to investigate the performance and integrity of highway bridges. This paper highlights the use of six structural health monitoring systems, which were deployed to monitor distinct behaviors on six bridges in Kentucky. The structural health monitoring systems are as follows: (1) Over-height truck impact detection and monitoring on the I-64 over US 60 bridge, (2) Barge impact detection and monitoring on northbound US 41 over the Ohio River, (3) effectiveness of carbon fiber-reinforced polymer retrofit evaluation based on prestressed concrete I-girder crack movement on the I-65 elevated expressway in Louisville, (4) effect of thermal loads on bridge substructure evaluation on KY 100 over Trammel Creek, (5) thermal movement of expansion joints evaluation on eastbound I-24 over the Tennessee River, and (6) crack growth monitoring on steel floor beam on I-275 over the Ohio River. The deployment of the different structural health monitoring systems on Kentucky bridges has provided valuable insights on their planning, implementation, and maintenance, which can be applied on future structural health monitoring projects. While several of the projects have proved immensely successful, with some still being continuously monitored, others, due to numerous complications, have met with only limited success. The best return on investment was realized from structural health monitoring instrumentation that was focused and limited in scope. The successful structural health monitoring systems had continuous communication between all stakeholders during planning, implementation, and monitoring phases of the projects. Following implementation, the availability of contingency funds through the funding source, to replace/upgrade sensors and networking equipment and costs for reprogramming and reinstallation outside of regular maintenance costs, was also important for the structural health monitoring to be successful.

Integration of High-Resolution Laser Displacement Sensors and 3D Printing for Structural Health Monitoring.

This paper presents a novel experimental design for complex structural health monitoring (SHM) studies achieved by integrating 3D printing technologies, high-resolution laser displacement sensors, and multiscale entropy SHM theory. A seven-story structure with a variety of composite bracing systems was constructed using a dual-material 3D printer. A wireless Bluetooth vibration speaker was used to excite the ground floor of the structure, and high-resolution laser displacement sensors (1-μm resolution) were used to monitor the displacement history on different floors. Our results showed that the multiscale entropy SHM method could detect damage on the 3D-printed structures. The results of this study demonstrate that integrating 3D printing technologies and high-resolution laser displacement sensors enables the design of cheap, fast processing, complex, small-scale civil structures for future SHM studies. The novel experimental design proposed in this study provides a suitable platform for investigating the validity and sensitivity of SHM in different composite structures and damage conditions for real life applications in the future.

A machine‐learning approach to load‐monitoring based on guided waves

AbstractThe research field of structural health monitoring (SHM) describes the way to assess the structural integrity. The research objectives are broadly diversified, which are all addressing the improvement of SHM technologies. It is important to optimise the capability of sensor networks by applying intelligent signal processing models. These intelligent systems predict the health state based on acquired data in a real‐time environment. Piezoelectric sensor networks and guided‐wave based analyses are combined with machine learning to create an efficient load‐monitoring routine. This load‐monitoring approach is chosen to track any changes in the environmental conditions, which causes most of the problems in reference signal based damage detection. Therefore, the proposed load‐monitoring method can be used to compensate the influence of external changes on the acquired signals and improve the performance and robustness of future SHM systems. (© 2017 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

In situ monitoring of particle fracture in aluminium alloys

AbstractMaterial damage at the microscale involves both initiation and interaction effects that are typically activated long before the appearance of macroscopically observable failure events. These early appearances of damage have been broadly classified as microstructure‐sensitive damage precursors or indicators. A particular class of such precursors which is of importance to aluminium and other precipitate‐hardened alloys is the focus of this article. Specifically, the hard, intermetallic particles in aluminium alloys fracture before significant failure occur in the surrounding matrix. In this investigation, an effort to directly assess particle fracture activity at the time and scale that it occurs is made by coupling mechanical testing inside a scanning electron microscope with nondestructive evaluation techniques including digital image correlation as well as real‐time acoustic emission monitoring. The use of a surface measurement technique along with a volumetric monitoring method at the microscope scale provides a way for coupling of fracture information at locations which are directly related to the particle activity. In this article, Al2024‐T3 specimens in the as‐received condition were subjected to tension as well as to tension‐tension cyclic loading. The obtained in situ results demonstrate, for the first time to the best knowledge of the authors, that particle fracture occurs early in the damage process which justifies its characterization as a material damage precursor. The overall approach provides datasets capable to detect particle fracture initiation, which could become useful in future structural health monitoring applications.

Biomechanically influenced mobile and participatory pedestrian data for bridge monitoring

Future structural health monitoring systems are evolving toward crowdsourced, autonomous, sustainable forms based on which damage-indicative structural features can be identified. Unlike conventional sensor systems, they serve as non-stationary, mobile, and distributed sensor network components. For example, smartphone sensors carried by pedestrians decouple from the structure of interest, making it difficult to measure structural vibration. Taking bridges as instances, smartphone sensor data contain not only the bridge vibration but also the pedestrians’ biomechanical features. In this article, pedestrians’ smartphone data are used to conduct force estimation and modal identification for structural health monitoring purposes. Two major pedestrian activities, walking and standing, are adopted to estimate walk-induced forces on structures and identify modal parameters, respectively. First, vibration time history of a walking pedestrian combined with pedestrian weight is a measure of dynamic forces imposed on the structure. Second, standing pedestrian’s smartphone sensors provide spectral peaks which are mixtures of structural and biomechanical vibrations. Eliminating biomechanical content reveals structural modal properties which are sensitive to structural integrity. This study presents the first structural health monitoring application recruiting pedestrians in a testbed bridge monitoring example. Orchestrating pervasive and participatory pedestrian data might bring new frontiers to structural health monitoring through a smart, mobile, and urban sensing framework.