Monitoring Of Large Structures Research Articles

Muography as a support technique for non-invasive research and three-dimensional localization of tombs in archaeological sites: a case study from Palazzone Necropolis (Perugia – Italy)

Transmission muography is a non-invasive imaging technique that exploits the penetrating power of atmospheric muons into matter to obtain two-dimensional and three-dimensional density images of the monitored structure. The detectors used are particle trackers. Muography enables the monitoring of large structures and it is also particularly useful in the archaeological field for a mapping of low-density underground anomalies potentially related to unknown or inaccessible tombs or tunnels. The Palazzone necropolis, located south of Perugia (Italy), dating back to Etruscan period, contains about 200 known tombs, some of which, such as the Volumni Hypogeum, can be visited thanks to a touristic route. The eastern area of the necropolis, on the other hand, does not have a touristic path and is partially unknown. The objective of the muographic measurement campaign is to support the re-evaluation of this archaeological area by searching for new anthropic cavities and identifying them three-dimensionally. One of the goals of this study is to obtain a three-dimensional localization of cavities starting from a single muographic measurement by exploiting an image focusing algorithm. For this purpose, an area that contains a known cavity was used as the reference cavity for the test of the three-dimensional reconstruction algorithm.

Robust and efficient feature-based method for structural health monitoring of large structures

Robust and efficient feature-based method for structural health monitoring of large structures

Research on a bimetallic-sensitized FBG temperature sensor.

Temperature measurement is of great significance for research in the health monitoring of large structures and earthquake precursors. Against the frequently reported low sensitivity of fiber Bragg grating (FBG) temperature sensors, a bimetallic-sensitized FBG temperature sensor was proposed. The sensitization structure of the FBG temperature sensor was designed, and the sensor sensitivity was analyzed; the lengths and materials of the substrate and strain transfer beam were analyzed theoretically; 7075 aluminum and 4J36 invar were chosen as bimetallic materials, and the ratio of the substrate length to the sensing fiber length was determined. The structural parameters were optimized; the real sensor was developed, and its performance was tested. The results suggested that the sensitivity of the FBG temperature sensor was 50.2 pm/°C, about five times than that of a bare FBG sensor, and its linearity was more than 0.99. The findings offer a reference for developing sensors of the same type and further improving the sensitivity of the FBG temperature sensors.

High-efficiency FBG array sensor interrogation system via a neural network working with sparse data.

FBG array sensors have been widely used in the multi-point monitoring of large structures due to their excellent optical multiplexing capability. This paper proposes a cost-effective demodulation system for FBG array sensors based on a Neural Network (NN). The stress variations applied to the FBG array sensor are encoded by the array waveguide grating (AWG) as transmitted intensities under different channels and fed to an end-to-end NN model, which receives them and simultaneously establishes a complex nonlinear relationship between the transmitted intensity and the actual wavelength to achieve absolute interrogation of the peak wavelength. In addition, a low-cost data augmentation strategy is introduced to break the data size bottleneck common in data-driven methods so that the NN can still achieve superior performance with small-scale data. In summary, the demodulation system provides an efficient and reliable solution for multi-point monitoring of large structures based on FBG array sensors.

Use of digital image correlation and machine learning for the optimal strain placement in a full-scale composite tidal turbine blade

One of the challenges testing and health monitoring of large structures represents is getting as much information as possible from a specimen with a limited number of sensors. In this work, a data-driven approach was pursued to decide the optimal location of single-point strain gauges using machine learning algorithms (MLA) and information from Digital Image Correlation (DIC) measurements. The optimal strain gauge placement was computed for a range of sensor numbers and the presence of sensors in the high-gradient regions was identified. Strain maps of almost 40,000 measurements were reconstructed successfully with fewer than twenty measured values using the method employed. However, certain loss of image contrast was identified which is likely to have resulted from the treatment of non-numerical values.

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

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

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.

Structural Health Monitoring of Large Structures via mmWave Sensing

Abstract Vibration and deformation measurement are of great significance for structural health monitoring (SHM). While the existing accelerometer, laser and vision-based approaches have limitations in multi-point vibration measurement, efficiency and environmental adaptability, especially for large structures. In this paper, we present a method for structural health monitoring with multi-point vibration and deformation measurement via millimetre- wave sensing. First, we describe the mathematical model and the principle of multi-target synchronous vibration and deformation monitoring. Second, with a built prototype of mmWave sensing system, we evaluate our method with both laboratory and field tests. The experimental results of different amplitudes, frequencies and velocities show that our approach can accurately monitor the vibration and deformation of multi-point with μm-level accuracy. The field test of Shanghai Songpu Bridge is provided to further validate the effectiveness of the method, offering a novel approach for contactless vibration and deformation monitoring of large structures in SHM.

Low-Cost and High-Performance Solution for Positioning and Monitoring of Large Structures.

Systems for accurate attitude and position monitoring of large structures, such as bridges, tunnels, and offshore platforms are changing in recent years thanks to the exploitation of sensors based on Micro-ElectroMechanical Systems (MEMS) as an Inertial Measurement Unit (IMU). Currently adopted solutions are, in fact, mainly based on fiber optic sensors (characterized by high performance in attitude estimation to the detriment of relevant costs large volumes and heavy weights) and integrated with a Global Position System (GPS) capable of providing low-frequency or single-update information about the position. To provide a cost-effective alternative and overcome the limitations in terms of dimensions and position update frequency, a suitable solution and a corresponding prototype, exhibiting performance very close to those of the traditional solutions, are presented and described hereinafter. The solution leverages a real-time Kalman filter that, along with the proper features of the MEMS inertial sensor and Real-Time Kinematic (RTK) GPS, allows achieving performance in terms of attitude and position estimates suitable for this kind of application. The results obtained in a number of tests underline the promising reliability and effectiveness of the solution in estimating the attitude and position of large structures. In particular, several tests carried out in the laboratory highlighted high system stability; standard deviations of attitude estimates as low as 0.04° were, in fact, experienced in tests conducted in static conditions. Moreover, the prototype performance was also compared with a fiber optic sensor in tests emulating actual operating conditions; differences in the order of a few hundredths of a degree were found in the attitude measurements.

Theory and method of multi-point synchronous deformation and vibration measurement based on millimeter-wave sensing

Although accelerometer-, vision-, and laser-based sensing technologies are widely used in deformation and vibration measurements, the multi-point simultaneous deformation and vibration measurement of large-scale structures with high accuracy remains a challenge. Herein, we developed a novel technology for contactless deformation and vibration measurement using millimeter-wave (mmWave) linear frequency-modulated continuous wave radar, establishing the theory and method for multi-point simultaneous deformation and vibration measurement based on mmWave sensing. The principle of mmWave multi-point vibration measurement and a method of displacement extraction were systematically described using the baseband signal model of multi-target perception. We built a prototype of the proposed mmWave vibration measurement system and a platform for simulated vibration tests. The accuracy of the mmWave multi-point vibration measurement technology was verified and compared with that of laser displacement sensors. Using the prototype system, we conducted experiments on the multi-point synchronous vibration measurement and model identification of the slender hinged flexible rod structure of a certain aerospace equipment in China and then described the dynamic response monitoring of a large bridge structure. Results showed that the deformation and vibration measurement method based on mmWave sensing developed in this work could conveniently and accurately extract the time domain information of multi-target vibration displacements. Our work provides an innovative method and approach for non-contact multi-point synchronous deformation and vibration measurements that could be applied to the mechanical performance test and health monitoring of large structures.

Aplicación de la fotogrametría con drones al control deformacional de estructuras y terreno

En este trabajo se estudia la viabilidad del empleo de drones para el control de deformaciones del terreno y las estructuras, analizando para cada caso, la resolución, precisión y validación con otras técnicas. Se presenta con detalle la técnica fotogramétrica Structure From Motion, empleada para elaborar ortofotografías y modelos 3D precisos sin necesidad de conocer previamente las posiciones y ángulos de incidencia. El uso de puntos de control precisos, así como drones con sistema integrado RTK, son factores relevantes para la obtención de resultados con alta precisión. Se presenta el caso de estudio de la monitorización de una gran estructura, en este caso una presa arco-gravedad. Los resultados obtenidos presentan una precisión en deformaciones de ±2 mm para la estructura. Esto confirma que la fotogrametría dron es aplicable al control deformacional de presas de hormigón, abriendo las posibilidades a la monitorización de otras grandes estructuras e infraestructuras.

Improved modal identification using wireless continuous dynamic monitoring systems without real time synchronization

In the context of vibration based Structural Health Monitoring using automated operational modal analysis, data synchronization methods permit a reduction in the cost of wireless monitoring systems, since the use of extra hardware for perfect data synchronization can be avoided. Furthermore, the relaxation of data synchronization requirements permits a more flexible topology (higher distance between sensors, no need of external antennas for data synchronization using GPS, lower power consumption), which is particularly advantageous in the dynamic monitoring of large structures. In this paper, continuous dynamic monitoring without real-time synchronization is studied. Firstly, using numerical simulations, the effects of synchronicity faults on operational modal analysis is deeply analysed, showing that time delays have a great impact on modal identification. Secondly, a new data synchronization method based on mode shapes identified with operational modal analysis methods is proposed to improve the accuracy of modal identification. This does not need the assumption of proportional damping. Therefore, it overcomes this limitation that up to the knowledge of the authors is present in all other available methods. The proposed processing strategy is validated in the processing of monitoring data continuously collected at a long span arch bridge (Infante D. Henrique Bridge) with two wireless acceleration nodes. The full-scale application shows high time synchronization accuracy and proves that the proposed synchronization method is adequate for continuous dynamic monitoring, providing reliable modal parameters estimates with any assumptions about the damping, permitting a reduction in the monitoring costs, easier installation and power consumption reductions (very important to increase the autonomy of wireless nodes). In short, hardware costs are replaced by a smarter processing.

Smart nano-engineered cementitious composite sensors for vibration-based health monitoring of large structures

Cement composites, generally with high resistivity, when incorporated with conductive nanoparticles, are found to be capable of reflecting strain state due to the change in electrical resistance of the matrix. These unique properties of cement-based nanocomposites (CNC) allow the development of smart cement sensors for continuous health monitoring of large concrete structures. Due to the limitation of strain-based sensing (for local behaviour), vibration-based sensing is gaining considerable importance. The performance of cement-based nanocomposites, unlike under the static load or slow varying load, for dynamic response sensing is very scanty. In the present study, an attempt has been made to develop robust smart cement sensors for capturing the multiple natural frequencies of a structure. First, a systemic way of fabrication of sensors (smart cement sensors) using cement matrix and nano-inclusions of carbon nanotubes is presented. Three different types of multi-walled nanotubes (MWCNTs) like pristine MWCNTs (P-MWCNTs), carboxyl acid (−COOH) MWCNTs and hydroxyl (−OH) MWCNTs with four different weight percentage (0.1-0.75 wt.%) of cement matrix are considered for the study. Strain sensibility of the developed sensors is explored under cyclic compression load. Further, the efficacy of the developed sensor for dynamic sensing is investigated using a large reinforced concrete bridge girder where instrumented impulse hammer is used to dynamically excite the bridge girder. It is found that the developed CNC is capable of capturing the natural frequencies (even up to the third mode) which are found to be sufficient for vibration-based health monitoring of structures. The performance of the developed sensors is compared with off-the-shelf accelerometers and is found to be in good agreement. The study provides a demonstrable technology for using the CNC as a vibration sensor for continuous health monitoring and assessment of structures, just by using ambient vibration information.

Guided wave sensitivity prediction for part and through-thickness crack-like defects

Guided waves allow for the efficient structural health monitoring of large structures using phased or distributed arrays of sensors. The sensitivity for specific defects can be improved by accounting for the angular scattering pattern. The scattering of the fundamental anti-symmetric guided wave mode (A0 Lamb mode) at through-thickness and part-through crack-like defects was studied experimentally and from three-dimensional finite element simulations. Experimentally, the scattered field around manufactured notches of different depths and lengths in an aluminium plate was measured using a laser interferometer. The scattered field was extracted by taking the complex difference in the frequency domain between baseline measurement and measurements around the defect. Good agreement was found between measurements and three-dimensional finite element simulations, and the amplitude and directionality pattern of the scattered field can be predicted accurately. The angular directionality pattern of the scattered field depending on the direction of the incident wave relative to the crack-like defect orientation, depth and length relative to the wavelength was investigated. For short and part-thickness defects, the main scattering effect is a reduction of the (forward) wave propagating past the defect with very limited backscattered amplitude. Significant energy scattered back towards the incident wave direction was only found for perpendicular incidence on long and deep defects. Even for large defects, almost no energy is scattered in certain directions from the defect, possibly complicating defect detection. Based on the predicted amplitude and angular dependency of the scattered guided waves, the sensitivity for defect detection using localized and distributed structural health monitoring sensor array systems can be quantified.

Structural Health Monitoring of Large Structures Using Acoustic Emission–Case Histories

Acoustic emission (AE) techniques have successfully been used for assuring the structural integrity of large rocket motorcases since 1963 [...]

Dynamic Assessment of Masonry Towers Based on Terrestrial Radar Interferometer and Accelerometers.

This paper discusses the performance of a terrestrial radar interferometer for the structural monitoring of ancient masonry towers. High-speed radar interferometry is an innovative and powerful remote sensing technique for the dynamic monitoring of large structures since it is contactless, non-destructive, and able to measure fast displacements on the order of tenths of millimeters. This methodology was tested on a masonry tower of great historical interest, the Saint Prospero bell tower (Northern Italy). To evaluate the quality of the results, data collected from the interferometer were compared and validated with those provided by two types of accelerometer-based measuring systems directly installed on the tower. Dynamic tests were conducted in operational conditions as well as during a bell concert. The first aimed at characterizing the dynamic behavior of the tower, while the second allowed to evaluate the bell swinging effects. Results showed a good agreement among the different measuring systems and demonstrated the potential of the radar interferometry for the dynamic monitoring of structures, with special focus on the need for an accurate design of the geometric aspects of the surveys.

Strain transfer mechanism of grating ends fiber Bragg grating for structural health monitoring

The grating ends bonding fiber Bragg grating (FBG) sensor has been widely used in sensor packages such as substrate type and clamp type for health monitoring of large structures. However, owing to the shear deformation of the adhesive layer of FBG, the strain measured by FBG is often different from the strain of actual matrix, which causes strain measurement errors. This investigation aims at improving the measurement accuracy of strain for the grating ends surface-bonded FBG. To fulfill this objective, a strain transfer equation of the grating ends bonding FBG is derived, and a theoretical model of the average strain transfer from the matrix to the optical fiber is developed. Moreover, parameters that influence the average strain transfer rate from the matrix to the optical fiber are analyzed. A selection scheme of bonding parameters by numerical simulation is provided, which is significantly advantageous over that of the grating bonding FBG. The theoretical equation is verified by finite element method (FEM). Compared with the existing model, the proposed model has higher measurement accuracy. Experimental tests are performed to validate the effectiveness of the proposed model on the equal-intensity cantilever beam, whose surface is attached to the bare FBG with grating ends bonding and strain gauge by using epoxy glue. The results show that there is a great agreement between the outcome of the bare FBG and that of the strain gauge, and the corrected strain is closer to the true strain. The proposed model provides a theoretical basis for the design of the grating ends surface-bonded FBG strain sensor for health monitoring of large structures.

Structural health monitoring: Closing the gap between research and industrial deployment

There has been a large volume of research on structural health monitoring since the 1970s but this research effort has yielded relatively few routine industrial applications. Structural health monitoring can include applications on very different structures with very different requirements; this article splits the subject into four broad categories: rotating machine condition monitoring, global monitoring of large structures (structural identification), large area monitoring where the area covered is part of a larger structure, and local monitoring. The capabilities and potential applications of techniques in each category are discussed. Condition monitoring of rotating machine components is very different to the other categories since it is not strictly concerned with structural health. However, it is often linked with structural health monitoring and is a relatively mature field with many routine applications, so useful lessons can be read across to mainstream structural health monitoring where there are many fewer industrial applications. Reasons for the slow transfer from research to practical application of structural health monitoring include lack of attention to the business case for monitoring, insufficient attention to how the large data flows will be handled and the lack of performance validation on real structures in industrial environments. These issues are discussed and ways forward proposed; it is concluded that given better focused research and development considering the key factors identified here, structural health monitoring has the potential to follow the path of rotating machine condition monitoring and become a widely deployed technology.

Characteristics of BeiDou Navigation Satellite System Multipath and Its Mitigation Method Based on Kalman Filter and Rauch-Tung-Striebel Smoother

Global Navigation Satellite System (GNSS) carrier phase measurement for short baseline meets the requirements of deformation monitoring of large structures. However, the carrier phase multipath effect is the main error source with double difference (DD) processing. There are lots of methods to deal with the multipath errors of Global Position System (GPS) carrier phase data. The BeiDou navigation satellite System (BDS) multipath mitigation is still a research hotspot because the unique constellation design of BDS makes it different to mitigate multipath effects compared to GPS. Multipath error periodically repeats for its strong correlation to geometry of satellites, reflective surface and antenna which is also repetitive. We analyzed the characteristics of orbital periods of BDS satellites which are consistent with multipath repeat periods of corresponding satellites. The results show that the orbital periods and multipath periods for BDS geostationary earth orbit (GEO) and inclined geosynchronous orbit (IGSO) satellites are about one day but the periods of MEO satellites are about seven days. The Kalman filter (KF) and Rauch-Tung-Striebel Smoother (RTSS) was introduced to extract the multipath models from single difference (SD) residuals with traditional sidereal filter (SF). Wavelet filter and Empirical mode decomposition (EMD) were also used to mitigate multipath effects. The experimental results show that the three filters methods all have obvious effect on improvement of baseline accuracy and the performance of KT-RTSS method is slightly better than that of wavelet filter and EMD filter. The baseline vector accuracy on east, north and up (E, N, U) components with KF-RTSS method were improved by 62.8%, 63.6%, 62.5% on day of year 280 and 57.3%, 53.4%, 55.9% on day of year 281, respectively.

Robust reference system for Digital Image Correlation camera recalibration in fieldwork

Digital Image Correlation (DIC) is a modern powerful tool to acquire displacement fields in both laboratory and field work. Still, a number of severe limitations persist that can hinder the application of the method to objects in field work, where perfect stability of the cameras or the object itself cannot be enforced, or image acquisition is to be performed during long periods and the equipment has to be moved in the meantime. A common example of this problem is found in large structure monitoring, such as auto or railway bridges, landslides, peers or any other structure that requires displacement monitoring due to damage, often associated with fatigue and fracture in metallic structures. Accurate detection of camera movement across images of the same surface enable the computation of 3D point clouds with correct camera calibration values and geometrically aligned with other point clouds from previous time instants. An algorithm, which uses feature detection to recalibrate a stereo camera-pair after movement, was developed in this work. The cameras are originally calibrated in their initial position, and the world coordinates of detected features in the background are computed to serve as reference. In subsequent image capture following camera setup movement, the new camera positions are computed using the new coordinates of the featured reference points, as well as newly detected point correspondences between images from both cameras.