Structural Health Monitoring Purposes Research Articles

Characterisation of the damaging micromechanisms in a pearlitic ductile cast iron and damage assessment by acoustic emission testing

AbstractThe damaging micromechanisms in a pearlitic (EN‐GJS700‐2) ductile cast iron (DCI) are investigated by means of scanning electron microscope (SEM) analysis and acoustic emission (AE) testing. Monotonic uniaxial tensile tests are performed on microtensile specimens under strain control. SEM analysis is applied under in situ conditions by means of a tensile holder. The multiple damaging micromechanisms are identified, and their evolution along with the mechanical response is characterised. The traditional AE features are found to be qualitatively correlated to the onset of the fracture damage over the elastic behaviour. The information entropy of the AEs evaluated according to both Shannon and Kullback‐Leibler formulations is proven to be well correlated to the ongoing damage and the incipient failure. Tentative failure criteria are finally proposed. The assessment approach is found to be promising for structural health monitoring purposes.

Crack monitoring in reinforced concrete beams by distributed optical fiber sensors

This paper investigates the use of distributed optical fiber sensors (DOFS) based on Optical Frequency Domain Reflectometry of Rayleigh backscattering for Structural Health Monitoring purposes in civil engineering structures. More specifically, the results of a series of laboratory experiments aimed at assessing the suitability and accuracy of DOFS for crack monitoring in reinforced concrete members subjected to external loading are reported. The experiments consisted on three-point bending tests of concrete beams, where a polyamide-coated optical fiber sensor was bonded directly onto the surface of an unaltered reinforcement bar and protected by a layer of silicone. The strain measurements obtained by the DOFS system exhibited an accuracy equivalent to that provided by traditional electrical foil gauges. Moreover, the analysis of the high spatial resolution strain profiles provided by the DOFS enabled the effective detection of crack formation. Furthermore, the comparison of the reinforcement strain profiles with measurements from a digital image correlation system revealed that determining the location of cracks and tracking the evolution of the crack width over time were both feasible, with most errors being below ±3 cm and ±20 µm, for the crack location and crack width, respectively.

Distributed Fiber Optic Sensing and Data Processing of Axial Loaded Precast Piles

The Pulse-Pre-Pump Brillouin Optical Time Domain Analysis (PPP-BOTDA) based distributed fiber optic sensing (DFOS) technique has a unique advantage of achieving full-distributed structural strain with cm-order spatial resolution, which is ideal for fine stress monitoring of the pile foundation. The high spatial resolution helped to detect local changes and defects while inevitably led to noisy data meanwhile, especially a certain part of anomalous sensing data. Therefore, a robust algorithm of random sampling consistency (RANSAC) was employed to extract and process the DFOS data. In a field static load test, the variation of strain along a precast pile was measured with PPP-BOTDA interrogator. The axial force and frictional resistance were calculated based on the RANSAC processed strain data. The pile-soil interface properties were estimated as well. It shows the feasibility and good performance of PPP-BOTDA in the measurement of precast piles stress test. The RANSAC proves to be an effective method for processing DFOS data with a certain number of outliers. This work presents a valuable reference for DFOS based pile stress test and data analysis which will promote its application in other related structural health monitoring purpose.

A Three-Dimensional Strain Rosette Sensor Based on Graphene Composite with Piezoresistive Effect

Obtaining the internal stress and strain state of concrete to evaluate the safety and reliability of structures is the important purpose of concrete structural health monitoring. In this paper, a three-dimensional (3D) strain rosette sensor was designed and fabricated using graphene-based piezoresistive composite to measure the strains in concrete structures. The piezoresistive composite was prepared using reduced graphene oxide (RGO) as conductive filler, cellulose nanofiber (CNF) as dispersant and structural skeleton, and waterborne epoxy (WEP) as polymer matrix. The mechanical, electrical, and electromechanical properties of RGO-CNF/WEP composite were tested. The results show that the tensile strength, elastic modulus, and conductivity of the composite are greatly improved by the addition of RGO and CNF. The relative resistance change of composite films demonstrates high sensitivity to mechanical strain with gauge factors of 16-52. Within 4% strain, the piezoresistive properties of composites are stable with good linearity and repeatability. The sensing performance of the 3D strain rosette was tested. The measured strains are close to the actual strains of measure point in concrete, and the error is small. The RGO-CNF/WEP composite has excellent mechanical and piezoresistive properties, which enable the 3D strain rosette to be used as embedded sensor to measure the internal strain of concrete structures accurately.

Three-stage Lamb-wave-based damage localization algorithm in plate-like structures for structural health monitoring applications

In this paper, a new two-dimensional localization algorithm based on the combination of detection theory and array processing frameworks is presented. Our proposed method is capable to extract locations of multi-damage in a plate-like structure for structural health monitoring purpose. The proposed technique is a three-stage localization algorithm. In the first stage, a signal activity detection (SAD) algorithm is applied to extract curved delays of interested damages, which are in the near-field of the array. Then, a new high-resolution technique, named compensated curved-time delay version of amplitude and phase estimation (CTD-APES), is proposed to estimate damage direction of arrivals. Finally, at the third stage of our proposed algorithm, the location of detected damages are found based on the obtained results of the SAD and CTD-APES algorithms. Unlike most existing works, the proposed method is developed based on the spatial-wideband effect as well as near-field nature across the receiving antenna array; thus the proposed method along with the problem formulation can be considered as a new analytical framework in this topic. Extensive Monte Carlo simulation results are provided to demonstrate the superiority of the proposed technique as well as to validate the theoretical calculations.

Capacitance-based sensor with layered carbon-fiber reinforced polymer and titania-filled epoxy

Advances in intelligent infrastructure can be achieved through the use of novel materials for increased system-level efficiency and multifunctionality. Carbon Fiber-Reinforced Polymer (CFRP) has been widely used in strengthening, rehabilitating, and retrofitting of existing structures because of its speed of deployment, low maintenance requirement, and high strength-to-weight ratio. In this work, the authors propose a novel method to augment CFRP with self-sensing capabilities. The sensor consists of two CFRP layers separated by a titania-filled epoxy dielectric layer, therefore forming a parallel plate capacitor. Sensing capability can be achieved through variations in the sensor’s capacitance provoked by strain, therefore providing an additional function that could be leveraged for structural health monitoring and structural health management purposes. Comprehensive testing, including (1) sensing properties on sensors with and without titania-doped epoxy and (2) electromechanical test on tension specimens subjected to both static and dynamic loading, was conducted. The test results show that doping the titania filler within the epoxy matrix can improve the sensor’s sensitivity, and that the sensor’s signal increases linearly with increasing strain. A static gauge factor of 0.92 was obtained, and the dynamic gauge factor characterized under 0.25, 0.5, and 1 Hz. It is also found that CFRP can be used as a self-sensing material without affecting its mechanical properties.

Equivalent neutral axis for structural condition assessment using multi-sensor fusion

Damage detection is one of the primary purposes of structural health monitoring. Since neutral axis location changes have been considered to be sensitive damage indicators, extensive research efforts have been devoted to developing effective and accurate neutral axis estimation methods, although challenges remain in real-world applications. This study proposes a novel structural condition assessment method using an equivalent neutral axis (ENA) that can be estimated based on acceleration and strain measurements at each location where a sensor is installed. Numerical simulations were conducted using a simply supported steel beam model to investigate the effect of the ENA with respect to the sensor configuration and damage level. The proposed method was also applied to a numerical reinforced concrete beam model to further validate the ENA as a damage indicator, thus proving the sensitivity of the ENA to structural damage.

An ultra-thin printable nanocomposite sensor network for structural health monitoring

A nanocomposite-based sensor ink made from carbon black and polyvinyl pyrrolidone was developed for fabricating a new breed of sensor by an inkjet printing approach, to accommodate the general purposes of structural health monitoring. This ink can be directly deposited onto the surface of various substrates or engineering structures such as polyimide film via computer-aided design to configure nanocomposite sensor arrays or dense sensor networks. Strong structure adaptability and high flexibility make this sensor a promising candidate to alternate traditional piezoresistive and piezoelectric sensors, in signal acquisition of dynamic disturbance on complex engineering structures. Lightweight and without the need to use wires or cables, the printed sensor network significantly reduces the weight and volume penalty imposed on the host structures, even when the network is deployed at a large scale. It also minimizes the possibility of exfoliation of the sensors from the host structure under cyclic load. The printed pattern distinguishes superior performance in the perception of acousto-ultrasonic signals from static up to 500 kHz, with high signal-to-noise ratio, sensitivity, and fidelity. By virtue of the tunneling current between two adjacent nanoparticles that are in close proximity (within several nanometers), the printed sensor network is capable of perceiving ultrasonic waves. The fabrication process of the sensor network does not entail any specially made printing facilities, and the carbon black/polyvinyl pyrrolidone hybrid can easily be injected into an inkjet cartridge for printing. Several confirmatory experiments and a proof-of-concept test were carried out based on the printed sensor network to validate the capability of the printed sensor for structural health monitoring.

Direct-write piezoelectric ultrasonic transducers for pipe structural health monitoring

Novel direct-write ultrasonic transducers comprising piezoelectric poly(vinylidenefluoride/trifluoroethylene) [P(VDF/TrFE)] polymer coatings which were in-situ deposited, crystallized and patterned on pipe for structural health monitoring purpose. Lamb ultrasonic wave signals, generated and measured by the direct-write transducers and propagating along the pipe structure, were used to monitor the integrity of the pipe structure. The experimental measurements of the axial Lamb wave on pipe structure showed the substantial reduction in the ultrasonic signal by the defects. In addition, pipe thickness was accurately determined with the direct-write transducers to generate and detect the ultrasonic wave in the pipe thickness direction using pulse-echo mode. Our result and analyses suggest that implementation of the unique direct-write ultrasonic transducer technology is promising for realizing structural health monitoring for pipeline structures with improved consistency and reliability.

Considering of a simplified model of passing train causing dynamic effects on railway bridge for SHM purposes

The aim of the structural health monitoring (SHM) is to provide useful information about the state of the structures. However, the data collected from monitoring needs to be first translated into actionable, quantitative or qualitative based characteristics, that indicate the condition of a bridge. The paper proposes a methodology for comparison of regularly measured data to monitor the state of stringers and floorbeams. Also, the dynamic factor is evaluated and compared with code values.

Quantitative Attribute Analyses with Ground Penetrating Radar for Infrastructure Assessments and Structural Health Monitoring.

In civil structures and infrastructure, assessing true performance and characterizing unusual structural behaviors can help avoid severe structural problems. To further refine or validate the conclusions from structural health monitoring (SHM) analyses, nondestructive evaluation or techniques (NDE or NDT) can be applied in conjunction with SHM approaches. Ground penetrating radar (GPR) is an NDT that has been used to investigate defects and internal features in concrete structures, but is not commonly used to assess mechanical properties for the purposes of SHM. As a preliminary investigation of the effectiveness of attribute analysis techniques, a GPR survey was conducted on Streicker Bridge (a pedestrian bridge on Princeton University campus with embedded fiber-optic strain and temperature sensors). The bridge was constructed in two phases, where different curing conditions produced different material properties (compressive strength of 51 MPa and 59 MPa). Both standard processing techniques and attribute analysis techniques were employed to interpret GPR reflections in each phase of construction to identify construction elements and to compare the attribute signatures of different strength concretes.Though this study presents primarily relative differences, the sensitivity of these attributes to material property differences is confirmed. This validates SHM studies of the bridge and indicates the potential of the attribute analysis method for material characterization, especially as a compliment to other SHM and NDE techniques.

Pattern recognition framework using asynchronous discrete binary data for condition and damage assessment in plate-like structures

Recent advances in energy harvesting technologies have led to the evolution of self-powered structural health monitoring techniques that are energy-efficient. Concurrent to the emergence of self-powered sensing has been the development of power-efficient data communication protocols. One such technology is an energy-aware pulse communication architecture that employs ultrasonic pulses through the material substrate for information forwarding. This results in limited discrete binary data that raises the need for new data analysis methods for structural health monitoring purposes. A pattern recognition framework that allows interpretation of the resulting asynchronous discrete binary data for condition and damage assessment in plate-like structures is presented in this article. The proposed pattern recognition framework is based on integration of image-based pattern recognition using anomaly detection, a pattern anomaly measure, a focal density concept, and the k-nearest neighbor algorithm. Using numerical simulations, damage indication parameters were determined from the strain response of dynamically loaded plates. Simulated test cases considering different levels of damage severity, single and multiple damage regions, loading conditions, and measurement noise were studied to evaluate the effectiveness and robustness of the strategy. Furthermore, the effect of sensor density on the proposed strategy was explored. Results demonstrate satisfactory performance and robustness of the proposed pattern recognition framework for localized damage detection in plate-like structures using limited and low-resolution discrete binary data.

Model selection for dynamic reduction-based structural health monitoring following the Bayesian evidence approach

Abstract There usually exist multitudinous finite element (FE) models with varying level of complexity which can be developed from the engineering judgment for the purpose of structural system identification and health monitoring. By following the theory of Bayesian evidence statistic, this paper proposes a methodology to investigate the issues of FE model-class selection for choosing suitable parameterized structural models utilized in dynamic reduction-based structural health monitoring (SHM). By employing the concept of information divergence, the amount of information needed to be extracted from the measured data is explicitly quantified during the procedure of FE model updating-based structural health monitoring. Then, for achieving a trade-off between the complexity of a parameterized FE model class and that of its corresponding information-theoretic interpretation, such information is utilized for penalizing the complexity of model class to ensure that a relatively simple parameterization scheme can be achieved. The proposed methodology consists of calibration and subsequent monitoring stages, and the information obtained in the former stage is utilized as pseudo-data which is learned by the latter stage to improve the model parameter estimation by implementing the delayed rejection adaptive Metropolis algorithm. Through numerical case studies conducted for a four-storey two-bay steel frame structure considering semi-rigid connections as well as laboratory experiment performed for a two-storey bolt-connected steel frame model, the feasibility and validity of proposed methodology is demonstrated.

Wireless sensor module for 3-axis vibration and tilt monitoring on the structural building

In this paper, a simple and low-cost wireless sensor module has been developed for structural health monitoring (SHM) purposes. The module can be divided into three main parts: sensing unit, data acquisition (DAQ) unit, and data communication (DC) unit. The sensing unit is three-axis vibration and tilt sensor. It is composed using single MEMS accelerometer e.g. MMA7361L, and signal conditioning circuit for both tilt and vibration parameters. The DAQ unit function for A/D converter and data formatting, built based on a microcontroller PIC16F873. Whereas DC unit is radio (RF) transceiver, it is used to communicate with the control module (monitoring room) wirelessly. In here, we use RF-transceiver YS-C20K. In the control room, vibrations and tilts data are analysed using the developed software to get information about dynamics condition of structural building being monitored. Results of experiment and evaluation show the tilt sensor is capable to detect up to ± 30 degrees dynamic range with resolution of about 0.05 degrees, while the vibration sensor can achieved up to sensitivity of (80 V/g) with noise compensation. To get portability and easiness in field installation, the sensor system was packaged into one module.

Neutral Axis-Based Health Monitoring and Condition Assessment Techniques for Concrete Box Girder Bridges

The neutral axis position is considered as a potential parameter of early abnormal and long term performance for flexural members of structures. This paper is to propose the Neutral Axis Indicator (NA) based on strain measurements, to evaluate the behavior of NA, and to investigate challenges associated with the neutral axis for structural health monitoring purpose. A numerical example of a simply supported reinforced concrete (RC) beam was first developed and analyzed followed by an experimental study for further verification. With the aim of application to real bridges, a case study was conducted on Gantao River Bridge, a typical concrete box girder bridge, for detailed analysis using field data. The numerical and experimental examples, as well as field tests, aim to figure out: (1) the sensitivity of NA to structural damages, especially at early age; (2) stability and uncertainty of NA using field data; (3) capability of NA on detecting abrupt and slowly changing structural degradation. The results show that: NA takes on good index accuracy and damage sensitivity on numerical and experimental cases; NA tends to keep constant during linear elastic phase, while responses quickly and sharply to nonlinear behaviors such as concrete cracking and hardening; and NA is a potentially reliable indicator both to capture nonlinear performance and to detect minute damage. Additionally, the uncertainty of multiple disturbing effects can be mostly eliminated through statistical analysis. Thus, it can be employed effectively in condition assessment of concrete box girder bridges for long-term health monitoring.

Evaluating the Performance of Functionalized Carbon Structures with Integrated Optical Fiber Sensors under Practical Conditions.

An Optical Frequency Domain Reflectometry (OFDR) based fiber optic sensor scheme “embedded” in concrete for the purpose of structural health monitoring (SHM) of carbon concrete composites (C3) is presented. The design, while strengthening the concrete structure, also aims to monitor common SHM parameters such as strain and cracks. This was achieved by weaving the carbon fiber together with optical fiber, based on a specialized technique that uses an embroidery setup where both the carbon and optical fiber are woven on a water dissolvable polymer substrate. The performance of the sensing scheme was characterized in-situ utilizing the OFDR based technique and the results presented. The sensors embedded on a custom made concrete block were subjected to varying strain via a three point bending test to destruction and the results discussed. The intended dual-achievement of the scheme thus proposed in SHM and strengthening the C3 is demonstrated. The suitability of the OFDR scheme for C3 is combined with a fibre Bragg grating (FBG)-based approach, and discussed in detail.

Optimal sensor placement for spatial lattice structure based on three-dimensional redundancy elimination model

Spatial lattice structures are widely applied in engineering fields attributed to their superiorities in weight and reasonable stress. It is essential to select the best sensor placement layout for structural health monitoring and safety purposes, yet it is neither realistic nor efficient to place sensors in every location possible on the structure. To meet the strong requirements for optimal sensor placement in spatial lattice structure, this paper aims to investigate a combined objective function based on effective independence method and three dimensional redundancy elimination model to balance between optimal sensor placement performance and elimination in redundancy. To eliminate redundant information and resource waste caused by the clustered sensor distribution, the three-dimensional redundancy elimination model is constructed with the consideration of nearer nodes and overall sensor distribution ranges in three-dimensional cases. In addition, the combined function is constructed by giving the two component functions equal significance using weighting factors and normalization, and solved by genetic algorithm. Finally, the proposed method for spatial lattice structure is supported by three numerical examples including a simple lattice structure, a ground spatial truss structure and a space docking modular in space solar power satellite.

Uncertainty quantification for structural health monitoring applications

The difficulty in modeling complex nonlinear structures lies in the presence of significant sources of uncertainties mainly attributed to sudden changes in the structure\'s behavior caused by regular aging factors or extreme events. Quantifying these uncertainties and accurately representing them within the complex mathematical framework of Structural Health Monitoring (SHM) are significantly essential for system identification and damage detection purposes. This study highlights the importance of uncertainty quantification in SHM frameworks, and presents a comparative analysis between intrusive and non-intrusive techniques in quantifying uncertainties for SHM purposes through two different variations of the Kalman Filter (KF) method, the Ensemble Kalman filter (EnKF) and the Polynomial Chaos Kalman Filter (PCKF). The comparative analysis is based on a numerical example that consists of a four degrees-of-freedom (DOF) system, comprising Bouc-Wen hysteretic behavior and subjected to El-Centro earthquake excitation. The comparison is based on the ability of each technique to quantify the different sources of uncertainty for SHM purposes and to accurately approximate the system state and parameters when compared to the true state with the least computational burden. While the results show that both filters are able to locate the damage in space and time and to accurately estimate the system responses and unknown parameters, the computational cost of PCKF is shown to be less than that of EnKF for a similar level of numerical accuracy.

Determination of the truss static state by means of the combined FE/GA approach, on the basis of strain and displacement measurements

ABSTRACTA real engineering inverse problem is considered in this paper, namely the recovery of the full static state of plane truss for Structural Health Monitoring (SHM) purposes. Available experimental data include strain or strain/displacement measurements done at few selected construction bars and nodes. All measurements are performed by means of sensors attached to the construction. The appropriate non-linear constrained optimization problem is formulated. Its numerical analysis is performed through the combination of the finite element method with genetic algorithms (FE/GA). Apart from the truss static load determination, the proposed approach may be applied to the identification of its bars with reduced tensile stiffness, as well as defective strain sensors. Moreover, the selection of the optimal number of strain sensors is considered. The work is illustrated with selected examples of truss analysis with various geometric complexity.

Prototyping and Validation of MEMS Accelerometers for Structural Health Monitoring—The Case Study of the Pietratagliata Cable-Stayed Bridge

In recent years, thanks to the simple and yet efficient design, Micro Electro-Mechanical Systems (MEMS) accelerometers have proven to offer a suitable solution for Structural Health Monitoring (SHM) in civil engineering applications. Such devices are typically characterised by high portability and durability, as well as limited cost, hence resulting in ideal tools for applications in buildings and infrastructure. In this paper, original self-made MEMS sensor prototypes are presented and validated on the basis of preliminary laboratory tests (shaking table experiments and noise level measurements). Based on the well promising preliminary outcomes, their possible application for the dynamic identification of existing, full-scale structural assemblies is then discussed, giving evidence of their potential via comparative calculations towards past literature results, inclusive of both on-site, Experimental Modal Analysis (EMA) and Finite Element Analytical estimations (FEA). The full-scale experimental validation of MEMS accelerometers, in particular, is performed using, as a case study, the cable-stayed bridge in Pietratagliata (Italy). Dynamic results summarised in the paper demonstrate the high capability of MEMS accelerometers, with evidence of rather stable and reliable predictions, and suggest their feasibility and potential for SHM purposes.