Health Monitoring Of Large-scale Structures Research Articles

A sparse, triangle-shaped sensor array for damage orientation and characterization of composite structures

Since numerous sensors are needed to create a sensor array for the structural health monitoring of large-scale structures, the equipment quantity and cost considerably increase. This study proposes a sparse, triangle-shaped sensor array to identify, orient, and assess the degree of structural damage in composite constructions in order to overcome this shortcoming. The damage-scattered Lamb waves are recorded by the sparse sensor array with a variety of features that are then extracted and fed into the support vector machine (SVM) classification method. The location and severity of the damage in composite constructions can be determined by training the SVM model. The principal component analysis technique is used to compress the wave feature vectors while maintaining the majority of the damage information because the high dimension of the wave feature vectors required a significant amount of calculation during the training phase. Proof-of-concept tests show that the trained model, by utilizing the many properties of Lamb wave signals, can orient and define the degree of damage with excellent accuracy. Multiple Lamb wave properties can be used to make up for the triangle sensor array’s loss of damage information. In conjunction with the SVM, the triangle-shaped sensor array that was proposed in this study can efficiently make it easier to identify and characterize damage to large-scale structures while using fewer sensors.

Reconstruction of full-field dynamic responses for large-scale structures using optimal sensor placement

This study explores an optimal sensor placement approach for reconstructing full-field dynamic responses using a set of basic vectors obtained from high-fidelity data. The novelty is the combination of the reduced-order model with sparse promotion, which makes the sensor optimization algorithm effective. Pivoted QR decomposition was performed to accelerate the full-field reconstruction of large-scale structures. To improve the reconstruction precision, an online–offline paradigm was used to create the reduced-order model offline and estimate the sparse coefficient online. The proposed method was verified using a high-rise building case study. The influential factors of reconstruction precision, such as measurement noise, different mode orders, number of sensors, and various types of dynamic responses, were investigated. The results show that the proposed method is accurate and reliable for reconstructing full-field responses, providing a potential alternative for structural health monitoring of large-scale structures.

Optimal sensor placement techniques for modal identification of historical masonry structures

Since destructive tests are not allowed for historical structures, numerical model updating using accelerometers has gained a lot of attraction in the last decade. Furthermore, another application of structural health monitoring is damage detection for near-real-time monitoring of cultural heritage assets of infrastructures such as masonry bridges. However, high cost is the main problem that discourages the use of large-scale structural health monitoring systems, and a modal pretest analysis is required to plan and optimize the modal tests procedure. For this purpose, various optimal sensor placement (OSP) techniques have been developed to derive the operational modal analysis results with a minimum number of sensors, leading to a lower cost. In this study, various OSP techniques have been applied to optimize sensor placement in two selected case studies. The first case study is a two-span masonry arch bridge in Rhodes, Greece and the second is a stone masonry tower located in Tønsberg, Norway. Baseline finite element models were developed before performing the ambient vibration tests and model updating process. The optimum sensor locations were detected using various techniques, and a comparative study was conducted on the results. Furthermore, the effect of considering soil-structure interaction on the OSP results was investigated.

A Modified Fully Convolutional Network for Crack Damage Identification Compared with Conventional Methods

Large-scale structural health monitoring and damage detection of concealed underwater structures are always the urgent and state-of-art problems to be solved in the field of civil engineering. With the development of artificial intelligence especially the combination of deep learning and computer vision, greater advantages have been brought to the concrete crack detection based on convolutional neural network (CNN) over the traditional methods. However, these machine learning (ML) methods still have some defects, such as it being inaccurate or not strong, having poor generalization ability, or the accuracy still needs to be improved, and the running speed is slow. In this article, a modified fully convolutional network (FCN) with more robustness and more effectiveness is proposed, which makes it convenient and low cost for long-term structural monitoring and inspection compared with other methods. Meanwhile, to improve the accuracy of recognition and prediction, innovations were conducted in this study as follows. Moreover, differed from the common simple deconvolution, it also includes a subpixel convolution layer, which can greatly reduce the sampling time. Then, the proposed method was verified its practicability with the overall recognition accuracy reaching up to 97.92% and 12% efficiency improvement.

Optical three-dimensional displacement measurement based on moiré methodology and its application to space structures

Optical methods providing full-field deformation information is of interest to mechanical engineers. In this study, an accurate optical three-dimensional displacement measurement method using a single camera attached to a precision 3-axis moving stage was developed. This paper presents the fundamental measurement principle and experiments for validating the measurement accuracy. The in-plane and out-of-plane displacements are simultaneously determined by calculating the phase difference and the phase gradient difference of the moiré fringe before and after deformation. The developed method was applied to displacement measurement in a loading test of a carbon fiber reinforced plastics (CFRP) cylindrical payload 2.5 m in diameter and 2.0 m long used in a solid rocket. The three-dimensional displacement results obtained from the developed method showed a good agreement with those measured with the extensometers conventionally employed in the displacement measurement. The developed method is useful for inspection in the space field and to secure the health monitoring of large-scale structures.

Brillouin scattering spectrum-based crack measurement using distributed fiber optic sensing

Brillouin scattering-based distributed fiber optic sensing (Brillouin-DFOS) technology is widely used in health monitoring of large-scale structures with the aim to provide early warning of structural degradation and timely maintenance and renewal. Material cracking is one of the key mechanisms that contribute to structural failure. However, the conventional strain measurement using the Brillouin-DFOS system has a decimeter-order spatial resolution, and therefore it is difficult to measure the highly localized strain generated by a sub-millimeter crack. In this study, a new crack analysis method based on Brillouin scattering spectrum (BSS) data is proposed to overcome this spatial resolution-induced measurement limitation. By taking the derivative of the BSS data and tracking their local minimums, the method can extract the maximum strain within the spatial resolution around the measurement points. By comparing the variation of the maximum strain within the spatial resolution around different measurement points along the fiber, cracks can be located. The performance of the method is demonstrated and verified by locating and quantifying a small gap created between two wood boards when one of the wood boards is pushed away from the other. The test result verifies the accuracy of the crack strain quantification of the method and proves its capability to measure a sub-millimeter crack. The method is also applied to a thin bonded concrete overlay of asphalt pavement field experiment, in which the growth of a transverse joint penetrating through the concrete–asphalt interface was monitored. The method successfully locates the position, traces the strain variation, and estimates the width of a crack less than wide using a Brillouin-DFOS system with spatial resolution.

Application of BP Neural Network in Cross- Sensitivity Solution of Temperature and Strain of FBG Sensor

Fiber Bragg grating (FBG) sensor has the advantages of long transmission distance and less channel occupied by data acquisition instrument, so it can be used for strain measurement of power engineering and electronic engineering and so on. FBG is sensitive to both strain and temperature, that is, both changes can change the central wavelength of the grating. In structural health monitoring, the load on the tested part is complicated, and the temperature also changes greatly in the long-term monitoring process. If the FBG sensor is fixed on the tested component, it will inevitably be affected by the combined action of temperature and strain at the same time, making the test result produce error or deviation. In this paper, the temperature compensation of FBG strain sensor is analyzed and studied. Based on the calibration data, the FBG strain sensor temperature compensation is realized by BP Neural Network Algorithm. The research results of this paper can be used as a reference for health monitoring of large-scale structures using FBG sensors.

Application of genetic algorithm in damage detection of reinforced concrete beams using piezo ceramic transducers

Health monitoring of reinforced concrete bridges and other large-scale civil infrastructures has received considerable attention in recent years. However, traditional inspection methods (x-ray, C-scan, etc) are expensive and sometimes in-effective for large-scale structures. Piezo-ceramic transducers have emerged as new tools for the health monitoring of large-scale structures due to their advantages of active sensing, low cost, quick response, availability in different shapes, and simplicity for implementation. This study presents an experimental effort for the detection of damages in the reinforcement bars at lap zone of the reinforced concrete beams using Piezo-ceramic Transducers (PZTs) with the help of Genetic Algorithm (GA). An Electromechanical admittance (EMA) approach is used to evaluate the damages in the RC beams for standard lap length provided in IS 456 code and optimized lap length of bars. Tests are performed in i) steel reinforced bars with standard lap length embedded in a reinforcing concrete beam of 1.3m span subjected to flexural load for detecting the response and damages ii) steel reinforcing bar with optimized lap length given to a reinforced concrete beam of span 1.3m subjected to flexural load for detecting the response of healthy state beam. Test measurements of healthy and damaged steel bars in the lap zone of the reinforced concrete beam have been conducted using the developed monitoring system and piezo ceramic transducers.

Truly Distributed Coaxial Cable Sensing Based on Random Inhomogeneities

Rayleigh backscattering-based distributed fiber optic sensing technology is well known and widely used for large-scale structural health monitoring. Inspired by the Rayleigh backscattering-based sensing methodology on an optical fiber, in this paper, we present a sensing concept based on the random inhomogeneities on a coaxial cable. As an analogy of Rayleigh backscattering along an optical fiber length, “backscattering” also exists from a commercial coaxial cable due to its inherent defects along a cable length which induce a local variation (i.e., impedance mismatch). This is because of the irregular microscopic structures of the inner/outer conductors, and the inhomogeneous density or permittivity of the inner dielectrics after the cables are manufactured. The accumulated backscattered signals along the coaxial cable can be obtained using frequency-domain reflectometry. By analyzing the shift in the local backscattered signal, the local environmental perturbations (e.g., local strain or temperature) can be determined, so that truly distributed sensing capability using a coaxial cable can be achieved. To verify the proposed concept, an intact and commercial coaxial cable was demonstrated for distributed temperature sensing. Compared with the existing coaxial cable-based distributed sensing technologies, the proposed sensing concept does not need extra modifications to the coaxial cable and offers a truly distributed sensing capability.

A fast acoustic emission beamforming localization method based on Hilbert curve

Abstract The low efficiency of the acoustic emission (AE) beamforming localization method in large-scale structural monitoring seriously restricts its application in practice. The Hilbert curve is introduced to improve the computational efficiency of AE beamforming method in this paper. Firstly, the Hilbert curve is introduced to traverse the monitored area, and quickly capture the approximate position of the AE source based on Beamforming. Then, the ‘Nine-Four bisection’ method is proposed to overcome the boundary problem in region division. Finally, the Hilbert curve-beamforming method (HCBF) is verified by simulation and experiment data. The results show that, the presented method not only greatly improves the computational efficiency, but also maintains the original localization accuracy and reliability. It creates a prerequisite for the further promotion and development of the beamforming method in the field of AE source localization in large-scale structural health monitoring.

EMI based monitoring of early-age characteristics of concrete and comparison of serial/parallel multi-sensing technique

Changes in admittance signatures measured using Electro-Mechanical Impedance (EMI) technique with a lead zirconate titanate (PZT) transducer, embedded in concrete is an indirect estimate of the localised changes in dynamic stiffness of the concrete in the proximity of the sensor. Experiments and observations of the EMI signatures obtained from PZT sensors bonded onto identical plates and embedded in concrete cubes with two sets of curing have been presented. Also, experiments are conducted on a concrete beam embedded with six PZT sensors bonded on carrier plates of differing thickness by subjecting the beam to differential curing. Feature extraction on the admittance signatures is carried out to identify the early age strength gain characteristics in the concrete. EMI measurements have been continued for 28 days until the signatures asymptote to vanishing changes. The contribution of the present work is the application of serial/parallel connected sensors embedded in concrete (in a Multi Input Single Output, MISO mode), that can be typically deployed in a large scale structural health monitoring (SHM) scenario. A novel methodology for separating the electrical and mechanical coupling in a multiplexed sensing scenario is a notable contribution. A hitherto unreported invariant factor which is obtained as the ratio of the parallel and serial admittance signatures is reasoned out and the removal of this results in separating the constant electrical admittance from the mechanical admittance. Well separated resonant peaks in the admittance signature is a pre-requisite for tracking and observing the changes, which is achieved through plates of different thickness resulting in different ultrasonic resonant modes. In the composite EMI signature which is representative of many sensors, individual peaks could be associated with separate sensors and their influencing regions.

Experimental investigation of aerial–ground network communication towards geospatially large-scale structural health monitoring

Recognizing the importance of realizing integrated remote sensing and structural monitoring for civil structures at a geospatially large scale, this paper first proposes a concept design toward developing an aerial–ground wireless sensing network (AG-WSN) system for improving the efficiency of systems-level structural health monitoring. Upon this design, this paper further investigates the communication interference within such sensing network, which if not understood would diminish the practical value of such a system. This interference arises because of the UAV’s and the WSN’s communication protocols, for which most UAVs use 2.4 GHz radio for flight control and Wi-Fi (802.11 b/g/n) for imagery data streaming, and the WSNs often use the low-power ZigBee 802.15.4 protocol at 2.4 GHz as well. With the development of the proposed AG-WSN prototype using commercially available products, this paper experimentally achieves two important findings: (1) the key parameters that affect the short-range Wi-Fi and ZigBee interference and the experimental relations; and (2) the ‘comfort zone’ and the sensitive parameters for the long-range optimal ZigBee transmission between the aerial UAV and the ground sensors.

Development and Application of a Structural Health Monitoring System Based on Wireless Smart Aggregates.

Structural health monitoring (SHM) systems can improve the safety and reliability of structures, reduce maintenance costs, and extend service life. Research on concrete SHMs using piezoelectric-based smart aggregates have reached great achievements. However, the newly developed techniques have not been widely applied in practical engineering, largely due to the wiring problems associated with large-scale structural health monitoring. The cumbersome wiring requires much material and labor work, and more importantly, the associated maintenance work is also very heavy. Targeting a practical large scale concrete crack detection (CCD) application, a smart aggregates-based wireless sensor network system is proposed for the CCD application. The developed CCD system uses Zigbee 802.15.4 protocols, and is able to perform dynamic stress monitoring, structural impact capturing, and internal crack detection. The system has been experimentally validated, and the experimental results demonstrated the effectiveness of the proposed system. This work provides important support for practical CCD applications using wireless smart aggregates.

A spectral-based clustering for structural health monitoring of the Sydney Harbour Bridge

This paper presents the results of a large scale Structural Health Monitoring application on the Sydney Harbour Bridge in Australia. This bridge has many structural components, and our work focuses on a subset of 800 jack arches under the traffic lane 7. Our goal is to identify which of these jack arches (if any) respond differently to the traffic input, due to potential structural damages or instrumentation issues.We propose a novel non-model-based method to achieve this objective, using a spectrum-driven feature based on the Spectral Moments (SMs) from measured responses from the jack arches. SMs contain information from the entire frequency range, thus subtle differences between the normal signals and distorted ones could be identified. Our method then applies a modified k-means−− clustering algorithm to these features, followed by a selection mechanism on the clustering results to identify jack arches with abnormal responses.We performed an extensive evaluation of the proposed method using real data from the bridge. This evaluation included a control component, where the approach successfully detected jack arches with already known damage or issues. It also included a test component, which applied the method to a large set of nodes over a month of data to detect any potential anomaly. The detected anomalies turned out to have indeed system issues after further investigations.

Large-Scale Structural Health Monitoring Based on Wireless Sensor Networks

For large-scale structural damage in various forms, a single regional monitoring is difficult to finish. The paper introduces a design method of a large-scale structural health monitoring system based on wireless sensor networks. ZigBee wireless communication technology is used for the system. The ZigBee-based double wireless sensor network architecture is designed. The wireless sensor node, sink node, a gateway node of this system is designed by using the CC2530 chip as core of data processing and wireless transceiver. In the IAR development platform, the lower position machine software is designed by using C language and the LabVIEW is used to finish the remote display in the host computer. The results show that the monitoring system meets the design requirements, has the advantage of good on-line and real-time monitoring etc.

A Bridge Health Monitoring System Based on Wireless Smart Aggregates

Researches on health monitoring technology of concrete structures by using piezoelectric smart aggregates have achieved a great development. However, the technique is not widely used so far in practical engineering. Because when constructing large-scale structural health monitoring (SHM) system using wire-based sensors, it requires a lot of cables to form a monitoring network, resulting in huge cost of abundant material of wires and labor for wire placement, and the relatively heavy maintenance work in case of failure of the SHM system. A kind of wireless sensor network based on the protocol for Zigbee802.15.4 and the passive piezoelectric smart health monitoring technology is developed in the paper. Through internal load monitoring tests of a concrete bridge model under impact loading, the developed wireless smart aggregate (WSA) health monitoring system is experimentally validated. The finite element method (FEM) is used to simulate the process as the same as the bridge model test, and the numerical results are consistent with those of the experiment. The experimental results show that the developed wireless system is stable and reliable, and can be applied in concrete bridge structure health monitoring under impact loading.

Optimal Sensor Placement for Latticed Shell Structure Based on an Improved Particle Swarm Optimization Algorithm

Optimal sensor placement is a key issue in the structural health monitoring of large-scale structures. However, some aspects in existing approaches require improvement, such as the empirical and unreliable selection of mode and sensor numbers and time-consuming computation. A novel improved particle swarm optimization (IPSO) algorithm is proposed to address these problems. The approach firstly employs the cumulative effective modal mass participation ratio to select mode number. Three strategies are then adopted to improve the PSO algorithm. Finally, the IPSO algorithm is utilized to determine the optimal sensors number and configurations. A case study of a latticed shell model is implemented to verify the feasibility of the proposed algorithm and four different PSO algorithms. The effective independence method is also taken as a contrast experiment. The comparison results show that the optimal placement schemes obtained by the PSO algorithms are valid, and the proposed IPSO algorithm has better enhancement in convergence speed and precision.

Structural Health Monitoring: Leveraging Pain in the Human Body

Tissue damage, or the perception thereof, is managed through pain experience. The neurobiological process of pain triggers most effective defense mechanisms for our safety. Structural health monitoring (SHM) is also a very similar function, albeit in engineering systems. SHM technology can leverage many aspects of pain mechanisms to progress in several critical areas. Discrimination between features from the undamaged and damaged structures can follow the threshold gate mechanism of the pain perception. Furthermore, the sensing mechanisms can be adaptive to changes by adjusting the threshold as does the pain perception. A distributed sensor network, often advanced by SHM, can be made fault-tolerant and robust by following the perception way of self-organization and redundancy. Data handling in real life is a huge challenge for large-scale SHM. As sensory data of pain is first cleaned, the threshold is then processed through experiential information gathering and use.

Mobile Multi-Agent Evaluation Method for Wireless Sensor Networks-Based Large-Scale Structural Health Monitoring

Wireless sensor networks are increasingly adopted both in aircraft and civil structural health monitoring. However, due to the limited resources on a wireless sensor node, structural evaluation ability of the wireless sensor network is also limited, especially for large-scale structures. This paper presents a multi-agent cooperation system based on mobile agent technology for wireless sensor network-based structural health monitoring which deals with the evaluation of large-scale structure. Mobile agent with the signal processing or evaluation algorithm is designed to migrate among different sensor nodes and perform network self-organization, data processing, information fusion, and damage estimation. This method can reduce the request to the sensor nodes and achieve a better monitoring and evaluation performance through multiagent cooperation. The proposed mobile multiagent system is evaluated by a large dimension aerospace aluminum structure monitoring experiment in which strain distribution and joint failure are monitored by different sensors. Experimental results show that the system can significantly improve the monitoring performance for large-scale structures.

A hybrid data-fusion system using modal data and probabilistic neural network for damage detection

This paper addresses a novel hybrid data-fusion system for damage detection by integrating the data fusion technique, probabilistic neural network (PNN) models and measured modal data. The hybrid system proposed consists of three models, i.e. a feature-level fusion model, a decision-level fusion model and a single PNN classifier model without data fusion. Underlying this system is the idea that we can choose any of these models for damage detection under different circumstances, i.e. the feature-level model is preferable to other models when enormous data are made available through multi-sensors, whereas the confidence level for each of multi-sensors must be determined (as a prerequisite) before the adoption of the decision-level model, and lastly, the single model is applicable only when data collected is somehow limited as in the cases when few sensors have been installed or are known to be functioning properly. The hybrid system is suitable for damage detection and identification of a complex structure, especially when a huge volume of measured data, often with uncertainties, are involved, such as the data available from a large-scale structural health monitoring system. The numerical simulations conducted by applying the proposed system to detect both single- and multi-damage patterns of a 7-storey steel frame show that the hybrid data-fusion system cannot only reliably identify damage with different noise levels, but also have excellent anti-noise capability and robustness.