Structural Heath Monitoring Research Articles

A piezoelectric buckling beam-type bistable energy harvester under rotational excitations

This paper proposes a novel piezoelectric buckling beam-type bistable energy harvester (PBBEH), which aims to efficiently harvest energy from rotational motions. The designed PBBEH is most made up of a piezoelectric buckling beam and a rotational disk which is used to provide low-speed rotational motions. A lumped parameter model is used for numerical analysis and the energy harvesting features of the PBBEH are analyzed. More importantly, experiments indicate that the PBBEH has excellent energy harvesting performance in the frequency range of 1–9 Hz, and experimental results indicate that the presented PBBEH has the output power of 28 μW. Additionally, the PBBEH has obvious nonlinear broadband dynamic characteristics. Both experimental and numerical results show that the output power curve has the tendency to increase and then decrease. Overall, in this paper, a good performance of this proposed harvester in ultra-low frequency rotational motions is verified, and it provides a possible design for self-powered wireless sensors used for structural heath monitoring in the wind turbine, the automobile wheel and other rotational machines.

Lamb wave-based damage detection of composite structures using deep convolutional neural network and continuous wavelet transform

Carbon fiber reinforced plastics composites have been widely used in aerospace, automotive and other fields for its superior performance. To avoid possible risks, Lamb waves-based structural heath monitoring techniques have been applied to detect the internal damage of these composites. However, due to their intrinsic high degree of anisotropy which makes the failure mode complicated under fatigue loads, an accurate and timely method for damage detection of these composites is still challenging to realize. This paper proposes a novel method for detecting the internal delamination of the carbon fiber reinforced plastics by combining deep convolutional neural network and continuous wavelet transform. This data-driven method can effectively leverage the large amount of data without relying on complex feature extraction. After converting the time series signal into a two-dimensional time–frequency image by the continuous wavelet transform technique, the convolutional neural network model is applied to classify the images. Based on the prediction results of different paths by the model, a damage localization method is also proposed for ply delamination. An experimental study is implemented to verify the effectiveness of this method. The result shows this method is a useful technique for precise diagnosis and positioning of delamination damage in the composite structures.

Artificial intelligence techniques for fault assessment in laminated composite structure: a review

There is a continuous quest in the research community for superior and more accurate methodology for fault diagnosis and condition monitoring of diverse composite structure. This is because, these structures suffer from various nonlinear mode of failures while in service those are recognised as delamination, voids, matrix crack etc. Early detection of failures is what the most research mainly aims at. In this regard, the implementation of Artificial Intelligence (AI) techniques has been proved to be a versatile method for damage assessment. The collective inevitable use of composite materials in various high-performance engineering industries requires preliminary testing (detection, location, and quantification) for damage to these materials in order to improve their integrity and order. The present paper aims to bring out a concise review on various methodologies employed for damage/fault detection in composite materials with a special emphasis on supervised and unsupervised machine learning techniques. The major observations are outlined with an objective to put forward a broad perspective of the state of art related to laminated composite structural heath monitoring.

Bottom-up image detection of water channel slope damages based on superpixel segmentation and support vector machine

The operation of water supply channels is threatened by the occasionally occurred slope damages. Timely detection of their occurrence is critical for the rapid enforcement of mitigation measures. However, current practices based on routine inspection and structural heath monitoring are inefficient, laborious and tend to be biased. As an attempt to address the limitations, this paper proposes a bottom-up image detection approach for slope damages, which includes four steps, i.e. superpixel segmentation, feature handcrafting, superpixel classification based on support vector machine (SVM), and slope damage recognition. The approach employs a bottom-up strategy to infer the upper-level slope condition from the classification results of individual superpixels in the bottom level. Experiments were conducted to demonstrate the effectiveness of the approach. The handcrafted feature “LBP + HSV” was demonstrated to be effective in characterizing the image features of slope damages. An SVM model with “LBP + HSV” as input can reliably identify the slope condition in superpixels. Based on the SVM model, the bottom-up strategy achieved high recognition performance, of which the overall accuracy can be up to 91.7%. The proposed approach has potential to facilitate the early and comprehensive awareness of slope damages along the entire route of water channel by the integration with unmanned aerial vehicles.

Foundations of population-based SHM, Part III: Heterogeneous populations – Mapping and transfer

This is the third and final paper in a series laying foundations for a theory/methodology of Population-Based Structural Health Monitoring (PBSHM). PBSHM involves utilising knowledge from one set of structures in a population and applying it to a different set, such that predictions about the health states of each member in the population can be performed and improved. Central ideas behind PBSHM are those of knowledge transfer and mapping. In the context of PBSHM, knowledge transfer involves using information from a source domain structure, where labels are known for given feature sets, and mapping these onto the unlabelled feature space of a different, target domain structure. This mapping means a classifier trained on the transformed source domain data will generalise to the unlabelled target domain data; i.e.a classifier built on one structure will generalise to another, making Structural Heath Monitoring (SHM) cost-effective and applicable to a wide range of challenging industrial scenarios. This process of mapping features and labels across source and target domains is defined here via domain adaptation, a subcategory of transfer learning. A mathematical underpinning for when domain adaptation is possible in a structural dynamics context is provided, with reference to topology within a graphical representation of structures. Subsequently, a novel procedure for performing domain adaptation on topologically different structures is outlined.

Beetle-Swarm Evolution Competitive Algorithm for Bridge Sensor Optimal Placement in SHM

In structural heath monitoring (SHM), for the sake of optimizing the bridge sensor placement, a beetle-swarm evolution competitive algorithm is proposed with the group evolutionary competition mechanism introducing into beetle antennae search optimization (BAS). This paper uses high-dimensional dynamic coverage coding to initialize the beetle's location as binary codes, which avoids that the original BAS can only solve the continuous optimization problem, and ensures the dispersion of effective information in individuals, so that the coding rules are consistent with the actual coverage density for bridge sensors. In the process of evolutionary, horizontal mutation, crossover, phagocytosis and elimination among individuals are added to improve the diversity of individual evolution, which may avoid the algorithm falling into local optimum, and make the excellent individuals tend to be better. Lastly, the example analysis of Ha-Qi bridge shows that beetle-swarm evolution competitive algorithm has fast convergence speed and global optimization ability, and it is suitable for solving the sensor placement optimization for large bridge.

Comparative Study of Damage Detection Methods Based on Long-Gauge FBG for Highway Bridges.

Damage detection of highway bridges is a significant part of structural heath monitoring. Conventional accelerometers or strain gauges utilized for damage detection have many shortcomings, especially their monitoring gauge length being too short, which would result in poor damage detection results. Under this circumstance, long-gauge FBG sensors as a novel optical sensor were developed to measure the macro-strain response of the structure. Based on this sensor, many derived damage detection methods were proposed. These methods exhibit various characteristics and have not been systematically compared. As a result, it is difficult to evaluate the state of the art and also leads to confusion for users to select. Therefore, a strict comparative study on three representative methods using long-gauge FBG was carried out. First, these methods’ theoretical backgrounds and formats were reformulated and unified for better comparison. Then, based on validated vehicle–bridge coupling simulation, these methods’ performances were tested through a series of parametric studies including various damage scenarios, vehicle types, speeds, road roughness and noise levels. The precision and reliability of three methods have been thoroughly studied and compared.

Scalable Monitoring System for the Localization of Damaging Events in Thin-Walled CFRP Structures Based on Acoustic Emission Analysis and Neural Networks

A constant challenge for the design and operation of CFRP primary structures is their sensitivity towards impact loading. This can lead to the formation of externally invisible delaminations which endanger the structural integrity. In practice, this circumstance is encountered with elaborate inspections or conservative design. Structural Heath Monitoring (SHM) systems offer the potential for permanent monitoring and represent an alternative approach that has drawn more attention in the last decade. The biggest barriers to market entry for this technology are system costs and reliability. This study is dedicated to these two points with the development of a low-cost system with which representative acoustic emission sources can be located reliably in a complex CFRP structure. The implementation is carried out using acoustic emission analysis, which represents a promising solution for the integral monitoring of primary structures. It is based on the detection of acoustic waves that are released during crack initiation and growth and propagate over large areas in thin-walled structures as Lamb waves. The challenges of source localization in thin-walled CFRP structures lie in the consideration of wave dispersion, anisotropic material properties, variable component geometry and interfaces. In this thesis, this complexity is captured by training a neural network. For this purpose, artificial sources are used which imitate acoustic emissions of typical damaging events in the material in frequency and mode content. The demonstrating component is an omega profile equipped with a network of piezoelectric sensors that is designed for reliable localization within a defined window. Signal processing takes place on a single-board computer which, together with a digital oscilloscope, completes the measurement chain. The system represents a modular, low-cost approach that can be transferred to other applications by adapting the hardware and training.

On decision optimality of terrorism risk mitigation measures for iconic bridges

This paper describes the assessment of the cost efficiency of risk mitigation strategies for terrorist attacks with Improvised Explosive Devices (IEDs) for an iconic bridge structure. The assessment is performed with a decision theoretical framework building upon very recent advances in the COST Action TU1402 on Quantifying the Value of Structural Heath Monitoring. The decision scenario is formulated for a decision maker constituting an authority responsible for the societal safety of the infrastructure and consequently the direct risks for the infrastructure owner and the indirect risk due to fatalities and importance of the infrastructure are considered. The mitigation strategies are classified within the decision theoretical context as prior analyses for the assessment of protection strategies and as control strategies requiring a pre-posterior decision analysis. The identification of efficient risk mitigation strategies is based (1) on the risk and expected cost based optimization of actions and information and their combination before implementation, (2) on quantifying and ensuring the significance in risk and expected cost reduction and (3) on quantifying and ensuring a high probability of cost efficiency. These criteria, i.e. the optimality, significance and efficiency ensure the performance of the strategies at the decision point in time before implementation. It is found that the strategies are relying on the identification of the threat level and that control strategies are in favor as their significance and probability of efficiency are higher and their costs are adjustable. However, for high threat levels, both the bridge protection strategies and control strategies are cost efficient.

Fabrication and characterization of a novel carbonated 0-3 piezoelectric γ-C2S composite

0-3 cement-based piezoelectric composites have been proposed as sensors and actuators in civil engineering for structural heath monitoring. Previous studies are focused exclusively on hydraulic binders such as ordinary Portland cement and sulfoaluminate cement. In this paper, a novel 0–3 piezoelectric composite is presented which consists of non-hydraulic gamma dicalcium silicate (γ-C2S) as the matrix and lead zirconate titanate (PZT) as the piezoelectric phase, utilizing carbonation reaction for the attainment of high mechanical strength. The effect of PZT content on the piezoelectric, dielectric and mechanical properties is investigated. When exposed to a CO2-rich environment for 2 h, the composite with 60 vol% PZT develops rapid strength to be 38.5 MPa and possesses a piezoelectric coefficient (d33) of 24.6 pC/N, a dielectric constant of 67.5 and tanδ of 0.11, which are comparable to what has been reported in other studies.

Vortex-induced vibration analysis of long-span bridges with twin-box decks under non-uniformly distributed turbulent winds

With the increase of span length, long-span bridges become more flexible and susceptible to vortex-induced vibrations (VIV). Large-amplitude VIV may cause fatigue damage to structural components, and therefore accurate predictions of vortex-induced responses (VIR) are important. This paper proposes a mode-by-mode VIV analysis method for long-span bridges with twin-box decks under non-uniformly distributed turbulent winds. A semi-empirical model of vortex-induced forces (VIF), developed by the authors and validated by direct force measurements on an elastically-mounted twin-box section model under turbulent winds, is embedded in the VIV analysis method. The proposed analysis method is then used to analyze the VIV of a real long-span suspension bridge of a twin-box deck. The computed VIR of the prototype-bridge is finally compared with the VIR measured on-site by a structural heath monitoring system installed in the bridge. The comparative results show that the proposed VIV analysis method can be used to predict the VIR of long-span bridges with twin-box decks. Further studies also show that the non-uniform distribution of mean wind speed reduces the VIV of the bridge. Turbulence also diminishes the VIV of the bridge, and the reduction effect is larger on lower-order modes of vibration than on higher-order modes of vibration.

Detection and localisation of structural damage based on the error in the constitutive relations in dynamics

In the present work the authors revisit the problem of detecting and localising errors in the finite element modelling of structures subjected to dynamic loading, with the objective of adapting it to the development of a reliable tool that may be used to indicate zones that may be damaged, when applied to structural heath monitoring. It can also be applied in the context of an updating process and in that case the aim is to obtain important information on zones that are not sufficiently well modelled, leading to the improvement of the numerical model. The problem is formulated by establishing an indicator based upon an error function focused on the constitutive relations of the material and on the discrepancy between the healthy and damaged states of a structure. In both cases the experimental results are introduced in the form of frequency response functions (FRFs), which are supposed to be known at just a few locations along the structure, as it happens in real applications. The developed indicator accommodates possible errors in the stiffness, mass and (proportional) damping properties. Numerical examples simulating the measured FRFs are given to assess the capability of the proposed indicator when applied to the damage detection and localisation problem.

System identification of a historic swing truss bridge using a wireless sensor network employing orientation correction

SUMMARY System identification was performed on the swing span of a steel truss bridge dating from 1896 using acceleration data collected from a wireless sensor network (WSN). The swing span can rotate 360° to allow river traffic to pass through the locks located under the bridge. The WSN installed on the swing span consists of 23 nodes that measure synchronized tri-axial acceleration. Five days of measured data were segmented according to three functional positions of the span (locked facing downstream, locked facing upstream, swung for river traffic) and load conditions. Subsequently, the modal parameters corresponding to the bridge's three functional positions were obtained using frequency domain decomposition method. The initial system identification of the bridge resulted in significant anomalies, in comparison with the finite element (FE) model. To improve the accuracy of the overall identification results, the sensor orientation correction technique is proposed for the measured data from the WSN. The improvement has been verified by comparing the orientation-corrected mode shapes with non-corrected mode shapes and FE mode shapes using the modal assurance criterion values. Analysis of the modal parameters inferred that the boundary conditions for the bridge positions are different because of the interaction of the locking mechanism with the abutments in those positions. The proposed approach for system identification, including sensor orientation correction, is to be used as part of the comprehensive structural heath monitoring strategy for this historic structure. Copyright © 2014 John Wiley & Sons, Ltd.

Reliability assessment of ship structures using Bayesian updating

This paper presents an approach for reducing the uncertainty in the performance assessment of ship structures by updating the wave-induced load effects with the data acquired from structural heath monitoring (SHM). The initial information on the wave-induced load effects is calculated based on strip theory. Bayesian updating method is used to incorporate the processed SHM data to update the wave-induced vertical bending moment modeled by the Rayleigh distribution and its extreme values modeled by the Type I extreme value (largest) distribution. Three general cases associated with updating (a) only one parameter, (b) two parameters separately, and (c) two correlated parameters simultaneously in the Type I extreme value distribution are investigated. Aging effect due to corrosion is considered in the resistance modeling. Time-variant reliabilities before and after updating are evaluated. The proposed approach is illustrated with the Joint High Speed Sealift.

Enhanced detection through low-order stochastic modeling for guided-wave structural health monitoring

Guided wave structural health monitoring offers the potential for efficient defect detection and localization on large plate-like structures. The aim of this article is to demonstrate that the performance of guided wave structural health monitoring on complex structures can be quantified, predicted, and enhanced through basic stochastic modeling and application of a likelihood-based detector. These are necessary steps in enabling such structural heath monitoring techniques to be used more robustly on safety-critical structures. Extensive ensembles of measurements were taken using a sparse array of transducers on an aluminum plate with geometric complexities (stiffeners), before and after introducing several sizes of damage at several locations, and in the presence of large noise processes. This enabled a full statistical treatment of performance evaluation using a modification of the standard receiver operating characteristic curve. The incorporation of stochastic modeling produced on average a 44% reduction in missed detections among the damage modes tested, with up to a 95% reduction in some cases.

Intelligent Wireless Sensors with Application to the Identification of Structural Modal Parameters and Steel Cable Forces: From the Lab to the Field

Wireless sensing systems have been proposed for structural heath monitoring in recent years. While wireless sensors are cost-competitive compared to tethered monitoring systems, their significant merit also lies in their embedded computational capabilities. In this paper, performance of the two embedded engineering algorithms, namely the fast Fourier transform and peak-picking algorithm implemented in the wireless sensing nodes codeveloped at Stanford University and the University of Michigan is investigated through laboratory and field experimental studies. Furthermore, the wireless sensor network embedded with the engineering algorithms is adopted for the identification of structural modal parameters and forces in steel bridge cables. Identification results by the embedded algorithms in the intelligent wireless sensors are compared with those obtained by conventional offline analysis of the measured time-history data. Such a comparison serves to validate the effectiveness of the intelligent wireless sensor network. In addition, it is shown that self-interrogation of measurement data based upon the two embedded algorithms in wireless sensor nodes greatly reduces the amount of data to be transmitted by the wireless sensing network. Thus, the intelligent wireless sensors offer scalable network solutions that are power-efficient for the health monitoring of civil infrastructures.

Monitoring the Mechanical Behavior of the Weathervane Sculpture Mounted Atop Seville Cathedral’s Giralda Tower

This article presents the application of monitoring and detection of structural damage techniques to a historic monument. Seville cathedral’s famous bell tower ‘La Giralda’ is 96 m tall and is crowned with a large 16th century sculpture known as ‘Giraldillo’. The sculpture is supported with an internal bar structure, which is fitted over the axis about which it rotates according to the wind direction, allowing it to function as a weathervane. Between 1999 and 2005 the Giraldillo was demounted and underwent an intensive restoration process, which included mechanical and structural repair work. As the sculpture is only accessible by means of complex and costly scaffolding systems, an instrumentation system consisting of different types of sensors was installed to study the assembly’s mechanical response, its functioning as a weathervane and its state of conservation while it was being remounted atop the Giralda Tower. Different damage detection techniques aimed at detecting possible deterioration in the Giraldillo’s support structure were employed as well. This article presents results obtained in 2 years of system operation, showing how structural heath monitoring techniques can be applied to historical monuments.

Controlled Space Radiation concept for mesh-free semi-analytical technique to model wave fields in complex geometries

Numerical modelling of the ultrasonic wave propagation is important for Structural Heath Monitoring and System Prognosis problems. In order to develop intelligent and adaptive structures with embedded damage detector and classifier mechanisms, detailed understanding of scattered wave fields due to anomaly in the structure is inevitably required. A detailed understanding of the problem demands a good modelling of the wave propagation in the problem geometry in virtual form. Therefore, efficient analytical, semi-analytical or numerical modelling techniques are required. In recent years a semi-analytical mesh-free technique called Distributed Point Source Method (DPSM) is being used for modelling various ultrasonic, electrostatic and electromagnetic wave field problems. In the conventional DPSM approach point sources are placed along the transducer faces, problem boundaries and interfaces to model incident and scattered fields. Every point source emits energy in all directions uniformly. Source strengths of these 360° radiation sources are obtained by satisfying interface and boundary conditions of the problem. In conventional DPSM modelling approach it is assumed that the shadow zone does not require any special consideration. 360° Radiation point sources should be capable of properly modelling shadow zones because all boundary and interface conditions are satisfied. In this paper it is investigated how good this assumption is by introducing the ‘shadow zone’ concept at the point source level and comparing the results generated by the conventional DPSM and by this modified approach where the conventional 360° radiation point sources are replaced by the Controlled Space Radiation (CSR) sources.

Adaptive magnitude spectrum algorithm for Hilbert–Huang transform based frequency identification

An innovative Hilbert–Huang transform (HHT) based frequency identification approach designated as adaptive magnitude spectrum algorithm (AMSA) is proposed in this paper. Characterized by the a posteriori property, the AMSA does not need the a priori information about the modal frequencies to be identified, and the situations that a modal frequency may be contained along specific segments of the whole time duration of one or more intrinsic mode functions (IMFs) are allowed for automatically in the resulting adaptive magnitude spectrum (AMS). The algorithm introduces a banded frequency sweep procedure, during which a series of digital filters are designed to process the original signal. Then upon applying HHT to the filtered signals, the forward weighted averages and the backward weighted averages are computed to construct the AMS, based on which the frequencies can be clearly identified. Two numerically simulated examples, i.e. the free vibration signal from a concrete slab subjected to impact loading and the random vibration signal generated by the Phase I IASC-ASCE structural health monitoring analytical benchmark problem, and one experimental example, i.e. the free vibration signal based on the Phase II IASC-ASCE structural heath monitoring experimental benchmark problem, are used to demonstrate the efficacy of the algorithm. The results indicate that the AMSA is an effective frequency identification technique.

Electro-Mechanical Impedance Method for Crack Detection in Thin Plates

This paper describes the utilization of Electro-Mechanical (E/M) impedance method for structural health monitoring of thin plates. The method allows the direct identification of structural dynamics by obtaining its E/M impedance or admittance signatures. The analytical model for two-dimensional structure was developed and verified with experiments. Good matching of experimental results and calculated spectra was obtained for axial and flexural components. The ability of the method to identify the presence of damage was investigated by performing an experiment where the damage in the form of crack was simulated with an EDM slit placed at various distances from the sensor. It was found that the crack presence dramatically modifies the E/M impedance spectrum and this modification decreases as the distance between the sensor and the crack increases. Several overall-statistics damage metrics, which may be used for on-line structural heath monitoring, were investigated. Among these candidate damage metrics, the αth power of the correlation coefficient deviation, CCDα,3< α <7, used in the high frequency band 300-450 kHz, was found to be most successful. Careful selection of the high frequency band and proper choice of the appropriate damage metric were found to be essential for successful damage detection and structural health monitoring.