Real-world Structural Health Monitoring Research Articles

Metal-coated high-temperature strain optical fiber sensor based on cascaded air-bubble FPI-FBG structure.

Metal coatings can protect the fragile optical fiber sensors and extend their life in harsh environments. However, simultaneous high-temperature strain sensing in a metal-coated optical fiber remains relatively unexplored. In this study, a nickel-coated fiber Bragg grating (FBG) cascaded with an air bubble cavity Fabry-Perot interferometer (FPI) fiber optic sensor was developed for simultaneous high temperature and strain sensing. The sensor was successfully tested at 545 °C for 0-1000 µɛ, and the characteristic matrix was used to decouple temperature and strain. The metal layer allows easy attachment to metal surfaces that operate at high temperatures, enabling sensor-object integration. As a result, the metal-coated cascaded optical fiber sensor has the potential to be used in real-world structural health monitoring.

Fatigue damage detection from imbalanced inspection data of Lamb wave

The core of structural health monitoring is to provide a real-time monitoring, inspection, and damage detection of structures. As one of the most promising technology to structural health monitoring, the Lamb wave method has attracted interest because it is sensitive to small-scale damage with a long detection range. However, in many real-world structural health monitoring applications, the nature of the problem implies structures work under normal condition in most of its operating phases; therefore, classes of data collected are not equally represented. The predictive capability of damage detection algorithms may significantly be impaired by class imbalance. This article presents a damage detection method using imbalanced inspection data which is collected through Lamb wave detection. Aiming at maximizing detection accuracy, an improved synthetic minority over-sampling technique using three-point triangle (triangle synthetic minority over-sampling technique) is proposed to conduct the over-sampling procedure and increase the number of minority samples. The iterative-partitioning filter is employed to remove the noisy examples which may be introduced by triangle synthetic minority over-sampling technique. Three conventional classification methods, namely, support vector machine, decision tree, and k-nearest neighbor, are used to perform the damage detection. A fatigue crack detection test using Lamb wave is performed to demonstrate the overall procedure of the proposed method. Three damage sensitive features, namely, normalized amplitude, correlation coefficient, and normalized energy, are extracted from signals as datasets. A cross-validation is performed to verify the performance of the proposed method for crack size identification.

Toward Structural Health Monitoring of Civil Structures Based on Self-Sensing Concrete Nanocomposites: A Validation in a Reinforced-Concrete Beam

Self-sensing concrete materials, also known as smart concretes, are emerging as a promising technological development for the construction industry, where novel materials with the capability of providing information about the structural integrity while operating as a structural material are required. Despite progress in the field, there are issues related to the integration of these composites in full-scale structural members that need to be addressed before broad practical implementations. This article reports the manufacturing and multipurpose experimental characterization of a cement-based matrix (CBM) composite with carbon nanotube (CNT) inclusions and its integration inside a representative structural member. Methodologies based on current–voltage (I–V) curves, direct current (DC), and biphasic direct current (BDC) were used to study and characterize the electric resistance of the CNT/CBM composite. Their self-sensing behavior was studied using a compression test, while electric resistance measures were taken. To evaluate the damage detection capability, a CNT/CBM parallelepiped was embedded into a reinforced-concrete beam (RC beam) and tested under three-point bending. Principal finding includes the validation of the material’s piezoresistivity behavior and its suitability to be used as strain sensor. Also, test results showed that manufactured composites exhibit an Ohmic response. The embedded CNT/CBM material exhibited a dominant linear proportionality between electrical resistance values, load magnitude, and strain changes into the RC beam. Finally, a change in the global stiffness (associated with a damage occurrence on the beam) was successfully self-sensed using the manufactured sensor by means of the variation in the electrical resistance. These results demonstrate the potential of CNT/CBM composites to be used in real-world structural health monitoring (SHM) applications for damage detection by identifying changes in stiffness of the monitored structural member.

Deterioration and damage identification in building structures using a novel feature selection method

Identifying structural defects in complex structures is one of the main objectives in real-world structural health monitoring (SHM) applications. In this article, a signal-based supervised methodology is proposed for detecting deterioration and damage in building structures. This method benefits from a novel feature selection method called signal simulation-based feature selection (SSFS) algorithm, which only relies on baseline signals to extract the most sensitive features from any type of structure. The results showed that the offered methodology is capable of identifying damage and deterioration precisely, and therefore, can be a viable alternative to conventional techniques that require additional information.

Vision-Based Building Seismic Displacement Measurement by Stratification of Projective Rectification Using Lines.

We propose a new flexible technique for accurate vision-based seismic displacement measurement of building structures via a single non-stationary camera with any perspective view. No a priori information about the camera’s parameters or only partial knowledge of the internal camera parameters is required, and geometric constraints in the world coordinate system are employed for projective rectification in this research. Whereas most projective rectifications are conducted by specifying the positions of four or more fixed reference points, our method adopts a stratified approach to partially determine the projective transformation from line-based geometric relationships on the world plane. Since line features are natural and plentiful in a man-made architectural building environment, robust estimation techniques for automatic projective/affine distortion removal can be applied in a more practical way. Both simulations and real-recorded data were used to verify the effectiveness and robustness of the proposed method. We hope that the proposed method could advance the consumer-grade camera system for vision-based structural measurement one more step, from laboratory environments to real-world structural health monitoring systems.

Structural health monitoring of high-speed railway tracks using diffuse ultrasonic wave-based condition contrast: theory and validation

Despite proven effectiveness and accuracy in laboratories, the existing damage assessment based on guided ultrasonic waves (GUWs) or acoustic emission (AE) confronts challenges when extended to real-world structural health monitoring (SHM) for railway tracks. Central to the concerns are the extremely complex signal appearance due to highly dispersive and multimodal wave features, restriction on transducer installations, and severe contaminations of ambient noise. It remains a critical yet unsolved problem along with recent attempts to implement SHM in bourgeoning high-speed railway (HSR). By leveraging authors' continued endeavours, an SHM framework, based on actively generated diffuse ultrasonic waves (DUWs) and a benchmark-free condition contrast algorithm, has been developed and deployed via an all-in-one SHM system. Miniaturized lead zirconate titanate (PZT) wafers are utilized to generate and acquire DUWs in long-range railway tracks. Fatigue cracks in the tracks show unique contact behaviours under different conditions of external loads and further disturb DUW propagation. By contrast DUW propagation traits, fatigue cracks in railway tracks can be characterised quantitatively and the holistic health status of the tracks can be evaluated in a real-time manner. Compared with GUW- or AE-based methods, the DUW-driven inspection philosophy exhibits immunity to ambient noise and measurement uncertainty, less dependence on baseline signals, use of significantly reduced number of transducers, and high robustness in atrocious engineering conditions. Conformance tests are performed on HSR tracks, in which the evolution of fatigue damage is monitored continuously and quantitatively, demonstrating effectiveness, adaptability, reliability and robustness of DUW-driven SHM towards HSR applications.

Agglomerative concentric hypersphere clustering applied to structural damage detection

The present paper proposes a novel cluster-based method, named as agglomerative concentric hypersphere (ACH), to detect structural damage in engineering structures. Continuous structural monitoring systems often require unsupervised approaches to automatically infer the health condition of a structure. However, when a structure is under linear and nonlinear effects caused by environmental and operational variability, data normalization procedures are also required to overcome these effects. The proposed approach aims, through a straightforward clustering procedure, to discover automatically the optimal number of clusters, representing the main state conditions of a structural system. Three initialization procedures are introduced to evaluate the impact of deterministic and stochastic initializations on the performance of this approach. The ACH is compared to state-of-the-art approaches, based on Gaussian mixture models and Mahalanobis squared distance, on standard data sets from a post-tensioned bridge located in Switzerland: the Z-24 Bridge. The proposed approach demonstrates more efficiency in modeling the normal condition of the structure and its corresponding main clusters. Furthermore, it reveals a better classification performance than the alternative ones in terms of false-positive and false-negative indications of damage, demonstrating a promising applicability in real-world structural health monitoring scenarios.

Compressive sensing for efficient health monitoring and effective damage detection of structures

Real world Structural Health Monitoring (SHM) systems consist of sensors in the scale of hundreds, each sensor generating extremely large amounts of data, often arousing the issue of the cost associated with data transfer and storage. Sensor energy is a major component included in this cost factor, especially in Wireless Sensor Networks (WSN). Data compression is one of the techniques that is being explored to mitigate the effects of these issues. In contrast to traditional data compression techniques, Compressive Sensing (CS) – a very recent development – introduces the means of accurately reproducing a signal by acquiring much less number of samples than that defined by Nyquist's theorem. CS achieves this task by exploiting the sparsity of the signal. By the reduced amount of data samples, CS may help reduce the energy consumption and storage costs associated with SHM systems. This paper investigates CS based data acquisition in SHM, in particular, the implications of CS on damage detection and localization. CS is implemented in a simulation environment to compress structural response data from a Reinforced Concrete (RC) structure. Promising results were obtained from the compressed data reconstruction process as well as the subsequent damage identification process using the reconstructed data. A reconstruction accuracy of 99% could be achieved at a Compression Ratio (CR) of 2.48 using the experimental data. Further analysis using the reconstructed signals provided accurate damage detection and localization results using two damage detection algorithms, showing that CS has not compromised the crucial information on structural damages during the compression process.

A novel unsupervised approach based on a genetic algorithm for structural damage detection in bridges

This paper proposes a novel unsupervised and nonparametric genetic algorithm for decision boundary analysis (GADBA) to support the structural damage detection process, even in the presence of linear and nonlinear effects caused by operational and environmental variability. This approach is rooted in the search of an optimal number of clusters in the feature space, representing the main state conditions of a structural system, also known as the main structural components. This genetic-based clustering approach is supported by a novel concentric hypersphere algorithm to regularize the number of clusters and mitigate the cluster redundancy. The superiority of the GADBA is compared to state-of-the-art approaches based on the Gaussian mixture models and the Mahalanobis squared distance, on data sets from monitoring systems installed on two bridges: the Z-24 Bridge and the Tamar Bridge. The results demonstrate that the proposed approach is more efficient in the task of fitting the normal condition and its structural components. This technique also revealed to have better classification performance than the alternative ones in terms of false-positive and false-negative indications of damage, suggesting its applicability for real-world structural health monitoring applications.

Instrumentation on structural health monitoring systems to real world structures

Instrumentation on structural health monitoring system imposes critical issues for applying the structural monitoring system to real world structures, for which not only on the configuration and geometry, but also aesthetics on the system to be monitored should be considered. To illustrate this point, two real world structural health monitoring systems, the structural health monitoring system of Shenzhen Vanke Center and the structural health monitoring system of Shenzhen Bay Stadium in China, are presented in the paper. The instrumentation on structural health monitoring systems of real world structures is addressed by providing the description of the structure, the purpose of the structural health monitoring system implementation, as well as details of the system integration including the installations on the sensors and acquisition equipment and so on. In addition, an intelligent algorithm on stress identification using measurements from multi-region is presented in the paper. The stress identification method is deployed using the fuzzy pattern recognition and Dempster-Shafer evidence theory, where the measurements of limited strain sensors arranged on structure are the input data of the method. As results, at the critical parts of the structure, the stress distribution evaluated from the measurements has shown close correlation to the numerical simulation results on the steel roof of the Beijing National Aquatics Center in China. The research work in this paper can provide a reference for the design and implementation of both real world structural health monitoring systems and intelligent algorithm to identify stress distribution effectively.

Warped Wigner-Hough Transform for Defect Reflection Enhancement in Ultrasonic Guided Wave Monitoring

To improve the defect detectability of Lamb wave inspection systems, the application of nonlinear signal processing was investigated. The approach is based on a Warped Frequency Transform (WFT) to compensate the dispersive behavior of ultrasonic guided waves, followed by a Wigner-Ville time-frequency analysis and the Hough Transform to further improve localization accuracy. As a result, an automatic detection procedure to locate defect-induced reflections was demonstrated and successfully tested by analyzing numerically simulated Lamb waves propagating in an aluminum plate. The proposed method is suitable for defect detection and can be easily implemented for real-world structural health monitoring applications.