Review: optical fiber sensors for civil engineering applications

Optical fiber sensor (OFS) technologies have developed rapidly over the last few decades, and various types of OFS have found practical applications in the field of civil engineering. In this paper, which is resulting from the work of the RILEM technical committee “Optical fiber sensors for civil engineering applications”, different kinds of sensing techniques, including change of light intensity, interferometry, fiber Bragg grating, adsorption measurement and distributed sensing, are briefly reviewed to introduce the basic sensing principles. Then, the applications of OFS in highway structures, building structures, geotechnical structures, pipelines as well as cables monitoring are described, with focus on sensor design, installation technique and sensor performance. It is believed that the State-of-the-Art review is helpful to engineers considering the use of OFS in their projects, and can facilitate the wider application of OFS technologies in construction industry

Master of Science

thesisCarbon fiber-reinforced composite materials have been increasingly used in aerospace and aeronautics industries due to their superior strength over metals, low fatigue life, high corrosion resistance, and temperature resistance. Since most damage, such as delaminations, manifest inside the composite material, we often cannot detect damage through visual inspection. As a replacement for visual inspection, ultrasonic guided waves have been widely researched to remotely detect, locate, and characterize damage in structures due to their unique capability to travel long distances and inspect inaccessible locations for damage. Yet the anisotropic nature of composites makes it difficult to identify the velocity characteristics of the guided waves and utilize them for damage localization. To address this challenge, we use sparse wavenumber analysis to determine anisotropic multimodal and dispersive frequency-wavenumber characteristics of guided waves. We then use these multimodal and dispersive properties to predict how guided waves propagate in the anisotropic plate through sparse wavenumber synthesis. Finally, these predictions, which form a wave propagation model for the composite, are integrated with matched field processing, a model-based localization framework, to locate damage on the composite

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

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

Machine learning at the interface of structural health monitoring and non-destructive evaluation

While both non-destructive evaluation (NDE) and structural health monitoring (SHM) share the objective of damage detection and identification in structures, they are distinct in many respects. This paper will discuss the differences and commonalities and consider ultrasonic/guided-wave inspection as a technology at the interface of the two methodologies. It will discuss how data-based/machine learning analysis provides a powerful approach to ultrasonic NDE/SHM in terms of the available algorithms, and more generally, how different techniques can accommodate the very substantial quantities of data that are provided by modern monitoring campaigns. Several machine learning methods will be illustrated using case studies of composite structure monitoring and will consider the challenges of high-dimensional feature data available from sensing technologies like autonomous robotic ultrasonic inspection. This article is part of the theme issue ‘Advanced electromagnetic non-destructive evaluation and smart monitoring’

An analytical approach to reconstruction of axisymmetric defects in pipelines using T(0,1) guided waves

Torsional guided waves have been widely utilized to inspect the surface corrosion in pipelines due to their simple displacement behaviors and the ability of longrange transmission. Especially, the torsional mode T(0, 1), which is the first order of torsional guided waves, plays the irreplaceable position and role, mainly because of its non-dispersion characteristic property. However, one of the most pressing challenges faced in modern quality inspection is to detect the surface defects in pipelines with a high level of accuracy. Taking into account this situation, a quantitative reconstruction method using the torsional guided wave T(0, 1) is proposed in this paper. The methodology for defect reconstruction consists of three steps. First, the reflection coefficients of the guided wave T(0, 1) scattered by different sizes of axisymmetric defects are calculated using the developed hybrid finite element method (HFEM). Then, applying the boundary integral equation (BIE) and Born approximation, the Fourier transform of the surface defect profile can be analytically derived as the correlative product of reflection coefficients of the torsional guided wave T(0, 1) and the fundamental solution of the intact pipeline in the frequency domain. Finally, reconstruction of defects is precisely performed by the inverse Fourier transform of the product in the frequency domain. Numerical experiments show that the proposed approach is suitable for the detection of surface defects with arbitrary shapes. Meanwhile, the effects of the depth and width of surface defects on the accuracy of defect reconstruction are investigated. It is noted that the reconstructive error is less than 10%, providing that the defect depth is no more than one half of the pipe thickness

Multi-helical Lamb Wave Imaging for Pipe-like Structures Based on a Probabilistic Reconstruction Approach

The special form of pipe-like structure provides the helical route for ultrasonic guided wave. Considering the pipe as a flattened plate but with periodical replications, the helical wave becomes intuitional and a corresponding imaging algorithm can be constructed. This work proposes the multihelical Lamb wave imaging method by utilizing the multiple arrival wavepackets which are denoted as different orders. The helical wave signal model is presented and the constant group velocity point is illustrated. The probabilistic reconstruction algorithm is combined with the separation and fusion of different helical routes. To verify the proposed scheme, finite element simulations and corresponding experiments are conducted. The cases of single-defect simulation and two-defect simulation indicate the successful and robust implementation of the imaging algorithm. The test on actual pipe damage is also investigated to show its capability in imaging an irregular defect. The comparison with imaging results from only first arrival demonstrates the advantage of multihelical wave imaging, including the better imaging resolution and higher localization accuracy

Monitoring system for long-distance pipelines subject to destructive attack

In an era of terrorism, it is important to protect critical pipeline infrastructure, especially in countries where life is strongly dependent on water and the economy on oil and gas. Structural health monitoring (SHM) using acoustic waves is one of the common solutions. However, considerable prior work has shown that pipes are cylindrical acoustic waveguides that support many dispersive, lossy modes; only the torsional T(0, 1) mode has zero dispersion. Although suitable transducers have been developed, these typically excite several modes, and even if they do not, bends and supports induce mode conversion. Moreover, the high-power transducers that could in principle be used to overcome noise and attenuation in long distance pipes present an obvious safety hazard with volatile products, making it difficult to distinguish signals and extract pipeline status information. The problem worsens as the pipe diameter increases or as the frequency rises (due to the increasing number of modes), if the pipe is buried (due to rising attenuation), or if the pipe carries a flowing product (because of additional acoustic noise). Any system is therefore likely to be short-range. This research proposes the use of distributed active sensor network to monitor long-range pipelines, by verifying continuity and sensing small disturbances. A 4-element cuboid Electromagnetic Acoustic Transducer (EMAT) is used to excite the longitudinal L(0,1) mode. Although the EMAT also excites other slower modes, long distance propagation allows their effects to be separated. Correlation detection is exploited to enhance signal-to-noise ratio (SNR), and code division multiplexing access (CDMA) is used to distinguish between nodes in a multi-node system. An extensive numerical search for multiphase quasi-orthogonal codes for different user numbers is conducted. The results suggest that side lobes degrade performance even with the highest possible discrimination factor. Golay complementary pairs (which can eliminate the side lobes completely, albeit at the price of a considerable reduction in speed) are therefore investigated as an alternative. Pipeline systems are first reviewed. Acoustic wave propagation is described using standard theory and a freeware modeling package. EMAT modeling is carried out by numerical calculation of electromagnetic fields. Signal propagation is investigated theoretically using a full system simulator that allows frequency-domain description of transducers, dispersion, multi-mode propagation, mode conversion and multiple reflections. Known codes for multiplexing are constructed using standard algorithms, and novel codes are discovered by an efficient directed search. Propagation of these codes in a dispersive system is simulated. Experiments are carried out using small, unburied air-filled copper pipes in a frequency range where the number of modes is small, and the attenuation and noise are low. Excellent agreement is obtained between theory and experiment. The propagation of pulses and multiplexed codes over distances up to 200 m are successfully demonstrated, and status changes introduced by removable reflectors are detected.Open Acces

Master of Science

thesisNondestructive evaluation (NDE) is a means of assessing the reliability and integrity of a structural component and provides such information as the presence, location, extent, and type of damage in the component. Structural health monitoring (SHM) is a subfield of NDE, and focuses on a continuous monitoring of a structure while in use. SHM has been applied to structures such as bridges, buildings, pipelines, and airplanes with the goal of detecting the presence of damage as a means of determining whether a structure is in need of maintenance. SHM can be posed as a modeling problem, where an accurate model allows for a more reliable prediction of structural behavior. More reliable predictions make it easier to determine if something is out of the ordinary with the structure. Structural models can be designed using analytical or empirical approaches. Most SHM applications use purely analytical models based on finite element analysis and fundamental wave propagation equations to construct behavioral predictions. Purely empirical models exist, but are less common. These often utilize pattern recognition algorithms to recognize features that indicate damage. This thesis uses a method related to the k-means algorithm known as dictionary learning to train a wave propagation model from full wavefield data. These data are gathered from thin metal plates that exhibit complex wavefields dominated by multipath interference. We evaluate our model for its ability to detect damage in structures on which the model was not trained. These structures are similar to the training structure, but variable in material type and thickness. This evaluation will demonstrate how well learned dictionaries can both detect damage in a complex wavefield with multipath interference, and how well the learned model generalizes to structures with slight variations in properties. The damage detection and generalization results achieved using this empirical model are compared to similar results using both an analytical model and a support vector machine model