Corrosion poses a great threat to ageing civil infrastructure in the world, and researchers are seeking methods to monitor the corrosion in reinforced concrete structures. Detection of corrosion at its incipient stage has been an impending task in the non-destructive testing of materials. Several non-destructive testing methods to assess the presence of corrosion exist. The limitation of the current methods is that either they require measurement at several points or they require a large network of sensors. Guided wave-based monitoring overcomes these limitations because a large area can be scanned using fewer sensors. The process of corrosion is complex, and it leads to a simultaneous reduction in the diameter and the debonding between concrete and reinforcing steel bars in reinforced concrete structures. However, some of the recent studies that explore the use of guided waves focus only on the detection of the individual effect of diameter reduction and debonding of the rebars in reinforced concrete by artificially inducing the damage. In this study, an accelerated corrosion setup is deployed to induce pitting corrosion in reinforced concrete beams using the impressed current method. These beams are continuously monitored using ultrasonic guided waves that are generated and received by piezoelectric wafer transducers that are attached to the rebars. It is shown that the incipient stage of pitting corrosion can be detected successfully, and the mechanism of corrosion process, which involves the corrosion initiation, progression, and diameter reduction-and-cracking phases, can be established from the signal characteristics of the longitudinal and flexural-guided wave modes. The impressed current flow in the corrosion cell also confirms the various phases of corrosion.
Several concrete dams all over the world exhibit severe cracks. It is very important to investigate the influence of cracks on the long-term behavior of dam structures to ensure safe operation. The interpretation of measured dam displacements is usually based on statistical hydrostatic-seasonal-time and hydrostatic-thermal-time models. The main purpose of this article is to present a statistical hydrostatic-thermal-crack-time model to interpret displacements of concrete arch dams with influential horizontal cracks. The hydrostatic-thermal-crack-time model is applied to analyze the Chencun dam, an arch-gravity dam with a large-scale horizontal crack on the downstream face. The crack stretches horizontally across most of the dam blocks. Its crack mouth opening displacement had been continually increasing even after reinforcement treatment, accompanied by abnormal deformation characteristics of the arch-cantilever system. A three-dimensional finite element model, containing the pre-existing crack using special gap elements, is built to reproduce the structural response, assess the contribution of the crack on the registered movements, and obtain the relationship between the crack mouth opening displacement and the dam crest displacement. Based on this, the hydrostatic-thermal-CMOD-time model considering crack mouth opening displacement is developed. Compared with the traditional models, the hydrostatic-thermal-crack-time model is expected to provide a better fit accuracy. The results also show that the crack and the corresponding reinforcement measure have a significant effect on the deformation behavior of the dam. This can provide some useful indications for concrete structures with similar problems.
In this article, we present a probabilistic approach for fault detection and prognosis of rolling element bearings based on a two-phase degradation model. One of the main issues in dealing with bearing degradation is that the degradation mechanism is unobservable and can only be inferred through appropriate surrogate measures obtained from indirect sensory measurements. Furthermore, the stochastic nature of the degradation path renders fault detection and estimating the end-of-life characteristics from such data extremely challenging. When such components are a part of a larger system, the exact degradation path depends on both the operating and loading conditions, which means that the most effective condition monitoring approach should estimate the degradation model parameters under operational conditions, and not solely from isolated component testing or historical information. Motivated by these challenges, a two-phase degradation model using surrogate measures of degradation from vibration measurements is proposed and a Bayesian approach is used to estimate the model parameters. The underlying methodology involves using priors from historical data, while the posterior calculations are undertaken using surrogate measures obtained from a monitored unit combined with the aforesaid priors. The problem of fault detection is posed as a change point location problem. This allows the prior knowledge obtained from the past failures to be integrated for maintenance planning of a currently working unit in a systematic way. The correlation between the degradation rate and the time of occurrence of the change point, an often overlooked aspect in prognosis, is also considered in here. A numerical example and a case study are presented to illustrate the overall methodology and the results obtained using this approach.
Fiber Bragg gratings are known being immune to electromagnetic interference and emerging as Lamb wave sensors for structural health monitoring of plate-like structures. However, their application for damage localization in large areas has been limited by their direction-dependent sensor factor. This article addresses such a challenge and presents a robust damage localization method for fiber Bragg grating Lamb wave sensing through the implementation of adaptive phased array algorithms. A compact linear fiber Bragg grating phased array is configured by uniformly distributing the fiber Bragg grating sensors along a straight line and axially in parallel to each other. The Lamb wave imaging is then performed by phased array algorithms without weighting factors (conventional delay-and-sum) and with adaptive weighting factors (minimum variance). The properties of both imaging algorithms, as well as the effects of fiber Bragg grating's direction-dependent sensor factor, are characterized, analyzed, and compared in details. The results show that this compact fiber Bragg grating array can precisely locate damage in plates, while the comparisons show that the minimum variance method has a better imaging resolution than that of the delay-and-sum method and is barely affected by fiber Bragg grating's direction-dependent sensor factor. Laboratory tests are also performed with a four-fiber Bragg grating array to detect simulated defects at different directions. Both delay-and-sum and minimum variance methods can successfully locate defects at different positions, and their results are consistent with analytical predictions.
Ambient excitations applied to structures may lead to non-stationary vibration responses. In such circumstances, it may be difficult or improper to extract meaningful and significant damage features through methods that mainly rely on the stationarity of data. This article proposes a new hybrid algorithm for feature extraction as a combination of a new adaptive signal decomposition method called improved complete ensemble empirical mode decomposition with adaptive noise and autoregressive moving average model. The major contribution of this algorithm is to address the important issue of feature extraction under ambient vibration and non-stationary signals. The improved complete ensemble empirical mode decomposition with adaptive noise method is an improvement on the well-known ensemble empirical mode decomposition technique by removing redundant intrinsic mode functions. In addition, a novel automatic approach is presented to select the most relevant intrinsic mode functions to damage based on the intrinsic mode function energy level. Fitting an autoregressive moving average model to each selected intrinsic mode function, the model residuals are extracted as the damage-sensitive features. The main limitation is that such features are high-dimensional multivariate time series data, which may make a difficult and time-consuming decision-making process for damage localization. Multivariate distance correlation methods are introduced to cope with this drawback and locate structural damage using the multivariate residual sets of the normal and damaged conditions. The accuracy and robustness of the proposed methods are validated by a numerical shear-building model and an experimental benchmark structure. The effects of sampling frequency and time duration are evaluated as well. Results demonstrate the effectiveness and capability of the proposed methods to extract sufficient and reliable features, identify damage location, and quantify damage severity under ambient excitations and non-stationary signals.
Diffuse ultrasound is highly sensitive to changes in mechanical properties. Based on the coda wave interferometry analysis, we investigate the environmental temperature-induced wave velocity variations in high-manganese steels with plastic deformations by diffuse ultrasound. We observe the velocity changes in the materials at test with similar to 10-6 relative resolution. We propose the temperature-dependent coefficient as the key parameter for damage assessment in the specimens with different plastic deformations. The results show that the early-stage damage caused by plastic deformation in the specimens at test varying from 6% to 14% are successfully characterized by temperature-dependent coefficients in the absence of external mechanical load. The theoretical analysis on the sensitivity of the temperature-dependent coefficient to plastic deformation as well as the potential on-site application is discussed in this article.
Lu, Shaowei;Du, Kai;Wang, Xiaoqiang;Tian, Caijiao;Chen, Duo;Ma, Keming;Xu, Tao
来源期刊：Structural health monitoring
年/卷/期：2019 / 18 / 2
A novel, omnidirectional, nanomaterial-based sensor technology which can provide wide area damage detection of composite structures was proposed in this work. The behaviors of the buckypaper sensors subjected to both tensile and low-velocity impact were investigated. The experimental results showed that the rectangle buckypaper sensor has a large range of sensing coefficients from 21.40 to 35.83 at different directions under tensile. However, the circular buckypaper sensor has a steady sensing coefficient of about 155.63. Thus, the circular buckypaper sensor as a kind of omnidirectional sensor was chosen to monitor the impact damage. The low-velocity impact damage of composite structures is characterized by the gauge factor of omnidirectional buckypaper sensors and the results of C-scanning. Omnidirectional buckypaper sensors' electrical resistance increases with repeated impact loading; composite structure elastic deformation and damage evolution can be identified from resistance change. Experiment results show that structure monitoring based on the omnidirectional buckypaper sensor not only can detect small barely visible impact damage flaws and the damage evaluation of composite structures subjected to impact but also can determine the location of low-velocity impact damage through the analysis of results. Through comparison with C-scan, the results have preliminarily demonstrated that the omnidirectional carbon nanotubes' buckypaper sensor can serve as an efficient tool for sensing the evolution of impact damage as well as serve structural health monitoring of composite structures.
In the past few years, intelligent structural damage identification algorithms based on machine learning techniques have been developed and obtained considerable attentions worldwide, due to the advantages of reliable analysis and high efficiency. However, the performances of existing machine learning-based damage identification methods are heavily dependent on the selected signatures from raw signals. This will cause the fact that the damage identification method, which is the optimal solution for a specific application, may fail to provide the similar performance on other cases. Besides, the feature extraction is a time-consuming task, which may affect the real-time performance in practical applications. To address these problems, this article proposes a novel method based on deep convolutional neural networks to identify and localise damages of building structures equipped with smart control devices. The proposed deep convolutional neural network is capable of automatically extracting high-level features from raw signals or low-level features and optimally selecting the combination of extracted features via a multi-layer fusion to satisfy any damage identification objective. To evaluate the performance of the proposed deep convolutional neural network method, a five-level benchmark building equipped with adaptive smart isolators subjected to the seismic loading is investigated. The result shows that the proposed method has outstanding generalisation capacity and higher identification accuracy than other commonly used machine learning methods. Accordingly, it is deemed as an ideal and effective method for damage identification of smart structures.
Many damage detection methods that use data obtained from contact sensors physically attached to structures have been developed. However, damage-sensitive features such as the modal properties of steel and reinforced concrete are sensitive to environmental conditions such as temperature and humidity. These uncertainties are difficult to address with a regression model or any other temperature compensation method, and these uncertainties are the primary causes of false alarms. A vision-based remote sensing system can be an option for addressing some of the challenges inherent in traditional sensing systems because it provides information about structural conditions. Using bolted connections is a common engineering practice, but very few vision-based techniques have been developed for loosened bolt detection. Thus, this article proposes a fully automated vision-based method for detecting loosened civil structural bolts using the Viola-Jones algorithm and support vector machines. Images of bolt connections for training were taken with a smartphone camera. The Viola-Jones algorithm was trained on two datasets of images with and without bolts to localize all the bolts in the images. The localized bolts were automatically cropped and binarized to calculate the bolt head dimensions and the exposed shank length. The calculated features were fed into a support vector machine to generate a decision boundary separating loosened and tight bolts. We tested our method on images taken with a digital single-lens reflex camera.
Guided wave (GW) testing has become one of the most important nondestructive tools for evaluation of structural in-service degradation. Although GW testing was widely used to inspect and screen many engineering structures, there are still challenges associated with its applications, which mostly originate from the dispersive and multi-mode nature of GW signals as well as noise contamination. To deal with these challenges, an effective GW signal processing technique is introduced that enables one to accurately recover multiple modes from noisy dispersive GW signals for defect localization. To characterize the dispersion phenomenon, the chirp model is introduced first, which is derived from GW dispersion characteristics. Based on this model, an over-complete dictionary is designed by considering all possible defect locations (by discretization) and the propagation paths of GW modes caused by these defects. Then the prior knowledge that structural defects or damage typically occur in localized areas and correspondingly only a small number of modes are included in the GW signals is exploited by a robust sparse Bayesian learning (SBL) framework to reduce the ill-conditioning in the inverse problem. During the implementation of the robust SBL algorithm, irrelevant basis vectors in the dictionary are pruned out and the posterior probabilities of the small number nonzero basis coefficients corresponding to those "active" GW modes are established. After sparse representation of the GW signal, the information of propagation paths and group delays of GW modes are used to identify each GW mode and then localize defects. Illustrative results from numerical simulations and experiment studies on plate structures are presented to demonstrate the capability of the proposed method.