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Research on quantitative evaluation of rebar stress based on weak magnetic effect
... There are many applications of magnetic methods in the construction industry. These methods can be used to detect reinforcement and determine the parameters of RC structures [9][10][11][12] or to detect corrosion of rebars [13][14][15][16][17], cracks or defects [18,19], and rebar stress [20][21][22]. A more detailed description of the applications of magnetic methods in the construction industry can be found in [6,22]. ...
... These methods can be used to detect reinforcement and determine the parameters of RC structures [9][10][11][12] or to detect corrosion of rebars [13][14][15][16][17], cracks or defects [18,19], and rebar stress [20][21][22]. A more detailed description of the applications of magnetic methods in the construction industry can be found in [6,22]. ...
This work presents how significantly the proper selection of the magnetization method can improve almost all parameters of the magnetic method and affect the effectiveness of the evaluation of reinforced concrete (RC) structures. Three magnetization methods are considered in this paper: opposite pole magnetization (typical solution), same pole magnetization, and (as a reference point) no magnetization. The experiments are carried out in a three-dimensional (XYZ) space. Measurements along each of the axes are discussed in a separate section. The results show that the appropriate selection of the magnetization method can affect noise reduction, signal strength, and the separation of measurements carried out on different samples. This paper also discusses the situations when the magnetization may change the shape, cause deformations of waveforms, affect the area testing, and be used to significantly increase the efficiency of simultaneous evaluation of three basic parameters of RC structure. Experiments and simulations have proven that properly applied magnetization may strongly affect the evaluation’s effectiveness, making the magnetic method one of the most promising techniques in testing RC constructions.
The bridge deflection data measured in field are greatly affected by temperature. In some situations, temperature can be the dominant factor comparing with loads and other factors. Generally, dynamic deflection contains information related to the health condition of bridge. It is wildly used in dynamic deflection warning system. The temperature-induced deflection might lead to the inaccuracy of such system. Therefore, it is important to separate the temperature-induced deflection when implement bridge health condition assessment using dynamic deflection data. However, the accuracy of existing methods of temperature-induced deflection separation is low. This low accuracy is normally caused by mode aliasing and excessive elimination of feature components, etc. To this end, this study proposed an innovative method of temperature-induced deflection separation based on the time-varying filtered empirical mode decomposition (TVFEMD), permutation entropy (PE) and Kullback–Leibler divergence (KLD). The proposed method is used to extract temperature-induced deflection from bridge deflection data. It overcomes the problems of mode aliasing and end-point effects by reconstructing the signals with the same composition. The separating accuracy is also improved by the non-linear feature extracting ability of the proposed method. First, TVFEMD was introduced to decompose the original bridge deflection data into a number of intrinsic mode functions (IMFs). Then, PE was adopted to measure the complexity of each IMF, by which all IMFs can be reconstructed into several new subseries. Furthermore, KLD was employed to calculate the divergence values between these reconstructed subseries and the measured ambient temperatures. The subseries with the smallest divergence value can be considered to be the temperature-induced deflection. Finally, numerical study and locale measurement were utilized to comprehensively validate the effectiveness of the proposed method. The results show that the identification accuracy of the proposed method exceeds 90%. It outperforms the other involved models in terms of separating daily and annual temperature-induced deflection.
More than 20 years of research progress regarding the nondestructive testing method of metal magnetic memory is reviewed and summarized in detail. Consequently, this overview is selective, covering what we feel are the most important trends of experimental phenomena, mechanism explanations, quantitative theories, simulations, testing, evaluation and application. From analyzing the current state of research on the method of metal magnetic memory, some key problems and future developmental trends are proposed. Although the research on magnetic memory method has made great progress, the practical application still faces problems such as complex influencing factors and less quantitative research. In the future, for magnetic memory method, it is necessary to strengthen the microscopic observations of magnetic domains, experiments of magnetomechanical constitutive, establishment of quantitative models, modeling of complex influencing factors, and the study of identification, inversion and criteria. In addition, the combination of other non-destructive testing methods can greatly improve the practical application of the magnetic memory method.
Metal magnetic memory (MMM) method is a non-destructive testing (NDT) technology which has potentials to detect early damage. A review is presented in this paper about the development of this method, including the theoretical studies of the magnetic/stress coupling effect, factors influencing the detection signals, the criteria for judging the damage state and defect identification. Directions of future research are also discussed.
Experimental investigation of bi-axial steel deformation is presented using new equipment for bi-axial loading. Main conclusion obtained from displayed results is the condition of the invariance of Barkhausen noise intensity relative to any changes of spherical (isotropic) strain/stress tensor, the last being influenced only by material microstructure. This conclusion is supported by independent measurement of Barkhausen noise (BN) intensity on cross-shaped specimens of optimized shape using novel biaxial loading equipment, and tubular specimens like pipe and a balloon loaded by means of oil pressure. Finite Element Modeling (FEM) simulation was also investigated. Thus the BN intensity depends only on the deviatoric (shear) stress tensor value. The presence of this symmetric effect yields too many uncertainties in strain/stress evaluation via BN. Further investigation should identify whether this condition is also present in other magnetic parameters, such as coercive force, remanence, permeability, associated with BN.
The impact of stress on changes in magnetisation is one of the most complex issues of magnetism. Magnetic methods make use of the impact of stress on permeability, hysteresis and magnetic Barkhausen noise, which are examined with fields with a high strength and a small frequency. The paper presents an analysis of the impact of residual stress resulting from inhomogeneous plastic deformations in the notch area of the examined samples on the changes in the strength of the residual magnetic field (RMF). The RMF on the surface of the component is the superposition of the simultaneous effect of the shape, the anisotropic magnetic properties of the material, as well as of the values of the components of a weak external magnetic field (most commonly—the magnetic field of the Earth). Distributions of the RMF components were measured on the surface of samples with a various degree of plastic strain. The finite element method was used to model residual stress in the samples. The impact of residual stress on changes in the residual magnetic field was shown. A qualitative correlation was found between places with residual stress and areas with increased values of the gradients of the RMF components. Further research is now in progress in order to develop the quantitative relationships.
Classification and regression trees are machine‐learning methods for constructing prediction models from data. The models are obtained by recursively partitioning the data space and fitting a simple prediction model within each partition. As a result, the partitioning can be represented graphically as a decision tree. Classification trees are designed for dependent variables that take a finite number of unordered values, with prediction error measured in terms of misclassification cost. Regression trees are for dependent variables that take continuous or ordered discrete values, with prediction error typically measured by the squared difference between the observed and predicted values. This article gives an introduction to the subject by reviewing some widely available algorithms and comparing their capabilities, strengths, and weakness in two examples. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 14‐23 DOI: 10.1002/widm.8
This article is categorized under: Technologies > Classification
Technologies > Machine Learning
Technologies > Prediction
Algorithmic Development > Statistics
Deformation is a paramount index of bridge health monitoring. Accurate prediction of bridge deformation is of great significance to evaluate bridge performance. However, owing to the complex mechanical mechanism of bridge performance evolution, it is arduous to obtain a satisfactory forecasting result by simple data-driven methods. To this end, this paper proposes an innovative data-driven method based on an improved variational mode decomposition (IVMD) and conditional kernel density estimation (CKDE). Specifically, the raw deformation data are firstly pretreated by IVMD. This newly developed technique could not only adaptively optimize the decomposition level number through Hilbert transform and empirical mode decomposition, but also hinder the disturbance of irrelevant components by Kullback-Leibler divergence and Gram-Schmidt orthogonal. Then, auto-regression integrated moving average model is established to excavate the linear feature hidden in the data. Finally, CKDE in conjunction with Gaussian distribution assumption is designed to describe the above modeling errors, by which both point and interval predictions are generated. Obviously, this method avoids exploring the complex internal mechanism of structural behavior evolution. Three case studies based on structural health monitoring data are used to systematically evaluate its effectiveness. The results manifest that this method possesses higher reliability in comparison with the other concerned models, and could lay a foundation for early warning purpose.
In the last decades, the use of structural adhesives has increased. Indeed, they are able to replace advantageously traditional assembly techniques such as riveting or bolting, and generally tend to reduce the global weight of the structure and/or improve the strength of the structure thanks to more homogeneous stress distribution. In addition, it allows the assembly of composite materials or materials having different nature, which is of great interest in the aeronautic industry. However, to be used for structural joining and critical application, reliable non-destructive testing techniques are compulsory for evident safety reasons, be it after fabrication or during the whole life of the structure. While linear ultrasounds have demonstrated their capability to detect easily decohesion or voids in a structure, their use for bond strength inspection is less straightforward. Another approach relies on the analysis of nonlinear signature of a bond defect inspected by high amplitude ultrasound. In the present paper, a Chaotic Cavity Transducer is used to inspect various metallic bonded plates (titanium or aluminum) with several bond defects. The defects were introduced by depositing localized surface pollution (introduction of PTFE spray, release agent, or fingerprints) before the adhesive is deposited on top of the surface. Combined with the pulse inversion technique, high amplitude plane waves were sent in the structure, leading to harmonic components observed in the defect region. Mechanical destructive tests were performed and display a good agreement with nondestructive tests.
The metal magnetic memory (MMM) method is a nondestructive testing method to inspect the early damage of steel structures. A four-point bending fatigue beam was inspected by this method during the whole fatigue life. The self-magnetic-leakage field (SMLF) values were measured during the test of the Q345B steel beam. Frequency of 3 Hz and stress ratio of 0.2 contributed to the fatigue life of 100180 cycles. Test results indicate that both the normal and tangential component of the SMLF values can identify the stress concentration and the crack zones. The gradient can reflect the degree of damage. Furthermore, in order to study the change law of the SMLF values in the crack zone, an improved magnetic dipole model considering the crack dimensions and shape is proposed. The model predictions are in very good agreement with the corresponding test results. Lastly, three characteristic parameters, i.e. average ΔHp(y) field values, resultant magnetic field value H and magnetic parameter m, were extracted from the test data. These parameters can judge the fatigue stage and the damage degree of the specimen. Therefore, the research lays the experimental and theoretical foundation for using the MMM method to inspect steel structures under fatigue bending loads.
X-ray digital radiology is increasingly used in aerospace industries because of their numerous advantages such as the non-use of consumables and chemicals products, capability to store information via digital media, control time reduction, etc. Nowadays, X-ray digital radiology provides new application perspectives for industrials. In this study, X-ray digital detector array radiology (DDA) was used to infer geometrical welding flaws such as sagging depths by measuring grey level differences. Two representative samples batches of real parts are tested according to different alloys (Nickel and Cobalt-based alloy). Sagging depths of welded samples were measured by X-ray technique, Laser profilometer and micrographic cuts, providing good correlation, in the range of 30–250 μm. These results show the X-ray DDA technique ability to evaluate such welding flaws and provide new perspectives for applications on real parts.
The correlation between the stress concentration and the spontaneous magnetic signals of metal magnetic memory (MMM) was investigated via tensile tests. Sheet specimens of the Q235 steel were machined into standard bars with rectangular holes to obtain various stress concentration factors. The tangential component Hp(x) of MMM signals and its related magnetic characteristic parameters throughout the loading process were presented and analyzed. It is found that the tangential component Hp(x) is sensitive to the abnormal magnetic changes caused by the local stress concentration in the defect area. The minimum magnetic field is positively correlated to the magnitude of the load and the distance from the notch. The tangential magnetic stress concentration factor presents good numerical stability during the entire loading process, and can be used to evaluate the stress concentration factor. The results obtained will be a complement to the MMM technique.
Cracks with inclination angles may potentially cause damage to a larger region in the tested structures. Their characterization, in terms of depth and angle, is therefore paramount for ensuring the integrity of the specimen under test. This study extracts features from Pulsed eddy current (PEC) signals obtained in a linear scan, perpendicular to the simulated surface cracks. The novel features extracted, termed skewness, LLS and LSmax, are capable of defining crack depth and inclination angles simultaneously. Multiple linear regression (MLR) was built to perform depth prediction, and the pre-determined depths were used in the hierarchical linear model (HLM) for angle prediction. The results were then compared with depth and angle prediction using artificial neural network (ANN). Better reliability of the ANN model with recorded RMSE of 0.198 mm and 2.903° in depth and angle prediction are highlighted. ANN is favourable in handling simultaneous prediction of crack depth and inclination angles, when using interdependent features. Meanwhile, HLM is still approved as a technique to provide a preliminary understanding of the crack parameters.
The existence of an extensive network of main pipelines, both already operating and being constructed, which are exploited under complex climatic conditions, specifies the need for the development of methods of a nondestructive control for testing the current state of both the pipes themselves and the welded joints in the process of manufacture and subsequent service. In this work, we give the results of microstructural studies, as well as the mechanical and magnetic properties of different zones of welded joints (base metal, heat-affected zone, and the weld) of pipe steels of the strength classes X70 and X80 produced using the method of controlled rolling. The influence of different modes of loading on the magnetic characteristic of the metal of all three zones of welded joints has been investigated. The magnetic parameters that unambiguously characterize changes in the stress-strain state of the separate zones of the welded joint (weld, heat-affected zone, base metal) have been determined in certain ranges of the applied stresses.
Variation of the stress-induced magnetic field of the U75V rail steel under tension was investigated in this research. Various magnetic responses were registered by a magnetometer in the elastic and plastic deformation stages, which can be explained by the microstructural changes in magnetic domains. Two types of defective specimens were also tested to correlate the stress concentration with the magnetic field. It is found that the tangential component of the magnetic field Bx is much more sensitive to local stress concentration than the normal component Bz. The tangential component Bx reaches a peak value at the rupture position, and the peak magnitude is proportional to the concentrated stress caused by the defect. This observation is different from the Q235 steel, whose tangential component Bx and the normal component Bz are equally effective. Such discrepancy might be due to that U75V fails in a more brittle pattern than the Q235 steel. The average value of the Bx along the loading axis can determine the overall stress state of the structure, while the peaks in the Bx curve tell the local stress concentration caused by cracks and dislocation.
The paper presents a residual stress evaluation method using the gradients of the residual magnetic field (RMF) components. Distributions of the RMF components were measured on the surface of samples with a various degree of plastic strain. The finite element method was used to model residual stress in samples. The impact of residual stress on changes in the residual magnetic field was shown. A very good qualitative correlation was found between places with residual stress and areas with increased values of the gradients of the RMF components. An algorithm was developed and verified for steel T/P24 to make a quantitative evaluation of residual equivalent (von Mises) stress based on the gradients of tangential component dH(T.Y)/dx and field gradient dH/dx. Directions of further research were formulated, which included the validation of the method and which took into consideration the factors affecting its accuracy. The developed algorithm can be a significant complement to the Metal Magnetic Memory (MMM) method.
Metal magnetic memory (MMM) is a new non-destructive (NDT) method for inspecting the early damage of ferromagnetic structures using the residual magnetic field (RMF) signals. Our previous experimental research has confirmed that ferromagnetic materials exhibit various RMF features in different deformation stages. This paper presents a 2-Dimensional (2D) numerical study of RMF characteristics of ferromagnetic samples subject to the mechanical deformation. A new criterion is proposed to determine the plastic deformation. Then some parameters are defined to characterize the RMF signal. After that, the dependence of these parameters on the defect width, depth, location, testing position and direction are analyzed. This work is useful for promoting the MMM from a qualitative NDT technique to quantitative technique.
The remanence of a ferromagnet is formed under the influence of many factors, the main of which are a magnetic field (DC and AC), temperature, mechanical stress, and chemical transformations. Remanences obtained by different ways are differently resistant to external influences. Remanence observed in structural elements may result from one factor or from all of the above. It is shown that the use of remanence as a parameter for assessing the stress-strain state, i. e. the “method of magnetic memory”, without taking into account the conditions of the formation of the remanence state in a product area under testing will have low assessment reliability.
The hole drilling core surface strain gauge method for measuring existing stresses in concrete structures has been discussed. First, the release detection process of hole drilling has been simulated using nonlinear three-dimensional finite element method, based on the concrete damaged plasticity theory. The technique of element birth and death has been used to simulate the drilling process. Laws of stress measurement described by stress release rates and h/d (where h is the depth of hole and d is its diameter) have been studied. Second, experiments have been carried out to prove the efficiency of this method. Compared with numerical results, the experimental stress release rates are obviously lower than simulation results. Finally, normal stresses have been loaded on the cylindrical surface of the concrete core to simulate the influence of core bit swaying, and the results of modified numerical data analysis have been obtained. The results indicate that the rules of stress measurement by hole drilling release which have been represented by stress release rates and h/d, are widely used.
The paper presents an analysis of the universality of the algorithm for the evaluation of residual stress of the first order using the gradients of the residual magnetic field (RMF) components. The impact of the component geometry on quantitative relationships between residual stress and the RMF component gradients are studied. Two kinds of flat samples with a different geometries (type A and B) are analysed. For different degrees of plastic strain, on the surface of the samples, the distributions of the RMF components are measured. The finite element method is used to model residual stress in the samples. The quantitative relationships between the RMF component gradients and equivalent residual (von Mises) stress are developed based on the measurements of samples with type A notch geometry. These relationships are verified by using them to evaluate residual stress in type B samples with a different geometry. It is found that the quantitative evaluation of residual stress in components which is based on the RMF gradients requires at every instance the development of a transition function for the specific geometry, material and orientation of the component in the magnetic field of the Earth.
The surface magnetic field intensity Hp(y) of low-carbon steel plate specimen was measured after tensile test and unloading; variation of Hp(y) was studied. It was found that Hp(y) signals first decreased with increase in stress, and then reversed to the initial field when the stress was greater than 160MPa. Under yield stress, Hp(y) reached its maximum, and then decreased slightly with further increase in stress. The initial magnetic signals have great impact on the variation of magnetic field.
This study is concerned with the development of a model theory of the changes in magnetization that a ferromagnetic material undergoes when subjected to a varying external applied stress.
The variation of the magnetic field, induced by applied stress under the geomagnetic field, can potentially be used to evaluate the degree of fatigue damage for ferromagnetic materials. To further investigate the physical mechanism of the metal magnetic memory phenomenon, measurements of the normal components of the stress-induced magnetic field intensity, Hp(y), were performed during rotary bending fatigue experiments under the geomagnetic field. The results showed that the process of metal magnetic memory includes reversible and irreversible magnetisation. The applied cyclic tensile-compressive stress would cause magnetisation to approach anhysteretic magnetisation irreversibly. In addition, magnetisation under tensile stress was different from that under compressive stress.
A hypothesis is presented to explain the mechanism by which externally applied stresses can affect the magnetic properties of ferromagnetic materials. Experiments have revealed coincident points in the second and fourth quadrants on stressed hysteresis loops of mild steel. The results are presented along with an explanation of this effect. An atomic level theory of the origins of the magnetomechanical effect is introduced whereby spin–spin and spin–orbit coupling interact with magnetic moments to alter the magnetocrystalline anisotropy and exchange energies.
Stress is an important parameter governing the magnetic properties of steel. This is normally a complicating factor for magnetic non-destructive inspection techniques but it may also be exploited for inspection of high performance steel structures such as gas pipelines. Magnetometer measurements of magnetization changes induced by bending stress above gas transmission lines are presented. These can be interpreted using recent advances in the theory of the magnetomechanical effect by separating them into reversible and irreversible components and may eventually be used as a basis for bending stress monitoring.
The new trend of long-span steel bridge design and development
- Liu