Automatic Classification of Impact-Echo Spectra II

2011 ◽  
pp. 199-205
Author(s):  
Addisson Salazar ◽  
Arturo Serrano
Keyword(s):  
Neural Networks ◽  
Wave Propagation ◽  
Propagation Velocity ◽  
Sampling Frequency ◽  
P Wave ◽  
Material Surface ◽  
S Wave ◽  
Wave Propagation Velocity ◽  
Impact Echo

We study the application of artificial neural networks (ANNs) to the classification of spectra from impact-echo signals. In this paper we focus on analyses from experiments. Simulation results are covered in paper I. Impact-echo is a procedure from Non-Destructive Evaluation where a material is excited by a hammer impact which produces a response from the material microstructure. This response is sensed by a set of transducers located on material surface. Measured signals contain backscattering from grain microstructure and information of flaws in the material inspected (Sansalone & Street, 1997). The physical phenomenon of impact-echo corresponds to wave propagation in solids. When a disturbance (stress or displacement) is applied suddenly at a point on the surface of a solid, such as by impact, the disturbance propagates through the solid as three different types of stress waves: a P-wave, an S-wave, and an R-wave. The P-wave is associated with the propagation of normal stress and the S-wave is associated with shear stress, both of them propagate into the solid along spherical wave fronts. In addition, a surface wave, or Rayleigh wave (R-wave) travels throughout a circular wave front along the material surface (Carino, 2001). After a transient period where the first waves arrive, wave propagation becomes stationary in resonant modes of the material that vary depending on the defects inside the material. In defective materials propagated waves have to surround the defects and their energy decreases, and multiple reflections and diffraction with the defect borders become reflected waves (Sansalone, Carino, & Hsu, 1998). Depending on the observation time and the sampling frequency used in the experiments we may be interested in analyzing the transient or the stationary stage of the wave propagation in impact- echo tests. Usually with high resolution in time, analyzes of wave propagation velocity can give useful information, for instance, to build a tomography of a material inspected from different locations. Considering the sampling frequency that we used in the experiments (100 kHz), a feature extracted from the signal as the wave propagation velocity is not accurate enough to discern between homogeneous and different kind of defective materials. The data set for this research consists of sonic and ultrasonic impact-echo signal (1-27 kHz) spectra obtained from 84 parallelepiped-shape (7x5x22cm. width, height and length) lab specimens of aluminium alloy series 2000. These spectra, along with a categorization of the quality of materials among homogeneous, one-defect and multiple-defect classes were used to develop supervised neural network classifiers. We show that neural networks yield good classifications (

scholarly journals Experiments on Rare-Earth Element Extractions from Umber Ores for Optimizing the Grinding Process

Minerals ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 239
Author(s):  
Takaya ◽  
Wang ◽  
Fujinaga ◽  
Uchida ◽  
Nozaki ◽  
...  
Keyword(s):  
Wave Propagation ◽  
Rare Earth ◽  
Physical Properties ◽  
Rare Earth Element ◽  
Propagation Velocity ◽  
Earth Element ◽  
Extraction Process ◽  
P Wave ◽  
Grinding Process ◽  
Wave Propagation Velocity

Ancient hydrothermal metalliferous sediments (umber) have recently attracted attention as a new rare-earth element resource. We conducted chemical leaching experiments on three different umber ores to optimize the hydrometallurgical extraction process, especially regarding the grinding process. The three umber ore samples, which were collected from Japanese accretionary complexes (Kuminiyama and Aki umber) and Troodos ophiolite (Cyprus umber), had different chemical, mineral, and physical properties, and showed different leaching behaviors. The experimental results revealed that the physical properties (density and P-wave propagation velocity) principally controlled the extent of REY (lanthanides and yttrium) extraction from the umber ore samples, and REY extraction from umber samples clearly increased with the decrease in the density and P-wave propagation velocity. The differences in physical properties of the umber samples are attributable to the pressure and thermal history of each ore sample, and it was revealed that umber samples which underwent strong metamorphism are not suitable for actual development. The results also suggested that the optimum particle size (optimum grinding level) of umber samples is simply predictable based on the physical properties. The results of this study should be valuable for future efforts to procure these important mineral resources.

Automatic Classification of Impact-Echo Spectra I

2011 ◽  
pp. 192-198
Author(s):  
Addisson Salazar ◽  
Arturo Serrano
Keyword(s):  
Neural Networks ◽  
Wave Propagation ◽  
Impact Echo ◽  
Multiple Defects ◽  
Material Condition ◽  
Resonant Modes ◽  
Defect Dimension ◽  
Four Levels ◽  
The Impact

We investigate the application of artificial neural networks (ANNs) to the classification of spectra from impact-echo signals. In this paper we provide analyses from simulated signals and the second part paper details results of lab experiments. The data set for this research consists of sonic and ultrasonic impact-echo signal spectra obtained from 100 3D-finite element models. These spectra, along with a categorization of the materials among homogeneous and defective classes depending on the kind of material defects, were used to develop supervised neural network classifiers. Four levels of complexity were proposed for classification of materials as: material condition, kind of defect, defect orientation and defect dimension. Results from Multilayer Perceptron (MLP) and Radial Basis Function (RBF) neural networks with Linear Discriminant Analysis (LDA), and k-Nearest Neighbours (kNN) algorithms (Duda, Hart, & Stork, 2000), (Bishop C.M., 2004) are compared. Suitable results for LDA and RBF were obtained. The impact-echo is a technique for non-destructive evaluation based on monitoring the surface motion resulting from a short-duration mechanical impact. It has been widely used in applications of concrete structures in civil engineering. Cross-sectional resonant modes in impact-echo signals have been analyzed in elements of different shapes, such as, circular and square beams, beams with empty ducts or cement fillings, etc. In addition, frequency analyses of the displacement of the fundamental frequency to lower values for detection of cracks have been studied (Sansalone & Street, 1997), (Carino, 2001). The impact-echo wave propagation can be analyzed from transient and stationary behaviour. The excitation signal (the impact) produces a short transient stage where the first P (normal stress), S (shear stress) and Rayleigh (superficial) waves arrive to the sensors; afterward the wave propagation phenomenon becomes stationary and a manifold of different mixtures of waves including various changes of S-wave to P-wave propagation mode and viceversa arrive to the sensors. Patterns of waveform displacements in this latter stage are known as the resonant modes of the material. The spectra of impact-echo signals provide of information for classification based on resonant modes the inspected materials. The classification tree approached in this paper has four levels from global to detailed classes with up to 12 classes in the lowest level. The levels are: (i) Material condition: homogeneous, one defect, multiple defects, (ii) Kind of defect: homogeneous, hole, crack, multiple defects, (iii) Defect orientation: homogeneous, hole in axis X or axis Y, crack in planes XY, ZY, or XZ, multiple defects, and (iv) Defect dimension: homogeneous, passing through and half passing through types of holes and cracks of level iii, multiple defects. Some examples of defective models are in Figure 1.

scholarly journals AE propagation velocity calculation for stiffness estimation in Pier Luigi Nervi’s concrete structures

2021 ◽  
Vol 8 (1) ◽  
pp. 109-118
Author(s):  
Erica Lenticchia ◽  
Amedeo Manuello Bertetto ◽  
Rosario Ceravolo
Keyword(s):  
Propagation Velocity ◽  
Concrete Structures ◽  
P Wave ◽  
Wave Propagation Velocity ◽  
Damage Level ◽  
Stiffness Characteristics ◽  
Vertical Bearing ◽  
The Relationship ◽  
Ae Signals ◽  
Elastic Characteristics

Abstract In the present paper, the acoustic emission (AE) device is used with an innovative approach, based on the calculation of P-wave propagation velocity (vp ), to detect the stiffness characteristics and the diffused damage of in-service old concrete structures. The paper presents the result of a recent testing campaign carried out on the slant pillars composing the vertical bearing structures designed by Pier Luigi Nervi in one of his most iconic buildings: the Hall B of Torino Esposizioni. In order to investigate the properties of these inclined pillars, localizations of artificial sources (hammer impacts), by the triangulation procedure, were performed on three different inclined elements characterized by stiffness discrepancies due to different causes: the casting procedures, executed in different stages, and the enlargement of the hall happened a few years later the beginning of the construction. In the present work, the relationship between the velocity of AE signals and the elastic characteristics (principally elastic modulus, E) is evaluated in order to discriminate the stiffness level of the slanted pillars. The procedure presented made it possible to develop an innovative investigation method able to estimate, by means of AE, the state of conservation and the elastic properties and the damage level of the monitored concrete and reinforced concrete structures.

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