A Non-Destructive Testing Based on Electromagnetic Measurements and Neural Networks for the Inspection of Concrete Structures

This paper presents a new non-destructive testing (NDT) for reinforced concrete structures, in order to identify the components of their reinforcement. A time varying electromagnetic field is generated close to the structure by electromagnetic devices specially designed for this purpose. The presence of ferromagnetic materials (the steel bars of the reinforcement) immersed in the concrete disturbs the magnetic field at the surface of the structure. These field alterations are detected by sensors coils placed on the concrete surface. Variations in position and cross section (the size) of steel bars immersed in concrete originate slightly different values for the induced voltages at the coils.. The values for the induced voltages were obtained in laboratory tests, and multi-layer perceptron artificial neural networks with Levemberg-Marquardt training algorithm were used to identify the location and size of the bar. Preliminary results can be considered very good.

A cost optimization-based design of precast concrete floors using genetic algorithms

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Numeric evaluation of the stiffness of log-concrete composite beams submitted the cyclical load

In this paper was evaluated, using the software ANSYS, the stiffness (El) of the log-concrete composite beams, of section T, with connectors formed by bonded-in steel rods, type CA-50, disposed in X, with application of cyclical load. The stiffness of the system was evaluated through the simulation of bending tests, considered 1/2 beam, with cyclical shipment varying among 40 % and 5 % of the strength of the connection with the load relationship R=0,125, for a total of 10 load cycles applied. The numeric results show a good agreement with experimental tests.

Non-linear analysis of reinforced concrete plates by the boundary element method and continuum damage mechanics

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Oriented texture classification based on self-organizing neural network and Hough transform

This paper presents a technique for oriented texture classification which is based on the Hough transform and Kohonen's neural network model. In this technique, oriented texture features are extracted from the Hough space by means of two distinct strategies. While the first operates on a non-uniformly sampled Hough space, the second concentrates on the peaks produced in the Hough space. The described technique gives good results for the classification of oriented textures, a common phenomenon in nature underlying an important class of images. Experimental results are presented to demonstrate the performance of the new technique in comparison, with an implemented technique based on Gabor filters.

Bibliographical review of the used tyre in concrete

Due to the progressive increase of vehicles, the number of used tires is globally one of the serious environmental problems faced now. Therefore, several researches have being developed for its reuse. The use of tires' rubber in the concrete is a possible form of its a pplication, aiming a the recycling of this material and the improvement of certain properties, as tenacity, impact resistance, thermal and acoustic isolation. This article presents conclusions that several researchers obtained using the rubberized concrete. Thus there were researched several works enclosing the period of 1993 to 2003, presenting then the results of some characteristics of this concrete such as: physical properties in fresh and hardened state, mechanical properties and properties that remit the durability. The bibliographical revision has as objective to subsidize future researches that can contribute to improve the use of this concrete in civil construction.

Structural analyzes of the use of residues of tires in reinforced concrete

The search for an adequate destination to the tires without use is a problem for many countries. The use of tire rubber in concrete through the partial substitution of the small aggregate has for objective the withdrawal of this material of the environment besides serving as alternative material in places that present sand scarcity. However, to use this type of concrete in civil construction it's necessary to verify its structural behavior. The behavior of the adherence enters the bar of armor and the concrete surrounding it has decisive importance with relation to the load capacity of the structures of reinforced concrete. In this context, this work presents, argues and evaluates the results of the experimental studies for determination of the adherence tension according to pulling up assays pull-out normalized for CEB RC6 and also related in the ASTM C-234 in concrete with and without rubber residues. Armors of nominal diameter of 10,0; 12,5 and 16 mm had been used and concrete contend 10% of rubber fibres in substitution to the sand in volume.

Steel bars identification in reinforced concrete structures by using ANN and magnetic fields

This work proposes a methodology for non destructive testing (NDT) of reinforced concrete structures, using superficial magnetic fields and artificial neural networks, in order to identify the size and position of steel bars, embedded into the concrete. For the purposes of this paper, magnetic induction curves were obtained by using a finite element program. Perceptron Multilayered (PML) ANNs, with Levemberg-Marquardt training algorithm were used. The results presented very good agreement with the expect ones, encouraging the development of real systems based upon the proposed methodology.

Brazilian results on structural masonry concrete blocks

The purpose of this paper is to study the mechanical behavior of concrete blocks and prisms when performing axial compression tests within the Brazilian base of knowledge, intending to foment data of this kind for a world-based network. The blocks were built using five different mixtures in which the quantity of cement and the compacting ratio (density) were varied (during the fabrication process). The three-course-high prisms were assembled using 1 cm (0.39 in.) thick full-bedded joints, always trying to leave the mortar's characteristics constant. The axial compression tests were conducted according to Brazilian practice code recommendations, because most of these standards are very similar to international practice codes. The compressive strength, strains, and rupture form of each mixture studied were recorded. Attempts were made to correlate the strength, efficiency ratio (block strength/prism strength) of the prisms, strains, and rupture form; with the quantity of cement and compacting ratio. The data are presented in tables and figures, and the obtained results are discussed throughout the text. Copyright © 2007, American Concrete Institute. All rights reserved.

A regularized nonlinear diffusion approach for texture image denoising

In this paper a new partial differential equation based method is presented with a view to denoising images having textures. The proposed model combines a nonlinear anisotropic diffusion filter with recent harmonic analysis techniques. A wave atom shrinkage allied to detection by gradient technique is used to guide the diffusion process so as to smooth and maintain essential image characteristics. Two forcing terms are used to maintain and improve edges, boundaries and oscillatory features of an image having irregular details and texture. Experimental results show the performance of our model for texture preserving denoising when compared to recent methods in literature. © 2009 IEEE.

Potential use of the veris apparent EC sensor to predict soil texture under the semi-arid conditions of central Arizona

The operational details of the apparent electrical conductivity (ECa) sensor manufactured by Veris Technologies have been extensively documented in literature reports, but the geographical distribution of these research studies indicate a strong regional concentration in the US Mid-west and Southern states. The agricultural lands of these states diverge significantly to the soil conditions and water regime of irrigated land in the US South-western states such as Arizona where there is no previous research reports of the use of this particular sensor. The objectives of the present study were to analyze the performance of this sensor under the conditions of typical soils in irrigated farms of Central Arizona. We tested under static conditions the performance of the sensor on three soils of contrasting texture. Observations were collected as time series data as soil moisture changed from saturation to permanent wilting point. Observations were repeated at the hours of lowest and highest temperatures. In addition, this study included soil penetration resistance and salinity determinations. Preliminary results indicate that soil temperature of the upper layer caused the most dynamic change in the sensor output. The ECa curves of the three soil textures tested had well defined distinctive characteristics. Final multivariate analysis is pending.

Measuring and analyzing color and texture information in anatomical leaf cross sections: An approach using computer vision to aid plant species identification

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Characterization of texture in image of skin lesions by support vector machine

Due to the increased incidence of skin cancer, computational methods based on intelligent approaches have been developed to aid dermatologists in the diagnosis of skin lesions. This paper proposes a method to classify texture in images, since it is an important feature for the successfully identification of skin lesions. For this is defined a feature vector, with the fractal dimension of images through the box-counting method (BCM), which is used with a SVM to classify the texture of the lesions in to non-irregular or irregular. With the proposed solution, we could obtain an accuracy of 72.84%. © 2012 AISTI.

Identification of steel bars immersed in reinforced concrete based on experimental results of eddy current testing and artificial neural network analysis

This paper presents an experimental research on the use of eddy current testing (ECT) and artificial neural networks (ANNs) in order to identify the gauge and position of steel bars immersed in concrete structures. The paper presents details of the ECT probe and concrete specimens constructed for the tests, and a study about the influence of the concrete on the values of measured voltages. After this, new measurements were done with a greater number of specimens, simulating a field condition and the results were used to generate training and validation vectors for multilayer perceptron ANNs. The results show a high percentage of correct identification with respect to both, the gauge of the bar and of the thickness of the concrete cover. © 2013 Copyright Taylor and Francis Group, LLC.