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Artificial neural networks application to predict bond steel-concrete in pull-out tests Aplicação de redes neurais artificiais na predição da aderência aço-concreto em ensaios do tipo pull-out Abstract ResumoThis study aims the possibility of using the pull-out test results -bond tests steel-concrete, that has been successfully carried out by the research group APULOT since 2008 [1]. This research demonstrates that the correlation between bond stress and concrete compressive strength allows estimate concrete compressive strength. However to obtain adequate answers testing of bond steel-concrete is necessary to control the settings test. This paper aims to correlate the results of bond tests of type pull-out with its variables by using Artificial Neural Networks (ANN). Though an ANN is possible to correlate the known input data (age rupture, anchorage length, covering and compressive strength of concrete) with control parameters (bond stress steel-concrete). To generate the model it is necessary to train the neural network using a database with known input and output parameters. This allows estimating the correlation between the neurons in each layer. This paper shows the modeling of an ANN capable of performing a nonlinear approach to estimate the concrete compressive strength using the results of steel-concrete bond tests.Keywords: bond steel-concrete, artificial neural networks, pull-out test, concrete strength, APULOT test.O estudo visa avaliar a possibilidade de se usar os resultados do ensaio de arrancamento "pull-out test" -ensaio de aderência aço-concreto para estimativa da resistência à compressão do concreto, este método vem sendo utilizado com sucesso pelo grupo de pesquisa APULOT, desde 2008 [1]. A pesquisa ora realizada evidencia a existência da correlação entre essas duas variáveis, aderência e resistência à compressão do concreto, o que permite determinar estimativas apropriadas da resistência à compressão do concreto, melhorando deste modo a capacidade do controle tecnológico "in situ" do concreto. Entretanto para se obter respostas adequadas dos ensaios de aderência aço-concreto é necessário controlar as configurações de ensaio, dado que existem diversos formatos de corpos de prova para estes tipos de ensaios na literatura. Deste modo, este trabalho tem por objetivo correlacionar os resultados obtidos em ensaios de aderência do tipo pull-out a suas variáveis por meio da utilização de Redes Neurais Artificiais (RNA). Com a utilização de uma RNA, pode-se correlacionar, de forma não linear, dados de entrada conhecidos (idade de ruptura, comprimento de ancoragem, cobrimento e resistência à compressão) com parâmetros de controle (tensão de aderência aço-concreto). Para gerar o modelo neural é necessário treinar a rede, expondo-a a uma série de dados com parâmetros de entrada e de saída conhecidos. Isto permite estimar os coeficientes de correlação entre os neurônios de cada camada. O presente trabalho apresenta a modelagem de uma RNA capaz de realizar uma aproximação não linear, visando estimar a res...
Artificial neural networks application to predict bond steel-concrete in pull-out tests Aplicação de redes neurais artificiais na predição da aderência aço-concreto em ensaios do tipo pull-out Abstract ResumoThis study aims the possibility of using the pull-out test results -bond tests steel-concrete, that has been successfully carried out by the research group APULOT since 2008 [1]. This research demonstrates that the correlation between bond stress and concrete compressive strength allows estimate concrete compressive strength. However to obtain adequate answers testing of bond steel-concrete is necessary to control the settings test. This paper aims to correlate the results of bond tests of type pull-out with its variables by using Artificial Neural Networks (ANN). Though an ANN is possible to correlate the known input data (age rupture, anchorage length, covering and compressive strength of concrete) with control parameters (bond stress steel-concrete). To generate the model it is necessary to train the neural network using a database with known input and output parameters. This allows estimating the correlation between the neurons in each layer. This paper shows the modeling of an ANN capable of performing a nonlinear approach to estimate the concrete compressive strength using the results of steel-concrete bond tests.Keywords: bond steel-concrete, artificial neural networks, pull-out test, concrete strength, APULOT test.O estudo visa avaliar a possibilidade de se usar os resultados do ensaio de arrancamento "pull-out test" -ensaio de aderência aço-concreto para estimativa da resistência à compressão do concreto, este método vem sendo utilizado com sucesso pelo grupo de pesquisa APULOT, desde 2008 [1]. A pesquisa ora realizada evidencia a existência da correlação entre essas duas variáveis, aderência e resistência à compressão do concreto, o que permite determinar estimativas apropriadas da resistência à compressão do concreto, melhorando deste modo a capacidade do controle tecnológico "in situ" do concreto. Entretanto para se obter respostas adequadas dos ensaios de aderência aço-concreto é necessário controlar as configurações de ensaio, dado que existem diversos formatos de corpos de prova para estes tipos de ensaios na literatura. Deste modo, este trabalho tem por objetivo correlacionar os resultados obtidos em ensaios de aderência do tipo pull-out a suas variáveis por meio da utilização de Redes Neurais Artificiais (RNA). Com a utilização de uma RNA, pode-se correlacionar, de forma não linear, dados de entrada conhecidos (idade de ruptura, comprimento de ancoragem, cobrimento e resistência à compressão) com parâmetros de controle (tensão de aderência aço-concreto). Para gerar o modelo neural é necessário treinar a rede, expondo-a a uma série de dados com parâmetros de entrada e de saída conhecidos. Isto permite estimar os coeficientes de correlação entre os neurônios de cada camada. O presente trabalho apresenta a modelagem de uma RNA capaz de realizar uma aproximação não linear, visando estimar a res...
Nondestructive tests that assess the constitution or degradation of structures are of great interest in Civil Engineering. Among the non-destructive testing techniques, the Ultrasonic Pulse Velocity (UPV) test stands out; however, although its use is widespread, there are still no applications that employ this method to determine the constitution of concrete in situ. Therefore, this article addresses the identification of the coarse aggregate content in concrete specimens by an Artificial Neural Network (ANN) trained with a database of numerical tests that simulated UPV. In this paper, the coarse aggregate content will be described as a percentage of the total area of a two-dimensional concrete model. Three artificial neural network architectures were evaluated. The first two, trained with 13 or 22 paths, solved a classification problem for five aggregate contents, and the third, trained with 22 paths, solved a regression problem. Its performance was compared with those of other regression solutions, namely XGB Regressor, Random Forest, and OLS (Ordinary Least Squares), and showed superior, with -2.55% to +2.17% average deviations. Thus, this paper demonstrated that the use of ANN in combination with UPV test has the potential to identify the coarse aggregate content in concretes. The positive results suggest that this approach is promising and highlights the need for further experimental validation in future research.
Retaining walls are important structures for stabilizing slopes next to buildings. As with any structure, the parts used in retaining walls need to undergo periodic evaluation; as such, it is important to study different inspection techniques and methodologies. Ultrasonic testing has been used for material classification and quality control of the structural parts of various materials, including reinforced concrete. This study aims to evaluate the inspection of a retaining wall made with a lock and load system using ultrasonic pulse velocity. We considered the type of measurement, type of transducer, frequency of the transducer, coupling of the transducer on the inspected part, and distance between the transducers for the indirect measurements. The 45 kHz frequency was the most suitable for inspections before installation. It was found that the exponential surface transducer without coupling holes was the only one that allowed us to distinguish the plates by their velocity. For feasible field tests, the ratio between the indirect and direct velocity was 0.60. A distance of 300 mm between the transducers was the most suitable for indirect tests.
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