. Using this model, it was possible to assess the contribution of each input variable to the compressive strength of the tested concretes. The results indicate an excellent performance of the ANN model developed to predict compressive strength from the input parameters studied, with an average error less than 5%. Several artificial
neural networks with different architectures (with various hidden neurons and layers) were studied
using software – Statistica Neural Network.
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Knowledge of the tension softening process of concrete is essential to understand fracture
mechanism, further to analyze fracture behaviour, and further to evaluate properties of concrete. High accuracy was obtained in the all
predicted tension softening curves and the fracture parameters were also well predicted. Please solve this CAPTCHA to request unblock to the website
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https://iopscience.
The work presents the results of an experimental campaign carried out on concrete elements in order to investigate the potential of using artificial neural networks (ANNs) to estimate the compressive strength based on relevant parameters, such as the water–cement ratio, aggregate–cement ratio, age of testing, and percentage cement/metakaolin ratios (5% and 10%).
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An ANN model was developed, different ANN configurations were tested and compared to identify the best ANN model. The paper is dedicated to predict tension
softening curve of concrete by using artificial neural networks (ANNs) based on experimental data of
five different mixtures of concrete (including High Performance Concrete). 84 on
October 02 2022, 16:13:30 UTC
. For
the last eight years, many different tests on uniaxial tension with elimination of secondary flexure
were performed in Tohoku Institute of Technology. .
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We prepared 162 cylindrical concrete specimens with dimensions of 10 cm in diameter and 20 click to investigate in height and 27 prismatic specimens with cross sections measuring 25 and 50 cm in length, with 9 different concrete mixture proportions. but your activity and behavior on this site made us think that you are a bot. In order to evaluate the prediction accuracy, tension
softening curve and other fracture parameters were predicted for each mix from the other four mixes
and compared with the omitted data of the relevant mix. org/article/10. 255. Together, the water–cement ratio and the percentage of metakaolin were shown to be the most influential factors for the compressive strength value predicted by the developed ANN model.
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Note: A number of things could be going on here. iop. It is an advantage to
predict a proper tension softening curve without performing uniaxial tension visit this website A longitudinal transducer with a frequency of 54 kHz was used to measure the ultrasonic look at these guys .