• Asian-Australasian Journal of Animal Sciences
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• v.28 no.7
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• pp.993-998
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• 2015

Possibilities of predicting eggshell ultrastructure from direct non-destructive and destructive measurements were examined using 120 Fayoumi eggs collected from the flock at 45 weeks of age. The non-destructive measurements included weight, length and width of the egg. The destructive measurements were breaking strength and shell thickness. The eggshell ultrastructure traits involved the total thickness of eggshell layer, thickness of palisade layer, cone layer and total score. Prediction of total thickness of eggshell layer based on non-destructive measurements individually or simultaneously was not possible ($R^2=0.01$ to 0.16). The destructive measurements were far more accurate than the non-destructive in predicting total thickness of eggshell layer. Prediction based on breaking strength alone was more accurate ($R^2=0.85$) than that based on shell thickness alone ($R^2=0.72$). Adding shell thickness to breaking strength (the best predictor) increased the accuracy of prediction by 5%. The results obtained indicated that both non-destructive and destructive measurements were not useful in predicting the cone layer ($R^2$ not exceeded 18%). The maximum accuracy of prediction of total score ($R^2=0.48$) was obtained from prediction based on breaking strength alone. Combining shell thicknesses and breaking strength into one equation was no help in improving the accuracy of prediction.

• Proceedings of the Korea Concrete Institute Conference
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• 2006.05b
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• pp.145-148
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• 2006

Schmidt hammer and ultra-sonic method are commonly used for crushed sand concrete compressive strength test in a construction field. At present, various of equations for prediction of strength are present, which have been used in a construction field. The purpose of this study is to evaluate the correlation between prediction strength by presentation equations and destructive strength to test specimen, and find out which is a suitable equation for the construction site, In this study, a strength test was carried out destructive test by means of core sampling and traditional test. Non-destructive test was conducted Schmidt hammer and ultra-sonic method, the experimental parameter were concrete age, curing condition, test method and strength level. It is demonstrated that the correlation behavior of crushed sand concrete strength in this study good due to the perform analysis of correlation between core, destructive strength and non-destructive strength.

• Geomechanics and Engineering
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• v.25 no.4
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• pp.317-330
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• 2021

This paper reports the results of advanced data analysis involving artificial neural networks for the prediction of the unconfined compressive strength of granite using only two non-destructive test indexes. A data-independent site-independent unbiased database comprising 182 datasets from non-destructive tests reported in the literature was compiled and used to train and develop artificial neural networks for the prediction of the unconfined compressive strength of granite. The results show that the optimum artificial network developed in this research predicts the unconfined compressive strength of weak to very strong granites (20.3-198.15 MPa) with less than ±20% deviation from the experimental data for 70% of the specimen and significantly outperforms a number of available models available in the literature. The results also raise interesting questions with regards to the suitability of the Pearson correlation coefficient in assessing the prediction accuracy of models.

• Proceedings of the Korea Concrete Institute Conference
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• 2005.05b
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• pp.133-136
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• 2005

Compressive strength of recycled aggregate concrete was tested by the core and by the non-destructive testing. A prediction model of compressive strength considering the replacement level of recycled aggregate was suggested by multi-regression analysis and was compared with test results. Also, Test results showed that the ratio of compressive strength by core and non-destructive testing to actual was somewhat affected by the replacement level of recycled aggregate.

• Computers and Concrete
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• v.29 no.6
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• pp.433-444
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• 2022

Concrete's compressive strength is widely studied in order to understand many qualities and the grade of the concrete mixture. Conventional civil engineering tests involve time and resources consuming laboratory operations which results in the deterioration of concrete samples. Proposing efficient non-destructive models for the prediction of concrete compressive strength will certainly yield advancements in concrete studies. In this study, the efficiency of using radial basis function neural network (RBFNN) which is not common in this field, is studied for the concrete compressive strength prediction. Complementary studies with back propagation neural network (BPNN), which is commonly used in this field, have also been carried out in order to verify the efficiency of RBFNN for compressive strength prediction. A total of 13 input parameters, including novel ones such as cement's and fly ash's compositional information, have been employed in the prediction models with RBFNN and BPNN since all these parameters are known to influence concrete strength. Three different train: test ratios were tested with both models, while different hidden neurons, epochs, and spread values were introduced to determine the optimum parameters for yielding the best prediction results. Prediction results obtained by RBFNN are observed to yield satisfactory high correlation coefficients and satisfactory low mean square error values when compared to the results in the previous studies, indicating the efficiency of the proposed model.

• Journal of the Korea Institute of Building Construction
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• v.4 no.4
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• pp.63-70
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• 2004

The objective of this study is to evaluate the compressive strength of recycled aggregate concrete by the non-destructive testing. Main experimental variables were the replacement level of recycled aggregate and blast-furnace slag, which were divided into two series according to recycled aggregate maximum size. Test results showed that a recycled aggregate had a significant influence on the non-destructive testing results, such as rebound number, Ultrasonic pulse velocity, and frequency. A prediction model of compressive strength considering the replacement level of recycled aggregate was suggested by multi-regression analysis and was compared with test results.

• Journal of the Korea Concrete Institute
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• v.19 no.1
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• pp.75-81
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• 2007

Percentage that aggregate of materials that concrete composed about $70{\sim}80%$ of whole volume, therefore influence that quality of aggregate gets in concrete characteristics are very important. Schmidt hammer and ultra-sonic velocity method are commonly used for crushed sand concrete compressive strength test in a construction field. At present, various equations for prediction of strength are present, which have been used in a construction field. The purpose of this study is to evaluate the correlation between prediction strength by present equations and destructive strength to test specimen, and find out which is a suitable equation for the construction site, a strength test was carried out destructive test by means of core sampling and traditional test. The experimental parameters were concrete age, curing condition, and strength level. It is demonstrated that the correlation behavior of crushed sand concrete strength in this study good due to the perform analysis of correlation between core, destructive strength and non-destructive strength.

• Computers and Concrete
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• v.16 no.6
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• pp.919-932
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• 2015

This paper presents a new method for assessing the three-point-bending (3PB) strength of mortar beams in a non-destructive manner, based on neural network (NN) models. The models are based on the radial basis function (RBF) architecture and the fuzzy means algorithm is employed for training, in order to boost the prediction accuracy. Data for training the models were collected based on a series of experiments, where the cement mortar beams were subjected to various bending mechanical loads and the resulting pressure stimulated currents (PSCs) were recorded. The input variables to the NN models were then calculated by describing the PSC relaxation process through a generalization of Boltzmannn-Gibbs statistical physics, known as non-extensive statistical physics (NESP). The NN predictions were evaluated using k-fold cross-validation and new data that were kept independent from training; it can be seen that the proposed method can successfully form the basis of a non-destructive tool for assessing the bending strength. A comparison with a different NN architecture confirms the superiority of the proposed approach.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2006.05a
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• pp.155-158
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• 2006

Deduction of compressive strength in concrete members is very important to decide stability of structures. In this study, we compare the compressive strength of concrete between nondestructive test done to the building which was to be demolished at residential reconstruction site and destructive test of core specimen from the site. The result is more reliable because ore can compare the measurement of nondestructive tell with the result from destructive test using drilled cores. Compressive strength of each material was calculated with the result of rebound number test. In addition, we performed ultrasonic test for another result of compressive strength. And we made a comparative study of compressive strength of concrete drawn from both nondestructive and destructive tests.

• Computers and Concrete
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• v.20 no.1
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• pp.49-56
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• 2017

A clear correlation exists between the compressive strength and elastic modulus of concrete. Unfortunately, determining the static elastic modulus requires destructive methods and determining the dynamic elastic modulus is greatly complicated by the shape and size of the specimens. This paper reports on a novel approach to the prediction of compressive strength in concrete cylinders using numerical calculations in conjunction with the impact-echo method. This non-destructive technique involves obtaining the speeds of P-waves and S-waves using correction factors through numerical calculation based on frequencies measured using the impact-echo method. This approach makes it possible to calculate the dynamic elastic modulus with relative ease, thereby enabling the prediction of compressive strength. Experiment results demonstrate the speed, convenience, and efficacy of the proposed method.