• Geomechanics and Engineering
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• 제25권4호
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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.

• Advances in concrete construction
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• 제14권2호
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• pp.115-130
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• 2022

This paper presents an application of the maturation effect on the strength of high-strength concrete which is produced with different origin aggregates. While investigating the maturation effect on HSC 384 specimens were prepared with 22 different origin aggregates. These prepared specimens were subjected to the standard compressive tests which were applied after curing for 2, 7, 28, and 56 days under appropriate conditions. The test results revealed that bright surface-low adherence behavior is valid in normal strength concretes, but is not as effective as expected in high-strength concretes. The application of artificial neural networks (ANNs) to predict 2, 7, 28, and 56 day compressive strength of HSC is also investigated in this paper. An ANN model is built, trained, and tested using the available test data gathered from experimental studies. The ANN model is found to predict 2, 7, 28, and 56 days of compressive strength of high-strength concrete well within the ranges of the input parameters considered. These comparisons show that ANNs have strong potential as a feasible tool for predicting the compressive strength of high-strength concrete within the range of the input parameters considered.

• Computers and Concrete
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• 제34권2호
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• pp.137-149
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• 2024

In order to study the triaxial mechanical behavior of steel fiber reinforced high performance concrete (SFRHPC), the standard triaxial compression tests with four different confining pressures are performed on the cylindrical specimens. Three different steel fiber volumes (0, 1% and 2%) are added in the specimens with diameter of 50 mm and height of 100 mm. Test results show that the triaxial compressive strength and peak strain increase with the increasing of fiber content at the same confining pressure. At the same steel fiber content, the triaxial compressive strength and peak strain increases with the confining pressure. The compressive strength growth rate declines as the confining pressure and steel fiber content increases. Longitudinal cracks are dominant in specimens with or without steel fiber under uniaxial compression loading. While with the confining pressure increases, diagonal crack due to shear is obvious. The Mohr-Coulomb criterion is illustrated can be used to describe the failure behavior, and the cohesive force increases as steel fiber content increases. Finally, the numerical model is built by using the PFC3D software. In the numerical model a index is introduced to reflect the effect of steel fiber content on the triaxial compressive behavior. The simulating stress-strain curve and failure mode of SFRHPC are agree well with the experimental results.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2006년도 춘계 학술발표회 논문집(II)
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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.

• 한국해양공학회지
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• 제10권2호
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• pp.35-41
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• 1996

This paper is aimed to solve local buckling problem that can frequently occur when the high speed ship's hull of sandwich structural type is crushed by rarbour and cargo. Experiment is performed on 36 specimens cut of 4-plates that made of sandwich type(Kevlar-Epoxy, Klegecell foam) and 16-Edgewise compressive test specimen, 16-Flatwise test specimen were tested by A.S.T.M. test method. The result of this study is analyzed and compared in test method and test jig to perorm Edgewise compressive test and Flatwise test.

• Computers and Concrete
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• 제21권3호
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• pp.345-353
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• 2018

This paper approaches to improve the mechanical and durability properties of low calcium fly ash geopolymer concrete with the addition of Alccofine as a mineral admixture. The mechanical and durability performance of GPC was assessed by means of compressive strength, flexural strength, permeability, water absorption and permeable voids tests. The correlation between compressive strength and flexural strength, depth of water penetration and percentage permeable voids are also reported. Test results show that addition of Alccofine significantly improves the mechanical as well as permeation properties of low calcium fly ash geopolymer concrete. Very good correlations were noted between the depth of water penetration and compressive strength, percentage permeable voids and compressive strength as well as between compressive strength and flexural strength.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2002년도 학술논문발표회
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• pp.69-74
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• 2002

The following results are achieved from a mortar flow test depending on stainless steel slag fineness, replacement ratio, and a research on material age compressive strength, strength activity index. 1. Flow is proportional to the stainless steel slag fineness within the limits of 4000~8000$\textrm{cm}^2$/g, but in the case of fineness 20000$\textrm{cm}^2$/g flow decreases at all conditions except the case of replacement ratio 10%. 2. As stainless steel slag replacement ratio increases, Mortar of flow somewhat decreases. 3. As stainless steel slag blends, compressive strength decreases, but in proportion to the increase of age, compressive strength increases. 4. As stainless steel slag replacement ratio, compressive strength decreases. 5. In the case of stainless steel slag fineness 6000$\textrm{cm}^2$/g and 20.000$\textrm{cm}^2$/g, compressive strength of revelation ratio has the maximum value when it's replacement ratio is 10%.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 1995년도 봄 학술발표회 논문집
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• pp.154-159
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• 1995

Cold weather concrete presents the many characteristic variation of quality, according to the mixing and cooling point, the cooling time and the quantity of air besides the compressive strength of concrete. Thus, in this study to verify the character of cold-weather concrete we make the concrete specimens at laboratory and cool them at cooling-melting machine and then test the 7days compressive strength of them, with the variation of compressive strength of concrete, cooling point, cooling time, cooling weather and air quantity. At the results, the compressive strength of concrete decrease in the case of early cooling point, long cooling time, low cooling temperature and the low design compressive strength

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 1999년도 봄 학술발표회 논문집(I)
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• pp.135-140
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• 1999

The size effect of axial compressive strength of concrete in notched specimens was experimentally investigated. Based on the concept of the fracture mechanics and size effect law, theoretical studies for axial compressive failure of concrete were reviewed, and two failure modes of concrete specimen under compression were discussed. In this study, experiment of axial compressive failure, which is one of the two failure modes, was carried out by using double cantilever fracture specimens. By varying the slenderness of cantilevers and the eccentricity of applied loads with respect to the axis of each cantilever, the size effect of axial compressive strength of concrete was investigated, and predicted by Bazant's size effect law. The test results show that size effect appears conspicuously for all series of specimens. For the eccentricity of loads, the influence of tensile and compressive stress at the notch tip are significant and so that the size effect is varied. In other words, if the influence of tensile stress at the notch tip grows up, the size effect of concrete increases. And the fact that the fracture process zone must be sufficiently secured for more accurate experiment was affirmed.

• Computers and Concrete
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• 제25권5호
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• pp.401-409
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• 2020

Artificial neural networks are used as a useful tool in distinct fields of civil engineering these days. In order to control long-term quality of Self-Compacting Semi-Lightweight Concrete (SCSLC), the 90 days compressive strength is considered as a key issue in this paper. In fact, combined artificial neural networks are used to predict the compressive strength of SCSLC at 28 and 90 days. These networks are able to re-establish non-linear and complex relationships straightforwardly. In this study, two types of neural networks, including Radial Basis and Multilayer Perceptron, were used. Four groups of concrete mix designs also were made with two water to cement ratios (W/C) of 0.35 and 0.4, as well as 10% of cement weight was replaced with silica fume in half of the mixes, and different amounts of superplasticizer were used. With the help of rheology test and compressive strength results at 7 and 14 days as inputs, the neural networks were used to estimate the 28 and 90 days compressive strengths of above-mentioned mixes. It was necessary to add the 14 days compressive strength in the input layer to gain acceptable results for 90 days compressive strength. Then proper neural networks were prepared for each mix, following which four existing networks were combined, and the combinatorial neural network model properly predicted the compressive strength of different mix designs.