• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 1998.10a
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• pp.146-149
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• 1998

In this study, the chloride ion penetration test and the compressive strength test should be done simultaneously on the standard cylinder specimen. And from analyzing the data from those tests, a strength estimation equation with high credibility is to be developed.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.11a
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• pp.181-182
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• 2020

This study investigates the feasibility tp estimate the compressive strength at early age in hardened concrete by applying Durometer D type. The result of the experiment showed that the compressive strength reached 5MPa 6 hours, and 55 HD when the hardness was measured with the type D of the Duometer. Through this, it is expected that it will be possible to estimate the compressive strength of the initial age and measure it by the age of 28 when using the Durometer D type in the ready-mixed concrete, which will be applied in practice.

• Geomechanics and Engineering
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• v.7 no.3
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• pp.247-261
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• 2014

Analytical and numerical modeling of soft or problematic soils stabilized with lime and cement require a number of soil parameters which are usually obtained from expensive and time-consuming laboratory experiments. The high shear strength of lime and cement stabilized soils make it extremely difficult to obtain high quality laboratory data in some cases. In this study, an alternative method is proposed, which uses the unconfined compressive strength and estimating functions available in literature to evaluate the shear strength parameters of the treated materials. The estimated properties were applied in finite element model to determine which estimating function is more appropriate for lime and cement treated granular soils. The results show that at the mid-range strength of the stabilized soils, most of applied functions have a good compatibility with laboratory conditions. However, application of some functions at lower or higher strengths would lead to underestimation or overestimation of the unconfined compressive strength.

• Journal of the Korea institute for structural maintenance and inspection
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• v.12 no.3
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• pp.199-207
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• 2008

As infrastructures are being deteriorated, nondestructive evaluation of existing structures and construction quality control are increasingly demanded. The importance of predicting compressive strength of concrete structure is also gradually increasing in construction industry. The estimation of concrete compressive strength is a critical factor of the construction management and quality control. This study has been conducted using Schmidt hammer test and Impact echo method, which are nondestructive test methods for the comparison of the concrete compressive strength. It is focusing to examine the relationship between compressive strength of concrete and rebound number by Schmidt hammer test result by Impact echo method. It was found that concrete compressive strength and rebound number have a close correlation.

• Journal of the Korean Geotechnical Society
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• v.38 no.10
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• pp.5-15
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• 2022

In this study, to propose simplified methods for estimating the cohesion and internal friction angle of volcanic rocks in Jeju Island using uniaxial compressive strength and/or Brazilian tensile strength, the estimated values of cohesion and internal friction angle from triaxial and uniaxial compression tests and Brazilian tests were compared in terms of estimation accuracy. This study proposed simplified methods for estimating the cohesion and internal friction angle using the uniaxial compressive strength and/or Brazilian tensile strength of volcanic rocks in Jeju Island. According to the findings, among the proposed simplified methods, the method using uniaxil compressive strength was most desirable to accurately estimate the cohesion and internal friction angle of volcanic intact rocks in Jeju Island.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.06a
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• pp.23-24
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• 2020

In this study, the concrete compressive strength estimation system Concrete IoT Management System (hereinafter referred to as CIMS) was developed, and CIMS was applied to domestic field structure slabs and wall concrete to check whether CIMS is practically available and to estimate the accuracy of the initial strength estimation of concrete. As a result, it shows a very high correlation when the compressive strength of the specimen for structural management is compared with the estimated strength of CIMS in terms of integrated temperature, and it is expected to be gradually applied to domestic construction sites in the future.

• Computers and Concrete
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• v.21 no.1
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• pp.21-30
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• 2018

In this study, we analyze the behavior of concrete which contains zeolite and diatomite. In order to achieve the goal, we utilize expert system methods. The utilized methods are artificial neural network and adaptive network-based fuzzy inference systems. In this respect, we exploit seven different mixes of concrete. The concrete mixes contain zeolite, diatomite, mixture of zeolite and diatomite. All seven concrete mixes are exposed to 28, 56 and 90 days' compressive strength experiments with 63 specimens. The results of the compressive strength experiments are used as input data during the training and testing of expert system methods. In terms of artificial neural network and adaptive network-based fuzzy models, data format comprises seven input parameters, which are; the age of samples (days), amount of Portland cement, zeolite, diatomite, aggregate, water and hyper plasticizer. On the other hand, the output parameter is defined as the compressive strength of concrete. In the models, training and testing results have concluded that both expert system model yield thrilling medium to predict the compressive strength of concrete containing zeolite and diatomite.

• Computers and Concrete
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• v.15 no.1
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• pp.89-101
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• 2015

In this paper, Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR) models were discussed to determine the compressive strength of clinker mortars cured for 1, 2, 7 and 28 days. In the experimental stage, 1288 mortar samples were produced from 322 different clinker specimens and compressive strength tests were performed on these samples. Chemical properties of the clinker samples were also determined. In the modeling stage, these experimental results were used to construct the models. In the models tricalcium silicate ($C_3S$), dicalcium silicate ($C_2S$), tricalcium aluminate ($C_3A$), tetracalcium alumina ferrite ($C_4AF$), blaine values, specific gravity and age of samples were used as inputs and the compressive strength of clinker samples was used as output. The approximate reasoning ability of the models compared using some statistical parameters. As a result, ANN has shown satisfying relation with experimental results and suggests an alternative approach to evaluate compressive strength estimation of clinker mortars using related inputs. Furthermore MLR model showed a poor ability to predict.

• International Journal of Concrete Structures and Materials
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• v.19 no.1E
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• pp.11-16
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• 2007

Recently, probabilistic neural network (PNN) has been proposed to predict the compressive strength of concrete for the known effect of improvement on PNN by the iteration method. However, an empirical method has been incorporated in the PNN technique to specify its smoothing parameter, which causes significant uncertainty in predicting the compressive strength of concrete. In this study, a modified probabilistic neural network (MPNN) approach is hence proposed. The global probability density function (PDF) of variables is reflected by summing the heterogeneous local PDFs which are automatically determined by the individual standard deviation of each variable. The proposed MPNN is applied to predict the compressive strength of concrete using actual test data from a concrete company. The estimated results of MPNN are compared with those of the conventional PNN. MPNN showed better results than the conventional PNN in predicting the compressive strength of concrete and provided promising results for the probabilistic approach to predict the concrete strength by using the individual standard deviation of a variable.

• Computers and Concrete
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• v.26 no.4
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• pp.309-316
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• 2020

The application of multi-variable adaptive regression spline (MARS) in predicting he long-term compressive strength of a concrete with various admixtures has been investigated in this study. The compressive strength of concrete specimens, which were made based on 24 different mix designs using various mineral and chemical admixtures in different curing ages have been obtained. First, The values of fly ash (FA), micro-silica (MS), water-reducing admixture (WRA), coarse and fine aggregates, cement, water, age of samples and compressive strength were defined as inputs to the model, and MARS analysis was used to model the compressive strength of concrete and to evaluate the most important parameters affecting the estimation of compressive strength of the concrete. Next, the proposed equation by the MARS method using particle swarm optimization (PSO) algorithm has been optimized to have more efficient equation from the economical point of view. The proposed model in this study predicted the compressive strength of the concrete with various admixtures with a correlation coefficient of R=0.958 rather than the measured compressive strengths within the laboratory. The final model reduced the production cost and provided compressive strength by reducing the WRA and increasing the FA and curing days, simultaneously. It was also found that due to the use of the liquid membrane-forming compounds (LMFC) for its lower cost than water spraying method (SWM) and also for the longer operating time of the LMFC having positive mechanical effects on the final concrete, the final product had lower cost and better mechanical properties.