• Journal of the Korea institute for structural maintenance and inspection
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• v.11 no.5
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• pp.81-88
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• 2007

Among deteriorations of concrete due to environmental exposure, carbonation problems of concrete structures have increased in urban and underground structures. But conventional carbonation-prediction equations that were proposed by foreign references, can not be applied directly to the prediction of carbonation for domestic concrete structures. The purpose of this study is to propose a prediction equation of carbonation depth by considering domestic exposure conditions of concrete structures. For the derivation of the equation, conventional carbonation-prediction equations are analyzed. Through considering the relationship between results of prediction equation and those of various domestic field survey data, the so-called correction factors for different domestic exposure condition of concrete structures are derived. Finally, a carbonation-prediction equation of concrete structures under domestic exposure conditions is proposed with consideration for concrete strength in core and correction factors.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2013.05a
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• pp.89-90
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• 2013

This study is to predict the compressive strength for the concrete of ground granulated blast-furnace slag, and use Plowman's, Gompertz's model. The results are as follows; The prediction compressive strength were simiar using Rastrup's equivalent age model. but The prediction compressive strength using Freiesleben's equivalent age model weren't simiar in bfs replacement Ratio of 50%, because it is analyzed as the activation energy.

• Journal of Korean Association for Spatial Structures
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• v.20 no.4
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• pp.111-121
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• 2020

There has been increasing interest in UHPC (Ultra-High Performance Concrete) materials in recent years. Owing to the superior mechanical properties and durability, the UHPC has been widely used for the design of various types of structures. In this paper, machine learning based compressive strength prediction methods of the UHPC are proposed. Various regression-based machine learning models were built to train dataset. For train and validation, 110 data samples collected from the literatures were used. Because the proportion between the compressive strength and its composition is a highly nonlinear, more advanced regression models are demanded to obtain better results. The complex relationship between mixture proportion and concrete compressive strength can be predicted by using the selected regression method.

• Structural Engineering and Mechanics
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• v.52 no.6
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• pp.1273-1287
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• 2014

Plates are most widely used in the hulls of floating concrete structures, bridge decks, walls of off-shore structures and liquid storage tanks. A method of analysis is presented for the determination of load-deflection response and ultimate strength of high-strength steel fiber reinforced concrete (HSSFRC) plates simply supported on all four edges and subjected to combined action of external compressive in-plane and transverse loads. The behavior of HSSFRC plate specimens subjected to combined uniaxial in-plane and transverse loads was investigated. The proposed analytical method is compared to the physical test results, and shows good agreement. To predict the constitutive behavior of HSSFRC in compression, a non-dimensional characteristic equation was proposed and found to give reasonable accuracy.

• Journal of the Korea institute for structural maintenance and inspection
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• v.8 no.2
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• pp.177-183
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• 2004

The maturity concept is based on the fact that concrete gains strength with time as a result of the cement hydration and, thus the strength of concrete is related to the degree of hydration of the cement in concrete. The rate of hydration, as in any chemical reaction, depends primarily on the concrete temperature during hydration. Therefore, the aim of the study is to investigate of the correlation between strength of high-strength concrete and maturity that is expressed as a function of an integral of the curing period and temperature.

• Proceedings of the Korea Concrete Institute Conference
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• 1989.10a
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• pp.9-13
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• 1989

The purpose of this paper is to propose a method predetermining the 28-days strength of concrete. In this paper, it was predicted by regression analysis of the relation between 7-days and 28-days strength of fresh concrete and the strength of concrete early cured at $70^{\circ}C$ for rour hours after wet screening and addition of accelerating agent. It is concluded that the formula predeterming the 28-days strength of concrete using 25M/M rubbles from Sam-Cheok and sands from Yon-Gok, by the strength of concrete early cured for 4 hours is Y=-11.45 + 3.686X, where the coefficient of determination of regression-expression is r2=0.938, S=17.94(kg/$\textrm{cm}^2$).

• Computers and Concrete
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• v.27 no.1
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• pp.21-28
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• 2021

The Artificial Neural Network (ANN) is a system, which is utilized for solving complicated problems by using nonlinear equations. This study aims to investigate compressive strength, rebound hammer number (RN), and ultrasonic pulse velocity (UPV) of sustainable concrete containing various amounts of fly ash, silica fume, and blast furnace slag (BFS). In this study, the artificial neural network technique connects a nonlinear phenomenon and the intrinsic properties of sustainable concrete, which establishes relationships between them in a model. To this end, a total of 645 data sets were collected for the concrete mixtures from previously published papers at different curing times and test ages at 3, 7, 28, 90, 180 days to propose a model of nine inputs and three outputs. The ANN model's statistical parameter R2 is 0.99 of the training, validation, and test steps, which showed that the proposed model provided good prediction of compressive strength, RN, and UPV of sustainable concrete with the addition of cement.

• Journal of the Korea Concrete Institute
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• v.16 no.6 s.84
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• pp.813-822
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• 2004

For the prediction of the shear strength of reinforced concrete members subjected to axial force, this paper presents a truss model, Transformation Angle Truss Model (TATM), that can predict the shear behavior of reinforced concrete members subjected to combined actions of shear, axial force, and bending moment. In TATM, as axial compressive stress increases, crack angle decreases and concrete contribution due to the shear resistance of concrete along the crack direction increases in order to consider the effect of the axial force. To verify if the prediction results of TATM have an accuracy and reliability for the shear strength of reinforced concrete members subjected to axial forces, the shear test results of a total of 67 RC members subjected to axial force reported in the technical literatures were collected and compared with TATM and existing analytical models(MCFT RA-STM and FA-STM). As a result of comparing with experimental and theoretical results, the test results was better predicted by TATM with 0.94 in average value of $\tau_{test}/\tau_{ana}$. and $11.2\%$ in coefficient of variation than other truss models. And theoretical results obtained from TATM were not effect by steel capacity ratio, axial force, shear span-to-depth ratio, and compressive steel ratio.

• Computers and Concrete
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• v.14 no.1
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• pp.19-40
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• 2014

Reinforced concrete deep beams are structural beams with low shear span-to-depth ratio, and hence in which the strain distribution is significantly nonlinear and the conventional beam theory is not applicable. A strut-and-tie model is considered one of the most rational and simplest methods available for shear strength prediction and design of deep beams. The strut-and-tie model approach describes the shear failure of a deep beam using diagonal strut and truss mechanism: The diagonal strut mechanism represents compression stress fields that develop in the concrete web between diagonal cracks of the concrete while the truss mechanism accounts for the contributions of the horizontal and vertical web reinforcements. Based on a database of 406 experimental observations, this paper proposes a new strut-and-tie-model for accurate prediction of shear strength of reinforced concrete deep beams, and further improves the model by correcting the bias and quantifying the scatter using a Bayesian parameter estimation method. Seven existing deterministic models from design codes and the literature are compared with the proposed method. Finally, a limit-state design formula and the corresponding reduction factor are developed for the proposed strut-andtie model.

• Proceedings of the Korea Concrete Institute Conference
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• 2005.05b
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• pp.1-4
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• 2005

This research is to present experimental materials model of high strength concrete for prediction of fire safety of structural members based on physical properties of materials during heating up to 800$^{circ}C$. The following conclusions are drawn from this study. First of all, between 100 to 200 $^{circ}C$, the physical models of concrete such as specific heat and thermal conductivity, show visible degradation, regardless of concrete strength. Second, between 300 to 600$^{circ}C$, the physical models of the 29MPa and 49MPa concrete show degradation continually at these temperatures. Finally, beyond 600$^{circ}C$, the physical models of 49MPa strength concrete show larger degradation than 29MPa strength concrete due to rise of pore pressure and melting of the interface between aggregate and cement paste.