• Journal of the Korea Concrete Institute
• /
• v.12 no.6
• /
• pp.35-42
• /
• 2000

In paper I, the relationships between compressive strength and splitting tensile strength or modulus of elasticity were proposed. In this paper, new prediction model is investigated from estimating splitting tensile strength and modulus of elasticity with curing temperature and aging without compressive strength. New prediction model is based on the model which was proposed to predict compressive strength, and splitting tensile strength and modulus of elasticity calculated by this model are compared with experimental values of paper I. To evaluate in-situ applicability of the model, strength and modulus of elasticity tested with variable temperatures are estimated by the prediction model. The prediction model reasonably estimates the strength and the modulus of elasticity of type I and V cement concretes tested in paper I and experimental results with variable temperature tested in this paper.

• Proceedings of the Korea Concrete Institute Conference
• /
• 2000.10a
• /
• pp.173-178
• /
• 2000

New prediction model is investigated estimating splitting tensile strength and modulus of elasticity with curing temperature and aging. New prediction model is based on the model which was proposed to predict compressive strength, and splitting tensile strength and modulus of elasticity calculated by this model are compared with experimental values. New prediction model well estimated splittinge tensile strength and elastic modulus as well as compressive strength.

• Computers and Concrete
• /
• v.12 no.4
• /
• pp.377-392
• /
• 2013

This paper investigates prediction models estimating the hydration properties of concrete, such as the compressive strength, the splitting tensile strength, the elastic modulus,and the autogenous shrinkage. A prediction model is suggested on the basis of an equation that is formulated to predict the compressive strength. Based on the assumption that the apparent activation energy is a characteristic property of concrete, a prediction model for the compressive strength is applied to hydration-related properties. The hydration properties predicted by the model are compared with experimental results, and it is concluded that the prediction model properly estimates the splitting tensile strength, elastic modulus, and autogenous shrinkage as well as the compressive strength of concrete.

• Computers and Concrete
• /
• v.6 no.2
• /
• pp.155-165
• /
• 2009

Splitting tensile strength (STS) of high-performance concrete (HPC) is one of the important mechanical properties for structural design. This property is related to compressive strength (CS), water/binder (W/B) ratio and concrete age. This paper presents a clustering-based fuzzy model for the prediction of STS based on the CS and (W/B) at a fixed age (28 days). The data driven fuzzy model consists of three main steps: fuzzy clustering, inference system, and prediction. The system can be analyzed directly by the model from measured data. The performance evaluations showed that the fuzzy model is more accurate than the other prediction models concerned.

• Advances in Computational Design
• /
• v.2 no.3
• /
• pp.225-240
• /
• 2017

This paper presents the application of multiple linear regression (MLR) and artificial neural network (ANN) techniques for developing the models to predict the unconfined compressive strength (UCS) and Brazilian tensile strength (BTS) of the fiber reinforced cement stabilized fly ash mixes. UCS and BTS is a highly nonlinear function of its constituents, thereby, making its modeling and prediction a difficult task. To establish relationship between the independent and dependent variables, a computational technique like ANN is employed which provides an efficient and easy approach to model the complex and nonlinear relationship. The data generated in the laboratory through systematic experimental programme for evaluating UCS and BTS of fiber reinforced cement fly ash mixes with respect to 7, 14 and 28 days' curing is used for development of the MLR and ANN model. The data used in the models is arranged in the format of four input parameters that cover the contents of cement and fibers along with maximum dry density (MDD) and optimum moisture contents (OMC), respectively and one dependent variable as unconfined compressive as well as Brazilian tensile strength. ANN models are trained and tested for various combinations of input and output data sets. Performance of networks is checked with the statistical error criteria of correlation coefficient (R), mean square error (MSE) and mean absolute error (MAE). It is observed that the ANN model predicts both, the unconfined compressive and Brazilian tensile, strength quite well in the form of R, RMSE and MAE. This study shows that as an alternative to classical modeling techniques, ANN approach can be used accurately for predicting the unconfined compressive strength and Brazilian tensile strength of fiber reinforced cement stabilized fly ash mixes.

• Proceedings of the Korean Society For Composite Materials Conference
• /
• 2001.10a
• /
• pp.129-132
• /
• 2001

Fiber waviness is one of manufacturing defects encountered frequently in thick composite structures. It affects significantly on the behavior as well as strength of thick composites. The effects of fiber waviness on tensile/compressive nonlinear elastic behavior and strength of thick composite with fiber waviness are studied theoretically and experimentally. FEA(Finite Element Analysis) models are proposed to predict tensile/compressive nonlinear behavior and strength of thick composites. In the FEA models, both material and geometric nonlinearities were incorporated into the model using energy density, iterative mapping and incremental method. Also Tsai-Wu criteria was adopted to predict the strength of thick composites with fiber waviness. Tensile and compressive tests were conducted on the specimens with uniform fiber waviness. It was observed that the degree of fiber waviness in composites significantly affected the nonlinear behavior and strength of the composites

• Smart Structures and Systems
• /
• v.14 no.5
• /
• pp.785-809
• /
• 2014

In this paper, an Adaptive nerou-based inference system (ANFIS) is being used for the prediction of shear strength of high strength concrete (HSC) beams without stirrups. The input parameters comprise of tensile reinforcement ratio, concrete compressive strength and shear span to depth ratio. Additionally, 122 experimental datasets were extracted from the literature review on the HSC beams with some comparable cross sectional dimensions and loading conditions. A comparative analysis has been carried out on the predicted shear strength of HSC beams without stirrups via the ANFIS method with those from the CEB-FIP Model Code (1990), AASHTO LRFD 1994 and CSA A23.3 - 94 codes of design. The shear strength prediction with ANFIS is discovered to be superior to CEB-FIP Model Code (1990), AASHTO LRFD 1994 and CSA A23.3 - 94. The predictions obtained from the ANFIS are harmonious with the test results not accounting for the shear span to depth ratio, tensile reinforcement ratio and concrete compressive strength; the data of the average, variance, correlation coefficient and coefficient of variation (CV) of the ratio between the shear strength predicted using the ANFIS method and the real shear strength are 0.995, 0.014, 0.969 and 11.97%, respectively. Taking a look at the CV index, the shear strength prediction shows better in nonlinear iterations such as the ANFIS for shear strength prediction of HSC beams without stirrups.

• Proceedings of the Korea Concrete Institute Conference
• /
• 1992.10a
• /
• pp.111-118
• /
• 1992

The mechanical behaviors related to the strain rate effect and the tensile strength of high-strength concrete were investigated in this study. For this purpose, concrete cylinder specimens with 4 different compressive strengths from 232kg/$\textrm{cm}^2$ to 1113kgf/$\textrm{cm}^2$ were tested and analysed on the mechanical properties(stress-strain relationship, compressive, modulus of elasticity, strain at peak compressive stress). From this experimental and analytical study, it seems that the current prediction model(ACI) for modulus of rupture need to be refined. Therefore, more refined equations for evaluation tensile strength of concrete are proposed.

• Journal of the Korean Recycled Construction Resources Institute
• /
• v.3 no.3
• /
• pp.206-211
• /
• 2015

Recently, for UHPC (Ulta High Performance Concrete) which is researched actively, as the tensile strength is absolutely influenced on the content of steel fiber, in this paper, experiments of compressive strength, elasticity modulus and tensile strength were performed according to compressive strength and content of steel fiber as variables. By the test results, compressive strength, elasticity modulus and tensile strength are proportioned and have a good correlation and according to content of steel fiber, compressive and tensile strength are also proportioned and have a good correlation. In case of elasticity modulus, the difference between test and present design code is not large, so it is possible to adapt to present design code. On the other hand, in case of tensile strength, as there is no specification of present design code, new prediction equation is proposed by using nonlinear regression analysis and the proposed equation have a good correlation to test results.

• Proceedings of the Korean Society of Propulsion Engineers Conference
• /
• 2017.05a
• /
• pp.936-938
• /
• 2017

In this paper, the progressive failure analysis of a bolted composite joint which is used in combustion tubes of projectiles and weapon systems is performed. Hashin's failure criterion is considered as fiber tensile failure mode, fiber compressive failure mode, matrix tensile failure mode, and matrix compressive failure mode for this analysis. And this criterion is used to make user subroutine, UMAT. Through the progressive failure analysis we predicted failure strength and compared failure strength with specimen test result.