• Title, Summary, Keyword: Strength Prediction

Search Result 1,447, Processing Time 0.057 seconds

Effect of Curing Temperature and Aging on the Mechanical Properties of Concrete (II) -Evaluation of Prediction Models- (콘크리트의 재료역학적 성질에 대한 양생온도와 재령의 효과(II) -예측 모델식을 중심으로-)

  • 한상훈;김진근;양은익
    • 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.

A Study on Development of Strength Prediction Model for Construction Field by Maturity Method (적산온도 기법을 활용한 건설생산현장에서의 강도예측모델 개발에 관한 연구)

  • Kim, Moo-Han;Nam, Jae-Hyun;Khil, Bae-Su;Choi, Se-Jin;Jang, Jong-Ho;Kang, Yong-Sik
    • Journal of the Korea Institute of Building Construction
    • /
    • v.2 no.4
    • /
    • pp.177-182
    • /
    • 2002
  • The purpose of this study is to develope the strength prediction model by Maturity Method. A maturity function is a mathematical expression to account for the combined effects of time and temperature on the strength development of a cementious mixture. The method of equivalent ages is to use Arrhenius equation which indicates the influence of curing temperature on the initial hydration ratio of cement. For the experimental factors of this study, we selected the concrete mixing of W/C ratio 45, 50, 55 and 60% and curing temperature 5, 10, 20 and $30^{\circ}C$. And we compare and evaluate with logistic model that is existing strength prediction model, because we have to verify adaption possibility of new strength prediction model which is proposed by maturity method. As the results, it is found that investigation of the activation energy that are used to calculate equivalent age is necessary, and new strength prediction model was proved to be more accurate in the strength prediction than logistic model in the early age. Moreover, the use of new model was more reasonable because it has low SSE and high decisive factor.

Prediction model of resistivity and compressive strength of waste LCD glass concrete

  • Wang, Chien-Chih
    • Computers and Concrete
    • /
    • v.19 no.5
    • /
    • pp.467-475
    • /
    • 2017
  • The purpose of this study is to establish a prediction model for the electrical resistivity ($E_r$) of self-consolidating concrete by using waste LCD (liquid crystal display) glass as part of the fine aggregate and then, to analyze the results obtained from a series of laboratory tests. A hyperbolic function is used to perform nonlinear multivariate regression analysis of the electrical resistivity prediction model, with parameters such as water-binder ratio (w/b), curing age (t) and waste glass content (G). Furthermore, the relationship of compressive strength and electrical resistivity of waste LCD glass concrete is also found by a logarithm function, while compressive strength is evaluated by the electrical resistivity of non-destructive testing (NDT). According to relative regression analysis, the electrical resistivity and compressive strength prediction models are developed, and the results show that a good agreement is obtained using the proposed prediction models. From the comparison between the predicted analysis values and test results, the MAPE value of electrical resistivity is 17.0-18.2% and less than 20%, the MAPE value of compressive strength evaluated by $E_r$ is 5.9-10.6% and nearly less than 10%. Therefore, the prediction models established in this study have good predictive ability for electrical resistivity and compressive strength of waste LCD glass concrete. However, further study is needed in regard to applying the proposed prediction models to other ranges of mixture parameters.

Characteristics and Prediction of Shear Strength for Unsaturated Residual Soil (풍화잔적토의 불포화전단강도 예측 및 특성연구)

  • 이인모;성상규;양일순
    • Proceedings of the Korean Geotechical Society Conference
    • /
    • /
    • pp.377-384
    • /
    • 2000
  • The characteristics and prediction model of the shear strength for unsaturated residual soils was studied. In order to investigate the influence of the initial water content on the shear strength, unsaturated triaxial tests were carried out varying the initial water content, and the applicability of existing prediction models for the unsaturated shear strength was testified. It was shown that the soil - water characteristic curve and the shear strength of the unsaturated soil varied with the change of the initial water content. A sample compacted in the lower initial water content needs a higher suction to get the same degree of saturation while the shear strength of a sample with the lower initial water content displays a lower value. In order to apply the existing prediction models of the unsaturated shear strength to granite residual soils, a correction coefficient, α, on the internal friction angle, ø'was added.

  • PDF

An evolutionary fuzzy modelling approach and comparison of different methods for shear strength prediction of high-strength concrete beams without stirrups

  • Mohammadhassani, Mohammad;Nezamabadi-pour, Hossein;Suhatril, Meldi;shariati, Mahdi
    • 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.

Prediction model for the hydration properties of concrete

  • Chu, Inyeop;Amin, Muhammad Nasir;Kim, Jin-Keun
    • 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.

Prediction of Mechanical Properties of Concrete by a New Apparent Activation Energy Function (새로운 겉보기 활성에너지 함수에 의한 콘크리트의 재료역학적 성질의 예측)

  • 한상훈;김진근
    • Proceedings of the Korea Concrete Institute Conference
    • /
    • /
    • 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.

  • PDF

Clustering-based identification for the prediction of splitting tensile strength of concrete

  • Tutmez, Bulent
    • 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.

Influence of net normal stresses on the shear strength of unsaturated residual soils (풍화잔적토의 불포화전단강도에 미치는 순연직응력의 영향)

  • 성상규;이인모
    • Proceedings of the Korean Geotechical Society Conference
    • /
    • /
    • pp.139-146
    • /
    • 2002
  • The characteristics and prediction model for the shear strength of unsaturated residual soils was studied. In order to investigate the influence of the net normal stress on the shear strength, unsaturated triaxial tests and SWCC tests were carried out varying the net normal stress, and the experimental data for unsaturated shear strength tests were compared with predicted shear strength envelopes using existing prediction models. It was shown that the soil - water characteristic curve and the shear strength of the unsaturated soil varied with the change of the net normal stress. Therefore, to achieve a truly descriptive shear strength envelope for unsaturated soils, tile effect of the normal stress on the contribution of matric suction to the shear strength has to be taken into consideration. In this paper, a modified prediction model for the unsaturated shear strength was proposed.

  • PDF

Prediction of concrete strength using serial functional network model

  • Rajasekaran, S.;Lee, Seung-Chang
    • Structural Engineering and Mechanics
    • /
    • v.16 no.1
    • /
    • pp.83-99
    • /
    • 2003
  • The aim of this paper is to develop the ISCOSTFUN (Intelligent System for Prediction of Concrete Strength by Functional Networks) in order to provide in-place strength information of the concrete to facilitate concrete from removal and scheduling for construction. For this purpose, the system is developed using Functional Network (FN) by learning functions instead of weights as in Artificial Neural Networks (ANN). In serial functional network, the functions are trained from enough input-output data and the input for one functional network is the output of the other functional network. Using ISCOSTFUN it is possible to predict early strength as well as 7-day and 28-day strength of concrete. Altogether seven functional networks are used for prediction of strength development. This study shows that ISCOSTFUN using functional network is very efficient for predicting the compressive strength development of concrete and it takes less computer time as compared to well known Back Propagation Neural Network (BPN).