• Structural Engineering and Mechanics
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• v.46 no.4
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• pp.447-458
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• 2013

Splitting tensile strength (STS) is a respectable mechanical property reflecting ability of the concrete. The STS of concrete is mainly related to compressive strength (CS), water/binder (W/B) ratio and concrete age. In this study, the assessment of STS is made by a novel uncertainty-oriented method which uses least square optimization and then predicts STS of concrete by uncertain (fuzzy) numbers. The approximation method addresses a novel integration of fuzzy set theory and multivariate statistics. The numerical examples showed that the method is applicable with relatively limited data. In addition, the prediction of uncertainty at various levels of possibility can be described. In conclusion, the uncertainty-oriented interval analysis can be suggested an effective tool for appraising the uncertainties in concrete technology.

• Computers and Concrete
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• v.17 no.6
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• pp.739-760
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• 2016

This paper reports on punching shear behavior of reinforced concrete panels, investigated experimentally and through finite element simulation. The aim of the study was to examine the punching shear of high strength concrete panels incorporating different types of aggregate and silica fume, in order to assess the validity of the existing code models with respect to the role of compressive and tensile strength of high strength concrete. The variables in concrete mix design include three types of coarse aggregates and three water-cementitious ratios, and ten-percent replacement of silica fume. The experimental results were compared with the results produced by empirical prediction equations of a number of widely used codes of practice. The prediction of the punching shear capacity of high strength concrete using the equations listed in this study, pointed to a potential unsafe design in some of them. This may be a reflection of the overestimation of the contribution of compressive strength and the negligence of the role of flexural reinforcement. The overall findings clearly indicated that the extrapolation of the relationships that were developed for normal strength concrete are not valid for high strength concrete within the scope of this study and that finite element simulation can provide a better alternative to empirical code Equations.

• Journal of the Korean Recycled Construction Resources Institute
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• v.7 no.4
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• pp.316-322
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• 2019

A probabilistic approach considering uncertainties was employed to investigate the effects on the material characteristics and strength of nuclear bio-logical shield concrete walls, when exposed to long-term radiation during the entire service life. Time-dependent compressive and tensile strengths were estimated by conducting the neutron fluence analysis. For the contaminated concrete, individual compressive and tensile failure probabilities can be possibly evaluated by not only establishing limit-state function withthe predefined critical values but also performing Monte Carlo Simulation. Nuclear power plant types similar to the Kori Unit 1, which was shut off permanently in 2017 after the 40-year operation, were herein selected for an illustrative purpose. Consequently, the probability-based performance assessment and prediction of contaminated concrete walls were well demonstrated.

• Structural Engineering and Mechanics
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• v.36 no.6
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• pp.759-783
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• 2010

In this paper, compressive strength of carbon fiber reinforced polymer (CFRP) confined concrete cylinders is formulated using a hybrid method coupling genetic programming (GP) and simulated annealing (SA), called GP/SA, and a robust variant of GP, namely multi expression programming (MEP). Straightforward GP/SA and MEP-based prediction equations are derived for the compressive strength of CFRP-wrapped concrete cylinders. The models are constructed using two sets of predictor variables. The first set comprises diameter of concrete cylinder, unconfined concrete strength, tensile strength of CFRP laminate, and total thickness of CFRP layer. The most widely used parameters of unconfined concrete strength and ultimate confinement pressure are included in the second set. The models are developed based on the experimental results obtained from the literature. To verify the applicability of the proposed models, they are employed to estimate the compressive strength of parts of test results that were not included in the modeling process. A sensitivity analysis is carried out to determine the contributions of the parameters affecting the compressive strength. For more verification, a parametric study is carried out and the trends of the results are confirmed via some previous studies. The GP/SA and MEP models are able to predict the ultimate compressive strength with an acceptable level of accuracy. The proposed models perform superior than several CFRP confinement models found in the literature. The derived models are particularly valuable for pre-design purposes.

• Computers and Concrete
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• v.32 no.6
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• pp.595-606
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• 2023

Dune sand (DS) has been widely used as a partial replacement for regular sand in concrete construction. Therefore, investigating its mechanical properties is critical for the analysis and design of structural elements using DS as a construction material. This paper presents a comprehensive investigation of the mechanical properties of DS concrete, considering different replacement ratios and strength grades. Regression analysis is utilized to develop strength prediction models for different mechanical properties of DS concrete. The proposed models exhibit high calculation accuracy, with R² values of 0.996, 0.991, 0.982, and 0.989 for cube compressive strength, axial compressive strength, splitting tensile strength, and elastic modulus, respectively, and an error within ±20%. Furthermore, a stress-strain relationship specific to DS concrete is established, showing good agreement with experimental results. Additionally, nonlinear finite element analysis is performed on concrete-filled steel tube columns incorporating DS concrete, utilizing the established stress-strain relationship. The analytical and experimental results exhibit good agreement, confirming the validity of the proposed stress-strain relationship for DS concrete. Therefore, the findings presented in this paper provide valuable references for the design and analysis of structures utilizing DS concrete as a construction material.

• Journal of the Korea Concrete Institute
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• v.18 no.3 s.93
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• pp.361-370
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• 2006

The results of previous tests by many researchers have been compiled to evaluate the flexural strength of steel-fiber reinforced concrete beams. Existing prediction equations for flexural strength of such beams were examined, and a new equation based on mechanical and empirical observations, was proposed. In other words, the constitutive models for steel fiber reinforced concrete(SFRC) were proposed, which incorporate compressive and tensile strength. A steel model might also exhibit stain-hardening characteristics. Predictions based on the model are compared with the experimental data. For the collection of tests, a variation of the Henager equations, modified to apply to fiber-reinforced concrete beams, provided reliable estimates of flexural strength. The proposed equations accounted for the influence of fiber-volume fraction, fiber aspect ratio, concrete compressive strength and flexural steel reinforcement ratio. The proposed equations gave a good estimation for 129 flexural specimens evaluated.

• Proceedings of the Korea Concrete Institute Conference
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• 2004.05a
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• pp.532-535
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• 2004

This paper presents a nonlinear finite element analysis procedure for the prediction of shear strength of reinforced concrete deep beams. A computer program, named RCAHESTC(Reinforced Concrete Analysis in Higher Evaluation System Technology), for the analysis of reinforced concrete structures was used. Material nonlinearity is taken into account by comprising tensile. compressive and shear models of cracked concrete and a model of reinforcing steel. The smeared crack approach is incorporated. The proposed numerical method for the prediction of shear strength of reinforced concrete deep beams is verified by comparison with the reliable experimental results.

• Advances in Computational Design
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• v.5 no.2
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• pp.195-207
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• 2020

The present study focuses on the application of artificial neural network (ANN) and Multiple linear Regression (MLR) analysis for developing a model to predict the unconfined compressive strength (UCS) and split tensile strength (STS) of the fiber reinforced clay stabilized with grass ash, fly ash and lime. Unconfined compressive strength and Split tensile strength are the nonlinear functions and becomes difficult for developing a predicting model. Artificial neural networks are the efficient tools for predicting models possessing non linearity and are used in the present study along with regression analysis for predicting both UCS and STS. The data required for the model was obtained by systematic experiments performed on only Kaolin clay, clay mixed with varying percentages of fly ash, grass ash, polypropylene fibers and lime as between 10-20%, 1-4%, 0-1.5% and 0-8% respectively. Further, the optimum values of the various stabilizing materials were determined from the experiments. The effect of stabilization is observed by performing compaction tests, split tensile tests and unconfined compression tests. ANN models are trained using the inputs and targets obtained from the experiments. Performance of ANN and Regression analysis is checked with statistical error of correlation coefficient (R) and both the methods predict the UCS and STS values quite well; but it is observed that ANN can predict both the values of UCS as well as STS simultaneously whereas MLR predicts the values separately. It is also observed that only STS values can be predicted efficiently by MLR.

• Geomechanics and Engineering
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• v.33 no.3
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• pp.279-289
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• 2023

Brittleness index (BI) is an important property of rocks because it is a good index to predict rockburst. Due to its importance, several empirical and soft computing (SC) models have been proposed in the literature based on the punch penetration test (PPT) results. These models are very important as there is no clear-cut experimental means for measuring BI asides the PPT which is very costly and time consuming to perform. This study used a novel Multivariate Adaptive regression spline (MARS), M5P, and white-box ANN to predict the BI of rocks using the available data in the literature for an improved BI prediction. The rock density, uniaxial compressive strength (σ_c) and tensile strength (σ_t) were used as the input parameters into the models while the BI was the targeted output. The models were implemented in the MATLAB software. The results of the proposed models were compared with those from existing multilinear regression, linear and nonlinear particle swarm optimization (PSO) and genetic algorithm (GA) based models using similar datasets. The coefficient of determination (R²), adjusted R² (Adj R²), root-mean squared error (RMSE) and mean absolute percentage error (MAPE) were the indices used for the comparison. The outcomes of the comparison revealed that the proposed ANN and MARS models performed better than the other models with R² and Adj R² values above 0.9 and least error values while the M5P gave similar performance to those of the existing models. Weight partitioning method was also used to examine the percentage contribution of model predictors to the predicted BI and tensile strength was found to have the highest influence on the predicted BI.

• Structural Engineering and Mechanics
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• v.73 no.5
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• pp.515-527
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• 2020

This paper presents a stress field approach for the shear capacity of stirrup-reinforced concrete beams that explicitly incorporates the contribution of principal tensile stresses in concrete. This formulation represents an extension of the variable strut inclination method adopted in the Eurocode 2. In this model, the stress fields in web concrete consist of principal compressive stresses inclined at an angle θ combined with principal tensile stresses oriented along a direction orthogonal to the former (the latter being typically neglected in other formulations). Three different failure mechanisms are identified, from which the strut inclination angle and the corresponding shear strength are determined through equilibrium principles and the static theorem of limit analysis, similar to the EC-2 approach. It is demonstrated that incorporating the contribution of principal tensile stresses of concrete slightly increases the ultimate inclination angle of the compression struts as well as the shear capacity of reinforced concrete beams. The proposed stress field approach improves the prediction of the shear strength in comparison with the Eurocode 2 model, in terms of both accuracy (mean) and precision (CoV), as demonstrated by a broad comparison with more than 200 published experimental results from the literature.