• Journal of the Korea institute for structural maintenance and inspection
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• v.16 no.4
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• pp.107-115
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• 2012

The present study evaluates the validity of different equations specified in code provisions and proposed by the existing researchers to predict the concrete tensile capacities (direct tensile strength, splitting tensile strength and modulus of rupture) using a comprehensible database including 361 lightweight concrete (LWC), 1,335 normal-weight concrete (NWC) and 221 heavy-weight concrete (HWC) specimens. Most of the equations express the concrete tensile strengths as a function of its compressive strength based on the limited NWC concrete test data. However, the present database shows that the concrete tensile capacities are significantly affected by its unit weight as well. As a result, the inconsistency between experiments and predictions by the different models increases when the concrete unit weight is below 2,100 kg/$m^3$ and concrete compressive strength is above 50 MPa. On the other hand, new models proposed by the present study considering the concrete unit weight predict the tensile strengths of concrete with more accuracy.

• Advances in concrete construction
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• v.13 no.2
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• pp.123-132
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• 2022

This paper aims to adapt Multilinear regression (MLR) to predict the strength and toughness of SIFCON containing various pozzolanic materials. Slurry Infiltrated Fibrous Concrete (SIFCON) is one of the most common terms used in concrete manufacturing, known for its benefits such as high ductility, toughness and high ultimate strength. Assessment of compressive strength (CS.), flexural strength (F.S.), splitting tensile strength (STS), dynamic elasticity modulus (DME) and impact energy (I.E.) using the experimental approach is too costly. It is time-consuming, and a slight error can lead to a repeat of the test and, to solve this, alternative methods are used to predict the strength and toughness properties of SIFCON. In the present study, the experimentally investigated SIFCON data about various mix proportions are used to predict the strength and toughness properties using regression analysis-multilinear regression (MLR) models. The input parameters used in regression models are cement, fibre, fly ash, Metakaolin, fine aggregate, blast furnace slag, bottom ash, water-cement ratio, and the strength and toughness properties of SIFCON at 28 days is the output parameter. The models are developed and validated using data obtained from the experimental investigation. The investigations were done on 36 SIFCON mixes, and specimens were cast and tested after 28 days of curing. The MLR model yields correlation between predicted and actual values of the compressive strength (C.S.), flexural strength, splitting tensile strength, dynamic modulus of elasticity and impact energy. R-squared values for the relationship between observed and predicted compressive strength are 0.9548, flexural strength 0.9058, split tensile strength 0.9047, dynamic modulus of elasticity 0.8611 for impact energy 0.8366. This examination shows that the MLR model can predict the strength and toughness properties of SIFCON.

• Computers and Concrete
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• v.10 no.4
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• pp.379-390
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• 2012

In this paper, the mechanical strength of different lightweight mortars made with 0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95 and 100 percentage of scoria instead of sand and 0.55 water-cement ratio and 350 $kg/m^3$ cement content is investigated. The experimental result showed 7.9%, 16.7% and 49% decrease in compressive strength, tensile strength and mortar density, respectively, by using 100% scoria instead of sand in the mortar. The normalized compressive and tensile strength data are applied for artificial neural network (ANN) generation using generalized regression neural network (GRNN). Totally, 90 experimental data were selected randomly and applied to find the best network with minimum mean square error (MSE) and maximum correlation of determination. The created GRNN with 2 input layers, 2 output layers and a network spread of 0.1 had minimum MSE close to 0 and maximum correlation of determination close to 1.

• Computers and Concrete
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• v.20 no.6
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• pp.645-654
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• 2017

Continuing from previous studies of sustainable concrete containing environmentally friendly materials and existing modeling approach to predicting concrete properties, this study developed an estimation methodology to predicting the strength of sustainable concrete using an advanced case-based reasoning approach. It was conducted in two steps: (i) establishment of a case database and (ii) development of an advanced case-based reasoning model. Through the experimental studies, a total of 144 observations for concrete compressive strength and tensile strength were established to develop the estimation model. As a result, the prediction accuracy of the A-CBR model (i.e., 95.214% for compressive strength and 92.448% for tensile strength) performed superior to other conventional methodologies (e.g., basic case-based reasoning and artificial neural network models). The developed methodology provides an alternative approach in predicting concrete properties and could be further extended to the future research area in durability of sustainable concrete.

• Journal of the Korean Society of Safety
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• v.18 no.4
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• pp.8-15
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• 2003

A study on predicting the joint strength of mechanically fastened woven glass/epoxy composite has been performed. An experimental and numerical study were carried out to determine the characteristic length and joint strength of composite joint. The characteristic lengths for tension and compression were determined from the tensile and compressive test with a hole respectively. The characteristic lengths were evaluated by applying the point stress failure criterion to a specimen containing a hole at the center subjected to tensile loading and a specimen containing a half circular notch at the center subjected to compressive load. The joint strength was evaluated by the Tsai-Wu and Yamada-Sun failure criterion on the characteristic curve. The predicted results of the joint strength were compared with experimental results.

• Journal of the Korea Concrete Institute
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• v.27 no.4
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• pp.331-341
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• 2015

The main purpose of this study is reducing the cost and effort for characterization of tensile strength of fiber reinforced concrete, in order to use in structural design. For this purpose, in this study, test for fiber reinforced concrete was carried out. Because fiber reinforced concrete is consisted of diverse material, it is hard to define the correlation between mix proportions and strength. Therefore, compressive strength test and tensile strength test were carried out for the range of smaller than 100 MPa of compressive strength and 0.25~1% of steel fiber volume fraction. as a results of test, two types of tensile strength were highly affected by compressive strength of concrete. However, increase rate of tensile strength was decreased with increase of compressive strength. Increase rate of tensile strength was decreased with increase of fiber volume fraction. Database was constructed using previous research data. Because estimation equations for tensile strength of fiber reinforced concrete should be multiple variable function, linear regression is hard to apply. Therefore, in this study, we decided to use the ANN(Artificial Neural Network). ANN was constructed using multiple layer perceptron architecture. Sigmoid function was used as transfer function and back propagation training method was used. As a results of prediction using artificial neural network, predicted values of test data and previous research which was randomly selected were well agreed with each other. And the main effective parameters are water-cement ratio and fiber volume fraction.

• Computers and Concrete
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• v.32 no.3
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• pp.263-286
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• 2023

Experimenting with concrete to determine its compressive and tensile strengths is a laborious and time-consuming operation that requires a lot of attention to detail. Researchers from all around the world have spent the better part of the last several decades attempting to use machine learning algorithms to make accurate predictions about the technical qualities of various kinds of concrete. The research that is currently available on estimating the strength of concrete draws attention to the applicability and precision of the various machine learning techniques. This article provides a summary of the research that has previously been conducted on estimating the strength of concrete by making use of a variety of different machine learning methods. In this work, a classification of the existing body of research literature is presented, with the classification being based on the machine learning technique used by the researchers. The present review work will open the horizon for the researchers working on the machine learning based prediction of the compressive strength of concrete by providing the recommendations and benefits and drawbacks associated with each model as determining the compressive strength of concrete practically is a laborious and time-consuming task.

• Composites Research
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• v.17 no.5
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• pp.39-46
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• 2004

In this study, the strength of spatially reinforced composites (SRC) are predicted by using stiffness reduction for each structural element composed of a rod stiffness in each direction and a matrix stiffness proportional to its rod volume fraction. Maximum failure strain criteria is applied to rod failure, and modified Tsai-Wu failure criteria to matrix failure. The material properties composed of the tensile failure strain of a rod, the compressive failure strain of 3D SRC, the tensile and compressive strength of the 3D SRC in the $45^{\cir}$ rotated direction from a rod and the shear strength of the 3D SRC are measured to predict the SRC strength. The strength distributions of the 3D/4D SRC in rod and off-rod direction have the largest and the smallest values, respectively. A variable load step is selected to increase an efficiency of strength distribution calculation. Uniform load step is applied when a load history is needed. The results of compressive strength from analysis and experiment show the 18 % difference though the initial slop is coincident with each other.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.5
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• pp.135-140
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• 2021

In this study, the concrete aggregate shape features were extracted from the cross-section of a normal concrete strength cylinder, and the compressive strength of the cylinder was predicted using artificial neural networks and image processing technology. The distance-angle features of aggregates, along with general aggregate shape features such as area, perimeter, major/minor axis lengths, etc., were numerically expressed and utilized for the compressive strength prediction. The results showed that compressive strength can be predicted using only the aggregate shape features of the cross-section without using major variables. The artificial neural network algorithm was able to predict concrete compressive strength within a range of 4.43% relative error between the predicted strength and test results. This experimental study indicates that various material properties such as rheology, and tensile strength of concrete can be predicted by utilizing aggregate shape features.

• Proceedings of the Korea Concrete Institute Conference
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• 2008.11a
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• pp.637-640
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• 2008

In this study, the characteristics of strength development on high volume fly ash concrete(HVFAC)with Type 4 cement was experimentally investigated. Three levels of W/B were selected. Four levels of fly ash replacement ratios and two levels of silica fume replacement ratios were adopted. In the concrete mix, the water content of 125kg/m$^3$ was used, which is less than that of usual water content. As a result, it appeared that the compressive strength gradually decreased with increasing fly ash replacement ratio until 91days. However, regarding the compressive strength, the proper replacement ratio is about 20%, which is low compared to Type I cement case. It was observed that the tensile strength is proportional to the 0.72 power of the compressive strength. It appears that the prediction equation presented in Concrete Standard Specification overestimate the tensile strength in the low strength range, underestimate the tensile strength in the hi호 strength range.