• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.6D
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• pp.599-605
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• 2012

It is important to decide traffic opening time for construction plan of epoxy asphalt pavement. For this purpose, strength prediction model of epoxy asphalt concrete is required. In this study, Marshall stability was measured according to temperature and time for making strength properties equation. Strength prediction model was developed using chemical kinetics considering temperature variation. The traffic opening time of epoxy asphalt pavement on bridge deck has been predicted using the developed model. The prediction and actual traffic opening times were different by 17-days, because weathers of year 2009-2011 used in prediction model were different from weather of year 2012. When the prediction model used the actually measured temperatures of pavement, the difference between real opening time and prediction opening time was two days. The correlation analysis result between measured strength and prediction strength revealed that the $R^2$ using accurate temperature of pavement was 0.95. An improved precise prediction result is to be obtained if the prediction model uses accurate temperature data of pavement.

• Proceedings of the Korea Concrete Institute Conference
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• 1994.04a
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• pp.47-52
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• 1994

In designing of slabs, a prediction of the punching shear capacity is one of important concerns. In this study, an equation was proposed to predict the punching shear strength of reinforced concrete slabs. The proposed equation depends on concrete compression strength, steel ratio, effective depth and slab radial length. The good correlation exists between the predicted punching shear strength and the measured.

• Proceedings of the Korea Concrete Institute Conference
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• 2005.05b
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• pp.133-136
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• 2005

Compressive strength of recycled aggregate concrete was tested by the core and by the non-destructive testing. A prediction model of compressive strength considering the replacement level of recycled aggregate was suggested by multi-regression analysis and was compared with test results. Also, Test results showed that the ratio of compressive strength by core and non-destructive testing to actual was somewhat affected by the replacement level of recycled aggregate.

• International Journal of Concrete Structures and Materials
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• v.7 no.4
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• pp.295-301
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• 2013

This paper deals with the interfacial effects of silica fume (SF) and styrene-butadiene rubber (SBR) on compressive strength of concrete. Analyzing the compressive strength results of 32 concrete mixes performed over two water-binder ratios (0.35, 0.45), four percentages replacement of SF (0, 5, 7.5, and 10 %) and four percentages of SBR (0, 5, 10, and 15 %) were investigated. The results of the experiments were showed that in 5 % of SBR, compressive strength rises slightly, but when the polymer/binder materials ratio increases, compressive strength of concrete decreases. A mathematical model based on Abrams' law has been proposed for evaluation strength of SF-SBR concretes. The proposed model provides the opportunity to predict the compressive strength based on time of curing in water (t), and water, SF and SBR to binder materials ratios that they are shown with (w/b), (s) and (p).This understanding model might serve as useful guides for commixture concrete admixtures containing of SF and SBR. The accuracy of the proposed model is investigated. Good agreements between them are observed.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.06a
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• pp.58-59
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• 2020

The change of temperature and humidity in early-age concrete has a great influence on the durability of the structure. In this study, a reliable wireless sensor network system and a concrete embedded type Compressive strength prediction sensor were designed using the Arduino platform. The accuracy of the compressive strength prediction sensor was verified through a mock-up experiment, and it was confirmed that the experiment had sufficient accuracy to be used in the field environment.

• Computers and Concrete
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• v.34 no.1
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• pp.33-48
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• 2024

This paper presents the detailed experimental and analytical investigation on the evolution of static (E_s) and dynamic modulus of elasticity (E_d) of concrete having 0%, 35%, and 50% FA used as partial cement replacement. Destructive and non-destructive tests were conducted on cylindrical specimens to evaluate the compressive strength and MoE of concrete in compression at the age of 28, 56, 90, and 150 days for all mixes. Experimental results show that the concrete having 35% FA achieved compressive strength and MoE similar to plain concrete at the age of 90 days, while 50% FA concrete attained satisfactory compressive strength and MoE at the age of 150 days. The comprehensive statistical analysis has been carried out in two ways on the basis of the experimental results. Firstly, the 28-day crushing strength of plain concrete in compression was used to design the models for the prediction of E_s and E_d of fly ash concrete at any age and percentage replacement of FA. Secondly, using the values of UPV and RHN, models have been developed to predict the age or time-dependent E_s and E_d of fly ash concrete. These models will be helpful in assessing the E_s and E_d of fly ash concrete without knowing the 28-day crushing strength of plain concrete in compression in the laboratory. Hence, the suggested models in the present study will be beneficial in conducting the health assessment of fly ash based concrete structures.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.4
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• pp.122-129
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• 2010

High-performance concrete (HPC) is a new terminology used in concrete construction industry. Several studies have shown that concrete strength development is determined not only by the water-to-cement ratio but also influenced by the content of other concrete ingredients. HPC is a highly complex material, which makes modeling its behavior a very difficult task. This paper aimed at demonstrating the possibilities of adapting artificial neural network (ANN) to predict the comprresive strength of HPC. Mahalanobis Distance (MD) outlier detection method used for the purpose increase prediction ability of ANN. The detailed procedure of calculating Mahalanobis Distance (MD) is described. The effects of outlier compared with before and after artificial neural network training. MD outlier detection method successfully removed existence of outlier and improved the neural network training and prediction performance.

• Proceedings of the Korea Concrete Institute Conference
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• 2006.05b
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• pp.485-488
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• 2006

The shrinkage properties of the high strength concrete using the cement of Type I, Type III and Type IV was examined, and the following results were obtained. (1) Consideration of the autogenous shrinkage when evaluating appropriately the shrinkage properties of the high strength concrete is indispensable. (2) The autogenous shrinkage prediction expression of JSCE can estimate the properties of autogenous shrinkage of the cement made from korea with in general sufficient accuracy. (3) It is necessary to advance examination which used Korean aggregate about dry shrinkage from now on, and to attain highly accuracy of the autogenous shrinkage prediction expression.

• International Journal of Concrete Structures and Materials
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• v.2 no.1
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• pp.57-62
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• 2008

When the yield line theory is used to estimate the ultimate strength of a concrete barrier, it is of primary importance that the correct assumption is made for the failure mode of the barrier. In this study, a static test was performed on two full-scale concrete barrier specimens of Korean standard shape that simulate the actual behavior of a longitudinally continuous barrier. This was conducted in order to verify the failure mode presented in the AASHTO LRFD specification. The resulting shape of the yield lines differed from that presented in AASHTO when subjected to an equivalent crash load. Furthermore, the ultimate strengths of the specimens were lower than the theoretical prediction. The main causes of these differences can be attributed to the characteristics of the barrier shape and to a number of limitations associated with the classical yield line theory. Therefore, a revised failure mode with corresponding prediction equations of the strength were proposed based on the yield lines observed in the test. As a result, a strength that was more comparable to that of the test could be obtained. The proposed procedure can be used to establish more realistic test levels for barriers that have a similar shape.

• Computers and Concrete
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• v.33 no.2
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• pp.137-145
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• 2024

The technique of experimentally determining concrete's compressive strength for a given mix design is time-consuming and difficult. The goal of the current work is to propose a best working predictive model based on different machine learning algorithms such as Gradient Boosting Machine (GBM), Stacked Ensemble (SE), Distributed Random Forest (DRF), Extremely Randomized Trees (XRT), Generalized Linear Model (GLM), and Deep Learning (DL) that can forecast the compressive strength of ternary geopolymer concrete mix without carrying out any experimental procedure. A geopolymer mix uses supplementary cementitious materials obtained as industrial by-products instead of cement. The input variables used for assessing the best machine learning algorithm not only include individual ingredient quantities, but molarity of the alkali activator and age of testing as well. Myriad statistical parameters used to measure the effectiveness of the models in forecasting the compressive strength of ternary geopolymer concrete mix, it has been found that GBM performs better than all other algorithms. A sensitivity analysis carried out towards the end of the study suggests that GBM model predicts results close to the experimental conditions with an accuracy between 95.6 % to 98.2 % for testing and training datasets.