• Journal of the Korea Institute of Building Construction
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• v.22 no.6
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• pp.619-630
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• 2022

The concrete mix design and compressive strength evaluation are used as basic data for the durability of sustainable structures. However, the recent diversification of mixing factors has created difficulties in calculating the correct mixing factor or setting the reference value concrete mixing design. The purpose of this study is to design a predictive model of bidirectional analysis that calculates the mixing elements of ternary concrete using deep learning, one of the artificial intelligence techniques. For the DNN-based predictive model for calculating the concrete mixing factor, performance evaluation and comparison were performed using a total of 8 models with the number of layers and the number of hidden neurons as variables. The combination calculation result was output. As a result of the model's performance evaluation, an average error rate of about 1.423% for the concrete compressive strength factor was achieved. and an average MAPE error of 8.22% for the prediction of the ternary concrete mixing factor was satisfied. Through comparing the performance evaluation for each structure of the DNN model, the DNN5L-2048 model showed the highest performance for all compounding factors. Using the learned DNN model, the prediction of the ternary concrete formulation table with the required compressive strength of 30 and 50 MPa was carried out. The verification process through the expansion of the data set for learning and a comparison between the actual concrete mix table and the DNN model output concrete mix table is necessary.

• Journal of the Korea Concrete Institute
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• v.28 no.4
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• pp.427-434
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• 2016

The purpose of this study is to propose a mixture proportioning approach based on the replacement level of natural sand for reducing $CO_2$ emissions from artificial lightweight aggregate concrete(LWAC) production. To assess the effect of natural sand on the reduction of $CO_2$ emissions and compressive strength of LWAC, a total of 379 specimens compiled from different sources were analyzed. Based on the non-linear regression analysis using the database and the previous mixture proportioning method proposed by Yang et al., simple equations were derived to determine the concrete mixture proportioning and the replacement level of natural sand for achieving the targeted performances(compressive strength, initial slump, air content, and $CO_2$ reduction ratio) of concrete. Furthermore, the proposed equations are practically applicable to straightforward determination of the $CO_2$ emissions from the provided mixture proportions of LWAC.

• Journal of the Korea Concrete Institute
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• v.27 no.4
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• pp.427-434
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• 2015

The objective of this study is to establish an rational mixture-proportioning procedure for low-$CO_2$ concrete using supplementary cementitious materials (SCMs) achieving the targeted $CO_2$ reduction ratio as well as the conventional requirements such as initial slump, air content, and 28-day compressive strength of concrete. To evaluate the effect of SCM level on the $CO_2$ emission and compressive strength of concrete, a total of 12537 data sets were compiled from the available literature and ready-mixed concrete plants. The amount of $CO_2$ emission of concrete was assessed under the system boundary from cradle to concrete production stage at a ready-mixed concrete plant. Based on regression analysis using the established database, simple equations were proposed to determine the mixture proportions of concrete such as the type and level of SCMs, water-to-binder ratio, and fine aggregate-to-total aggregate ratio. Furthermore, the $CO_2$ emissions for a given concrete mixture can be straightforwardly calculated using the proposed equations. Overall, the developed mixture-proportioning procedure is practically useful for determining the initial mixture proportions of low-$CO_2$ concrete in the ready-mixed concrete field.

• Journal of the Korea Institute of Building Construction
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• v.13 no.5
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• pp.491-497
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• 2013

The objective of the present study is to establish a straightforward mixture proportioning procedure for structural lightweight aggregate concrete (LWAC), and evaluate the selection range of the targeted dry density of concrete against the designed concrete compressive strength. In developing this procedure, mathematical models were formulated based on a nonlinear regression analysis over 347 data sets and two boundary conditions of the absolute volume and dry density of concrete. The proposed procedure demonstrated the appropriate water-to-cement ratio and dry density of concrete to achieve the designed strength decrease with the increase in volumetric ratio of coarse aggregates. This trend was more significant in all-LWAC than in sand-LWAC. Overall, the selection range of the dry density of LWAC exists within a certain range according to the designed strength, which can be obtained using the proposed procedure.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.05a
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• pp.223-224
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• 2021

Recently, the demand for high fluidity concrete has been increased due to skyscrapers. However, it has its own limits. First of all, high fluidity concrete has large variation and through trial & error it costs lots of money and time. Neural network model has repetitive learning process which can solve the problem while training the data. Therefore, the purpose of this study is to predict optimum mix design of 40MPa, 60MPa high fluidity concrete by using neural network model and verifying compressive strength by applying real data. As a result, comparing collective data and predicted compressive strength data using MATLAB, 40MPa mix design error rate was 1.2%~1.6% and 60MPa mix design error rate was 2%~3%. Overall 40MPa mix design error rate was less than 60MPa mix design error rate.

• Journal of Ocean Engineering and Technology
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• v.18 no.1
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• pp.53-62
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• 2004

The Purpose of this study is to recycle the waste concrete, which is generated in huge quantities, from construction works. in order to achieve this goal, it is important to determine the compressive strength, workability, slump, and ultrasonic velocity of recycled aggregate concrete. Thus, several experiment parameters are considered, such as water-cement ratios, sand percentage, and fine aggregate composition ratios, in order to apply the recycled aggregate concrete to pre-cast artificial fishing reefs. From the results, it has been shown that the proper mix designs for reef concrete are W/C=45%, S/a=50%, SR50:SN50 in recycled sand and natural sand mix combination case, W/C=45%, S/a=50%, SC50:SN50 in crushed sand and natural sand mix combination case, W/C=45%, S/a=50%, SR50:SC50 in recycled sand and crushd sand mix combination case. Also, this study shows that the shape and surface roughness of fine aggregate particles have an effect on the strength, slump, ultrasonic velocity of tested concrete, and the compressive strength ratios of 7days' and 90days' curing ages of recycled aggregate concrete are about 70% and 110% of 28days' curing age.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2012.11a
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• pp.253-254
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• 2012

The purpose of this study is to suggest and verify high-strength concrete mix design model applying neural network theory, in order to minimize effort and time wasted by using trial and error method utill now. There are 7 input and 2 output to predict mix design. 40 data of mix design were learned with back-propagation algorithm. Then they are repeatedly learned back-propagation in neural network theory. Also, to verify predicted model, we analyzed and compared value predicted from 60MPa mix design with value measured by actual compressive strength test.

• Journal of Biosystems Engineering
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• v.9 no.1
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• pp.53-61
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• 1984

배합사료공장(配合飼料工場)의 생산규모(生産規模) 및 생산사료종류별(生産飼料種類別) 소요(所要) 투자(投資) 자본(資本)을 추정(推定)할 수 있는 모델을 개발(開發)하였다. 켄사스 주립대학교(Park, 1982)에서 개발(開發)된 배합사료공장(配合飼料工場)의 설계(設計) 모델을 이용(利用)하여 기계(機械) 및 비품(備品)값, 설치비용(設置費用), 건축비용(建築費用), 전기기구(電氣器具) 및 전기시설비(電氣施設費) 등을 관련업체의 도움을 얻어 분석(分析)하였으며, 모델에 적용(適用)된 공장(工場)의 규모(規模)는 10 ton/hr에서 40 ton/hr이고, 공장대상(工場對象)은 양돈(養豚) 및 양계용(養鷄用) 사료공장(飼料工場)(1일 8시간 작업(作業), 1일 16시간 작업(作業)과 유우용(乳牛用) 펠릿 사료공장(飼料工場)(1일 8시간 작업(作業))이었다.

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.6
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• pp.35-43
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• 2010

This study was carried out to find reliable relations between various concrete strength properties which are used as input data in concrete pavement design program. Concretes were made from different sources of coarse grained(granite, limestone and sandstone) and fine grained aggregates such as natural sand, washed sand and crushed sand. From strength test results, model equations were obtained based on the relation between strengths. For each coarse grained aggregate, models for compression-flexural strengths, compression-split tensile strengths, compressive strength-modulus and flexural-split tensile strengths with age were obtained. For concrete mixed with gneiss granite aggregates, concrete strengths were obtained from numerical mean values of concrete strengths mixed with fine grained aggregates. In addition models for concrete split tensile strengths and modulus values were provide by averaging numerically the estimated values obtained from the derived relationship and the experimental values. This is due to more scattered values of split tensile strengths and modulus values than other strength properties. Finally criteria for drying shrinkage strain as well as Poisson's ratio for concrete used in pavement were presented for all mixes with differed coarse grained aggregates.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.3
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• pp.26-38
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• 2020

Mixture design of self-compacting concrete is a typical multi-criteria decision making problem and conventional mixture designs are based on the low level engineering method like trials and errors through iteration method to satisfy the various requirements. This study concerns with performing the straightforward multiobjective design optimization of economic SCC mixture considering relative importances of the various requirements and cost-effectives of SCC. Total five requirements of 28day compressive strength, filling ability, segregation stability, material cost and mass were taken into consideration to prepare the objective function to be formulated in form of the weighted-multiobjective mixture design optimization problem. Economic SCC mixture computational design can be given in a rational way which considering material costs and the relative importances of the requiremets and from the result of this study it is expected that the development of SCC mixtue computational design and the consequent univeral concrete material design optimization methodology can be advanced.