• 베이커리
• /
• no.6 s.371
• /
• pp.140-143
• /
• 1999

• 베이커리
• /
• no.11 s.400
• /
• pp.187-192
• /
• 2001

• 베이커리
• /
• no.12 s.365
• /
• pp.145-150
• /
• 1998

• 베이커리
• /
• no.9 s.386
• /
• pp.145-152
• /
• 2000

• 베이커리
• /
• no.7 s.384
• /
• pp.146-149
• /
• 2000

• Proceedings of the Korea Concrete Institute Conference
• /
• 1994.04a
• /
• pp.215-220
• /
• 1994

본 연구의 목적은 당사 콘크리트 배합설계 프로그램인 쌍용배합설계시스템(Ssangyong Mix Proportioning Design System ; 이하 SMPD라 칭한다)을 기본으로해서 슬래그시멘트에 대한 콘크리트 배합설계(안)을 제안함으로써 콘크리트 현장에서 합리적으로 콘크리트를 제조할 수 있도록 하는데 있다. 연구 내용은 슬래그시멘트와 보통시멘트간의 콘크리트 물성차이를 실험실적으로 규명하기 위해서 슬래그 함유량 및 양생 온도별로 슬래그시멘트의 콘크리트 강도발현특성, 물시멘트비, 단위수량변화 및 응결특성 등을 검토하였으며 그 결과를 이용하여 슬래그시멘트의 콘크리트 배합설계를 시행, 표준배합과 현장배합표를 제시하였다.

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

• 베이커리
• /
• no.10 s.375
• /
• pp.110-111
• /
• 1999

잡지나 서적을 참고하여 제품만들기를 시도했던 사람이라면 누구나 한 번쯤 배합표에 적혀있는 재료나 구입처를 잘 몰라서 포기했던 경우가 종종 있다. 본지는 '재료색인' 지면을 마련하여 그 달에 소개되었단 재료 중 생소하거나 특이한 재료들에 대한 간단한 설명과 구입처 안내를 통해 독자들의 편의를 돕고있다.

• 베이커리
• /
• no.7 s.360
• /
• pp.160-160
• /
• 1998

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