[논문]인공신경망을 이용한 플라이애시 및 실리카 흄 복합 콘크리트의 압축강도 예측

번위결; 최영지; 왕소용

doi:10.22805/jit.2021.41.1.001

인공신경망을 이용한 플라이애시 및 실리카 흄 복합 콘크리트의 압축강도 예측
Prediction of strength development of fly ash and silica fume ternary composite concrete using artificial neural network 원문보기

産業技術硏究 : 江原大學校産業技術硏究所 = Journal of industrial technology, v.41 no.1, 2021년, pp.1 - 6

번위결 (NingboTech University) , 최영지 (Department of Architectural Engineering, Kangwon National University) , 왕소용 (Department of Integrated Energy and Infra System, Department of Architectural Engineering, Kangwon National University)

Abstract ▼ AI-Helper

Fly ash and silica fume belong to industry by-products that can be used to produce concrete. This study shows the model of a neural network to evaluate the strength development of blended concrete containing fly ash and silica fume. The neural network model has four input parameters, such as fly ash replacement content, silica fume replacement content, water/binder ratio, and ages. Strength is the output variable of neural network. Based on the backpropagation algorithm, the values of elements in the hidden layer of neural network are determined. The number of neurons in the hidden layer is confirmed based on trial calculations. We find (1) neural network can give a reasonable evaluation of the strength development of composite concrete. Neural network can reflect the improvement of strength due to silica fume additions and can consider the reductions of strength as water/binder increases. (2) When the number of neurons in the hidden layer is five, the prediction results show more accuracy than four neurons in the hidden layer. Moreover, five neurons in the hidden layer can reproduce the strength crossover between fly ash concrete and plain concrete. Summarily, the neural network-based model is valuable for design sustainable composite concrete containing silica fume and fly ash.

주제어

표/그림 (7)

표 Table 1 Mixtures and strength of specimens
그림 Fig. 1 Set up of 4 neurons hidden layer neural network
그림 Fig. 2 Analysis results of 4 neurons hidden layer neural network
그림 Fig. 3 Trends of analysis results of 4 neurons hidden layer neural network
그림 Fig. 4 Set up of 5 neurons hidden layer neural network
그림 Fig. 5 Analysis results of 5 neurons hidden layer neural network
그림 Fig. 6 Trends of analysis results of 5 neurons hidden layer neural network

참고문헌 (15)

X.-Y. Wang, 2021, Evaluation of the Phase Assemblage and Strength Progress of Hybrid Blends of Cement and Fly Ash Using Kinetic and Thermodynamic Hydration Model, Ceramics - Silikaty 1-9.
E. Harrison, et al., 2020, Recycling of waste glass as aggregate in cement-based materials, Environmental Science and Ecotechnology 4 100064.

상세보기
X.-Y. Wang, 2021, Evaluation of the properties of cement-calcined Hwangtoh clay-limestone ternary blends using a kinetic hydration model, Construction and Building Materials 303 124596.

상세보기
M.Y. Xuan, et al., 2021, Effect of Cement Types and Superabsorbent Polymers on the Properties of Sustainable Ultra-High-Performance Paste, Materials (Basel) 14(6) 1497-1517.

상세보기
S. Tian, et al., 2021, Global patterns and changes of carbon emissions from land use during 1992-2015, Environmental Science and Ecotechnology 7 100108.

상세보기
X. Zhao, et al., 2021, On the applicability of the 'humic acids' nomenclature from natural ecosystems to engineering sciences, Environmental Science and Ecotechnology 6 100082.

상세보기
Y. Han, et al., 2021, Performance and sustainability of quaternary composite paste comprising limestone, calcined Hwangtoh clay, and granulated blast furnace slag, Journal of Building Engineering 43 102655.

상세보기
M. Abu Yaman, et al., 2017, Predicting the ingredients of self compacting concrete using artificial neural network, Alexandria Engineering Journal 56(4) 523-532.

상세보기
G. Pazouki, et al., 2021, Predicting the compressive strength of self- compacting concrete containing Class F fly ash using metaheuristic radial basis function neural network, Structural Concrete 1-23.
K.T. Nguyen, et al., 2020, Analyzing the compressive strength of green fly ash based geopolymer concrete using experiment and machine learning approaches, Construction and Building Materials 247 118581.

상세보기
A. Behnood, E.M. Golafshani, 2018, Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves, Journal of Cleaner Production 202 54-64.

상세보기
M. Serraye, et al., 2021, Prediction of Compressive Strength of Self-Compacting Concrete (SCC) with Silica Fume Using Neural Networks Models, Civil Engineering Journal 7(1) 118-139.
A.J. Paulson, et al., 2019, APPLICATION OF NEURAL NETWORK FOR PREDICTION OF COMPRESSIVE STRENGTH OF SILICA FUME CONCRETE, International Journal of Civil Engineering and Technology 10 1859-1867.
J. Abellan-Garcia, 2021, Artificial Neural Network Model for Strength Prediction of Ultra-High-Performance Concrete, ACI Materials Journal 118(4) 1-12.
L. Lam, et al., 1998, EFFECT OF FLY ASH AND SILICA FUME ON COMPRESSIVE AND FRACTURE BEHAVIORS OF CONCRETE, Cement and Concrete Research 28(2) 271-283.

상세보기

내보내기 구분	파일저장 인쇄 메일전송
구성항목	기본정보 상세정보 관리번호, 논문명, 저널/프로시딩명, 저자 , 발행년, 권, 호, 시작페이지, 끝페이지, 발행기관 관리번호, 논문명, 대등논문명, 저자 , 저널/프로시딩명, 발행기관, 발행년, 발행언어, 권, 호, 시작페이지, 끝페이지, ISBN, ISSN, 주제분야, 키워드, 초록(한글), 초록(영문), 저자(소속기관)
저장형식	Text(ASCII format) Excel format RefWorks Direct Export RIS format (for Reference Manager, ProCite, EndNote), Scholar's Aids, Mendeley
메일정보	받는사람 (필수) @ 보내는사람 (선택) @ 제목 내용 KISTI 검색결과 이메일 서비스
안내	총 건의 자료가 검색되었습니다. 다운받으실 자료의 인덱스를 입력하세요. (1-10,000) 검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다. 데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요) 다운로드 파일은 UTF-8 형태로 저장됩니다. 파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오. ~ Text(ASCII format) Excel format

연합인증