최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기한국전산구조공학회논문집 = Journal of the computational structural engineering institute of Korea, v.36 no.1, 2023년, pp.9 - 18
곽윤지 (국립 한밭대학교 건설환경공학과) , 고채연 (국립 한밭대학교 건설환경공학과) , 곽신영 (국립 한밭대학교 건설환경공학과) , 임승현 (경북대학교 융복합시스템공학과 플랜트시스템전공)
Predicting the compressive strength of high-performance concrete (HPC) is challenging because of the use of additional cementitious materials; thus, the development of improved predictive models is essential. The purpose of this study was to develop an HPC compressive-strength prediction model using...
Ahmad, S., Alghamdi, S.A. (2014) A Statistical approach to?Optimizing Concrete Mixture Design, The Scientific World?J., 2014.
Alpaydin, E. (2020) Introduction to Machine Learning, MIT?Press, p.683.
Apostolopoulou, M., Armaghani, D.J., Bakolas, A., Douvika, M.?G., Moropoulou, A., Asteris, P.G. (2019) Compressive?Strength of Natural Hydraulic Lime Mortars using Soft?Computing Techniques, Procedia Struct. Integr., 17, pp.914~923.
Asteris, P.G., Kolovos, K.G., Douvika, M.G., Roinos, K. (2016)?Prediction of Self-Compacting Concrete Strength using?Artificial Neural Networks, Eur. J. Environ. & Civil Eng.,?20(sup1), pp.s102~s122.
Asteris, P.G., Mokos, V.G. (2020) Concrete Compressive?Strength using Artificial Neural Networks, Neural Comput. &?Appl., 32(15), pp.11807~11826.
Asteris, P.G., Skentou, A.D., Bardhan, A., Samui, P., Pilakoutas,?K. (2021) Predicting Concrete Compressive Strength using?Hybrid Ensembling of Surrogate Machine Learning Models,?Cement & Concr. Res., 145, 106449.
Atici, U. (2011) Prediction of the Strength of Mineral Admixture?Concrete using Multivariable Regression Analysis and an?Artificial Neural Network, Expert Syst. with Appl., 38(8),?pp.9609~9618.
Breiman, L. (1996) Bagging Predictors, Mach. Learn., 24(2),?pp.123~140.
Burges, C.J. (1998) A Tutorial on Support Vector Machines for?Pattern Recognition, Data Min. & Knowl. Discov., 2(2), pp.?121~167.
Cheng, M.Y., Chou, J.S., Roy, A.F., Wu, Y.W. (2012) High-Performance Concrete Compressive Strength Prediction using?Time-Weighted Evolutionary Fuzzy Support Vector Machines?Inference Model, Automat. Constr., 28, pp.106~115.
Chou, J.S., Chiu, C.K ., Farfoura, M., Al-Taharwa, I. (2011)?Optimizing the Prediction Accuracy of Concrete Compressive?Strength based on a Comparison of Data-Mining Techniques,?J. Comput. Civil Eng., 25(3), pp.242~253.
Gartner, E. (2004) Industrially Interesting Approaches to "low-CO2" Cements, Cem. & Concr. Res., 34(9), pp.1489~1498.
Jerath, S. (1983) Computer-aided Concrete Mix Proportioning, J.?Proc., 80(4), pp.312~317.
Juenger, M.C.G., Winnefeld, F., Provis, J.L., Ideker, J.H. (2011)?Advances in Alternative Cementitious Binders, Cem. &?Concr. Res., 41(12), pp.1232~1243.
Kasperkiewicz, J., Racz, J., Dubrawski, A. (1995) HPC Strength?Prediction using Artificial Neural Network, J. Comput. Civil?Eng., 9(4), pp.279~284.
Kwag, S., Gupta, A., Dinh, N. (2018) Probabilistic Risk?Assessment based Model Validation Method using Bayesian?Network, Reliab. Eng. & Syst. Safety, 169, pp.380~393.
Lam, L., Wong, Y.L., Poon, C.S. (1998) Effect of Fly Ash and?Silica Fume on Compressive and Fracture behaviors of?Concrete, Cem. & Concr. Res., 28(2), pp.271~283.
McClelland, J.L., Rumelhart, D.E., Hinton, G.E. (1986) The?Appeal of Parallel Distributed Processing, MIT Press,?Cambridge MA, 3, 44.
Neshat, M., Adeli, A., Sepidnam, G., Sargolzaei, M. (2012)?Predication of Concrete Mix Design using Adaptive Neural?Fuzzy Inference Systems and Fuzzy Inference Systems, Int. J.
Adv. Manuf. Technol., 63(1), pp.373~390.?Ozbay, E., Gesoglu, M., Guneyisi, E. (2011) Transport Properties?Based Multi-Objective Mix Proportioning Optimization of?High Performance Concretes, Mater. & Struct., 44(1), pp.?139~154.
Oztas, A., Pala, M., Ozbay, E., Kanca, E., Caglar, N., Bhatti, M.?A. (2006) Predicting the Compressive Strength and Slump of?High Strength Concrete using Neural Network, Constr. &?Build. Mater., 20(9), pp.769~775.
Rasmussen, C.E. (2003) Summer School on Machine Learning,?Gaussian Processes in Machine Learning, Springer, Berlin,?Heidelberg. pp.63~71.
Rutkowska, G., Wichowski, P., Franus, M., Mendryk, M.,?Fronczyk, J. (2020) Modification of Ordinary Concrete using?Fly Ash from Combustion of Municipal Sewage Sludge,?Mater., 13(2), 487.
Sun, L., Koopialipoor, M., Jahed Armaghani, D., Tarinejad, R.,?Tahir, M.M. (2021) Applying a Meta-Heuristic Algorithm to?Predict and Optimize Compressive Strength of Concrete?Samples, Eng. Comput., 37(2), pp.1133~1145.
Syarif, I., Zaluska, E., Prugel-Bennett, A., Wills, G. (2012)?Application of Bagging, Boosting and Stacking to Intrusion?Detection, In International Workshop on Machine Learning?and Data Mining in Pattern Recognition, Springer, Berlin,?Heidelberg, pp.593~602.
Yeh, I.C. (1998) Modeling of Strength of High-Performance?Concrete using Artificial Neural Networks, Cem. & Concr.?Res., 28(12), pp.1797~1808.
Yilmaz, I., Erik, N.Y., Kaynar, O. (2010) Different Types of?Learning Algorithms of Artificial Neural Network (ANN)?Models for Prediction of Gross Calorific Value (GCV) of?Coals, Sci. Res. & Essays, 5(16), pp.2242~2249.
Zain, F.M., Abd, M.S. (2009) Multiple Regression Model for?Compressive Strength Prediction of High Performance?Concrete, J. Appl. Sci., 9(1), pp.155~160.
Zhang, J., Huang, Y., Wang, Y., Ma, G. (2020) Multi-Objective?Optimization of Concrete Mixture Proportions using Machine?Learning and Metaheuristic Algorithms, Constr. & Build.?Mater., 253, 119208.
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
오픈액세스 학술지에 출판된 논문
※ AI-Helper는 부적절한 답변을 할 수 있습니다.