پیوندهای مفید
آمار
| تعداد نشریات | 7 |
| تعداد شماره‌ها | 399 |
| تعداد مقالات | 5,389 |
| تعداد مشاهده مقاله | 5,288,168 |
| تعداد دریافت فایل اصل مقاله | 4,882,896 |
Developing a Novel Machine Learning Method to Predict the Compressive Strength of Fly Ash Concrete in Different Ages | ||
| AUT Journal of Civil Engineering | ||
| مقاله 3، دوره 4، شماره 4، اسفند 2020، صفحه 423-436 اصل مقاله (1004.97 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/ajce.2019.16124.5569 | ||
| نویسندگان | ||
| Hamed Naseri1؛ Hamid Jahanbakhsh2؛ Fereidoon Moghadas Nejad* 1؛ amir golroo3 | ||
| 1Amirkabir University of Technology | ||
| 2Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran | ||
| 3Civil Engineering Department, Amir Kabir University of Technology | ||
| چکیده | ||
| Estimating the compressive strength of concrete before fabricating, has been one of the most important challenges because designing a mixture proportion by experimental methods needs expert workers, consumes energy, and wastes materials. Therefore, in this study, the influences of materials and the age of samples on the compressive strength of fly ash concrete are investigated, and a novel method for predicting the compressive strength is presented. To this end, the water cycle algorithm and genetic algorithm are utilized, and their outcomes are compared with the classical regression models. Various performance indicators are used to gauge the accuracy of the models. By analyzing the results, it is comprehended that the water cycle algorithm is the most accurate model according to all performance indicators. Besides, the outcomes of the water cycle algorithm and genetic algorithm are by far better than those of classical methods. The mean absolute error of water cycle algorithm, genetic algorithm, linear regression, partial-fractional regression, and fractional regression are 3.01, 3.12, 5.47, 9.70, and 5.37 MPa for training data and 2.90, 3.44, 5.47, 9.70, and 5.37 MPa for testing data respectively. Furthermore, the water cycle algorithm is the only algorithm whose mean absolute error of testing data is less than that of training data. At last, it was concluded that the mixture with less than 35% fly ash (weight of the binder) had maximum amounts of compressive strength. Also, the compressive strength of concrete decreased significantly as the amount of fly ash increased more than this definite level. | ||
| کلیدواژه‌ها | ||
| Water cycle algorithm؛ Genetic algorithm؛ Regression؛ Fly ash concrete؛ Compressive strength | ||
| مراجع | ||
|
[1] A.M.J.J.O.C.P. Rashad, An exploratory study on high-volume fly ash concrete incorporating silica fume subjected to thermal loads, 87 (2015) 735-744.
[2] R. Feiz, J. Ammenberg, L. Baas, M. Eklund, A. Helgstrand, R.J.J.O.C.P. Marshall, Improving the CO2 performance of cement, part I: utilizing life-cycle assessment and key performance indicators to assess development within the cement industry, 98 (2015) 272-281.
[3] S. Ghavami, B. Farahani, H. Jahanbakhsh, F.J.A.J.O.C.E. Moghadas Nejad, Effects of Silica Fume and Nano-silica on the Engineering Properties of Kaolinite Clay, 2(2) (2018) 135-142.
[4] F.M. Nejad, M. Habibi, P. Hosseini, H.J.J.o.c.p. Jahanbakhsh, Investigating the mechanical and fatigue properties of sustainable cement emulsified asphalt mortar, 156 (2017) 717-728.
[5] B. Lothenbach, K. Scrivener, R.J.C. Hooton, c. research, Supplementary cementitious materials, 41(12) (2011) 1244-1256.
[6] M.H. Shehata, M.D.J.C. Thomas, C. Research, The effect of fly ash composition on the expansion of concrete due to alkali-silica reaction, 30(7) (2000) 1063-1072.
[7] R.J.C. Siddique, C. Research, Performance characteristics of high-volume Class F fly ash concrete, 34(3) (2004) 487-493.
[8] A. Behnood, V. Behnood, M.M. Gharehveran, K.E.J.C. Alyamac, B. Materials, Prediction of the compressive strength of normal and high-performance concretes using M5P model tree algorithm, 142 (2017) 199-207.
[9] F. Deng, Y. He, S. Zhou, Y. Yu, H. Cheng, X.J.C. Wu, B. Materials, Compressive strength prediction of recycled concrete based on deep learning, 175 (2018) 562-569.
[10] A. Behnood, E.M.J.J.o.c.p. Golafshani, Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves, 202 (2018) 54-64.
[11] B. Vakhshouri, S.J.N. Nejadi, Prediction of compressive strength of self-compacting concrete by ANFIS models, 280 (2018) 13-22.
[12] Z.M. Yaseen, R.C. Deo, A. Hilal, A.M. Abd, L.C. Bueno, S. Salcedo-Sanz, M.L.J.A.i.E.S. Nehdi, Predicting compressive strength of lightweight foamed concrete using extreme learning machine model, 115 (2018) 112-125.
[13] H.J.I.J.O.I. Naseri, Management, Technology, Cost Optimization of No-Slump Concrete Using Genetic Algorithm and Particle Swarm Optimization, 10(1) (2019).
[14] J.-S. Chou, C.-F.J.A.i.C. Tsai, Concrete compressive strength analysis using a combined classification and regression technique, 24 (2012) 52-60.
[15] D.-K. Bui, T. Nguyen, J.-S. Chou, H. Nguyen-Xuan, T.D.J.C. Ngo, B. Materials, A modified firefly algorithm-artificial neural network expert system for predicting compressive and tensile strength of high-performance concrete, 180 (2018) 320-333.
[16] H. Naderpour, A.H. Rafiean, P.J.J.O.B.E. Fakharian, Compressive strength prediction of environmentally friendly concrete using artificial neural networks, 16 (2018) 213-219.
[17] T. Kim, J.M. Davis, M.T. Ley, S. Kang, P.J.C. Amrollahi, B. Materials, Fly ash particle characterization for predicting concrete compressive strength, 165 (2018) 560-571.
[18] N. Rajamane, J.A. Peter, P.J.C. Ambily, c. composites, Prediction of compressive strength of concrete with fly ash as sand replacement material, 29(3) (2007) 218-223.
[19] I.B. Topcu, M.J.C.M.S. Sarıdemir, Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic, 41(3) (2008) 305-311.
[20] M.M. Khotbehsara, B.M. Miyandehi, F. Naseri, T. Ozbakkaloglu, F. Jafari, E.J.C. Mohseni, B. Materials, Effect of SnO2, ZrO2, and CaCO3 nanoparticles on water transport and durability properties of self-compacting mortar containing fly ash: Experimental observations and ANFIS predictions, 158 (2018) 823-834.
[21] I.-C.J.C. Yeh, C. research, Modeling of strength of high-performance concrete using artificial neural networks, 28(12) (1998) 1797-1808.
[22] I.-C.J.J.C.I.C.H.E. Yeh, Prediction of strength of fly ash and slag concrete by the use of artificial neural networks, 15(4) (2003) 659-663.
[23] J. Sobhani, M. Najimi, A.R. Pourkhorshidi, T.J.C. Parhizkar, B. Materials, Prediction of the compressive strength of no-slump concrete: A comparative study of regression, neural network and ANFIS models, 24(5) (2010) 709-718.
[24] J.R. Sampson, Adaptation in natural and artificial systems (John H. Holland), in, Society for Industrial and Applied Mathematics, 1976.
[25] H. Naseri, M.A.E.J.I.J.o.I. Ghasbeh, Management, Technology, Time-Cost Trade off to Compensate Delay of Project Using Genetic Algorithm and Linear Programming, 9(6) (2018).
[26] D. Neeraja, T. Kamireddy, P. Santosh Kumar, V. Simha Reddy, Weight optimization of plane truss using genetic algorithm, in: Materials Science and Engineering Conference Series, 2017, pp. 032015.
[27] Z. Aydın, Y.J.K.J.O.C.E. Ayvaz, Overall cost optimization of prestressed concrete bridge using genetic algorithm, 17(4) (2013) 769-776.
[28] H. Eskandar, A. Sadollah, A. Bahreininejad, M.J.C. Hamdi, Structures, Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems, 110 (2012) 151-166.
[29] A. Sadollah, H. Eskandar, A. Bahreininejad, J.H.J.S.C. Kim, Water cycle algorithm for solving multi-objective optimization problems, 19(9) (2015) 2587-2603.
[30] A. Sadollah, H. Eskandar, J.H.J.A.S.C. Kim, Water cycle algorithm for solving constrained multi-objective optimization problems, 27 (2015) 279-298.
[31] E.M. Golafshani, A.J.C. Behnood, C. Composites, Estimating the optimal mix design of silica fume concrete using biogeography-based programming, 96 (2019) 95-105.
[32] E.A. Ramalho, J.J. Ramalho, P.D.J.J.o.P.A. Henriques, Fractional regression models for second stage DEA efficiency analyses, 34(3) (2010) 239-255.
[33] E.A. Ramalho, J.J. Ramalho, J.M.J.J.o.E.S. Murteira, Alternative estimating and testing empirical strategies for fractional regression models, 25(1) (2011) 19-68.
[34] M. Mokhtari, s. Abedian, S.A. Almasi, Rockfall Susceptibility Mapping Using Artificial Neural Network, Frequency Ratio, and Logistic Regression: A Case Study in Central Iran, Taft County %J AUT Journal of Civil Engineering, (2019) -.
[35] A. Committee, I.O.f. Standardization, Building code requirements for structural concrete (ACI 318-08) and commentary, in, American Concrete Institute, 2008.
[36] M. Mirzahosseini, P. Jiao, K. Barri, K.A. Riding, A.H.J.E.C. Alavi, New machine learning prediction models for compressive strength of concrete modified with glass cullet, 36(3) (2019) 876-898. | ||
|
آمار تعداد مشاهده مقاله: 1,621 تعداد دریافت فایل اصل مقاله: 972 |
||