Prediction of mechanical and fresh properties of self-consolidating concrete (SCC) using multi-objective genetic algorithm (MOGA)
محورهای موضوعی : Structural Engineering
Reza Jelokhani Niaraki
1
(Department of Civil Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran)
Reza Farokhzad
2
(Assistance Professor, Department of Civil Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran)
کلید واژه: Genetic Algorithm, Strength, Neural Networks, self-compacting concrete, Slump,
چکیده مقاله :
Compressive strength and concrete slump are the most important required parameters for design, depending on many factors such as concrete mix design, concrete material, experimental cases, tester skills, experimental errors etc. Since many of these factors are unknown, and no specific and relatively accurate formulation can be found for strength and slump, therefore, the concrete properties can be improved to an acceptable level using the neural networks and genetic algorithm. In this research, having results of experimental specimens including soil classification parameters, water to cement ratio, cement content, super-lubricant content, compressive strength, and slump flow, using the MATLAB software, the perceptron neural network training, general regression neural network, and radial base function neural network are considered, and then, with regard to coefficient of determination (R2) criteria and mean absolute error, the above network
[1] Sonebi, M., Medium strength self-compacting concrete containing fly ash: Modelling using factorial experimental plans. Cement and Concrete research, 2004. 34(7): p. 1199-1208.
[2] Farokhzad, R., Yaseri, S., Entezarian, M. H., & Yavari, A. , Investigating Effects of Sulfates on Compressive Strength of Different Types of Pozzolan Concrete and Measuring Penetration Rate by Ultrasound Tests at Different Ages. 2016.
[3] Siddique, R. and G. Kaur, Strength and permeation properties of self-compacting concrete containing fly ash and hooked steel fibres. Construction and Building Materials, 2016. 103: p. 15-22.
[4] Topçu, İ.B., A.R. Boğa, and F.O. Hocaoğlu, Modeling corrosion currents of reinforced concrete using ANN. Automation in Construction, 2009. 18 (2): p. 145-152.
[5] Orejarena, L. and M. Fall, The use of artificial neural networks to predict the effect of sulphate attack on the strength of cemented paste backfill. Bulletin of engineering geology and the environment, 2010. 69 (4): p. 659-670.
[6] Sarıdemir, M., Predicting the compressive strength of mortars containing metakaolin by artificial neural networks and fuzzy logic. Advances in Engineering Software, 2009. 40 (9): p. 920-927.
[7] Mashhadban, H., S.S. Kutanaei, and M.A. Sayarinejad, Prediction and modeling of mechanical properties in fiber reinforced self-compacting concrete using particle swarm optimization algorithm and artificial neural network. Construction and Building Materials, 2016. 119: p. 277-287.
[8] Chithra, S., Kumar, S. S., Chinnaraju, K., & Ashmita, F. A., A comparative study on the compressive strength prediction models for High Performance Concrete containing nano silica and copper slag using regression analysis and Artificial Neural Networks. Construction and Building Materials, 2016. 114: p. 528-535.
[9] Yeh, I.-C., Modeling of strength of high-performance concrete using artificial neural networks. Cement and Concrete research, 1998. 28 (12): p. 1797-1808.
[10] Bagheri, A., Farrokhi, F., Mahdikhani, M., Farokhzad, R., & Baghdadi, J., REPRESENTING APPROPRIATE AGGREGATES GRADING ZONE FOR SELF-CONSOLIDATING CONCRETE BY USING SOIL CLASSIFYING PARAMETERS.
[11] Lai, S. and M. Serra, Concrete strength prediction by means of neural network. Construction and Building Materials, 1997. 11 (2): p. 93-98.
[12] Yeh, I.-C., Modeling concrete strength with augment-neuron networks. Journal of Materials in Civil Engineering, 1998. 10 (4): p. 263-268.
[13] Yeh, I.-C., Design of high-performance concrete mixture using neural networks and nonlinear programming. Journal of Computing in Civil Engineering, 1999. 13 (1): p. 36-42.
[14] Sebastia, M., I.F. Olmo, and A. Irabien, Neural network prediction of unconfined compressive strength of coal fly ash–cement mixtures. Cement and Concrete Research, 2003. 33 (8): p. 1137-1146.
[15] Kim, J. I., Kim, D. K., Feng, M. Q., & Yazdani, F., Application of neural networks for estimation of concrete strength. Journal of Materials in Civil Engineering, 2004. 16 (3): p. 257-264.
[16] Dias, W. and S. Pooliyadda, Neural networks for predicting properties of concretes with admixtures. Construction and Building Materials, 2001. 15 (7): p. 371-379.
[17] Ni, H.-G. and J.-Z. Wang, Prediction of compressive strength of concrete by neural networks. Cement and Concrete Research, 2000. 30 (8): p. 1245-1250.
[18] Ren, L. and Z. Zhao, An optimal neural network and concrete strength modeling. Advances in Engineering Software, 2002. 33 (3): p. 117-130.
[19] Lee, S.-C., Prediction of concrete strength using artificial neural networks. Engineering Structures, 2003. 25 (7): p. 849-857.
[20] Holland, J., A d aptation in nat u ral an d artifi cial s y s tem s. U niversity of Michigan Press. Ann Arbor, 1975. 1 (975): p. 1.
[21] Yeh, I.-C. and L.-C. Lien, Knowledge discovery of concrete material using genetic operation trees. Expert Systems with Applications, 2009. 36 (3): p. 5807-5812.
[22] Farokhzad, R., Mohebi, B., Amiri, G.G. and Ashtiany, M.G., Detecting structural damage in Timoshenko beams based on optimization via simulation (OVS). Journal of Vibroengineering, 2016. 18 (8).
[23] Farokhzad, R., Ghodrati Amiri, G., Mohebi, B., & Ghafory-Ashtiany, M., Multi-Damage Detection for Steel Beam Structure. Journal of Rehabilitation in Civil Engineering, 2016. 4 (2): p. 25-44.
[24] Eskandari-Naddaf, H. and R. Kazemi, ANN prediction of cement mortar compressive strength, influence of cement strength class. Construction and Building Materials, 2017. 138: p. 1-11.
[25] Farokhzad, R., Mahdikhani, M., Bagheri, A. and Baghdadi, J., Representing a logical grading zone for self-consolidating concrete. Construction and Building Materials, 2016. 115: p. 735-745.
