Prediction of Bending Strength of Self-Leveling Glass Fiber Reinforced Concrete
AbstractMany studies have been conducted on the prediction of fiber reinforced concrete strength; however, there are very rare data concerning the prediction of bending strength values of self-leveling glass fiber reinforced concrete. And there is no study for prediction of bending strength of self-leveling glass fiber reinforced concrete from mixture ingredients and slump values. In the present study, relationships between the bending strength and the mixture proportions are explored. An artificial neural network model (ANN) is designed with an extensive experimental data including 395 four-point bending tests, and input parameters as white cement amount, maximum aggregate size, glass fiber content, water cement ratio, superplasticizer and metakaolin content and slump test results. Effect of each parameter on the bending strength is investigated with the developed model. An empirical and user-friendly formula was obtained with the generalization capabilities of the ANN. Results showed that the prediction results are in good agreement with the field data. And these numerical results with high efficiency can make it possible to use the neural design for real-life self-leveling glass fiber reinforced concrete applications.
F. Almeida Filho, Hardened properties of self-compacting concrete—a statistical approach, Constr. Build. Mater. 24 (9) (2010), 1608–1615, doi: 10.1016/j.conbuildmat.2010.02.0.32
E.P. Koehler, Aggregates in Self-Consolidating Concrete, ProQuest, 2007.
G.S. Rampradheep, M. Sivaraja, Experimental investigation on self-compacting self-curing concrete incorporated with the light weight aggregates (no. spe2, pp. 1-spe11), Braz. Arch. Biol. Technol. 59 (2016), doi:10.1590/1678-4324-2016161075.
N. K. Murthy, A.V. N. Rao, I.V. R. Reddy, M. Reddy, V. Sekhar, Mix Design procedure for self-compacting concrete, IOSR J. Eng. 2 (9) (2012), 33–41.
H.A.F. Dehwah, Mechanical properties of self-compacting concrete incorporating quarry dust powder, silica fume or fly ash, Constr. Build. Mater. 26, (2012), 547–551.
Z. L. Wang, Y. S. Liu, R.F. Shen, Stress-Strain Relationship of Steel Fiber-Reinforced Concrete under Dynamic Compression, Constr. Build. Mater. J.22, (2008), 811–819, doi: 10.1016/j.conbuildmat.2007.01.005.
P. Pujadas, A. Blanco, S. Cavalaro, A. Aguado, Plastic fibres as the only reinforcement for flat suspended slabs: experimental investigation and numerical simulation, Constr. Build. Mater. 57, (2014), 92–104, doi: 10.1016/j.conbuildmat.2014.01.082.
G. Tiberti, F. Minelli, G. Plizzari, Reinforcement optimization of fiber reinforced concrete linings for conventional tunnels, Compos. Part B Eng. 58, (2014), 199–207, doi: 10.1016/j.compositesb.2013.10.012.
B. K. Rao, V. R. Ravindra, Steel fiber reinforced self-compacting concrete incorporating class F fly ash, Int. J. Eng. Sci. Technol. 2 (9), (2010), 4936–4943.
Y. Fritih, T. Vidal, A. Turatsinze, G. Pons, Flexural and shear behavior of steel fiber reinforced SCC beams, KSCE J. Civil Eng. 17, (2013),1383–1393, doi: 10.1007/s12205-013-1115-1.
S. Assie, G. Escadeillas, V. Waller, Estimates of self-compacting concrete ‘potential’durability, Construction and Building Materials, 21(10), (2007),1909-1917, doi: 10.1016/j.conbuildmat.2006.06.034.
P.S. Rao, K.C. Mouli, T.S. Sekhar, Durability studies on glass fibre reinforced concrete. J. Civ. Eng. Sci. 1, (2012), 37–42.
F.A. Mirza, P. Soroushian, Effects of alkali-resistant glass fiber reinforcement on crack and temperature resistance of lightweight concrete. Cem. Concr. Compos. 24, (2002), 223–227, doi: 10.1016/S0958-9465(01)00038-5.
A. Khashman, An Emotional System with Application to Blood Cell Type Identification, Transactions of the Institute of Measurement and Control, SAGE, (2012), 34(2-3): 125-147, doi: 10.1177/0142331210366640.
F. Khademi, Predicting strength of recycled aggregate concrete using artificial neural network, adaptive neuro-fuzzy inference system and multiple linear regression, Int. J. Sustain. Built Environ. 5 (2), (2016) 355–369, doi: 10.1016/j.ijsbe.2016.09.003.
F. Khademi, M. Akbari, S.M. Jamal, Prediction of compressive strength of concrete by data-driven models, i-manager’s J. Civ. Eng. 5 (2), (2015), 16.
M. Nehdi, M. Bassuoni, Fuzzy logic approach for estimating durability of concrete, Proc. Ins. Civ. Eng.-Constr. Mater. 162 (2) (2009) 81–92, doi: 10.1680/coma.2009.162.2.81.
B. Vakhshouri, S. Nejadi, Predicition of compressive strength in light-weight self-compacting concrete by ANFIS analytical model, Arch. Civ. Eng. 61 (2), (2015) 53–72, doi:10.1515/ace-2015-0014.
I. Mansouri, Predicting behavior of FRP-confined concrete using neuro fuzzy, neural network, multivariate adaptive regression splines and M5 model tree techniques, Mater. Struct. 49 (10), (2016) 4319–4334, doi: 10.1617/s11527-015-0790-4.
A. Sadrmomtazi, J. Sobhani, M. Mirgozar, Modeling compressive strength of EPS lightweight concrete using regression, neural network and ANFIS, Constr. Build. Mater. 42, (2013), 205–216, doi: 10.1016/j.conbuildmat.2013.01.016.
EN 197-1. Cement - Part 1: Composition, specifications and conformity criteria for common cements, 2011.
Copyright (c) 2019 International Journal of Intelligent Systems and Applications in Engineering
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.