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http://dx.doi.org/10.12989/cac.2017.19.2.133

Fuzzy inference systems based prediction of engineering properties of two-stage concrete  

Najjar, Manal F. (Department of Civil Engineering, Tripoli University)
Nehdi, Moncef L. (Department of Civil and Environmental Engineering, Western University)
Azabi, Tareq M. (Department of Civil and Environmental Engineering, Western University)
Soliman, Ahmed M. (Department of Building, Civil and Environmental Engineering, Concordia University)
Publication Information
Computers and Concrete / v.19, no.2, 2017 , pp. 133-142 More about this Journal
Abstract
Two-stage concrete (TSC), also known as pre-placed aggregate concrete, is characterized by its unique placement technique, whereby the coarse aggregate is first placed in the formwork, then injected with a special grout. Despite its superior sustainability and technical features, TSC has remained a basic concrete technology without much use of modern chemical admixtures, new binders, fiber reinforcement or other emerging additions. In the present study, an experimental database for TSC was built. Different types of cementitious binders (single, binary, and ternary) comprising ordinary portland cement, fly ash, silica fume, and metakaolin were used to produce the various TSC mixtures. Different dosages of steel fibres having different lengths were also incorporated to enhance the mechanical properties of TSC. The database thus created was used to develop fuzzy logic models as predictive tools for the grout flowability and mechanical properties of TSC mixtures. The performance of the developed models was evaluated using statistical parameters and error analyses. The results indicate that the fuzzy logic models thus developed can be powerful tools for predicting the TSC grout flowability and mechanical properties and a useful aid for the design of TSC mixtures.
Keywords
two-stage concrete; fuzzy logic; efflux time; spread flow; compressive strength; tensile strength;
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Times Cited By KSCI : 4  (Citation Analysis)
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1 Abdelgader, H.S. and Elgalhud, A.A. (2008), "Effect of grout proportions on strength of two-stage concrete", Struct. Concrete, 9(3), 163-170.   DOI
2 Abdelgader, H.S., Jaroslaw, G., Jamal, M.K. and El-Baden, A. (2016), "Two-stage concrete: Effect of silica fume and superplasticizers on strength", BFT Concrete Prec. Plant Technol., 82(3), 38-47.
3 Abdul Awal, A.S. (1984), "Manufacture and properties of prepacked aggregate concrete", M.S. Dissertation, University of Melbourne, Australia.
4 ACI 304.1 (2005), Guide for the Use of Preplaced Aggregate Concrete for Structural and Mass Concrete Applications, Farmington Hills, Michigan, U.S.A.
5 ASTM C 939 (2010), Standard Test Method for Flow of Grout for Preplaced-Aggregate Concrete (Flow Cone Method), West Conshohocken, U.S.A.
6 ASTM C 942 (2010), Standard Test Method for Compressive Strength of Grouts for Preplaced-Aggregate Concrete in the Laboratory, West Conshohocken, U.S.A.
7 ASTM C496/C496M (2011), Standard Test Method for Splitting Tensile Strength of Cylindrical Concrete Specimens, West Conshohocken, U.S.A.
8 ASTM C938 (2010), Standard Practice for Proportioning Grout Mixtures for Preplaced-Aggregate Concrete, West Conshohocken, U.S.A.
9 ASTM C943 (2010), Standard Practice for Making Test Cylinders and Prisms for Determining Strength and Density of Preplaced-Aggregate Concrete in the Laboratory, West Conshohocken, U.S.A.
10 Bassuoni, M.T. and Nehdi, M.L. (2008), "Neuro-fuzzy based prediction of the durability of self-consolidating concrete to various sodium sulfate exposure regimes", Comput. Concrete, 5(6), 573-597.   DOI
11 Bedirhanoglu, I. (2014), "A practical neuro-fuzzy model for estimating modulus of elasticity of concrete", Struct. Eng. Mech., 51(2), 249-265.   DOI
12 Feng, M.Q., Chung, L. and Park, T.W. (2009), "Neuro-fuzzy application for concrete strength prediction using combined non-destructive tests", Mag. Concrete Res., 61(4), 245-256.   DOI
13 Coo, M. and Pheeraphan, T. (2015), "Effect of sand, fly ash, and coarse aggregate gradation on preplaced aggregate concrete studied through factorial design", Constr. Build. Mater., 93, 812-821.   DOI
14 Da Silva, W.R. and Stemberk, P. (2013), "Optimized fuzzy logic model for predicting self-compacting concrete shrinkage", Mechanika, 19(1), 67-72.
15 Demir, F. (2005), "A new way of prediction elastic modulus of normal and high strength concrete-fuzzy logic", Cement Concrete Res., 35(8), 1531-1538.   DOI
16 Hunger, M. and Brouwers, H.J.H. (2009), "Flow analysis of waterowder mixtures: Aplication to specific surface area and shape factor", Cement Concrete Compos., 31(1), 39-59.   DOI
17 Kute, S.Y. and Kale, R.S. (2013), "Five-layer fuzzy inference system to design a concrete", ACI Mater. J., 110(6), 629-639.
18 Mamdani, E.H. and Assilian, S. (1975), "An experiment in linguistic synthesis with a fuzzy logic controller", J. Man-Machine Stud., 7(1), 1-13.   DOI
19 Najjar, M., Soliman, A. and Nehdi, M. (2014), "Critical overview of two stage concrete: Properties and applications", Constr. Build. Mater., 62, 47-58.   DOI
20 Najjar, M., Soliman, A. and Nehdi, M. (2016), "Two-stage concrete made with single, binary and ternary binders", Mater. Struct., 49(1), 317-327.   DOI
21 Nehdi, M.L. and Bassuoni, M.T. (2009), "Fuzzy logic approach for estimating durability of concrete", Proceedings of the Institution of Civil Engineers-Construction Materials, 162(2), 81-92.   DOI
22 O'Malley, J. and Abdelgader, H. (2010), "Investigation into viability of using two stage (preplaced aggregate) concrete in an Irish setting", Front. Architect. Civil Eng. China, 4(1), 127-132.   DOI
23 Tsai, P.W., Hayat, T., Ahmad, B. and Chen, C.W. (2015), "Structural system simulation and control via NN based fuzzy model", Struct. Eng. Mech., 56(3), 385-407.   DOI
24 Sivanandam, S.N., Sumathi, S. and Deepa, S.N. (2007), Introduction to Fuzzy Logic using MATLAB, Springer-Verlag Berlin Heidelberg, New York, U.S.A.
25 Subasi, S., Beycioglu, A., Sancak, E. and Sahin, I. (2013), "Rulebased mamdani type fuzzy logic model for the prediction of compressive strength of silica fume included concrete using non-destructive test results", Neur. Comput. Appl., 22(6), 1133-1139.   DOI
26 Topcu, I.B. and Saridemir, M. (2008), "Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic", Comput. Mater. Sci., 41(3), 305-311.   DOI
27 Zadeh, L.A. (1965), "Fuzzy set", Informat. Control, 8(3), 338-353.   DOI
28 Zhang, Y. (2015), "A fuzzy residual strength based fatigue life prediction method", Struct. Eng. Mech., 56(2), 201-221.   DOI
29 Abdelgader, H.S. (1996), "Effect of quantity of sand on the compressive strength of two-stage concrete", Mag. Concrete Res., 48(177), 353-360.   DOI
30 Ross, T.J. (2010), Fuzzy Logic with Engineering Applications, 3rd Edition, John Wiley & Sons Ltd., Chichester, U.K.
31 Abdelgader, H.S. (1999), "How to design concrete produced by a two-stage concreting method", Cement Concrete Res., 29(3), 331-337.   DOI