ThomasBSGuptaRCKallaP, et al.Strength, abrasion and permeation characteristics of cement concrete containing discarded rubber fine aggregates. Constr Build Mater2014; 59: 204–212.
2.
Directive (EC) 98/2008 of the European Parliament and of the Council of 19 November 2008 on waste and replacing certain Directives. 2008: OJ L312/3.
3.
MohammedBSAnwar HossainKMEng SweeJT, et al.Properties of crumb rubber hollow concrete block. J Clean Prod2012; 23: 57–67.
4.
MavroulidouMFigueiredoJ. Discarded tire rubber as concrete aggregate: a possible outlet for used tires. Global Nest J2010; 12: 359–367.
5.
NajimKHallM. A review of the fresh/hardened properties and applications for plain-(PRC) and self-compacting rubberised concrete (SCRC). Constr Build Mater2010; 24: 2043–2051.
6.
MontellaGCalabreseASerinoG. Mechanical characterization of a tire derived material: experiments, hyperelastic modeling and numerical validation. Constr Build Mater2014; 66: 336–347.
7.
AtahanAOYücelAO. Crumb rubber in concrete: static and dynamic evaluation. Constr Build Mater2012; 36: 617–622.
8.
Arabali P, Sakhaeifar M, Freeman T, et al. Concrete pavement preservation in general aviation airports management. Pavement and asset management. In: Proceedings of the world conference on pavement and asset management (WCPAM 2017), 2019, pp.383–390. London: Taylor and Francis Group.
Al-TayebMMAbu BakarBIsmailH, et al.Effect of partial replacement of sand by recycled fine crumb rubber on the performance of hybrid rubberized-normal concrete under impact load: experiment and simulation. J Clean Prod2013; 59: 284–289.
11.
JalalMNoorzadA. Effect of binder content, pozzolanic admixtures and SiO2 nanoparticles on thermal properties and capillary water absorption of high performance concrete. J Am Sci2012; 8: 395–399.
12.
JalalM. Corrosion resistant self-compacting concrete using micro and nano silica admixtures. Struct Eng Mech2014; 51: 403–412.
13.
VazinramFJalalMForoushaniMY. Effect of nano ZnO2 and lime water curing on strength and water absorption of concrete. Int J Mater Prod Technol2015; 50: 356–365.
14.
JalalMMansouriE. Thermal and mechanical characteristics of cement nanocomposites. Sci Eng Compos Mater2013; 20: 35–40.
15.
JalalMMortazaviAAHassaniN. Thermal properties of TiO2 nanoparticles binary blended cementitious composites. J Am Sci2012; 8: 391–394.
16.
JalalMRamezanianpourAAPoolMK. Split tensile strength of binary blended self-compacting concrete containing low volume fly ash and TiO2 nanoparticles. Compos Part B: Eng2013; 55: 324–337.
17.
JalalMMansouriE. Effects of fly ash and cement content on rheological, mechanical, and transport properties of high-performance self-compacting concrete. Sci Eng Compos Mater2012; 19: 393–405.
18.
JalalMMansouriESharifipourM, et al.Mechanical, rheological, durability and microstructural properties of high performance self-compacting concrete containing SiO2 micro and nanoparticles. Mater Des2012; 34: 389–400.
19.
Arabali P and Shekarchi M. Durability of jointed reinforced concrete pavements under sever exposure conditions: a case study in Iran. In: XIII international conference on durability of building materials and components, Sao Paulo, Brazil, 2015, pp.866–873.
20.
GarmsiriKJalalM. Multiobjective optimization of composite cylindrical shells for strength and frequency using genetic algorithm and neural networks. Sci Eng Compos Mater2014; 21: 529–536.
21.
JodaeiAJalalMYasMH. Free vibration analysis of functionally graded annular plates by state-space based differential quadrature method and comparative modeling by ANN. Compos Part B: Eng2012; 43: 340–353.
22.
JodaeiAJalalMYasMH. Three-dimensional free vibration analysis of functionally graded piezoelectric annular plates via SSDQM and comparative modeling by ANN. Math Comput Model2013; 57: 1408–1425.
23.
Saeidi MarzangooHRJalalM. A semi-analytical three-dimensional free vibration analysis of functionally graded curved panels integrated with piezoelectric layers. Sci Eng Compos Mater2014; 21: 571–587.
24.
JalalMMoradi-DastjerdiRBidramM. Big data in nanocomposites: ONN approach and mesh-free method for functionally graded carbon nanotube-reinforced composites. J Comput Des Eng2018; 6: 209–223.
25.
GoharzayMNoorzadAMahboubi ArdakaniA, et al.A worldwide SPT-based soil liquefaction triggering analysis utilizing gene expression programming and Bayesian probabilistic method. J Rock Mech Geotech Eng2017; 9: 683–693.
26.
AshrafiHRJalalMGarmsiriK. Prediction of load–displacement curve of concrete reinforced by composite fibers (steel and polymeric) using artificial neural network. Expert Syst Appl2010; 37: 7663–7668.
27.
FathiMJalalMRostamiS. Compressive strength prediction by ANN formulation approach for CFRP confined concrete cylinders. Earthq Struct2015; 8: 1171–1190.
28.
JalalM. Soft computing techniques for compressive strength prediction of concrete cylinders strengthened by CFRP composites. Sci Eng Compos Mater2015; 22: 97–112.
29.
JalalMRamezanianpourAAPouladkhanAR, et al.Application of genetic programming (GP) and ANFIS for strength enhancement modeling of CFRP-retrofitted concrete cylinders. Neural Comput Appl2013; 23: 455–470.
30.
JalalMRamezanianpourAA. Strength enhancement modeling of concrete cylinders confined with CFRP composites using artificial neural networks. Compos Part B: Eng2012; 43: 2990–3000.
31.
JalalMMukhopadhyayAKGoharzayM. Bat algorithm as a metaheuristic optimization approach in materials and design: optimal design of a new float for different materials. Neural Comput Appl2018; 31(10): 6151–6161.
32.
JalalMMukhopadhyayAKGrasleyZ. Design, manufacturing, and structural optimization of a composite float using particle swarm optimization and genetic algorithm. Proc IMechE, Part L: J Materials: Design and Applications2019; 233: 1404–1418.
33.
JalalMGoharzayM. Cuckoo search algorithm for applied structural and design optimization: float system for experimental setups. J Comput Des Eng2019; 6: 159–172.
34.
TeimortashluEDehestaniMJalalM. Application of Taguchi method for compressive strength optimization of tertiary blended self-compacting mortar. Constr Build Mater2018; 190: 1182–1191.
35.
JalalMTeimortashluEGrasleyZ. Performance-based design and optimization of rheological and strength properties of self-compacting cement composite incorporating micro/ nano admixtures. Compos Part B: Eng2019; 163: 497–510.
36.
KeshavarzZTorkianH. Application of ANN and ANFIS models in determining compressive strength of concrete. J Soft Comput Civil Eng2018; 2: 62–70.
37.
DeshpandeNLondheSKulkarniSS. Modeling compressive strength of recycled aggregate concrete using neural networks and regression analysis. Concr Res Lett2013; 4(2): 187–189.
38.
Akbarzadeh BengarHAbdollahtabarMShayanfarJ. Predicting the ductility of RC beams using nonlinear regression and ANN. Iran J Sci Technol Trans Civ Eng2016; 40: 297–310.
39.
Wang C-C, Wang H-Y, Tang C-W, et al. A nonlinear-multivariate regression prediction of compressive strength of waste glass concrete. In: Proceedings of the 2nd international conference on intelligent technologies and engineering systems. New York: Springer International Publishing, 2014, pp.561–567.
40.
SadowskiŁPiechówka-MielnikMWidziszowskiT, et al.Hybrid ultrasonic-neural prediction of the compressive strength of environmentally friendly concrete screeds with high volume of waste quartz mineral dust. J Clean Prod2019; 212: 727–740.
41.
RezaifarOHasanzadehMGholhakiM. Concrete made with hybrid blends of crumb rubber and metakaolin: optimization using response surface method. Constr Build Mater2016; 123: 59–68.
42.
JalalMNassirNJalalH. Waste tire rubber and pozzolans in concrete: a trade-off between cleaner production and mechanical properties in a greener concrete. J Clean Prod2019; 238: 117882–117882.
43.
JalalMNassirNJalalH, et al.On the strength and pulse velocity of rubberized concrete containing silica fume and zeolite: prediction using multivariable regression models. Constr Build Mater2019; 223: 530–543.
44.
WaliaNSinghHSharmaA. ANFIS: adaptive neuro-fuzzy inference system - a survey. Int J Comput Appl2015; 123: 32–38.
45.
AshtianiRSLittleDNRashidiM. Neural network based model for estimation of the level of anisotropy of unbound aggregate systems. Transport Geotech2018; 15: 4–12.
46.
PramanikNPandaKR. Application of neural network and adaptive neuro-fuzzy inference systems for river flow prediction. Hydrol Sci J2009; 54: 247–260.
47.
SumithiraTRKumarANRameshkumarR. An adaptive neuro-fuzzy inference system (ANFIS) based prediction of solar radiation. J Appl Sci Res2012; 8: 346–351.
48.
TranYTLeeJKumarP, et al.Natural zeolite and its application in concrete composite production. Compos Part B: Eng2019; 165: 354–364.
49.
MermerdaşKMoafak ArbiliM. Explicit formulation of drying and autogenous shrinkage of concretes with binary and ternary blends of silica fume and fly ash. Constr Build Mater2015; 94: 371–379.
50.
British Standards Institute. Testing hardened concrete: compressive strength of test specimens. BS EN 2001. London: BIS, 2001.
51.
JangJSR. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybernet1993; 23: 665–685.
52.
Jang JSR and Sun CT. Neuro-fuzzy modeling and control. In: Proceedings of the IEEE, vol. 83, 1995. New York: IEEE.
53.
SadrmomtaziASobhaniJMirgozarMA. Modeling compressive strength of EPS lightweight concrete using regression, neural network and ANFIS. Constr Build Mater2013; 42: 205–216.
54.
TopcuIBSarıdemirM. Prediction of mechanical properties of recycled aggregate concretes containing silica fume using artificial neural networks and fuzzy logic. Comput Mater Sci2008; 42: 74–82.
55.
DunlopPSmithS. Estimating key characteristics of the concrete delivery and placement process using linear regression analysis. Civil Eng Environ Syst2003; 20: 273–290.
56.
SmithGN. Probability and statistics in civil engineering, London: Collins, 1986.
57.
Fuzzy Logic Toolbox, MATLAB R2017a, 2017.
58.
BezdecJC. Pattern recognition with fuzzy objective function algorithms, New York: Plenum Press, 1981.
59.
ChiuS. Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst1994; 2: 267–278.
60.
YuanZWangLNJiX. Prediction of concrete compressive strength: research on hybrid models genetic based algorithms and ANFIS. Adv Eng Softw2014; 67: 156–163.
61.
DeveliISorgucuU. Prediction of temperature distribution in human BEL exposed to 900 MHz mobile phone radiation using ANFIS. Appl Soft Comput2015; 37: 1029–1036.