Compressive strength prediction of eco-efficient GGBS-based geopolymer concrete using GEP method
Tài liệu tham khảo
Hassan, 2019, Cleaner production of one-part white geopolymer cement using pre-treated wood biomass ash and diatomite, J. Clean. Prod., 209, 1420, 10.1016/j.jclepro.2018.11.137
Khasreen, 2009, Life-cycle assessment and the environmental impact of buildings: a review, Sustainability, 1, 674, 10.3390/su1030674
Tam, 2016, Carbon-conditioned recycled aggregate in concrete production, J. Clean. Prod., 133, 672, 10.1016/j.jclepro.2016.06.007
Kocak, 2013, The effect of using natural zeolite on the properties and hydration characteristics of blended cements, Construct. Build. Mater., 47, 720, 10.1016/j.conbuildmat.2013.05.033
Akbarzadeh Bengar, 2020, A new anchorage system for CFRP strips in externally strengthened RC continuous beams, J. Build. Eng., 101230, 10.1016/j.jobe.2020.101230
Metz, 2005
Taylor, 2006, Energy efficiency and CO2 emissions from the global cement industry, Korea Times, 50, 61
Yang, 2013, Assessment of CO2 reduction of alkali-activated concrete, J. Clean. Prod., 39, 265, 10.1016/j.jclepro.2012.08.001
Ávalos-Rendón, 2018, Synthesis of belite cements at low temperature from silica fume and natural commercial zeolite, Mater. Sci. Eng., B, 229, 79, 10.1016/j.mseb.2017.12.020
Pacheco-Torgal, 2012, Durability of alkali-activated binders: a clear advantage over Portland cement or an unproven issue?, Construct. Build. Mater., 30, 400, 10.1016/j.conbuildmat.2011.12.017
Samimi, 2017, Influence of pumice and zeolite on compressive strength, transport properties and resistance to chloride penetration of high strength self-compacting concretes, Construct. Build. Mater., 151, 292, 10.1016/j.conbuildmat.2017.06.071
Taji, 2019, Application of statistical analysis to evaluate the corrosion resistance of steel rebars embedded in concrete with marble and granite waste dust, J. Clean. Prod., 210, 837, 10.1016/j.jclepro.2018.11.091
Tang, 2019, Sulfate attack resistance of sustainable concrete incorporating various industrial solid wastes, J. Clean. Prod., 218, 810, 10.1016/j.jclepro.2019.01.337
Khale, 2007, Mechanism of geopolymerization and factors influencing its development: a review, J. Mater. Sci., 42, 729, 10.1007/s10853-006-0401-4
Davidovits, 2008
Duxson, 2007, The role of inorganic polymer technology in the development of ‘green concrete’, Cement Concr. Res., 37, 1590, 10.1016/j.cemconres.2007.08.018
Sarker, 2009, Analysis of geopolymer concrete columns, Mater. Struct., 42, 715, 10.1617/s11527-008-9415-5
Pile, 2016
Shrestha, 2014
Jafari, 2018, A novel method for quantifying damage to cast‐in‐place self‐centering concrete stepping walls, Struct. Concr., 19, 1713, 10.1002/suco.201700247
Jafari, 2018, Seismic performance and damage incurred by monolithic concrete self-centering rocking walls under the effect of axial stress ratio, Bull. Earthq. Eng., 16, 831, 10.1007/s10518-017-0227-2
Cristelo, 2012, Soil stabilisation using alkaline activation of fly ash for self compacting rammed earth construction, Construct. Build. Mater., 36, 727, 10.1016/j.conbuildmat.2012.06.037
Cristelo, 2013, Effects of alkaline-activated fly ash and Portland cement on soft soil stabilisation, Acta Geotech., 8, 395, 10.1007/s11440-012-0200-9
Zhang, 2013, Experimental feasibility study of geopolymer as the next-generation soil stabilizer, Construct. Build. Mater., 47, 1468, 10.1016/j.conbuildmat.2013.06.017
Komljenović, 2010, Mechanical and microstructural properties of alkali-activated fly ash geopolymers, J. Hazard Mater., 181, 35, 10.1016/j.jhazmat.2010.04.064
Xu, 2000, The geopolymerisation of alumino-silicate minerals, Int. J. Miner. Process., 59, 247, 10.1016/S0301-7516(99)00074-5
Hardjito, 2005
Kong, 2008, Damage behavior of geopolymer composites exposed to elevated temperatures, Cement Concr. Compos., 30, 986, 10.1016/j.cemconcomp.2008.08.001
Al Bakri, 2009
Yazdani, 2017, Optimizing the sum of maximum earliness and tardiness of the job shop scheduling problem, Comput. Ind. Eng., 107, 12, 10.1016/j.cie.2017.02.019
Yazdani, 2017, A single-machine scheduling problem with multiple unavailability constraints: a mathematical model and an enhanced variable neighborhood search approach, J. Comput. Des. Eng., 4, 46
Aghapour, 2019, Capacity planning and reconfiguration for disaster-resilient health infrastructure, J. Build. Eng., 26, 100853, 10.1016/j.jobe.2019.100853
Ilkhani, 2019, A proposed novel approach for torsional strength prediction of RC beams, J. Build. Eng., 10.1016/j.jobe.2019.100810
Naderpour, 2019, Innovative models for prediction of compressive strength of FRP-confined circular reinforced concrete columns using soft computing methods, Compos. Struct., 215, 69, 10.1016/j.compstruct.2019.02.048
Behnood, 2020, Machine learning study of the mechanical properties of concretes containing waste foundry sand, Construct. Build. Mater., 243, 118152, 10.1016/j.conbuildmat.2020.118152
Cristofaro, 2020, New predictive models to evaluate concrete compressive strength using the SonReb method, J. Build. Eng., 27, 100962, 10.1016/j.jobe.2019.100962
Zéhil, 2020, Soft computing approaches to homogenized properties of inclusion-modified concrete mixtures: application to XLPE-modified concrete, J. Build. Eng., 101161, 10.1016/j.jobe.2019.101161
Akkurt, 2004, Can, Fuzzy logic model for the prediction of cement compressive strength, Cement Concr. Res., 34, 1429, 10.1016/j.cemconres.2004.01.020
Mansouri, 2016, Predicting behavior of FRP-confined concrete using neuro fuzzy, neural network, multivariate adaptive regression splines and M5 model tree techniques, Mater. Struct., 49, 4319, 10.1617/s11527-015-0790-4
Shayanfar, 2016, Numerical model to simulate shear behaviour of RC joints and columns, Comput. Concr., 18, 877, 10.12989/cac.2016.18.6.877
Naderpour, 2019, A Neuro-Fuzzy model for punching shear prediction of slab-column connections reinforced with FRP, J. Soft Comput. Civ. Eng., 3, 16
Akbarzadeh Bengar, 2016, Predicting the ductility of RC beams using nonlinear regression and ANN, Iran. J. Sci. Technol. Trans. Civ. Eng., 40, 297, 10.1007/s40996-016-0033-0
Azadeh, 2016, An integrated support vector regression–imperialist competitive algorithm for reliability estimation of a shearing machine, Int. J. Comput. Integrated Manuf., 29, 16
Naderpour, 2018, Compressive strength prediction of environmentally friendly concrete using artificial neural networks, J. Build. Eng., 16, 213, 10.1016/j.jobe.2018.01.007
Arora, 2019, Strength performance of recycled aggregate concretes containing mineral admixtures and their performance prediction through various modeling techniques, J. Build. Eng., 24, 100741, 10.1016/j.jobe.2019.100741
Koçer, 2019, Determination of moment, shear and ductility capacities of spiral columns using an artificial neural network, J. Build. Eng., 26, 100878, 10.1016/j.jobe.2019.100878
Naderpour, 2019, A new proposed approach for moment capacity estimation of ferrocement members using Group Method of Data Handling, Eng. Sci. Technol. Int. J.
Yazdani, 2019, Reliability estimation using an integrated support vector regression–variable neighborhood search model, J. Ind. Inf. Integr.
Azimi-Pour, 2020, Linear and non-linear SVM prediction for fresh properties and compressive strength of high volume fly ash self-compacting concrete, Construct. Build. Mater., 230, 117021, 10.1016/j.conbuildmat.2019.117021
Yazdani, 2016, Lion optimization algorithm (LOA): a nature-inspired metaheuristic algorithm, J. Comput. Des. Eng., 3, 24
Najimi, 2019, Modeling chloride penetration in self-consolidating concrete using artificial neural network combined with artificial bee colony algorithm, J. Build. Eng., 22, 216, 10.1016/j.jobe.2018.12.013
Golafshani, 2020, Predicting the compressive strength of normal and high-performance concretes using ANN and ANFIS hybridized with grey wolf optimizer, Construct. Build. Mater., 232, 117266, 10.1016/j.conbuildmat.2019.117266
Shahnewaz, 2020, Genetic algorithm for predicting shear strength of steel fiber reinforced concrete beam with parameter identification and sensitivity analysis, J. Build. Eng., 101205, 10.1016/j.jobe.2020.101205
Samuel, 2019, Modelling of concrete compressive strength admixed with GGBFS using gene expression programming, J. Soft Comput. Civ. Eng., 3, 44
Shahmansouri, 2019, Predicting compressive strength and electrical resistivity of eco-friendly concrete containing natural zeolite via GEP algorithm, Construct. Build. Mater., 229, 116883, 10.1016/j.conbuildmat.2019.116883
Ahmadi, 2020, New empirical approach for determining nominal shear capacity of steel fiber reinforced concrete beams, Construct. Build. Mater., 234, 117293, 10.1016/j.conbuildmat.2019.117293
Emamian, 2020, Genetic programming based formulation for compressive and flexural strength of cement mortar containing nano and micro silica after freeze and thaw cycles, Construct. Build. Mater., 241, 118027, 10.1016/j.conbuildmat.2020.118027
Murad, 2020, Joint shear strength models for exterior RC beam-column connections exposed to biaxial and uniaxial cyclic loading, J. Build. Eng., 101225, 10.1016/j.jobe.2020.101225
Ferreira, 2006
Elhakam, 2012, Influence of self-healing, mixing method and adding silica fume on mechanical properties of recycled aggregates concrete, Construct. Build. Mater., 35, 421, 10.1016/j.conbuildmat.2012.04.013
2019
2013
2015
2009
Ferreira, 2001
Ferreira, 2002, 635
Ferreira, 2006, 21
Mahdinia, 2019, Effect of cement strength class on the prediction of compressive strength of cement mortar using GEP method, Construct. Build. Mater., 198, 27, 10.1016/j.conbuildmat.2018.11.265
Bhargava, 2011, Stress corrosion cracking resistant aluminum alloys: optimizing concentrations of alloying elements and tempering, Mater. Manuf. Process., 26, 363, 10.1080/10426914.2010.536938
Ganguly, 2009, Genetic algorithm-based search on the role of variables in the work hardening process of multiphase steels, Comput. Mater. Sci., 45, 158, 10.1016/j.commatsci.2008.01.074
GEPsoft GeneXproTools
Kaplan, 1959, The effects of age and water/cement ratio upon the relation between ultrasonic pulse velocity and compressive strength of concrete, Mag. Concr. Res., 11, 85, 10.1680/macr.1959.11.32.85
Moon, 2014, Characterization of natural pozzolan-based geopolymeric binders, Cement Concr. Compos., 53, 97, 10.1016/j.cemconcomp.2014.06.010
Sarıdemir, 2010, Genetic programming approach for prediction of compressive strength of concretes containing rice husk ash, Construct. Build. Mater., 24, 1911, 10.1016/j.conbuildmat.2010.04.011
Ghaemi‐Fard, 2018, Genetic prediction of cement mortar mechanical properties with different cement strength class after freezing and thawing cycles, Struct. Concr., 19, 1341, 10.1002/suco.201700196
Thomas, 2018, Strength and durability of concrete containing recycled concrete aggregates, J. Build. Eng., 19, 349, 10.1016/j.jobe.2018.05.007
Yeddula, 2020, Experimental investigations and GEP modelling of compressive strength of ferrosialate based geopolymer mortars, Construct. Build. Mater., 236, 117602, 10.1016/j.conbuildmat.2019.117602