Review on compressive strength and durability of fly-ash-based geopolymers using characterization techniques

被引:1
|
作者
Singaram, Kailash Kumar [1 ]
Khan, Mohd Ataullah [1 ]
Talakokula, Visalakshi [1 ]
机构
[1] Mahindra Univ, Ecole Cent Sch Engn, Dept Civil Engn, Hyderabad 500043, India
关键词
Geopolymer concrete; Mix design; Alkaline liquid; Curing temperature; Predictive model; Artificial neural networks; Fly-ash-based GPC; Durability; ALKALI-ACTIVATED BINDERS; MECHANICAL-PROPERTIES; NANO-SILICA; DRYING SHRINKAGE; PORE STRUCTURE; SETTING TIME; REACTION-KINETICS; MIX DESIGN; CONCRETE; SLAG;
D O I
10.1007/s43452-025-01116-7
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The adoption of geopolymer, an inorganic amorphous material, reduces carbon dioxide emissions associated with ordinary portland cement (OPC) concrete and increases the usage of fly-ash (FA). Geopolymer concretes (GPCs) hold a lot of potential as cement substitutes as they can provide high early strength and resistance under aggressive environments. This paper reviews the compositions, curing regimes, mix designs, predictive models, and durability issues of FA-based GPC, drawing on recent credible publications. The role of microstructure on the strength and durability parameters is highlighted. Recent attempts, such as the utilization of multi-layered GPC-OPC, curing of geopolymer paste before casting, and the effect of mechanical milling of FA, are discussed. It was inferred that the porosity decreases and the microstructure gets denser as the volume fraction of nanomaterials increases. Research on durability issues indicates that the alumina-silicate determines the structural integrity of GP binders, exhibiting higher early age strength, reduced creep and shrinkage, and enhanced durability against hostile acids and sulphates. The primary carbonation and efflorescence reaction products for FA-based GPC are highly soluble Na2CO3 and K2CO3, which can increase the porosity of the concrete. Furthermore, it was found that deep residual networks and artificial neural network models were effective tools for predicting compressive strength, and the hybrid ensemble machine learning models outperformed conventional machine learning models. Reviewing large data might provide crucial information for the general use of FA-based GPC with suitable mechanical and durability features.
引用
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页数:34
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