Properties of cellular lightweight high calcium bottom ash-portland cement geopolymer mortar

Suksiripattanapong, Cherdsak; Krosoongnern, Kitsada; Thumrongvut, Jaksada; Sukontasukkul, Piti; Horpibulsuk, Suksun; Chindaprasirt, Prinya

doi:10.1016/j.cscm.2020.e00337

Cited by 27 publications

(16 citation statements)

References 44 publications

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“…Consumption of these waste materials benefits both the environment and the economy, as the demand for inexpensive housing will increase as the population expands [ 31 , 32 , 33 ]. GPC has been the topic of broad research and development on a global scale, and it may one day become the best green construction material [ 34 , 35 , 36 , 37 ]. GPC, on the other hand, has the potential to make a significant contribution to the prolonged sustainability of both CBCC technology and the building sector.…”

Section: Introductionmentioning

confidence: 99%

Application of Soft Computing Techniques to Predict the Strength of Geopolymer Composites

Wang

Ahmad

et al. 2022

Polymers

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Geopolymers may be the best alternative to ordinary Portland cement because they are manufactured using waste materials enriched in aluminosilicate. Research on geopolymer composites is accelerating. However, considerable work, expense, and time are needed to cast, cure, and test specimens. The application of computational methods to the stated objective is critical for speedy and cost-effective research. In this study, supervised machine learning approaches were employed to predict the compressive strength of geopolymer composites. One individual machine learning approach, decision tree, and two ensembled machine learning approaches, AdaBoost and random forest, were used. The coefficient correlation (R2), statistical tests, and k-fold analysis were used to determine the validity and comparison of all models. It was discovered that ensembled machine learning techniques outperformed individual machine learning techniques in forecasting the compressive strength of geopolymer composites. However, the outcomes of the individual machine learning model were also within the acceptable limit. R2 values of 0.90, 0.90, and 0.83 were obtained for AdaBoost, random forest, and decision models, respectively. The models’ decreased error values, such as mean absolute error, mean absolute percentage error, and root-mean-square errors, further confirmed the ensembled machine learning techniques’ increased precision. Machine learning approaches will aid the building industry by providing quick and cost-effective methods for evaluating material properties.

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Section: Introductionmentioning

confidence: 99%

Application of Soft Computing Techniques to Predict the Strength of Geopolymer Composites

Wang

Ahmad

et al. 2022

Polymers

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“…Estudios han a rmado que con un reemplazo del 20 % del cemento con BA en concretos ligeros esterilizados en autoclaves, superan considerablemente al concreto patrón a base de cemento, pues forman tobermorita, debido al contenido de sílice del (BA) (Wongkeo y Chaipanich, 2010;Wongkeo et al, 2012). Por otra parte, Suksiripattanapong et al (2020) elaboraron un concreto celular liviano a base de BA como aglomerante, con un activador alcalino líquido y arena de cuarzo como agregado; logrando obtener el menor peso unitario (11, 09KN/m 3 ), con una relación de arena/BA de 1 : 1, con un contenido de espuma del 3 %.…”

Section: Residuos De Centrales Eléctricasunclassified

Uso de residuos sólidos en la elaboración de concreto celular: una revisión

Pérez¹,

Chumacero

Pretel

2021

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Cada año a nivel mundial se generan millones de toneladas de diferentes tipos de desechos y se espera que aumente en el futuro. La mayoría de estos residuos se vierten a través de vertederos o incineración, lo que genera grandes preocupaciones ambientales. Una alternativa para la reutilización de estos desechos es incorporándolos como remplazantes parciales o totales de los componentes del concreto celular. En este sentido, el hormigón celular podría proporcionar un medio excelente para incorporar estos residuos en un gran volumen principalmente debido al requisito de baja resistencia. El concreto celular se clasifica en dos tipos según su método de fabricación, el método de arrastre de aire y el método espumoso. Po lo tanto, este documento recopila y revisa el uso de varios tipos de desechos, como residuos sólidos industriales y residuos sólidos agrícolas, como un reemplazo potencial del cemento, el agregado fino o el agente espumante en el concreto aireado y espumado. Cada tipo de desperdicio contribuye de manera diferente al desempeño del concreto celular; generalmente incorporando los materiales de desecho se puede producir un hormigón celular con baja densidad, con un aumento de la resistencia a la compresión y mejoras en las propiedades de aislamiento térmico.

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“…Geopolymers are a type of cementitious material synthesized from raw materials containing a high content of aluminum and silicon [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 ]. Since the chemical reaction occurs in the polymerization process, it is usually called a geopolymer.…”

Section: Introductionmentioning

confidence: 99%

Influence of Graphene Oxide Nanoparticles on Bond-Slip Reponses between Fiber and Geopolymer Mortar

et al. 2022

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In this study, the influence of graphene oxide nanoparticles on the bond-slip behavior of fiber and fly-ash-based geopolymer paste was examined. Geopolymer paste incorporating a graphene oxide nanoparticle solution was cast in half briquetted specimens and embedded with a fiber. Three types of fiber were used: steel, polypropylene, and basalt. The pullout test was performed at two distinct speeds: 1 mm/s and 3 mm/s. The results showed that the addition of graphene oxide increased the compressive strength of the geopolymer by about 7%. The bond-slip responses of fibers embedded in the geopolymer mixed with graphene oxide exhibited higher peak stress and toughness compared to those embedded in a normal geopolymer. Each fiber type also showed a different mode of failure. Both steel and polypropylene fibers showed full bond-slip responses due to their high ductility. Basalt fiber, on the other hand, because of its brittleness, failed by fiber fracture mode and showed no slip in pullout responses. Both bond strength and toughness were found to be rate-sensitive. The sensitivity was higher in the graphene oxide/geopolymer than in the conventional geopolymer.

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Properties of cellular lightweight high calcium bottom ash-portland cement geopolymer mortar

Cited by 27 publications

References 44 publications

Application of Soft Computing Techniques to Predict the Strength of Geopolymer Composites

Application of Soft Computing Techniques to Predict the Strength of Geopolymer Composites

Uso de residuos sólidos en la elaboración de concreto celular: una revisión

Influence of Graphene Oxide Nanoparticles on Bond-Slip Reponses between Fiber and Geopolymer Mortar

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