Determination of compressive strength of perlite-containing slag-based geopolymers and its prediction using artificial neural network and regression-based methods

ALAKARA, Erdinç Halis; Nacar, Sinan; Sevim, Özer; Korkmaz, Serdar; Demir, İlhami

doi:10.1016/j.conbuildmat.2022.129518

Cited by 24 publications

(4 citation statements)

References 77 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results showed that the UCS and ITS (indirect tensile strength) of this base material were optimized when the dosage of the geopolymer was 30% and the ratio of sodium silicate to sodium hydroxide was 7:3. Alakara E H et al [13] studied the influencing factors of the UCS of a geopolymer, and the results indicated that 48 h of thermal curing did not significantly affect the UCS of geopolymers. Feng B et al [14] explored the effect of different silane coupling agents and dosage metakaolin-based geopolymers on waterproof performance.…”

Section: Introductionmentioning

confidence: 99%

Mechanical Response and Deterioration Mechanisms in Freeze–Thaw Environments for Crushed Stone Stabilized with Industrial Solid Waste

Li,

Ji,

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

The conflict between industrial solid waste treatment and environmental protection in Inner Mongolia is becoming increasingly prominent. Using industrial solid waste such as mineral powder, fly ash and wet calcium carbide slag as raw materials, using the alkali excitation method to prepare geopolymer, and replacing part of the cement for pavement base can effectively absorb industrial solid waste and realize the dual goals of waste utilization and environmental protection. Through mechanical properties tests before and after a freeze–thaw cycle and micro tests such as scanning electron microscopy (SEM) and mercury intrusion porosimetry (MIP), the strength variation rule and mechanism of geopolymer-cement stabilized aggregate under freeze–thaw cycles were deeply investigated. The relationship between different porosity indexes and mechanical properties in mercury intrusion porosimetry (MIP) was established by grey relation analysis. The results prove that a mixture with impaired properties after freeze–thaw cycles and the anti-freezing performance of the mixture with 20% geopolymer content are better than that of the mixture with no geopolymer content and 40% geopolymer content. The loss rates of unconfined compressive strength (UCS) after 5, 10 and 20 freeze–thaw cycles were 9.5%, 27.6% and 36.4%, respectively. The appropriate addition of geopolymer can enhance the anti-freezing performance of a stable aggregate. Following freezing and thawing cycles, the unconfined compressive strength (UCS) damage of the mixture is mainly influenced by a rise in total porosity, and the grey correlation degree is 0.75. The increase in more harmful pores and total porosity mainly results in an indirect tensile strength (ITS) loss. The grey correlation degree is 0.91. The compressive rebound modulus (CRM) is not affected by the change in pores but decreases with a rise in the geopolymer dosage.

show abstract

Section: Introductionmentioning

confidence: 99%

Mechanical Response and Deterioration Mechanisms in Freeze–Thaw Environments for Crushed Stone Stabilized with Industrial Solid Waste

Li,

Ji,

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…41 Over the last three decades, ANN has also widely been employed to model the f 0 c of various types of sustainable concretes. [42][43][44][45][46] Recently, metaheuristic optimization algorithms have been used to look for efficient solutions and increase the accuracy of predictive methods. [47][48][49][50][51] A few studies have also been conducted to predict the f 0 c of concretes containing WFS.…”

Section: Introductionmentioning

confidence: 99%

“…Among different predictive techniques, the artificial neural network (ANN) method has been widely utilized as an efficient tool for several reasons: (i) recognizing patterns with high proficiency; (ii) identifying an accurate link between input and output variables; (iii) error‐tolerability; and (iv) solving complex problems through training process 41 . Over the last three decades, ANN has also widely been employed to model the

f_{c}^{'}

of various types of sustainable concretes 42–46 . Recently, metaheuristic optimization algorithms have been used to look for efficient solutions and increase the accuracy of predictive methods 47–51 .…”

Section: Introductionmentioning

confidence: 99%

Compressive strength prediction of sustainable concrete containing waste foundry sand using metaheuristic optimization‐based hybrid artificial neural network

Kazemi,

Golafshani,

Behnood

2023

Structural Concrete

View full text Add to dashboard Cite

This study seeks to present a sophisticated artificial intelligence (AI) framework to model the compressive strength () of concrete containing waste foundry sand (WFS), with the aim of minimizing the need for time‐consuming laboratory tests and skilled technicians. For this purpose, artificial neural network (ANN) is hybridized with two metaheuristic algorithms—particle swarm optimization (PSO) and ant colony optimization (ACO) to predict the of 340 samples containing WFS collected from the literature. Results indicated that the ACO + ANN model showed the best performance with the Pearson coefficient of 0.9971, mean absolute error of 0.0221 MPa, and root mean squared error of 0.7473 MPa. The values of prediction errors exhibited that more than 90% of them in the ACO + ANN model fall within the range of (−1.5 MPa, 1.5 MPa), while this range for the PSO + ANN and traditional ANN models was obtained as (−3 MPa, 3 MPa) and (−4 MPa, 4 MPa), respectively. Furthermore, the proposed ACO + ANN model predicted the in the range of 5.24–54.48 MPa. Besides, the results indicated that the water‐to‐cement ratio, cement strength class, and cement content had the most significant impact on the of WFS‐containing concrete. Finally, a comparison was made between the proposed ACO + ANN model and four other AI models recently proposed in the literature, in which the performance criteria demonstrated that the proposed ACO + ANN model outperformed the models in the literature.

show abstract

“…The depletion of raw material resources and the escalation of environmental pollution have prompted researchers to conduct various studies for the sustainability of the construction industry. In some studies, the usability of industrial waste has been investigated in cementitious composites, geopolymers, asphalt coatings, and road base and subbase layers, either as aggregates or binders [3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Effects of High Temperature and Cooling Regimes on Properties of Marble Powder-Based Cementitious Composites

Bayer,

Sevim,

Demir

2023

Buildings

Self Cite

View full text Add to dashboard Cite

The demand for cement is increasing every day worldwide. To meet this demand, natural resources are rapidly being depleted. The excessive consumption of natural resources encourages researchers to conduct studies on the use of waste materials instead of cement. Marble waste is one of the major natural wastes abundantly generated worldwide. It has been evaluated that there is a gap in the literature regarding a study comparing the effects of different cooling regimes on cementitious composites with a marble powder (MP) replacement that has been exposed to high temperatures. In this study, waste marble powder (MP) was used as a replacement for cement at percentages of 5%, 10%, 15%, 20%, and 25% by mass. The water-to-binder ratio was kept constant at 0.5 for all mixture groups. Subsequently, the prepared cementitious composites were exposed to high temperatures (300 °C, 600 °C, and 800 °C) and subjected to air- and water-cooling regimes. Within the scope of this study, unit weight (Uw), ultrasonic pulse velocity (UPV), flexural strength (ffs), compressive strength (fcs), and mass loss tests were conducted. Additionally, a microstructure analysis was carried out using scanning electron microscopy (SEM) to examine the effect of MP replacement and the cooling regime. When examining the results of the samples tested in the laboratory, it was observed that the mortar with 5% MP replacement exhibited better mechanical properties compared with the others. In general, it can be said that the mechanical properties of samples cooled in air after exposure to high temperatures were better than those of samples cooled in water. As a result of this study, it was determined that MP replacement could positively contribute to the resistance of cementitious composites to high temperatures. Additionally, the use of a significant amount of waste MP can lead to savings in cement usage and significant reductions in CO2 emissions.

show abstract

Determination of compressive strength of perlite-containing slag-based geopolymers and its prediction using artificial neural network and regression-based methods

Cited by 24 publications

References 77 publications

Mechanical Response and Deterioration Mechanisms in Freeze–Thaw Environments for Crushed Stone Stabilized with Industrial Solid Waste

Mechanical Response and Deterioration Mechanisms in Freeze–Thaw Environments for Crushed Stone Stabilized with Industrial Solid Waste

Compressive strength prediction of sustainable concrete containing waste foundry sand using metaheuristic optimization‐based hybrid artificial neural network

Effects of High Temperature and Cooling Regimes on Properties of Marble Powder-Based Cementitious Composites

Contact Info

Product

Resources

About