2023
DOI: 10.1007/978-3-031-25088-0_14
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Prediction of Compressive Strength of Geopolymer Concrete by Using Random Forest Algorithm

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Cited by 8 publications
(2 citation statements)
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“…Figure 7 illustrates the percentage difference between each of the concrete mixes and M1, which serves as the reference Mix. In the recent research, predict the strength by using machine learning techniques without destructive tests [25,[69][70][71][72][73][74].…”
Section: Compressive Strength Apllied Load or Force Cross Sectional Areamentioning
confidence: 99%
“…Figure 7 illustrates the percentage difference between each of the concrete mixes and M1, which serves as the reference Mix. In the recent research, predict the strength by using machine learning techniques without destructive tests [25,[69][70][71][72][73][74].…”
Section: Compressive Strength Apllied Load or Force Cross Sectional Areamentioning
confidence: 99%
“…At elevated temperatures, the flexural strength was more vulnerable to the formation of microstructural defects, such as the propagation of fractures and the formation of porous structures. [77][78][79][80][81][82] Losses in compressive strength are varied from 31% to 85% in fibreless geopolymer specimens, but losses in polypropylene fibrous geopolymer specimens from 32% to 86%. This rate has risen considerably in comparison to samples lacking fibres.…”
Section: Ultrasonic Pulse Velocity Test (Upvt)mentioning
confidence: 99%