Random Forest-Based Algorithms for Prediction of Compressive Strength of Ambient-Cured AAB Concrete—A Comparison Study

Ramagiri, Kruthi Kiran; Boindala, Sriman Pankaj; Zaid, -; Kar, Arkamitra

doi:10.1007/978-3-030-80312-4_61

Cited by 4 publications

(1 citation statement)

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“…The ranger algorithm with reliefF feature selection exhibited the most accurate predictions based on MAE and RMSE values. The paper recommends the application of the mentioned RF algorithm for the prediction of compressive strength and the determination of important input variables for AAB concrete [8].…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence Methods for Predicting the Compressive Strength of Green Concretes with Rice Husk Ash

Kovačević,

Hadzima-Nyarko,

Grubeša

et al. 2023

Mathematics

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To promote sustainable growth and minimize the greenhouse effect, rice husk fly ash can be used instead of a certain amount of cement. The research models the effects of using rice fly ash as a substitute for regular Portland cement on the compressive strength of concrete. In this study, different machine-learning techniques are investigated and a procedure to determine the optimal model is provided. A database of 909 analyzed samples forms the basis for creating forecast models. The derived models are assessed using the accuracy criteria RMSE, MAE, MAPE, and R. The research shows that artificial intelligence techniques can be used to model the compressive strength of concrete with acceptable accuracy. It is also possible to evaluate the importance of specific input variables and their influence on the strength of such concrete.

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Section: Introductionmentioning

confidence: 99%