Abstract
Poor frost resistance of concrete will accelerate the deterioration of concrete structure and shorten the service life of concrete. This paper predicts the frost resistance of concrete and selects the initial index of factors that affect the frost resistance of concrete. The random forest algorithm is introduced to remove the unimportant indicators in the initial indicator system of the concrete mix ratio and determine the optimal indicator combination. The relative dynamic elastic modulus is used as the output index of random forest to predict the frost resistance of concrete. The proposed random forest model provides a reasonable and effective method for concrete frost resistance prediction.
Highlights
Compared with other building materials, concrete is economical, easy to manufacture on site, and has good durability
Lin et al used the relative dynamic elastic modulus to calculate the performance degradation index and showed that the relative dynamic elastic modulus has a linear relationship with the number of freeze-thaw cycles[3]
Ji established a concrete strength and slump prediction model based on artificial neural networks (ANNs)[4]
Summary
Compared with other building materials, concrete is economical, easy to manufacture on site, and has good durability. It is one of the most widely used materials in construction[1]. Amini K proposed statistical univariate and multivariate regression models to predict concrete durability[2]. Behforouz B et al used ANN prediction model to predict the durability of concrete such as frost resistance[6]. Aiming at the deficiencies of the above methods, this paper proposes a random forest frost resistance prediction model based on the ratio of raw materials. The relative dynamic elastic modulus is used as the output index of the random forest to predict the frost resistance
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