Prediction of the compressive strength of normal and high-performance concretes using M5P model tree algorithm

By Ali Behnood, Venous Behnood, Mahsa Modiri Gharehveran, K. E. Alyamaç. 2017

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Prediction Compressive Strength of Concrete Containing GGBFS using Random Forest Model (2021, methodology): “Although many machine learning models have been proposed to predict the compressive strength of concrete in the available literature (i.e., [28–32]), the reliability assessment of the models still needs to be quantiﬁed.”
Classification-Based Regression Models for Prediction of the Mechanical Properties of Roller-Compacted Concrete Pavement (2020, methodology): “formulated mechanical properties of poplar concretes based on tree method [25, 26].”
Estimation of the dynamic modulus of asphalt concretes using random forests algorithm (2020, methodology): “|) since some previous research studies have shown that a logarithmic transformation could improve the overall performance of the developed models (Behnood et al. 2015, 2017).”