Predicting modulus elasticity of recycled aggregate concrete using M5′ model tree algorithm

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Compressive strength prediction of recycled concrete based on deep learning (2018, methodology): “Furthermore, M5’ model tree algorithm is also used in concrete strength prediction [18].”
A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model (2020, background): “Through the experimental procedure implemented, the WA of RAC samples was determined in the range of 0.338 to 21.604%; WCR lay in the range of 0.307 to 0.736; FA lay in the range of 278.1 to 1211.8 kg/m 3 ; RCA lay in the range of 0 to 1324.2 kg/m 3 ; NCA lay in the range of 0 to 1340.3 kg/m 3 , and”
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): “In this regard, it has been proven that the use of ML is an effective technique to derive prediction models in many civil engineering applications including the performance of asphalt mixtures (Ozsahin and Oruc 2008, Behnood et al. 2015, Gong et al. 2018, Fathi et al. 2019, Majidifard et al. 2019).”