Effect montmorillonite clay as aggregate in lightweight concrete cement-free

Light weight concrete has many advantages that can be used in the construction of buildings. Perhaps one of the most important of these features is its light weight, which contributes a lot to reducing stress on the soil, which provides the possibility of rising buildings and increasing the number of floors. In addition to its role in thermal insulation and its impact on reducing the consumption of energy sources in cooling and heating, light weight concrete is considered one of the sustainability factors in buildings. One of the second major factors in sustainability is to reduce or avoid the use of cement in the manufacture of this concrete, because of the harmful effects of cement on the environment and global warming. Cement-free concrete is considered a sustainable material in terms of its depletion of the waste materials and spin-off products from different industries apposite of consumption of natural resources in the cement industry (mud, limestone). In this research first aim is to produce lightweight cement-free concrete using pozolanic material and montmorillonite clay as coarse and fine aggregate. Studying some properties of producing light weight concrete (density, compression, tensile,) with different ages (7, 28, 56) days

Development of Prediction Model of Steel Fiber-reinforced Concrete Compressive Strength Using Random Forest Algorithm Combined with Hyperparameter Tuning and K-fold Cross-validation

Because of the incorporation of discontinuous fibers, steel fiber-reinforced concrete (SFRC) outperforms regular concrete. However, due to its complexity and limited available data, the development of SFRC strength prediction techniques is still in its infancy when compared to that of standard concrete. In this paper, the compressive strength of steel fiber-reinforced concrete was predicted from different variables using the Random forest model. Case studies of 133 samples were used for this aim. To design and validate the models, we generated training and testing datasets. The proposed models were developed using ten important material parameters for steel fiber-reinforced concrete characterization. To minimize training and testing split bias, the approach used in this study was validated using the 10-fold Cross-Validation procedure. To determine the optimal hyperparameters for the Random Forest algorithm, the Grid Search Cross-Validation approach was utilized. The root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE) between measured and estimated values were used to validate and compare the models. The prediction performance with RMSE=5.66, R2=0.88 and MAE=3.80 for the Random forest model. Compared with the traditional linear regression model, the outcomes showed that the Random forest model is able to produce enhanced predictive results of the compressive strength of steel fiber-reinforced concrete. The findings show that hyperparameter tuning with grid search and cross-validation is an efficient way to find the optimal parameters for the RF method. Also, RF produces good results and gives an alternate way for anticipating the compressive strength of SFR