Krzysztof Ostrowski

TL;DR: This study includes the collection of data from the experimental work and the application of ML techniques to predict the CS of concrete containing fly ash, and shows high accuracy towards the prediction of outcome as indicated by its high coefficient correlation (R2) value.

TL;DR: In this article, a comparison of algorithms between individuals and ensemble approaches, such as bagging Optimization for bagging is done by making 20 sub-models to depict the accurate one Variables like cement content, fine and coarse aggregate, water, binder-to-water ratio, fly-ash, and superplasticizer are used for modeling Model performance is evaluated by various statistical indicators like mean absolute error (MAE), mean square error (MSE), and root Mean Square Error (RMSE) individual algorithms show a moderate bias result However, the ensemble model gives

TL;DR: In this article, a scientometric review on waste material utilization in concrete is presented, and the most active research areas are identified and discussed, while the sustainability aspects of recycling waste materials in concrete are discussed and finally, future research is proposed.

TL;DR: In this article, the impact of coarse aggregate morphology on the behavior of fresh concrete mixture, as well as on the deformation and compressive strength of the hardened concrete, favors the creation of the efficiency of Self-Compacting High-Performance Concrete (SCHPC).

TL;DR: The morphology of the coarse aggregate has an impact on the strength and stiffness properties of self-compacting high-performance fibre-reinforced concrete (SCHPFRC), and the hypothesized mechanism of the formation of SCHPFRC was proposed.