Knowledge Tree
concrete
Facts (23)
Sources
Recent breakthroughs in the valorization of lignocellulosic biomass ... pubs.rsc.org Jun 7, 2025 23 facts
referenceAnwar et al. (2023) utilized regression machine learning models to predict the compressive strength of concrete reinforced with cellulose nanofibers.
referenceKaplan, Tufan, and Özel investigated the effects of cellulose derivatives on concrete properties using response surface methodology, published in Construction and Building Materials in 2024.
claimKashyap et al. designed their Random Forest model to provide the highest possible mechanical properties for concrete with minimal resource use, aiming to reduce water pollution and land degradation.
referenceThe combined effect of cellulose rate and cellulose derivative on the compressive strength of concrete can be represented by 3D and 2D response surface plots, as detailed in research published by Elsevier in 2024 (ref. 263).
referenceXu et al. (2024) conducted experimental and mesoscopic studies on the mechanical properties of concrete incorporating rice husk ash and glass powder.
referenceMiao et al. (2024) studied the effect of shrinkage-induced initial damage on the frost resistance of concrete in cold regions.
measurementThe Random Forest model evaluated by Kashyap et al. achieved an R2-value of 0.95, a mean absolute error of 1.05 kN mm−2, and a mean square error of 5.25 kN mm−2.
referenceS. Gupta, H. W. Kua, and S. D. Pang studied the effect of biochar on the mechanical and permeability properties of concrete exposed to elevated temperatures in 2020.
claimKashyap et al. concluded that the Random Forest model is the most effective machine learning model for evaluating the compressive strength of concrete, based on testing over 54 different concrete mixes.
claimSun et al. identified the water-to-cement ratio as the most crucial factor in determining the compressive strength of concrete, followed by curing time, and the proportions of coarse and fine aggregates.
measurementAt a 100% replacement level of natural coarse aggregate with artificial lightweight aggregate in concrete, tensile strength was reduced by 34.7% and flexural strength was reduced by 20%.
measurementBashir et al. utilized a database of 192 data points, each representing different environmental conditions and concrete mix proportions, to train their hybrid machine learning model.
referenceD. K. Panesar and B. Shindman investigated the mechanical, transport, and thermal properties of mortar and concrete containing waste cork in a 2012 study published in Cement and Concrete Composites.
referenceS. D. Kore and J. S. Sudarsan evaluated hemp concrete as a sustainable green material for conventional concrete in a 2021 study published in the Journal of Building Material Science.
claimA drying shrinkage model study found that concrete samples cured at 40% relative humidity experienced more critical freeze-thaw damage, including higher apparent deterioration, greater mass loss rates, and more pronounced decay in compressive strength, compared to samples cured at standard conditions.
referenceWang et al. (2024) conducted numerical simulations of concrete mix proportions based on fluidity.
referenceH. Khan, J. Ahmad, Z. B. Zahid, S. Irfan, and M. Umer authored the paper 'Development of Extrusion Based Artificial Lightweight Aggregates from Sand-Plastic Waste Composite for Sustainable Concrete Production: Performance Evaluation and Life Cycle Assessment,' published in Case Studies in Construction Materials in 2025 (DOI: 10.1016/j.cscm.2025.e04663).
referenceBashir et al. (2025) developed a strategy using intelligent hybrid learning to predict the water-binder ratio of concrete that uses rice husk ash as a supplementary cementitious material.
claimA life cycle assessment (LCA) study concluded that using artificial lightweight aggregates from sand-plastic composite in concrete can reduce the potential for global warming by up to 54.83%.
referenceSun et al. (2024) investigated the effectiveness of carbon nanotubes on the compressive strength of concrete using AI-aided tools.
referenceS. Wang and L. Baxter conducted a comprehensive study of biomass fly ash in concrete, covering strength, microscopy, kinetics, and durability, in a 2007 paper published in Fuel Processing Technology.
claimA three-phase model of concrete based on the boundary element method, used for simulating chloride-induced corrosion of steel-reinforced concrete, was found to be more accurate than the finite element method (FEM).
claimBashir et al. developed a hybrid machine learning model using explainable artificial intelligence to determine the optimal water-to-binder ratio for improving the durability, performance, and sustainability of concrete.