Knowledge Tree
compressive strength
Facts (14)
Sources
Recent breakthroughs in the valorization of lignocellulosic biomass ... pubs.rsc.org Jun 7, 2025 14 facts
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).
measurementA study on sustainable concrete developed from artificial lightweight aggregate originating from sand-plastic composite found an 84.5% retention of compressive strength at a 30% replacement level of natural coarse aggregate, while 100% replacement resulted in 52% of the original compressive strength.
claimA sensitivity analysis using a Random Forest model determined that water and cement proportions are the key factors influencing the compressive strength of reinforced concrete, while the addition of coarse aggregates negatively affects compressive strength.
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.
measurementIn a research study, 7 out of 10 machine learning models predicted the effectiveness of cellulose nanofiber (CNF) addition in enhancing the compressive strength of concrete materials with an R2-value greater than 0.6.
referenceSun et al. employed K-nearest neighbour, linear regression, and artificial neural network models on 282 data points to evaluate the effect of various parameters on the compressive strength of cementitious material.
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.
measurementIn the study by Sun et al., the artificial neural network model was the most effective model for evaluating cementitious material compressive strength, achieving an R2-value of 0.885.
referenceSun et al. (2024) investigated the effectiveness of carbon nanotubes on the compressive strength of concrete using AI-aided tools.
referenceNawaz et al. conducted a study on the use of Municipal waste incinerator bottom ash (MIBA) in fabricating alkali-activated concrete materials, evaluating their mechanical performance through compressive strength, SEM analysis, and dry density tests, alongside an LCA study on environmental impact.
claimThe use of Municipal Incinerator Bottom Ash (MIBA) as a replacement material in construction is limited to a 15% replacement level to ensure that the compressive strength of the material remains above the required threshold.
claimAnwar et al. utilized various regression machine learning models—including Random Forest, Support Vector Regressor, Gradient Boosting Regressor, Bagging Regressor, and Decision Tree—to predict the compressive strength of CNF-modified sustainable concrete composite.
referenceThe durability properties of low-carbon mortar with similar compressive strength influence its life-cycle carbon reduction, as investigated in a 2025 study by Z. Jiang, K. Lu, M. Limdevit, C. Fu, Y. Ling, and B. Dong.