Ensemble machine learning models for predicting strength of concrete with foundry sand and coal bottom ash as fine aggregate replacements. [PDF]
Paruthi S +4 more
europepmc +1 more source
Cementitous Binders/Superplasticizers Compatibilities and Incompatibilities
La résistance mécanique des bétons exigée actuellement impose une diminution du rapport eau/ciment et l'utilisation de superplastifiant pour faciliter la mise en oeuvre du matériau frais. Les produits de type polycarboxylate sont très efficaces à court terme, mais peuvent mener à une perte rapide de l'ouvrabilité en cas d'incompatibilité liant ...
openaire +1 more source
Prediction and parametric modeling of compressive strength of waste marble dust concrete through machine learning and experimental analysis. [PDF]
Alzlfawi A +7 more
europepmc +1 more source
Study on optimization of compressive properties of phosphorus gypsum-blast furnace slag-phosphorus tailings unburned brick. [PDF]
Wang J, ShengqingWang, Tuo B.
europepmc +1 more source
Bayesian machine learning for inverse design of ultra-high-performance concrete. [PDF]
Childs C +6 more
europepmc +1 more source
Experimental design and analysis of toughness and modulus of rupture (MOR) of cement mortar reinforced with PET fibers. [PDF]
Wang X +5 more
europepmc +1 more source
Fracture Toughness, Compressive Strength and Fracture Surface Morphology of Cement Mortars Modified with Nano-SiO<sub>2</sub>. [PDF]
Iskra-Kozak W, Konkol J, Poręba M.
europepmc +1 more source

