Predicting the compressive strength of polymer-infused bricks: A machine learning approach with SHAP interpretability. [PDF]
Chandra SS +5 more
europepmc +1 more source
Microscale hydrogels (microgels) feature improved mass transport characteristics supportive of fast actuation and chemical tunability amenable to programmed stimuli response. A unique soft actuator architecture is realized by encapsulating microgels in soft microcirculatory systems which enable the convenient delivery of liquid stimuli for powering and
Nengjian Huang +2 more
wiley +1 more source
Real-time prediction of early concrete compressive strength using AI and hydration monitoring. [PDF]
Marchewka A +2 more
europepmc +1 more source
Comparison of deep LSTM and machine learning models for predicting compressive strength of fly ash/slag-based geopolymer concrete. [PDF]
Kina C +4 more
europepmc +1 more source
Compressive and shear strength as well as compaction of soils with different textures
W. Owczarzak, S. Rzasa
openalex +1 more source
Prediction of uniaxial compressive strength of limestone from ball mill grinding characteristics using supervised machine learning techniques. [PDF]
Swamy SV +5 more
europepmc +1 more source
Evaluating Effects of Wrinkle Defects on Impact Response and Residual Compressive Strength After Impact in CFRP. [PDF]
Wang J +5 more
europepmc +1 more source
Evaluating the impact of waste marble on the compressive strength of traditional concrete using machine learning. [PDF]
Onyelowe KC +8 more
europepmc +1 more source
Comparison of polymerization behaviors, microhardness and compressive strength between bulk-fill resin and dual-cured core resin. [PDF]
Kim HJ +5 more
europepmc +1 more source

