The GEP-based formulations are developed for splitting tensile strength of concrete as a function of age of specimen and cylinder compressive strength.
The different parameters considered were fineness of fly ash, binder components, type of fine aggregate, molarity of alkaline solution, age of specimen, fluid-to-binder ratio, binder-to-aggregate ratio, degree of saturation, etc.
The data used in the artificial neural networks and fuzzy logic models are arranged in a format of eight input parameters that cover the age of specimen, cement, water, sand, aggregate, recycled aggregate, superplasticizer and silica fume.
In the set of the models, the age of specimen, cement, water, sand, aggregate, superplasticizers, fly ash and CaO are entered as input parameters, while the compressive strength values of concrete containing fly ash are used as output parameter.
The data used in the multilayer feed forward neural networks models are arranged in a format of eight input parameters that cover the age of specimen, cement, metakaolin (MK), silica fume (SF), water, sand, aggregate and superplasticizer.
The data used in the multilayer feed-forward neural networks and Sugeno-type fuzzy inference models are arranged in a format of five input parameters that cover the age of specimen, metakaolin replacement ratio, water binder ratio, superplasticizer and binder sand ratio.
Participating physicians collected specimens from patients whose illnesses included meningitis, encephalitis, influenza-like illness, HFMD, herpangina, and gastroenteritis, and documented patient age, date of specimen collection, symptoms, and suspected diagnosis.
It should increase the age of specimens' immersion in the sulfate to see the effect of this latter.
We should increase the age of specimens' immersion in the sulfate to see the effect of this latter.
The resulting correlation curve for each test is obtained by changing the level of compaction, water/cement ratio and concrete age of specimens.
Due to the high age of specimens the bone quality was low and labrums were calcified, which made it more difficult to determine the border between the labrum and the acetabular bone on the CT-scans.
