The data used in NN model are arranged in a format of seven input parameters that cover the water to binder ratio, water content, fine aggregate ratio, fly ash content, air entraining agent, superplasticizer and silica fume replacement.
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.
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.
A multilayer feed forward network has been used with input data arranged in a format of three input parameters that cover the density of timber, the time of fire exposure and the distance from exposed side and the output parameter being the temperature in timber.
The data used in the ANN and FIS models are arranged in a format of four input parameters that cover the water/cement ratio, cement abundance coefficient, cross-section area of specimens and curing time, and output parameter, which is the free expansion strain of SSC.
The data used in the multilayer feed forward neural networks models and input variables of genetic programming models are arranged in a format of eight input parameters that cover the cement content (C), nanoparticle content (N), aggregate type (AG), water content (W), the amount of superplasticizer (S), the type of curing medium (CM), Age of curing (AC) and number of testing try (NT).
The data used for the input data in ANFIS models are arranged in a format of seven input parameters that cover the thickness of layers, the number of layers, the adhesive type, the crack tip configuration, the content of SiC particles, the content of methacrylated butadiene styrene particles and the number of test trial.
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.
The data used in the multilayer feed forward neural networks models and input variables of genetic programming models have been arranged in a format of eight input parameters that cover the cement content, nanoparticle content, aggregate type, water content, the amount of superplasticizer, the type of curing medium, age of curing and number of testing try.
28 (2002) 243 262], has been extended with respect to the blocking factor ℓ. Unlike the unique blocking factor ℓ=2p in the original algorithm running on p processors, the current blocking factor is a variable parameter that covers the values in two different regions––namely, ℓ=p/k and ℓ=2kp for some integer k.
For the above reasons, our aim with this work is to provide EEM parameter sets that cover most of the drug-like molecules and with accuracy comparable to QM charges.
