Formulation of flow number of asphalt mixes using a hybrid computational method

206 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Compressive Strength of Fly-Ash-Based Geopolymer Concrete by Gene Expression Programming and Random Forest (2021, background): “+e literature also recommended that the square of correlation coefficient between the experimental and model predictive output (R′2 o ) or between model predictive output and experimental values (R(2)o) should come near 1 [112].”
Assessment of artificial neural network and genetic programming as predictive tools (2015, methodology): “To overcome these difficulties, the weights and biases were frozen after the network was well trained; the trained ANN models were then translated into explicit forms [3].”
Energy-based numerical models for assessment of soil liquefaction (2012, methodology): “Contrary to these methods, conventional methods (e.g., regression and finite element method) assume the structure of the model in advance, which may be suboptimal (Alavi et al., 2011).”
Machine learning in geosciences and remote sensing (2016, background): “A notable feature of GP and its variants is that they can produce prediction equations without a need to pre-de ﬁ ne the form of the existing relationship (Alavi et al., 2010; Alavi and Gandomi, 2011; Alavi et al., 2011a; Gandomi and Alavi, 2011).”