An evolutionary approach for modeling of shear strength of RC deep beams

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Compressive Strength of Fly-Ash-Based Geopolymer Concrete by Gene Expression Programming and Random Forest (2021, background): “Genetic programming (GP), one of the strong soft computing techniques, is worthy as it develops a model without considering the previously established relationships [48, 49].”
Prediction of the residual strength of clay using functional networks (2016, methodology): “Based on an experiment on 80 specimens, Tiwari and Marui (2005) presented a triangular correlation chart to calculate fr based on mineralogical composition of soils.”
Estimation of monthly evaporative loss using relevance vector machine, extreme learning machine and multivariate adaptive regression spline models (2016, methodology): “Following earlier works (e.g., (Gandomi et al. 2013; Samui and Dixon 2012)), we performed the sensitivity percentage of the output (E) to each of the predictor (x) variable in the following manner Zi ¼ fmaxðxiÞ fminðxiÞ ð26:1Þ Si ¼ ZiPn i¼1 Zj 100 ð26:2Þ where fmax(xi) and fmax(xi) are,…”