Fusing of optimized intelligence models by virtue of committee machine for estimation of the residual shear strength of clay

Owing to high dependency of landslide stability to residual shear strength (RSS) of clay, provide a sophisticated strategy for modeling of this parameter is advantageous. This paper present strategy based upon fusing of optimized intelligence models for estimation of RSS of clay as a function of readily available data. The developed model is achieved through implementing two following steps. In the first step, two optimized models including optimized neural network, and optimized fuzzy logic are developed for estimation of RSS of clay. Optimizing method which implanted in predictive models for improving those performance is bat-inspired algorithm. In second step, committee machine (CM) is employed for combining outputs of aforementioned optimized models. Bat-inspired is incorporated in CM for determining optimal contribution of optimized elements in final prediction. The superior performance of CM rather than its elements is ascertain through those evaluation based on statistical criteria. Results of this study infer that proposed methodology provide an alternative way for making quantitative formulation between RSS of clay and its index properties.