Manh Duc Nguyen1, Ha Nguyen Hai1, Nadhir Al-Ansari2,*, Mahdis Amiri3, Hai-Bang Ly4, Indra Prakash5, Binh Thai Pham4,*
CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.1, pp. 149-166, 2022, DOI:10.32604/cmes.2022.017355
Abstract One of the important geotechnical parameters required for designing of the civil engineering structure is the
compressibility of the soil. In this study, the main purpose is to develop a novel hybrid Machine Learning (ML)
model (ANFIS-DE), which used Differential Evolution (DE) algorithm to optimize the predictive capability of
Adaptive-Network-based Fuzzy Inference System (ANFIS), for estimating soil Compression coefficient (Cc) from
other geotechnical parameters namely Water Content, Void Ratio, Specific Gravity, Liquid Limit, Plastic Limit, Clay
content and Depth of Soil Samples. Validation of the predictive capability of the novel model was carried out using
statistical indices: Root Mean Square… More >
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