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Fiber-reinforced soil (FRS) has been used as a promising alternative material for civil and construction engineering. Shear strength of FRS is influenced complexly by many factors including fiber properties, soil properties, and stress conditions. This inherent complexity limits the ability of designers to assess shear strength parameters and has made it difficult to establish a mathematical model for accurately predicting the FRS shear strength. Accurately estimating the shear strength of FRS is vital for civil engineers in designing geotechnical structures and management. Thus, this work proposed a novel computational method, namely a swarm intelligence optimized regression (SIOR) system to estimate the peak shear strength of randomly distributed FRS. The SIOR system integrates a machine learning technique with an enhanced swarm intelligence algorithm to obtain reliable and good performance in …