Calibration of a Multiphase Poroelasticity Model Using Genetic Algorithms

Clibration of constitutive models is a critical step in geotechnical design, ensuring that numerical predictions accurately reproduce observed behavior. This study explores the use of Genetic Algorithms (GAs) for the calibration of Biot’s poroelastic model based on oedometer test data. A two-stage calibration procedure was applied. In Stage 1, when five parameters were varied simultaneously, the results showed strong parameter intercorrelation and structural non-identifiability, as different parameter sets produced nearly identical objective function values. To address this limitation, an identifiability analysis was performed, leading to parameter reduction. In Stage 2, only A, R, and k were calibrated, with N and H fixed, which resulted in stable and interpretable solutions. The GA-based approach demonstrated convergence, the absence of local minima, and good agreement with experimental data. The study highlights both the potential and the current limitations of GA-based calibration and should be regarded as a proof-of-concept rather than a complete identifiability study.