Precise calibration of constitutive models for cyclic liquefaction is essential but often time-consuming and requires significant expertise, limiting broader application in geotechnical practice. This paper introduces an automatic calibration tool designed to streamline the process for advanced constitutive models under both monotonic and cyclic loading. The tool supports various types of monotonic and cyclic laboratory test data, offers multiple choices of suitable comparison planes for error calculation, with a focus to also suit cyclic liquefaction problems, and employs advanced optimization techniques. The calibration follows a two-stage approach: first, optimizing parameters governing monotonic response using monotonic test data; second, refining these and additional parameters with both monotonic and cyclic data. The critical state parameters are fixed throughout, while the elasticity parameters are fixed in the second stage, all within defined bounds. Using this automatic calibration tool and the adapted calibration strategy, extensive element-level test data was used to determine the parameters of the SANISAND-MSf model for a given sand. These calibrated parameters were then used to simulate boundary value problems, including centrifuge tests of liquefiable sand slopes and sheet-pile-supported liquefiable sand deposits, all subjected to base excitations, demonstrating excellent alignment with experimental results. This validation highlights the robustness, reproducibility, and accuracy of the tool to model cyclic liquefaction while significantly reducing the expertise and time required for calibration. This represents a significant advancement toward the broader adoption of advanced constitutive soil models in geotechnical engineering practice.

This paper introduces DEEM (Differential Evolution with Elitism and Multi-populations), a novel heuristic optimisation algorithm of the Differential Evolution family. DEEM integrates elitism and multi-population strategies to improve convergence speed and accuracy. Additionally, a diversity-based restart strategy is employed to significantly reduce the algorithm's susceptibility to being trapped in local minima. The influence of algorithm parameter choices on optimisation success is demonstrated through a sensitivity study. The algorithm's effectiveness is validated against benchmark functions from CEC 2015, 2017, 2020, and 2022, showing superior performance compared to state-of-the-art DE algorithms. Additionally, DEEM's application is showcased through a complex optimisation problem in the field of geotechnical engineering: the calibration of advanced constitutive models for predicting the stress-strain behaviour of soils under monotonic and cyclic loading. This calibration process is notably time-consuming. DEEM not only achieves better objective values but also does so in fewer iterations, thus significantly reducing computational time.