Calibration of the Hypoplastic Clay model with a deep neutral network

Advanced constitutive models, here represented by the hypoplastic clay model, are powerful tools which provide engineers with reliable responses in various practical applications. However, the model calibration is not an easy task. Calibration of these models can be addressed with several approaches, which are generally distinguished as stochastic or deterministic approaches. In general, these approaches extract information from the experimental data and the subsequent optimisation process finds the best combination of parameters to fit the desired constraints. . The deterministic approach was integrated and combined in development of the online automated calibration tool ExCalibre. This paper presents a Machine Learning approach for automated calibration of the Hypoplastic Clay model. By using pairs of input experimental data and calibrated results performed by ExCalibre as training data, a Deep Neural Networks (DNNs) model is constructed to recognise how the experimental data can be used to derive the asymptotic state parameters such as the slope and the interception of the Normal Compression Line (NCL), or the critical friction angle, and the optimised stiffness parameters. The training and testing data comprise of In-house protocols and User-upload data over 3 years of launching the ExCalibre, and synthetic data with small distortion to prevent overfitting. Finally, investigations on how the DNNs model recognises the asymptotic patterns, as well as its calibration results will be presented.