A virtual calibration chamber for cone penetration test based on deep-learning approaches

Abstract

The interpretation of the cone penetration test (CPT) still relies largely on empirical correlations that have been predominantly developed in resource-intensive and time-consuming calibration chambers. This paper presents a CPT virtual calibration chamber using deep learning (DL) approaches, which allow for the consideration of depth-dependent cone resistance profiles through the implementation of two proposed strategies: (1) depth-resistance mapping using a multilayer perceptron (MLP) and (2) sequence-to-sequence training using a long short-term memory (LSTM) neural network. Two DL models are developed to predict cone resistance profiles ( q c ) under various soil states and testing conditions, where Bayesian optimization (BO) is adopted to identify the optimal hyperparameters. Subsequently, the BO-MLP and BO-LSTM networks are trained using the available data from published datasets. The results show that the models with BO can effectively improve the prediction accuracy and efficiency of neural networks compared to those without BO. The two training strategies yielded comparable results in the testing set, and both can be used to reproduce the whole cone resistance profile. An extended comparison and validation of the prediction results are carried out against numerical results obtained from a coupled Eulerian-Lagrangian (CEL) model, demonstrating a high degree of agreement between the DL and CEL models. Ultimately, to demonstrate the usability of this new virtual calibration chamber, the predicted q c is used to enhance the preceding correlations with the relative density ( D r ) of the sand. The improved correlation with superior generalization has an R 2 of 82% when considering all data, and 89.6% when examining the pure experimental data.

Topics & Concepts