Deep neural network based framework for complex correlations in engineering metrics

Linear or polynomial regression and artificial neural networks are often adopted to obtain correlation models between various attributes in engineering fields. Although these are straightforward, they may not perform well for datasets that involve complex correlations among multiple attributes, and overfitting can occur when high-order polynomials are used to match the data from one scenario but fail to produce accurate predictions elsewhere. This paper presents a Deep Neural Networks (DNN) based framework for obtaining complex correlations in engineering metrics and provides guidelines to assess data adequacy, remove outliers and to identify and resolve overfitting problems. Moreover, a clear and concise set of procedures for tuning hyperparameters of DNN is discussed. As an illustration, a DNN model was trained to predict the undrained shear strength of clays based on liquid limit, plastic limit, water content, vertical effective stress, and preconsolidation stress. This analysis is conducted with 1101 samples gathered from different sites all over the world. Prediction of soil strengths often involved significant uncertainties due to the natural variations in earth materials and site conditions, contributing to complex relationships among various material properties. Our results show that the proposed framework performs better than conventional correlation models established from previous studies. The developed framework and accompanying Python script can be readily applied to the prediction of clay properties at other sites, and also to other types of engineering metrics.