Evaluation of soil liquefaction using AI technology incorporating a coupled ENN / t-SNE model

This paper presents a new evolutionary neural network (ENN) algorithm coupled with the dimensionality reduction technique ‘t-distributed stochastic neighbour embedding’ (t-SNE). The ENN model features the crossbreeding of a differential evolution method and a stochastic gradient optimisation algorithm. The t-SNE is used to visualise the training and testing datasets and the ENN model performance. The proposed ENN model is applied to a relatively large soil liquefaction database. The good convergence and generalisation ability of the proposed model and the negligible misclassification results demonstrate that the proposed ENN model can provide accurate, efficient, and flexible results. The prominent and practical abilities of t-SNE to recover the structure of the initial conditions and to demonstrate the ENN model performance are discussed. This coupled approach simplifies the analysis and/or prediction of hazards for which large quantities of data are required. •The drawbacks of existing optimisation techniques are summarised.•An evolutionary neural network to overcome the limitations of existing methods is proposed.•The dimensionality reduction technique t-SNE to real data is implemented.•An updated liquefaction database is report.•The proposed coupled methodology is applied to assess soil.