Correlation between Cone Penetration Test parameters, soil type, and soil liquidity index using long short-term memory neural network

Accuracy and quality of recognizing soil properties are crucial for optimal building design and for ensuring safety in the construction and exploitation stages. This article proposes use of long short-term memory (LSTM) neural network to establish a correlation between Cone Penetration Test (CPTU) results, the soil type, and the soil liquidity index I_L. LSTM artificial neural network belongs to the class of networks requiring deep machine learning and is qualitatively different from artificial neural networks of the multilayer perceptron type, which have long been widely used to interpret the results of geotechnical experiments. The article outlines the methodology of CPTU testing and laboratory testing of the liquidity index, as well as construction and preparation of data for the network. The proposed network achieved good results when considering a database consisting of the parameters of eight CPTU soundings, soil stratifications, and laboratory test results.