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2 2023

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Reduction of Numerical Model in Some Geotechnical Problems

Góral Artur, Lefik Marek, Wojciechowski Marek

Studia Geotechnica et Mechanica

2023

Vol. 45, nr s1

333--349

The concept of equivalence of the realistic, initial reference model and the simplified, reduced model is proposed. In reduced models, the action of the soil on the structure is replaced by the action of a layer with prescribed properties, defined by a set of parameters. The main difficulty here is to find the parameter values required by the simplified theory. The subject of this work is to find the dependence of the parameters of the reduced model on the parameters of the full model, including realistic soil behavior, in order to ensure the equivalence of both models. We show the potential of the method by presenting two examples: Winkler and Pasternak's model of a plate on the ground. We assume that both models are equivalent if they give identical results (displacements) at a finite number of observation points. An artificial neural network (ANN) is built in order to approximate and record the dependence of the parameters of the reduced model (at the network output) from the parameters of the full model (given at the network input). The complex network acts as a formula that assigns the parameters of the reduced model to a realistic description of the soil structure that is used for finite element method (FEM) modeling. The formalism we propose is quite general and can be applied to many engineering problems. The presented procedure is entirely numerical; it allows to calculate the parameters of the reduced model without resorting to symbolic calculations or additional theoretical considerations.

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

Jocz Mateusz, Lefik Marek

Studia Geotechnica et Mechanica

2023

Vol. 45, nr s1

405--415

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 IL. 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.