<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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 <italic>I<sub>L</sub></italic>. 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.</p>
</abstract>

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.

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

Division of Geotechnics and Engineering Structures Department of Concrete Structures

Studia Geotechnica et Mechanica

This work is licensed under the Creative Commons Attribution 4.0 International License.

Accuracy and quality of recognizing soil properties are crucial for optimal building design and for ensuring safety in the construction and exploitation...

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

Article Category: Special Issue 19th KKMGiIG

Data publikacji: 13 lis 2023

Zakres stron: 405 - 415

Otrzymano: 03 mar 2023

Przyjęty: 04 paź 2023

DOI: https://doi.org/10.2478/sgem-2023-0023

Słowa kluczowe
geotechnical parameters, Cone Penetration Test (CPTU), liquidity index, Long Short-Term Memory (LSTM) neural network

© 2023 Mateusz Jocz et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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

Article Category: Special Issue 19th KKMGiIG

Data publikacji: 13 lis 2023

Zakres stron: 405 - 415

Otrzymano: 03 mar 2023

Przyjęty: 04 paź 2023

DOI: https://doi.org/10.2478/sgem-2023-0023

Słowa kluczowegeotechnical parameters, Cone Penetration Test (CPTU), liquidity index, Long Short-Term Memory (LSTM) neural network

© 2023 Mateusz Jocz et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
geotechnical parameters, Cone Penetration Test (CPTU), liquidity index, Long Short-Term Memory (LSTM) neural network