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

Mateusz Jocz

Mateusz Jocz

Interdisciplinary Doctoral School, Lodz University of Technology, Division of Geotechnics and Engineering Structures Department of Concrete Structures, Lodz University of TechnologyŁódź,
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Jocz, Mateusz
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Marek Lefik

Marek Lefik

Division of Geotechnics and Engineering Structures Department of Concrete Structures, Lodz University of TechnologyŁódź,
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Lefik, Marek
  
13 nov 2023
Correlation between Cone Penetration Test parameters, soil type, and soil liquidity index using long short-term memory neural network's Cover Image
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Categoría del artículo: Special Issue 19th KKMGiIG
Publicado en línea: 13 nov 2023
Páginas: 405 - 415
Recibido: 03 mar 2023
Aceptado: 04 oct 2023
DOI: https://doi.org/10.2478/sgem-2023-0023
Palabras clave
© 2023 Mateusz Jocz et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1:

CPTU probe test scheme.
CPTU probe test scheme.

Figure 2:

An example of a research point consisting of: a) a cross section of the borehole, where Sa – sand, Si – silt, Cl – clay, Mg – made ground, Gr – gravel; b–d) graphs of the basic parameters of CPTU probing, such as qc, fs, and u2; e) a graph of the calculated laboratory values of the liquidity index IL for the selected layers.
An example of a research point consisting of: a) a cross section of the borehole, where Sa – sand, Si – silt, Cl – clay, Mg – made ground, Gr – gravel; b–d) graphs of the basic parameters of CPTU probing, such as qc, fs, and u2; e) a graph of the calculated laboratory values of the liquidity index IL for the selected layers.

Figure 3:

Liquidity index IL before (green line) and after (orange line) transformation for the example research point.
Liquidity index IL before (green line) and after (orange line) transformation for the example research point.

Figure 4:

Results of identification of liquidity index with the developed LSTM network in comparison with known values of the liquidity index. Explanation in the text.
Results of identification of liquidity index with the developed LSTM network in comparison with known values of the liquidity index. Explanation in the text.

Figure 5:

Comparison of the obtained results with the correlation proposed by PN-B-04452:2002 and with laboratory results transformed by Equation 3 for profile number 7.
Comparison of the obtained results with the correlation proposed by PN-B-04452:2002 and with laboratory results transformed by Equation 3 for profile number 7.

Figure 5:

Borehole profiles.
Borehole profiles.

Figure 6:

Results of identification of soil type with the developed LSTM network (predicted data) in comparison with known borehole profiles (actual data). The profile includes the following data numbers: profile 1 (0–540), profile 2 (540–2115), profile 3 (2115–2837), profile 4 (2837–3547), profile 5 (3547–4600), profile 6 (4600–5318), profile 7 (5318–6304), profile 8 (6304–8060).
Results of identification of soil type with the developed LSTM network (predicted data) in comparison with known borehole profiles (actual data). The profile includes the following data numbers: profile 1 (0–540), profile 2 (540–2115), profile 3 (2115–2837), profile 4 (2837–3547), profile 5 (3547–4600), profile 6 (4600–5318), profile 7 (5318–6304), profile 8 (6304–8060).

Division of soil types into categories_

Soil types Symbol Category
Made ground Mg 1
Fine sand FSa 2
Medium sand MSa 3
Glacial till siSa, clSa, sasiCl 4
Settled deposits clSi, saSi, Si, 5
Settled deposits with gravel grsaSi, grclSi 6
Gravel Gr 7
Idioma:
Inglés
Calendario de la edición:
4 veces al año
Temas de la revista:
Geociencias, Geociencias, otros, Ciencia de los materiales, Compuestos, Materiales porosos, Física, Mecánica y dinámica de fluidos
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