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Comparison of machine learning models predicting the pull-off strength of modified epoxy resin floors

Mateusz Moj

Mateusz Moj

Wroclaw University of Science and Technology, Faculty of Civil Engineering, WrocławWrocław, Poland
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Moj, Mateusz
, 
Łukasz Kampa

Łukasz Kampa

Wroclaw University of Science and Technology, Faculty of Civil Engineering, WrocławWrocław, Poland
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Kampa, Łukasz
 y 
Sławomir Czarnecki

Sławomir Czarnecki

Wroclaw University of Science and Technology, Faculty of Civil Engineering, WrocławWrocław, Poland
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Czarnecki, Sławomir
  
10 nov 2024
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Categoría del artículo: Original Study
Publicado en línea: 10 nov 2024
Páginas: 377 - 388
Recibido: 07 jul 2024
Aceptado: 18 sept 2024
DOI: https://doi.org/10.2478/sgem-2024-0024
Palabras clave
© 2024 Mateusz Moj et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1:

Diagram of the basic factors that damage epoxy resin floors.
Diagram of the basic factors that damage epoxy resin floors.

Figure 2:

Search volume chart for publications based on keywords in Google Scholar and Science Direct on 03/07/2024.
Search volume chart for publications based on keywords in Google Scholar and Science Direct on 03/07/2024.

Figure 3:

Granite powder used.
Granite powder used.

Figure 4:

Linen fibers used.
Linen fibers used.

Figure 5:

Pull-off test.
Pull-off test.

Figure 6:

Violin plot of the parameter: a) amount of Component A [%], b) amount of Component B [%], c) amount of granite powder [%], d) amount of linen fibers [%], e) density [g/cm3], and f) fb [MPa].
Violin plot of the parameter: a) amount of Component A [%], b) amount of Component B [%], c) amount of granite powder [%], d) amount of linen fibers [%], e) density [g/cm3], and f) fb [MPa].

Figure 7:

Pearson correlation matrix.
Pearson correlation matrix.

Figure 8:

Pull-off strength of the modified epoxy resin coating.
Pull-off strength of the modified epoxy resin coating.

Figure 9:

Relationship between the predicted value and the experimental value of the pull-off strength fb for the: a) RL model, b) ANN model, c) DT model, and d) RF model.
Relationship between the predicted value and the experimental value of the pull-off strength fb for the: a) RL model, b) ANN model, c) DT model, and d) RF model.

Figure 10:

Relative errors for data sets for selected artificial intelligence algorithms.
Relative errors for data sets for selected artificial intelligence algorithms.

Figure 11:

Histograms of absolute error values for a) ANN, b) DT, c) RF, and d) LR.
Histograms of absolute error values for a) ANN, b) DT, c) RF, and d) LR.

Figure 12:

Visualization of SHAP values for the RF ML model.
Visualization of SHAP values for the RF ML model.

Elements of the decision tree and random forest algorithm_

Number of input categories Depth of trees Number of trees (only for RF) Minimum subset to be divided Minimum number of categories in the leaf
5 1–20 20–200 5 2

Descriptive statistics of the input and output parameters_

Min. Max. St.dev. Mean Range
Amount of Component A [%] 0,455 0,752 0,077 0,560 0,297
Amount of Component B [%] 0,155 0,310 0,035 0,252 0,105
Amount of granite powder [%] 0,000 0,375 0,112 0,182 0,375
Amount of linen fibers [%] 0,000 0,015 0,005 0,006 0,015
Density [g/cm3] 1,100 1,306 0,060 1,196 0,206
fb [MPa] 1,950 3,520 0,223 2,546 1,570

Summary of correlation coefficients R, RMSE, and average percentage forecast errors MAPE for selected models_

AI Model Statistical metrics
R [-] RMSE [MPa] MAPE [%]
Linear regression 0,6277 0,2299 7,4244
Decision tree 0,8310 0,1643 4,0814
Random forest 0,8848 0,1376 3,7156
Artificial neural networks 0,8744 0,1312 3,8098
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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