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. 2022 Oct 5;15(19):6899.
doi: 10.3390/ma15196899.

A Deep Learning Method for the Prediction of the Index Mechanical Properties and Strength Parameters of Marlstone

Mohammad Azarafza  1 , Masoud Hajialilue Bonab  1 , Reza Derakhshani  2
Affiliations

A Deep Learning Method for the Prediction of the Index Mechanical Properties and Strength Parameters of Marlstone

Mohammad Azarafza et al. Materials (Basel). .

Abstract

The index mechanical properties, strength, and stiffness parameters of rock materials (i.e., uniaxial compressive strength, c, ϕ, E, and G) are critical factors in the proper geotechnical design of rock structures. Direct procedures such as field surveys, sampling, and testing are used to estimate these properties, and are time-consuming and costly. Indirect methods have gained popularity in recent years due to their time-saving and highly accurate results, which are comparable to those obtained through direct approaches. This study presents a procedure for establishing a deep learning-based predictive model (DNN) for obtaining the geomechanical characteristics of marlstone samples that have been recovered from the South Pars region of southwest Iran. The model was implemented on a dataset resulting from the execution of numerous geotechnical tests and the evaluation of the geotechnical parameters of a total of 120 samples. The applied model was verified by using benchmark learning classifiers (e.g., Support Vector Machine, Logistic Regression, Gaussian Naïve Bayes, Multilayer Perceptron, Bernoulli Naïve Bayes, and Decision Tree), Loss Function, MAE, MSE, RMSE, and R-square. According to the results, the proposed DNN-based model led to the highest accuracy (0.95), precision (0.97), and the lowest error rate (MAE = 0.13, MSE = 0.11, and RMSE = 0.17). Moreover, in terms of R2, the model was able to accurately predict the geotechnical indices (0.933 for UCS, 0.925 for E, 0.941 for G, 0.954 for c, and 0.921 for φ).

Keywords: deep learning; geomechanical properties; marlstone; rock material; rock strength parameters.

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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Figure 1
Figure 1
Location map of the South Pars region in Iran.
Figure 2
Figure 2
A histogram of the geomechanical indices.
Figure 3
Figure 3
Topical shallow and deep neural network architectures [49].
Figure 4
Figure 4
The implemented DNN model flowchart.
Figure 5
Figure 5
Correlation between the measured data and the predicted model for the geotechnical values.
Figure 6
Figure 6
A comparison between the measured and predicted values based on the proposed method.
Figure 7
Figure 7
The loss function for the DNN model.
Figure 8
Figure 8
Prediction error evaluation for the DNN model and its components.
Figure 9
Figure 9
Variation chart for geotechnical parameters.
See this image and copyright information in PMC

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