Application of soft computing methods to estimate uniaxial compressive strength and elastic modulus of soft sedimentary rocks

Shahani, Niaz Muhammad; Zheng, Xigui; Liu, Cancan; Li, Peng; Hassan, Fawad Ul

doi:10.1007/s12517-022-09671-6

Cited by 10 publications

(2 citation statements)

References 57 publications

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“…They found that the KNN and RF are reliable approaches to predict both UCS and E. Also, it was found that P-wave velocity has strong correlations with the UCS and E. Based on predictive performance, the RF model was proposed to predict the UCS and E as the best model. Shahani et al [ 21 , 27 ] in a comprehensive study measured the UCS, E, dry and wet densities, and Brazilian tensile strength of soft sedimentary rocks and predicted the UCS and E by employing the MLR models, ANN, and ANFIS from other rock parameters. Their research indicated that the approaches are suitable ways to predict the UCS and E. It is also revealed that the prediction accuracy of the ANFIS is the best among all the employed models.…”

Section: Introductionmentioning

confidence: 99%

Non-destructive test-based assessment of uniaxial compressive strength and elasticity modulus of intact carbonate rocks using stacking ensemble models

Fereidooni,

Karimi,

Ghasemi

2024

PLoS ONE

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The uniaxial compressive strength (UCS) and elasticity modulus (E) of intact rock are two fundamental requirements in engineering applications. These parameters can be measured either directly from the uniaxial compressive strength test or indirectly by using soft computing predictive models. In the present research, the UCS and E of intact carbonate rocks have been predicted by introducing two stacking ensemble learning models from non-destructive simple laboratory test results. For this purpose, dry unit weight, porosity, P‐wave velocity, Brinell surface harnesses, UCS, and static E were measured for 70 carbonate rock samples. Then, two stacking ensemble learning models were developed for estimating the UCS and E of the rocks. The applied stacking ensemble learning method integrates the advantages of two base models in the first level, where base models are multi-layer perceptron (MLP) and random forest (RF) for predicting UCS, and support vector regressor (SVR) and extreme gradient boosting (XGBoost) for predicting E. Grid search integrating k-fold cross validation is applied to tune the parameters of both base models and meta-learner. The results demonstrate the generalization ability of the stacking ensemble method in the comparison of base models in the terms of common performance measures. The values of coefficient of determination (R2) obtained from the stacking ensemble are 0.909 and 0.831 for predicting UCS and E, respectively. Similarly, the stacking ensemble yielded Root Mean Squared Error (RMSE) values of 1.967 and 0.621 for the prediction of UCS and E, respectively. Accordingly, the proposed models have superiority in the comparison of SVR and MLP as single models and RF and XGBoost as two representative ensemble models. Furthermore, sensitivity analysis is carried out to investigate the impact of input parameters.

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Section: Introductionmentioning

confidence: 99%

Non-destructive test-based assessment of uniaxial compressive strength and elasticity modulus of intact carbonate rocks using stacking ensemble models

Fereidooni,

Karimi,

Ghasemi

2024

PLoS ONE

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“…In recent years, artificial intelligence (AI) technology has been rapidly developed, as well as its ability to establish a robust relationship between input and output variables. This technology has been demonstrated through the successful resolution of various engineering challenges in the fields of medicine [14], agriculture [15], machinery [16], and mining [17][18][19][20]. Unlike the empirical and the theoretical models, the AI technology represented by the machine learning (ML) models does not have limitations on the number of input variables and does not rely on constants [13,21], addressing some of the drawbacks associated with traditional models.…”

Section: Introductionmentioning

confidence: 99%

Tunnel Boring Machine Performance Prediction Using Supervised Learning Method and Swarm Intelligence Algorithm

Yu,

Li,

Zhou

2023

Mathematics

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This study employs a supervised learning method to predict the tunnel boring machine (TBM) penetration rate (PR) with high accuracy. To this end, the extreme gradient boosting (XGBoost) model is optimized based on two swarm intelligence algorithms, i.e., the sparrow search algorithm (SSA) and the whale optimization algorithm (WOA). Three other machine learning models, including random forest (RF), support vector machine (SVM), and artificial neural network (ANN) models, are also developed as the drawback. A database created in Shenzhen (China), comprising 503 entries and featuring 10 input variables and 1 output variable, was utilized to train and test the prediction models. The model development results indicate that the use of SSA and WOA has the potential to improve the XGBoost model performance in predicting the TBM performance. The performance evaluation results show that the proposed WOA-XGBoost model has achieved the most satisfactory performance by resulting in the most reliable prediction accuracy of the four performance indices. This research serves as a compelling illustration of how combined approaches, such as supervised learning methods and swarm intelligence algorithms, can enhance TBM prediction performance and can provide a reference when solving other related engineering problems.

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Prediction of the Uniaxial Compressive Strength of Rocks by Soft Computing Approaches

Khajevand

2023

Geotech Geol Eng

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Application of soft computing methods to estimate uniaxial compressive strength and elastic modulus of soft sedimentary rocks

Cited by 10 publications

References 57 publications

Non-destructive test-based assessment of uniaxial compressive strength and elasticity modulus of intact carbonate rocks using stacking ensemble models

Non-destructive test-based assessment of uniaxial compressive strength and elasticity modulus of intact carbonate rocks using stacking ensemble models

Tunnel Boring Machine Performance Prediction Using Supervised Learning Method and Swarm Intelligence Algorithm

Prediction of the Uniaxial Compressive Strength of Rocks by Soft Computing Approaches

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