Prediction of Uniaxial Compression Strength of Limestone Based on the Point Load Strength and SVM Model

Li, Shaoqian; Wang, Yu; Xie, Xuefang

doi:10.3390/min11121387

Cited by 7 publications

(4 citation statements)

References 38 publications

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“…However, both the AI models possessed higher a degree of accuracy and can be applied where necessary. While comparing the performance of other AI models for stabilized soil mixes, it was revealed that the support vector machine (SVM) model prediction performance is significantly better than the traditional fitting function [42]. In another study a comparison was made between the results obtained by the prediction techniques.…”

Section: Prediction Performance Of Thementioning

confidence: 99%

Prediction of Strength and CBR Characteristics of Chemically Stabilized Coal Gangue: ANN and Random Forest Tree Approach

et al. 2022

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Coal mining waste in the form of coal gangue (CG) was established recently as a potential fill material in earthworks. To ascertain this potential, this study forecasts the strength and California Bearing Ratio (CBR) characteristics of chemically stabilized CG by deploying two widely used artificial intelligence approaches, i.e., artificial neural network (ANN) and random forest (RF) regression. In this research work, varied dosage levels of lime (2, 4, and 6%) and gypsum (0.5, 1, and 1.5%) were employed for determining the unconfined compression strength (UCS) and CBR of stabilized CG mixes. An experimental study comprising 384 datasets was conducted and the resulting database was used to develop the ANN and RF regression models. Lime content, gypsum dosage, and 28 d curing period were considered as three input attributes in obtaining three outputs (i.e., UCS, unsoaked CBR, and soaked CBR). While modelling with the ANN technique, different algorithms, hidden layers, and the number of neurons were studied while selecting the optimum model. In the case of RF regression modelling, optimal grid comprising maximal depth of tree, number of trees, confidence, random splits, enabled parallel execution, and guess subset ratio were investigated, alongside the variable number of folds, to obtain the best model. The optimum models obtained using the ANN approach manifested relatively better performance in terms of correlation coefficient values, equaling 0.993, 0.995, and 0.997 for UCS, unsoaked CBR and soaked CBR, respectively. Additionally, the MAE values were observed as 45.98 kPa, 1.41%, and 1.18% for UCS, unsoaked CBR, and soaked CBR, respectively. The models were also validated using 2-stage validation processes. In the first stage of validation of the model (using unseen 30% of the data), it was revealed that reliable performance of the models was attained, whereas in the second stage (parametric analysis), results were achieved which are corroborated with those in existing literature.

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Section: Prediction Performance Of Thementioning

confidence: 99%

Prediction of Strength and CBR Characteristics of Chemically Stabilized Coal Gangue: ANN and Random Forest Tree Approach

et al. 2022

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“…With the advances in computer-based algorithms, the geo-material strength indexes for different materials can be predicted and can achieve the expected level of reliability. Subsequently, many scientific researchers have launched various aspects of related research related to these predictions [8]. These studies have been implemented to complete the advancements of traditional procedures to estimate the geomechanical index properties of rock materials based on UCS, point-load [9], or Schmidt hammer [10] results.…”

Section: Introductionmentioning

confidence: 99%

Support Vector Machine (SVM) Application for Uniaxial Compression Strength (UCS) Prediction: A Case Study for Maragheh Limestone

et al. 2023

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The geomechanical properties of rock materials, such as uniaxial compression strength (UCS), are the main requirements for geo-engineering design and construction. A proper understanding of UCS has a significant impression on the safe design of different foundations on rocks. So, applying fast and reliable approaches to predict UCS based on limited data can be an efficient alternative to regular traditional fitting curves. In order to improve the prediction accuracy of UCS, the presented study attempted to utilize the support vector machine (SVM) algorithm. Multiple training and testing datasets were prepared for the UCS predictions based on a total of 120 samples recorded on limestone from the Maragheh region, northwest Iran, which were used to achieve a high precision rate for UCS prediction. The models were validated using a confusion matrix, loss functions, and error tables (MAE, MSE, and RMSE). In addition, 24 samples were tested (20% of the primary dataset) and used for the model justifications. Referring to the results of the study, the SVM (accuracy = 0.91/precision = 0.86) showed good agreement with the actual data, and the estimated coefficient of determination (R2) reached 0.967, showing that the model’s performance was impressively better than that of traditional fitting curves.

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“…In civil engineering, Grubbs' test was used to identify outliers in geotechnical data, such as soil properties, rock mechanics, and foundation performance [20,21]. Several studies applied Grubbs' test to find the possible outliers in the shear strength data of rock at the 5% confidence level [22,23]. Grubbs' test assumes that the data are normally distributed, which is why it may not be appropriate for datasets that are not normally distributed.…”

Section: Introductionmentioning

confidence: 99%

Review of Applicable Outlier Detection Methods to Treat Geomechanical Data

2023

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The reliability of geomechanical models and engineering designs depend heavily on high-quality data. In geomechanical projects, collecting and analyzing laboratory data is crucial in characterizing the mechanical properties of soils and rocks. However, insufficient lab data or underestimating data treatment can lead to unreliable data being used in the design stage, causing safety hazards, delays, or failures. Hence, detecting outliers or extreme values is significant for ensuring accurate geomechanical analysis. This study reviews and categorizes applicable outlier detection methods for geomechanical data into fence labeling methods and statistical tests. Using real geomechanical data, the applicability of these methods was examined based on four elements: data distribution, sensitivity to extreme values, sample size, and data skewness. The results indicated that statistical tests were less effective than fence labeling methods in detecting outliers in geomechanical data due to limitations in handling skewed data and small sample sizes. Thus, the best outlier detection method should consider this matter. Fence labeling methods, specifically, the medcouple boxplot and semi-interquartile range rule, were identified as the most accurate outlier detection methods for geomechanical data but may necessitate more advanced statistical techniques. Moreover, Tukey’s boxplot was found unsuitable for geomechanical data due to negative confidence intervals that conflicted with geomechanical principles.

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Prediction of Uniaxial Compression Strength of Limestone Based on the Point Load Strength and SVM Model

Cited by 7 publications

References 38 publications

Prediction of Strength and CBR Characteristics of Chemically Stabilized Coal Gangue: ANN and Random Forest Tree Approach

Prediction of Strength and CBR Characteristics of Chemically Stabilized Coal Gangue: ANN and Random Forest Tree Approach

Support Vector Machine (SVM) Application for Uniaxial Compression Strength (UCS) Prediction: A Case Study for Maragheh Limestone

Review of Applicable Outlier Detection Methods to Treat Geomechanical Data

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