Assessment of Böhme Abrasion Value of Natural Stones through Artificial Neural Networks (ANN)

Strzałkowski, Paweł; Köken, Ekin

doi:10.3390/ma15072533

Cited by 7 publications

(4 citation statements)

References 48 publications

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“…The influences of waste PP fibres and palm oil fuel ash (POFA) on the abrasion resistance (using a Böhme surface abrasion machine) of the concrete were investigated. Strzałkowski and Köken [46] developed an alternative to the standard Böhme test method using neural networks to assess rock abrasiveness based on physical and mechanical properties such as dry density (ρ d ), water absorption by weight (w a ), Shore hardness value (SHV), pulse wave velocity (V p ) and uniaxial compressive strength (UCS) of rocks.…”

Section: Rock Abrasiveness Test Methods Applicationmentioning

confidence: 99%

Application of Rock Abrasiveness and Rock Abrasivity Test Methods—A Review

Mucha

2023

Sustainability

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The processes of rock formation have long been known and widely described in many literature items worldwide. Due to the multitude of occurring rock types, they are distinguished by various properties. For many decades, scientists worldwide have been determining various parameters by which these properties of rocks can be described. Tests of these parameters are commonly performed in many research centres worldwide. Depending on the scientific discipline, some researchers focus on geological properties (colour, structure, texture, chemical composition). Other researchers focus on physical and mechanical properties: hardness, density, strength properties, compactness, etc. Among them, abrasiveness and abrasivity can also be distinguished. In terms of nomenclature, they are very similar and often confused. Even within the academic community, researchers often use the names interchangeably, which needs to be corrected. This article aims to explain the difference between rock abrasiveness and rock abrasivity, classify methods for their assessment and present their practical applications in the mining and construction industry. It should be emphasized here that abrasiveness is determined when we are interested in the abrasive wear of natural stone and abrasivity when we are interested in the wear of the tool with which we cut the stone. The purpose of this article is also to let the reader decide whether to carry out an abrasiveness or abrasivity test and which method to use.

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Section: Rock Abrasiveness Test Methods Applicationmentioning

confidence: 99%

Application of Rock Abrasiveness and Rock Abrasivity Test Methods—A Review

Mucha

2023

Sustainability

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“…This analysis will help to understand which properties are more indicated to abrasion resistance, which is the aim of this paper. The detailed analysis is presented in the following charts, [6], [8]. The correlations of testing parameters presented in the charts will be analysed with equations in Table 2.…”

Section: Experimental Workmentioning

confidence: 99%

Abrasion parameter of natural stone for public infrastructure and correlations with other properties

Kabashi,

Krasniqi,

Basha

et al. 2023

E-Zbornik, Elektronički Zbornik Radova Građevinskog Fakulteta

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The abrasion resistance is one of the crucial parameters to consider when evaluating natural stone for use in public infrastructure with light traffic. The assessment and evaluation of abrasion parameters can be carried out according to the EN 14157 standard, using two typical natural stones from different sources in order to compare the necessary parameters. The parameters will be analysed in correlation with other factors in order to find optimizations. The results showed a significant correlation coefficient between the dry sample and water saturation tests. This suggests that the abrasion resistance of natural stone when dry is closely related to its resistance when saturated with water. This information is valuable in selecting appropriate natural stone materials for public infrastructure, as it provides insights into how the materials will perform under different conditions. In public squares, natural stone is often used as paving material because of its durability, resistance to weathering, aesthetics and other parameters. However, different types of natural stone have different levels of abrasion resistance, which can affect their suitability for use in public squares.

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“…These methods are now widely used in rock mechanics because of how easily and adaptably they can anticipate the required values depending on a variety of inputs. These techniques are well suited for usage when conventional statistical techniques are less accurate in making predictions [38][39][40][41][42]. In rock mechanics, these methods are getting more attention now because they are flexible and make it easy to predict required values based on the input variables.…”

Section: Introductionmentioning

confidence: 99%

Appraisal of Different Artificial Intelligence Techniques for the Prediction of Marble Strength

et al. 2023

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Rock strength, specifically the uniaxial compressive strength (UCS), is a critical parameter mostly used in the effective and sustainable design of tunnels and other engineering structures. This parameter is determined using direct and indirect methods. The direct methods involve acquiring an NX core sample and using sophisticated laboratory procedures to determine UCS. However, the direct methods are time-consuming, expensive, and can yield uncertain results due to the presence of any flaws or discontinuities in the core sample. Therefore, most researchers prefer indirect methods for predicting rock strength. In this study, UCS was predicted using seven different artificial intelligence techniques: Artificial Neural Networks (ANNs), XG Boost Algorithm, Random Forest (RF), Support Vector Machine (SVM), Elastic Net (EN), Lasso, and Ridge models. The input variables used for rock strength prediction were moisture content (MC), P-waves, and rebound number (R). Four performance indicators were used to assess the efficacy of the models: coefficient of determination (R2), Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE). The results show that the ANN model had the best performance indicators, with values of 0.9995, 0.2634, 0.0694, and 0.1642 for R2, RMSE, MSE, and MAE, respectively. However, the XG Boost algorithm model performance was also excellent and comparable to the ANN model. Therefore, these two models were proposed for predicting UCS effectively. The outcomes of this research provide a theoretical foundation for field professionals in predicting the strength parameters of rock for the effective and sustainable design of engineering structures

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Assessment of Böhme Abrasion Value of Natural Stones through Artificial Neural Networks (ANN)

Cited by 7 publications

References 48 publications

Application of Rock Abrasiveness and Rock Abrasivity Test Methods—A Review

Application of Rock Abrasiveness and Rock Abrasivity Test Methods—A Review

Abrasion parameter of natural stone for public infrastructure and correlations with other properties

Appraisal of Different Artificial Intelligence Techniques for the Prediction of Marble Strength

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