Assessment of Böhme Abrasion Value of Natural Stones through Artificial Neural Networks (ANN)
Özet
This present study explored the Böhme abrasion value (BAV) of natural stones through
artificial neural networks (ANNs). For this purpose, a detailed literature survey was conducted to
collect quantitative data on the BAV of different natural stones from Turkey. As a result of the ANN
analyses, several predictive models (M1–M13) were established by using the rock properties, such
as the dry density (ρd), water absorption by weight (wa), Shore hardness value (SHV), pulse wave
velocity (Vp), and uniaxial compressive strength (UCS) of rocks. The performance of the established
predictive models was evaluated by using several statistical indicators, and the performance analyses
indicated that four of the established models (M1, M5, M10, and M11) could be reliably used to
estimate the BAV of natural stones. In addition, explicit mathematical formulations of the proposed
ANN models were also introduced in this study to let users implement them more efficiently. In this
context, the present study is believed to provide practical and straightforward information on the
BAV of natural stones and can be declared a case study on how to model the BAV as a function of
different rock properties. *This research was funded by the Ministry of Education and Science Subsidy 2021 and 2022 for the Department of Mining WUST, the grant number is 8211104160.
*Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT)
