Prediction of thermal conductivity and damage in Indian Jalore granite for design of underground research laboratory

Verma, A. K.; Jha, Manish Kumar; Gautam, Prabhat; Mishra, Arun Kumar; Vardhan, Harsh; Singh, Shashank

doi:10.1007/s00521-021-05944-5

Cited by 9 publications

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Once the ANN analyses were trained, the predictive equations could be established by using the weights and biases extracted from each ANN analysis. In this regard, predictive models for estimating the BAV of natural stones were derived by using Equation (2) [ 67 , 68 ]:

where W 0 and W i are the weight vectors of the output and input layers, respectively; B 0 and B i are the bias vectors of the output and input layers, respectively; x i is the normalized input parameter; and f 0 and f i are the transfers functions (tansig).…”

Section: Artificial Neural Network (Ann) Analysesmentioning

confidence: 99%

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

Strzałkowski

Köken

2022

Materials

View full text Add to dashboard Cite

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.

show abstract