Leveraging Artificial Neural Networks to Predict Igneous Rock Strength Parameters from Petrological Contents

compressive strength (UCS), Brazilian tensile strength (BTS), and
Los Angeles Abrasion (LAA), through conventional methods is both
time-consuming and resource-intensive. To address this challenge,
this study develops efficient artificial neural network (ANN) models
optimized with Levenberg-Marquardt (LM), Bayesian
Regularization (BR), and Scaled Conjugate Gradient (SCG)
algorithms to predict UCS, BTS, and LAA from petrographic
properties, advancing civil engineering applications. A total of 100
dolerite samples were analyzed to assess their strength and
petrographic characteristics, with the ANNs trained on the three
aforementioned algorithms. The results indicate that the BR model
achieved the highest accuracy, with a correlation coefficient (R) of
0.9999 and a root mean square error (RMSE) of 0.3164. The LM
model also demonstrated strong performance with an R-value of
0.9997 and an RMSE of 0.8619. The BR and LM models
significantly outperformed the SCG model, which had an R-value of
0.9954 and an RMSE of 2.398. Sensitivity analysis identified
plagioclase and chlorite as the most influential factors in predicting
rock mechanical parameters. The effectiveness of the BR and LM
techniques highlights their potential to offer substantial time and cost
savings in rock strength prediction.