Mojtaba Rahimi Shahid, Gholam Reza Lashkaripour, Naser Hafezi Moghaddas,
Volume 19, Issue 6 (12-2025)
The Sanandaj–Sirjan Structural-Sedimentary Zone is one of the most important geological regions in Iran, where the limestone formations play a key role in civil engineering and mining projects. Precise knowledge of the mechanical properties of these rocks, especially the Uniaxial Compressive Strength (UCS Dry) and the Dry Point Load Index (Is ₅₀-Dry), is essential for the safe and economical design of structures. Due to the high cost and time-consuming nature of direct testing methods, this study employs indirect modeling techniques, including regression and neural networks, to predict these properties.Initially, a comprehensive database was compiled by collecting physical, mechanical, dynamic, and chemical data of limestone samples from the region. Then, univariate, bivariate, and multivariate regression analyses were conducted to extract statistical relationships among the variables. Subsequently, multilayer perceptron neural network models with various architectures based on the Levenberg–Marquardt learning algorithm were developed. The comparison results of model performance indicated that neural networks, due to their ability to identify complex and nonlinear relationships between parameters, provide more accurate predictions of the limestone mechanical properties compared to statistical models. A comparison of the correlation coefficients of multivariate regression equations and neural network models showed that, overall, using neural network models improves the accuracy of UCS Dry predictions by 14.89% and the Is ₅₀-Dry predictions by 4.70%. The application of these models can contribute to cost reduction, increased speed of rock engineering studies, and improved safety in civil projects.
