Köken, E.2025-09-252025-09-2520229786050114942https://hdl.handle.net/20.500.12573/4222The present study encompasses a quantitative investigation on rock comminution using statistical and soft computing analyses. For this purpose, physical and mechanical rock aggregate properties were determined for nine different rock types (R1-R9) in Turkey. Then, crushability tests were performed to determine the size reduction ratio (SRR) using a laboratory-scale jaw crusher. Based on statistical and soft computing analyses, five different predictive models (M1 to M5) were established to estimate the SRR in this study. Consequently, the SRR values are associated with water absorption by weight (w<inf>a</inf>), dry unit weight (γ<inf>d</inf>), and aggregate impact value (AIV) of the investigated rocks. However, the individual use of these independent variables results in undulating SRR estimations. Therefore, among the established predictive models, the empirical formulation based on artificial neural networks (ANN) (M5) was found to be the most reliable model with a correlation of determination value (R2) of 0.88. However, the predictive models stated in this study should be implemented to several portable jaw crushers to observe the similarities or difficulties in quantifying SRR as a function of rock properties in future studies. © 2022 Elsevier B.V., All rights reserved.eninfo:eu-repo/semantics/closedAccessCrushed StoneJaw CrusherRock CrushabilitySize Reduction RatioSoft ComputingAggregatesComminutionNeural NetworksRocksSize DeterminationWater AbsorptionComputing AnalysisCrushed StonesJaw CrushersPredictive ModelsReduction RatiosRock CrushabilitySize Reduction RatioSize-ReductionSoft-ComputingStatistical ComputingSoft ComputingConference Object2-s2.0-85138348271