Köken, E.2025-09-252025-09-2520222064-5228https://doi.org/10.14513/actatechjaur.00661https://hdl.handle.net/20.500.12573/3788In this study, the Young modulus (E) of different coals was investigated using artificial neural networks (ANN). For this purpose, a comprehensive literature survey was carried out to compile such datasets available for the ANN analyses. As a result of the literature survey, a database composed of 81 datasets was formed. In the ANN analyses, uniaxial compressive strength (UCS) and dry density (ρ<inf>d</inf>) of coals were adopted as input parameters. The ANN analysis results demonstrated that the predictive model established in this study could be reliably used to estimate the E for different coals. The correlation of determination value (R2) for the developed model is 0.85, which shows its relative success. In this context, this study can be declared a case study showing the applicability of ANN for the evaluation of E for a wide range of coal types. However, the number of samples and independent variables should be increased to obtain more comprehensive models in future studies. © 2025 Elsevier B.V., All rights reserved.eninfo:eu-repo/semantics/openAccessArtificial Neural NetworksCoalDeformation PropertiesYoung ModulusArticle10.14513/actatechjaur.006612-s2.0-85138315403