@ARTICLE{Köken_Ekin_Assessment_2022,
 author={Köken, Ekin},
 volume={vol. 67},
 number={No 3},
 journal={Archives of Mining Sciences},
 pages={401-422},
 howpublished={online},
 year={2022},
 publisher={Committee of Mining PAS},
 abstract={It has been acknowledged that two important rock aggregate properties are the Los Angeles abrasion value (LAAV) and magnesium sulphate soundness (M wl). However, the determination of these properties is relatively challenging due to special sampling requirements and tedious testing procedures. In this study, detailed laboratory studies were carried out to predict the LAAV and M wl for 25 different rock types located in NW Turkey. For this purpose, mineralogical, physical, mechanical, and aggregate properties were determined for each rock type. Strong predictive models were established based on gene expression programming (GEP) and artificial neural network (ANN) methodologies. The performance of the proposed models was evaluated using several statistical indicators, and the statistical analysis results demonstrated that the ANN-based proposed models with the correlation of determination (R2) value greater than 0.98 outperformed the other predictive models established in this study. Hence, the ANN-based predictive models can reliably be used to predict the LAAV and M wl for the investigated rock types. In addition, the suitability of the investigated rock types for use in bituminous paving mixtures was also evaluated based on the ASTM D692/D692M standard. Accordingly, most of the investigated rock types can be used in bituminous paving mixtures. In conclusion, it can be claimed that the proposed predictive models with their explicit mathematical formulations are believed to save time and provide practical knowledge for evaluating the suitability of the rock aggregates in pavement engineering design studies in NW Turkey.},
 type={Article},
 title={Assessment of Los Angeles Abrasion Value (LAAV) and Magnesium Sulphate Soundness (M wl) of Rock Aggregates Using Gene Expression Programming and Artificial Neural Networks},
 URL={http://journals.pan.pl/Content/124553/PDF/Archiwum-67-3-02-Ekin%20Koken.pdf},
 doi={10.24425/ams.2022.142407},
 keywords={Rock aggregate, Aggregate properties, Los Angeles abrasion loss, Magnesium sulphate soundness, Gene expression programming, Artificial Neural Network},
}