WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Citation - WoS: 6Citation - Scopus: 7Assessment of Los Angeles Abrasion Value (LAAV) and Magnesium Sulphate Soundness (MWL) of Rock Aggregates Using Gene Expression Programming and Artificial Neural Networks(Polska Akad Nauk, Polish Acad Sciences, 2023-07-24) Koken, EkinIt has been acknowledged that two important rock aggregate properties are the Los Angeles abrasion value (LAAV) and magnesium sulphate soundness (Mwl). However, the determination of these properties is relatively challenging due to special sampling requirements and tedious testing procedures. In this stu-dy, detailed laboratory studies were carried out to predict the LAAV and Mwl 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 demonstra-ted 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 Mwl 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.Article Citation - WoS: 1Assessment of Installed Power for Inclined Belt Conveyors Using Genetic Algorithm and Artificial Neural Networks(Konya Teknik Univ, 2022-06-01) Koken, EkinIn this study, the installed power (P inst , kW) of several inclined belt conveyors operating in the mining industry of Turkey was investigated through two soft computing algorithms (i.e., genetic expression programming (GEP) and artificial neural networks (ANN)). For this purpose, the most crucial belt (i.e., belt length (L), belt width (W), belt inclination (alpha)), operational (i.e., belt speed (Vb) b ) and throughput (Q)) and infrastructural (belt weight (Wb) b ) and idler weight (Wid)) id )) features of 42 belt conveyors were collected for each investigated belt conveyor. The collected data was transformed into a comprehensive dataset for soft computing analyses. Based on the GEP and ANN analyses, two robust predictive models were proposed to estimate the P inst . The performance of the proposed models was evaluated using several statistical indicators, and the statistical evaluations demonstrated that the models yielded a correlation of determination (R2) 2 ) greater than 0.95. Nevertheless, the ANN-based model has slightly overperformed in predicting the P inst values. In conclusion, the proposed models can be reliably used to estimate the P inst for the investigated conveyor belts. In addition, the mathematical expressions of the proposed models were given in the present study to let users implement them more efficiently.