Assessment of Los Angeles Abrasion Value (LAAV) and Magnesium Sulphate Soundness (M wl) of Rock Aggregates Using Gene Expression Programming and Artificial Neural Networks

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 th...

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Bibliographic Details
Published in:Archives of mining sciences = Archiwum górnictwa 2022-01, Vol.67 (3), p.401
Main Author: Köken, Ekin
Format: Article
Language:English
Subjects:
Abrasion
Aggregates
Artificial neural networks
Design engineering
Formulations
Gene expression
Magnesium
Magnesium sulfate
Magnesium sulphates
Mathematical models
Mixtures
Neural networks
Paving
Prediction models
Rock
Rocks
Statistical analysis
Statistical methods
Statistics
Sulfates
Testing procedures
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Summary: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.
ISSN:0860-7001
1689-0469
DOI:10.24425/ams.2022.142407