Abstract
Aggregate is the most extracted material from the world's mines and widely used in civil and construction projects. The Micro-Deval abrasion test (MD) is one of the most important tests that provides characteristics of crushed aggregates that show their resistance against mechanical abrasive factors such as repeated impact loading. The impact of various factors on abrasive resistance properties of aggregates has led researchers to seek correlations, often focusing on limited data samples, leading to reduced accuracy. This study employs machine learning (ML) methods to predict MD abrasion values, considering diverse aggregate properties. Various ensemble ML methods were applied, revealing the exceptional performance of the stacking model, which achieved an R2 score of 0.95 in predicting aggregate abrasion resistance. The feature importance analysis highlights the influence of factors such as Magnesium Sulfate Soundness (MSS), Water Absorption (ABS), and Los Angeles Abrasion (LAA) on aggregate abrasion values, suggesting that the use of multiple test methods could yield a more dependable assessment of aggregate durability.
Recommended Citation
A. Roshan and M. Abdelrahman, "Improving Aggregate Abrasion Resistance Prediction Via Micro-Deval Test using Ensemble Machine Learning Techniques," Engineering Journal, vol. 28, no. 3, pp. 15 - 24, Chulalongkorn University, Jan 2024.
The definitive version is available at https://doi.org/10.4186/ej.2024.28.3.15
Department(s)
Civil, Architectural and Environmental Engineering
Keywords and Phrases
Aggregate abrasion resistance and durability; ensemble machine learning; friction assessment; micro-Deval abrasion test
International Standard Serial Number (ISSN)
0125-8281
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Chulalongkorn University, All rights reserved.
Publication Date
01 Jan 2024
