Predicting the maximum dry density and optimum moisture content from soil index properties using efficient soft computing techniques

Ali, Hunar Farid Hama; Omer, Bashdar; Mohammed, Ahmed Salih; Faraj, Rabar H.

doi:10.1007/s00521-024-09734-7

Neural Comput & Applic

2024

DOI: 10.1007/s00521-024-09734-7

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Predicting the maximum dry density and optimum moisture content from soil index properties using efficient soft computing techniques

Hunar Farid Hama Ali,

Bashdar Omer,

Ahmed Salih Mohammed

et al.

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Cited by 3 publications

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Ahmed

2025

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Estimating compressive strength of high-performance concrete using different machine learning approaches

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2025

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Ijaz

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Transportation Geotechnics

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The Use of Waste Materials Red Mud and Bottom Ash as Road Embankment Fill

Soğancı,

Özkan,

Yenginar

et al. 2024

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This study provides a comprehensive evaluation of red mud as a sustainable material for road base construction, particularly in combination with bottom ash. Red mud, a by-product of the Bayer process used in alumina extraction, is known for its high alkalinity and heavy metal content. For this reason, this waste material causes environmental challenges. Red mud sourced from the Eti Aluminum Factory in Seydişehir, Konya (Turkey), was stabilized with bottom ash. Then, these waste materials were tested through a number of experiments, such as in relation to their Atterberg limits, compaction characteristics, unconfined compressive strength (UCS), California bearing ratio (CBR), and microstructure through a scanning electron microscopy (SEM) analysis. The results highlight that the UCS of stabilized red mud samples significantly improved with the addition of bottom ash and longer curing periods. Specifically, the UCS values increased from 0.5 MPa to 2.5 MPa after 28 days of curing. Moreover, RM specimens stabilized with 25% bottom ash achieved a CBR value of 146.64% after 28 days, far exceeding Turkey’s road fill material requirement, which mandates a minimum unsoaked CBR value of 15%. These findings indicate that red mud stabilized with bottom ash not only meets but exceeds the structural requirements for road base materials. This approach provides a sustainable solution for the environmental management of red mud while contributing to infrastructure development. Through the recycling of these industrial by-products, this study presents a viable method to reduce waste and support economic and environmental sustainability in road construction projects.

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Predicting the maximum dry density and optimum moisture content from soil index properties using efficient soft computing techniques

Cited by 3 publications

References 75 publications

Estimating compressive strength of high-performance concrete using different machine learning approaches

Estimating compressive strength of high-performance concrete using different machine learning approaches

Big data-driven global modeling of cohesive soil compaction across conceptual and arbitrary energies through machine learning

The Use of Waste Materials Red Mud and Bottom Ash as Road Embankment Fill

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