Computational Modeling of Desiccation Properties (CW, LS, and VS) of Waste-Based Activated Ash-Treated Black Cotton Soil for Sustainable Subgrade Using Artificial Neural Network, Gray-Wolf, and Moth-Flame Optimization Techniques
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
Advances in Materials Science and Engineering
Abstract
Artificial neural network (ANN), gray-wolf, and moth-flame optimization (GWO and MFO) techniques have been used in this
research work to predict the effect of activated sawdust ash (ASDA) on the crack width (CW), linear shrinkage (LS), and
volumetric shrinkage (VS) of a black cotton soil utilized as a subgrade material. Problematic soils or black cotton soils are not good
pavement foundation materials except that they are pretreated in order to meet the basic strength characteristics required for
roads in Nigeria. Due to this reason, there has been ongoing research to evaluate the best practices in which black cotton soils can
be favorably utilized in earthwork construction. On the other hand, there is a huge concern on the solid waste management system
in the wood processing environment and the recycling of sawdust into ash and its reuse as an alternative binder has offered a
sustainable disposal system. +e work tries to use AI-based techniques to predict the crack and shrinkage behaviors of BCS treated
with saw dust ash activated with alkali materials. +ere was appreciable improvement in the shrinkage and crack parameters over
the 30-day drying period due to the addition of ASDA. +e intelligent model results showed that the three techniques successfully
predicted the CW, LS, and VS with a performance accuracy above 90%, while ANN produced the minimal error in performance
outperforming the other techniques. Sensitivity study showed that the drying time (T) was the most influential of the studied
parameter. Hence, soil stabilization has shown its potential system of waste management in the wood processing industry.
Description
Keywords
Computational Modeling, Desiccation Properties (CW, LS, and VS), Artificial Neural Network, Gray-Wolf
Citation
Onyelowe, K. C., Shakeri, J., Amini-Khoshalan, H., Usungedo, T. F., & Alimoradi-Jazi, M. (2022). Computational Modeling of Desiccation Properties (CW, LS, and VS) of Waste-Based Activated Ash-Treated Black Cotton Soil for Sustainable Subgrade Using Artificial Neural Network, Gray-Wolf, and Moth-Flame Optimization Techniques. Advances in Materials Science and Engineering, 2022. https://doi.org/10.1155/2022/4602064