Predicting the Unconfined Compressive Strength of Rice Husk Ash – Treated Fine-grained Soils
This study aims to develop novel and accurate data-driven predictive models to replace labor-intensive laboratory testing for estimating the unconfined compressive strength (UCS) of problematic soils treated with rice husk ash (RHA) Full Quadratic, Interaction, M5P-tree, and Artificial Neural Netwo...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Koya University
2025-06-01
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| Series: | ARO-The Scientific Journal of Koya University |
| Subjects: | |
| Online Access: | https://88.198.206.215/index.php/aro/article/view/1967 |
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| Summary: | This study aims to develop novel and accurate data-driven predictive models to replace labor-intensive laboratory testing for estimating the unconfined compressive strength (UCS) of problematic soils treated with rice husk ash (RHA) Full Quadratic, Interaction, M5P-tree, and Artificial Neural Network (ANN) were trained and evaluated using a dataset of 211 samples that involved seven key geotechnical parameters, including RHA content (0–30%), liquid limit (22–108%), plasticity index (1.3–82%), maximum dry density (1.2–1.9 g/cm3), optimum moisture content (10.5–42.6%), and curing time (CT) (0–112 days). Among all these models, the ANN model demonstrated superior performance (R2 = 0.97, RMSE = 24 kPa, MAE = 17 kPa, SI = 0.10). Sensitivity analysis revealed CT as the most influence factor (21.9%), followed by moisture content (16.1%) and RHA content (15.3%). The findings present that these predictive models provide a hybrid empirical–machine learning approach, and an accurate alternative to traditional UCS testing, significantly reducing the need for laboratory experiments. They also emphasize enhanced geotechnical performance and the sustainable reuse of agricultural waste. Furthermore, the models can offer a time-efficient solution with practical applications in areas such as highway development and foundation engineering.
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| ISSN: | 2410-9355 2307-549X |