AI-Driven Analysis of Tuff and Lime Effects on Basalt Fiber-Reinforced Clay Strength
In this study, free compression tests were conducted to examine the changes in the strength of soil after adding 24 mm long basalt fiber (1%), lime (3%, 6%, 9% by dry weight), and tuff (10%, 20%, 30% by dry weight) before curing and after 28, 42, and 56 days of curing. Instead of the K + BF 1% + SL...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
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| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/15/14/2433 |
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| Summary: | In this study, free compression tests were conducted to examine the changes in the strength of soil after adding 24 mm long basalt fiber (1%), lime (3%, 6%, 9% by dry weight), and tuff (10%, 20%, 30% by dry weight) before curing and after 28, 42, and 56 days of curing. Instead of the K + BF 1% + SL 9% mixture, where the SL ratio is high, it has been revealed that T, which has a lower SL content and is environmentally friendly (as in the K + BF 1% + SL 6% + T 10% mixture), can be used considering environmental factors and costs. However, due to the length and cost of experimental studies, the use of artificial intelligence to reduce the need for physical tests/experiments and to accelerate processes will provide savings in terms of labor, time, and cost. Unconfined compressive strength (q<sub>u</sub>) prediction was performed using the artificial neural network (ANN) technique. The accuracy of the ANN model was proven using the R and MSE metrics. In addition, a q<sub>u</sub> prediction of the mixture with 30% water content was performed according to the curing times. The experimental and predicted q<sub>u</sub> values for the curing times were compared and presented. |
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| ISSN: | 2075-5309 |