AI-Driven Analysis of Tuff and Lime Effects on Basalt Fiber-Reinforced Clay Strength - Topcuoglu, Yasemin Aslan; Duranay, Zeynep Bala; Gurocak, Zuelfu; Guldemir, Hanifi (Mdpi, 2025)
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 (qu) 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 qu prediction of the mixture with 30% water content was performed according to the curing times. The experimental and predicted qu values for the curing times were compared and presented.
Keywords: artificial intelligence; basalt fiber; kaolin clay; neural network; slaked lime; tuff