ArticleMaterials (Basel, Switzerland)2025
Estimation of Compressive Strength of Basalt Fiber-Reinforced Kaolin Clay Mixture Using Extreme Learning Machine.
Article in Materials (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed.
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3 authors.
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Abstract
backgroundIn this study, the unconfined compressive strength (q
methodsThe experimental studies included data derived from a series of mixtures where water contents of 20%, 25%, 30%, and 35% were combined with kaolin clay reinforced with 24 mm-long basalt fiber at reinforcement rates of 0%, 1%, 2%, and 3%. Based on the experimental results obtained for these mixtures, an ELM model was developed to predict the q
resultsELM, recognized for its computational efficiency and high predictive accuracy, demonstrated exceptional performance in this application, achieving an R value of 0.9976 and an RMSE of 0.0001. Furthermore, this study includes a figure representation illustrating that the ELM-based predictions align closely with the experimental results, underscoring its reliability.
conclusionsTo further validate its performance, ELM was compared with other artificial intelligence models through a 5-fold cross-validation approach. The analysis revealed that ELM outperformed its counterparts, achieving a remarkable RMSE value of 0.000174, thereby solidifying its capability to accurately estimate the compressive strength of the soil under varying reinforcement and water content conditions. Thus, it is aimed to save labor, material, and time.
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