2021
DOI: 10.1007/s12517-021-06894-x
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A comparative study on the application of artificial intelligence networks versus regression analysis for the prediction of clay plasticity

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Cited by 6 publications
(3 citation statements)
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“…Table6indicate that Fuzzy logic (FL) and artificial neuron network gave more accurate results (𝑅𝑀𝑆𝐸, 𝑀𝐴𝐸 and 𝑅 2 ) than traditional non-linear regression analyses. Similar finding has been reported by several researchers(Işık 2009;Akan and Keskin 2019;Taleb Bahmed et al 2019;Mbarak et al 2020;Akbay Arama et al 2021;Gökçeoğlu 2022;Saadat and Bayat 2022) for prediction of geotechnical engineering parameters. The best performance was observed in Eq.…”
supporting
confidence: 91%
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“…Table6indicate that Fuzzy logic (FL) and artificial neuron network gave more accurate results (𝑅𝑀𝑆𝐸, 𝑀𝐴𝐸 and 𝑅 2 ) than traditional non-linear regression analyses. Similar finding has been reported by several researchers(Işık 2009;Akan and Keskin 2019;Taleb Bahmed et al 2019;Mbarak et al 2020;Akbay Arama et al 2021;Gökçeoğlu 2022;Saadat and Bayat 2022) for prediction of geotechnical engineering parameters. The best performance was observed in Eq.…”
supporting
confidence: 91%
“…In this study, the experimental data set is divided randomly such that 70 % of samples were used for training, 15% for validation and, 15 % for testing. Similar percent of training, validation and testing have been recommended by several researchers(Kalkan et al 2009;Dehghanbanadaki et al 2019;Moayedi and Hayati 2019;Taleb Bahmed et al 2019;Abu-Farsakh and Mojumder 2020;Moayedi et al 2020;Akbay Arama et al 2021;Tabarsa et al 2021;Armaghani et al 2022;Gökçeoğlu 2022;Lin et al 2022).The average coefficient of correlation values (𝑅) from ANN model were determined as 0.9761, 0.9778, 0.9663, and 0.9748 for training, validation, test and finally using complete dataset, respectively as presented in Fig.6.…”
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confidence: 82%
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