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• W4311185752 abstract "Developing new optimization algorithms and data mining has improved traditional engineering structural analysis models (meaning basically swarm-based solutions). Additionally, an accurate quantification of in situ friction capacity (ISFC) of driven piles is of paramount importance in design/construction of geotechnical infrastructures. A number of studies have underscored the use of models developed via artificial neural networks (ANNs) in anticipation of the bearing capacity of driven piles. Nonetheless, the main drawbacks of implementing the techniques relying on artificial neural networks are their slow convergence rate and reliable testing outputs. The current research focused on establishing an accurate/reliable predictive network of ISFC. Therefore, an adaptive neuro-fuzzy inference system (ANFIS) coupled with Harris hawk optimization (HHO), salp swarm algorithm (SSA), teaching-learning-based optimization (TLBO), and water-cycle algorithm (WCA) is employed. The findings revealed that the four models could accurately assimilate the correlation of ISFC to the referenced parameters. The values of the root mean square error (RMSE) realized in the prediction phase were 8.2844, 7.4746, 6.6572, and 6.8528 for the HHO-ANFIS, SSA-ANFIS, TLBO-ANFIS, and WCA-ANFIS, respectively. The results depicted WCA-ANFIS as more accurate than the three other algorithms at the testing and training phase, and could probably be utilized as a substitute for laboratory/classical methods." @default.
• W4311185752 created "2022-12-24" @default.
• W4311185752 creator A5036763708 @default.
• W4311185752 date "2022-12-01" @default.
• W4311185752 modified "2023-09-30" @default.
• W4311185752 title "In Situ Skin Friction Capacity Modeling with Advanced Neuro-Fuzzy Optimized by Metaheuristic Algorithms" @default.
• W4311185752 cites W1998151312 @default.
• W4311185752 cites W1999284878 @default.
• W4311185752 cites W2001422417 @default.
• W4311185752 cites W2019207321 @default.
• W4311185752 cites W2032270680 @default.
• W4311185752 cites W2040650998 @default.
• W4311185752 cites W2063907334 @default.
• W4311185752 cites W2065731394 @default.
• W4311185752 cites W2066380554 @default.
• W4311185752 cites W2073117529 @default.
• W4311185752 cites W2096673585 @default.
• W4311185752 cites W2118044993 @default.
• W4311185752 cites W2160829579 @default.
• W4311185752 cites W2417430847 @default.
• W4311185752 cites W2738900493 @default.
• W4311185752 cites W2768643127 @default.
• W4311185752 cites W2768692478 @default.
• W4311185752 cites W2783827472 @default.
• W4311185752 cites W2790332992 @default.
• W4311185752 cites W2801674441 @default.
• W4311185752 cites W2802700742 @default.
• W4311185752 cites W2804798422 @default.
• W4311185752 cites W2808327663 @default.
• W4311185752 cites W2900682747 @default.
• W4311185752 cites W2902956521 @default.
• W4311185752 cites W2913772749 @default.
• W4311185752 cites W2919913203 @default.
• W4311185752 cites W2919979744 @default.
• W4311185752 cites W2947339550 @default.
• W4311185752 cites W2965013539 @default.
• W4311185752 cites W2965364867 @default.
• W4311185752 cites W2979370854 @default.
• W4311185752 cites W2980498517 @default.
• W4311185752 cites W2990723369 @default.
• W4311185752 cites W2995993097 @default.
• W4311185752 cites W2996669499 @default.
• W4311185752 cites W2996850699 @default.
• W4311185752 cites W2998398504 @default.
• W4311185752 cites W2998553334 @default.
• W4311185752 cites W3002653678 @default.
• W4311185752 cites W3004837573 @default.
• W4311185752 cites W3011262494 @default.
• W4311185752 cites W3012628800 @default.
• W4311185752 cites W3013632036 @default.
• W4311185752 cites W3014647989 @default.
• W4311185752 cites W3015395776 @default.
• W4311185752 cites W3025826476 @default.
• W4311185752 cites W3026105097 @default.
• W4311185752 cites W3035795080 @default.
• W4311185752 cites W3044487099 @default.
• W4311185752 cites W3046790131 @default.
• W4311185752 cites W3047029276 @default.
• W4311185752 cites W3048264598 @default.
• W4311185752 cites W3099567700 @default.
• W4311185752 cites W3103254176 @default.
• W4311185752 cites W3109584340 @default.
• W4311185752 cites W3119886363 @default.
• W4311185752 cites W3120283229 @default.
• W4311185752 cites W3120349973 @default.
• W4311185752 cites W3120522579 @default.
• W4311185752 cites W3123888838 @default.
• W4311185752 cites W3127206013 @default.
• W4311185752 cites W3128797186 @default.
• W4311185752 cites W3129133132 @default.
• W4311185752 cites W3134674513 @default.
• W4311185752 cites W3179017527 @default.
• W4311185752 cites W3184382432 @default.
• W4311185752 cites W3191800245 @default.
• W4311185752 cites W3195454097 @default.
• W4311185752 cites W3196797324 @default.
• W4311185752 cites W3197813564 @default.
• W4311185752 cites W3199464050 @default.
• W4311185752 cites W3206918010 @default.
• W4311185752 cites W3210193654 @default.
• W4311185752 cites W4212829650 @default.
• W4311185752 cites W4220969554 @default.
• W4311185752 cites W4224931312 @default.
• W4311185752 cites W4229333028 @default.
• W4311185752 cites W4243116943 @default.
• W4311185752 cites W4280537970 @default.
• W4311185752 cites W4280553325 @default.
• W4311185752 cites W4280620320 @default.
• W4311185752 cites W4281746414 @default.
• W4311185752 cites W4283797458 @default.
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• W4311185752 doi "https://doi.org/10.3390/geotechnics2040049" @default.
• W4311185752 hasPublicationYear "2022" @default.
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