Table 1 Overview of AI-driven models for CBR prediction in literature.
Reference | Soil type | Independent variables | Applicated model | No. of datasets (training/testing) | Statistical output |
|---|---|---|---|---|---|
Taskiran24 | Fine-grained soils | LL, PI, MDD, OMC, (C + S), Sa, Gr | ANN, GEP | 119/32 | R2 = 0.91, MSE = 2.207 |
Yildirim and Gunaydin22 | – | Gr, Sa, MDD, OMC | ANN | 88/36 | R2 = 0.95, SE = 1.282 |
Kumar et al.38 | – | LL, PI, OMC, MDD, Sa, (C + S) | ANN (GRNN, MLPN) | 42/18 | R2 = 0.98, MSE = 0.22 |
Varghese et al39. | Fine-grained soils | LL, PL, OMC, MDD | ANN | Total no. of datasets = 145 | R2 = 0.86, MAE = 0.5 |
Bhatt et al31. | – | Gr, Sa, OMC, MDD | ANN | 102/12 | R2 = 0.98, NA |
Sabat40 | Stabilized expansive soil using lime and quarry dust | LL, PI, OMC, MDD | ANN, SVM | 36/13 | R2 = 0.96, MAE = 1.42, RMSE = 0.56 |
Erzin and Turkoz32 | Aegean sands | Gs, Cu, Cc, ρdry, MC, Q, Fel, Ca, Co, A | ANN | 49/12 | R2 = 0.93, MAE = 2.53, RMSE = 3.65, VAF = 92.28% |
Ghorbani and Hasanzadehshooiili23 | Sulfate silty sand stabilized with Microsilica and Lime | L, M, CC, CD | ANN | 63/27 | R2 = 0.99, RMSE = 0.057 |
Suthar and Aggarwal33 | Pond ash stabilized with lime and lime sludge | MDD, OMC, L, LS, CD | ANN | 36/15 | R2 = 0.96, MAE = 2.51, RMSE = 2.91 |
Ho and Tran42 | Stabilized soil containing industrial waste | LL, PL, PI, OMC, MDD, QD, BA, CA, GSA, SDA, OPC | RF | 203/87 | R2 = 0.98, RMSE = 2.397, MAE = 1.168 |
Ikeagwuani20 | Modified expansive soil | LL, PL, PI, MDD, OMC, SDA, QD, OPC | RF | 76/33 | R2 = 0.76, RMSE = 5.315, MAE = 3.92 |
González Farias et al.34 | Granular and fine soil | Soils with Gr ≤ 35% F, PI Soils with Gr ≥ 35% F, PI, OMC | ANN | Total no. of datasets = 96 | Soils with Gr ≤ 35%: R2 = 0.84, MAE = 5.2 Soils with Gr ≥ 35%: R2 = 0.68, MAE = 11.6 |
Fikret Kurnaz and Kaya35 | - | Gr, Sa, F, LL, PI, OMC, MDD | ANN, GMDH | 110/48 | R2 = 0.94, MAE = 2.86, RMSE = 1.69 |
Alam et al.36 | Fine-grained soils | Gs, Cu, Cc, LL, PL, PI, OMC, MDD | ANN, GEP | Total no. of datasets = 20 | R2 = 0.99, NA |
Rafizul and Chandra37 | Fine-grained soil stabilized with quarry dust, lime, and rice husk ash | QD, L, RHA, CD, OMC, MDD | ANN, SVM | Total no. of datasets = 60 | R2 = 0.99, MSE = 2.88 |
Taha et al.41 | Granular materials | D60, MDD | ANN | 174/44 | R2 = 0.97, MAE = 3.32, RMSE = 4.18 |
Tenpe and Patel25 | - | Gr, Sa, PI, MDD, OMC | SVM, GEP | Total no. of datasets = 389 | R2 = 0.8, MSE = 3.5, RMSE = 1.85 |
