Prediction of the Subgrade Soil California Bearing Ratio Using Machine Learning and Neuro-Fuzzy Inference System Techniques: A Sustainable Approach in Urban Infrastructure Development
Abstract
:1. Introduction
2. Soil Database and Laboratory Testing
3. Data Analysis
3.1. MLR Analysis
3.2. Artificial Neural Network
3.3. Adaptive Neuro-Fuzzy Inference System (ANFIS)
3.4. Performance Criteria
4. Results and Discussion
4.1. MLRA Results
4.2. ANN Results
4.3. ANFIS Results
4.4. Sensitivity Analysis
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Methodology Used | Input Parameters Considered | No. of Samples | R2 | Ref. |
---|---|---|---|---|
GP | OMC, MDD, S, G, LL, and PI | 151 | 0.92 | [24] |
MLRA ANN | Sieve analysis, Atterberg limits, MDD, and OMC. | 124 | 0.88 0.95 | [25] |
ANN | OMC, MDD, L, and LS | 51 | 0.84 | [26] |
GMDH | Gravel content (GC), Sand content (SC), Fine content (FC), LL, PI, OMC, and MDD | 158 | 0.96 | [27] |
MLRA ANN | D60 and MDD | 207 | 0.93 0.97 | [28] |
ANN | Gradation, OMC, MDD, LL, PI, and percentages of SO3, Soluble salt, Gypsum, and Organic materials. | 358 | 0.78 | [29] |
ERF ANFIS | Hydrated lime-activated rice husk ash, LL, PL, PI, OMC, MDD, and Clay activity. | 121 | 1.00 0.99 | [30] |
MLRA ANN ANFIS | LL, PL, PI, S, G, C/Si, MDD, and OMC | 264 | 0.80 0.90 0.98 | [31] |
ELM-CSO | Gravel %, Sand %, Fines %, LL, PL, OMC, and MDD. | 149 | 0.90 | [32] |
Particulars | Test Codes | Mean | Standard Deviation | Sample Variance | Kurtosis | Skewness | Minimum | Maximum |
---|---|---|---|---|---|---|---|---|
PI | ASTM D4318-00 | 10.80 | 9.37 | 87.82 | −0.68 | 0.44 | 0 | 39 |
MDD | ASTM D698 | 1.91 | 0.16 | 0.02 | −0.16 | −0.30 | 1.5 | 2.31 |
CBR | ASTM D1883-16 | 11.48 | 5.77 | 33.33 | −0.68 | 0.44 | 2.11 | 27.4 |
Sample No | Soil Type | Soil Description | Encoded to | PI (%) | MDD (g/cc) | CBR (%) |
---|---|---|---|---|---|---|
1 | CI | Intermediate-Plasticity Clay | 1 | 19.00 | 1.83 | 6.40 |
2 | CL | Low-Plasticity Clay | 2 | 10.60 | 1.68 | 6.50 |
3 | GM | Silty Gravel | 3 | 9.00 | 1.88 | 10.89 |
4 | GP | Poorly Graded Gravel | 4 | 10.00 | 1.95 | 7.11 |
5 | SC | Clayey Sand | 5 | 14.00 | 1.77 | 4.05 |
6 | SM | Silty Sand | 6 | 26.00 | 1.98 | 11.60 |
7 | SP | Poorly Graded Sand | 7 | 0 | 2.11 | 17.60 |
8 | SW | Well-Graded Sand | 8 | 0 | 1.94 | 10.58 |
Regression Statistics | |
---|---|
Multiple R | 0.67 |
R Square | 0.45 |
Adjusted R Square | 0.45 |
RMSE | 4.270 |
Observations | 2191 |
Coefficients | Standard Error | T Stat | p-Value | |
---|---|---|---|---|
Intercept | 2.13 | 1.14 | 1.86 | <0.05 |
Soil Type | 1.24 | 0.05 | 26.16 | <0.05 |
PI | −0.25 | 0.01 | −25.39 | <0.05 |
MDD | 2.67 | 0.63 | 4.24 | <0.05 |
Analysis Performed | R2 Value | Root Mean Square Error (RMSE) | ||||
---|---|---|---|---|---|---|
Training | Testing | Validation | Training | Testing | Validation | |
MLRA | 0.45 | 4.6 | ||||
ANN | 0.67 | 0.65 | 0.66 | 2.63 | 2.70 | 2.64 |
ANFIS | 0.81 | 0.82 | 0.82 | 2.26 | 2.29 | 2.23 |
Soil Type | Encoded To | PI, % | MDD, g/cc | Actual CBR, % | Predicted CBR, % | ||
---|---|---|---|---|---|---|---|
MLRA Output | ANN Output | ANFIS Output | |||||
SP | 7.00 | 0.00 | 2.11 | 17.60 | 15.96 | 16.52 | 16.45 |
SC | 5.00 | 11.70 | 1.47 | 10.43 | 11.40 | 8.59 | 9.75 |
SM | 6.00 | 30.00 | 1.99 | 9.04 | 7.84 | 10.75 | 8.97 |
CL | 2.00 | 10.60 | 1.68 | 6.50 | 7.15 | 6.04 | 5.67 |
GP | 4.00 | 0.00 | 2.09 | 14.80 | 11.79 | 17.48 | 16.95 |
GM | 3.00 | 28.00 | 2.11 | 7.09 | 3.92 | 7.93 | 6.51 |
SW | 8.00 | 23.00 | 1.77 | 10.51 | 12.56 | 11.39 | 11.30 |
CI | 1.00 | 19.00 | 1.83 | 6.40 | 3.60 | 2.47 | 5.99 |
SC | 5.00 | 15.00 | 1.88 | 7.11 | 10.05 | 8.22 | 6.91 |
CI | 1.00 | 16.00 | 1.75 | 4.13 | 4.41 | 3.56 | 5.17 |
SW | 8.00 | 22.60 | 2.00 | 9.50 | 12.32 | 11.31 | 10.64 |
SW | 8.00 | 12.15 | 1.93 | 11.40 | 14.83 | 11.31 | 12.85 |
CL | 2.00 | 17.00 | 1.65 | 2.71 | 5.72 | 2.62 | 3.93 |
GP | 4.00 | 0.00 | 1.96 | 15.50 | 11.98 | 17.77 | 16.95 |
SM | 6.00 | 15.67 | 2.14 | 12.50 | 10.91 | 9.60 | 11.25 |
SM | 6.00 | 19.22 | 2.02 | 10.48 | 10.27 | 9.89 | 10.50 |
GP | 4.00 | 0.00 | 1.84 | 18.40 | 12.15 | 18.02 | 18.10 |
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Gowda, S.; Kunjar, V.; Gupta, A.; Kavitha, G.; Shukla, B.K.; Sihag, P. Prediction of the Subgrade Soil California Bearing Ratio Using Machine Learning and Neuro-Fuzzy Inference System Techniques: A Sustainable Approach in Urban Infrastructure Development. Urban Sci. 2024, 8, 4. https://doi.org/10.3390/urbansci8010004
Gowda S, Kunjar V, Gupta A, Kavitha G, Shukla BK, Sihag P. Prediction of the Subgrade Soil California Bearing Ratio Using Machine Learning and Neuro-Fuzzy Inference System Techniques: A Sustainable Approach in Urban Infrastructure Development. Urban Science. 2024; 8(1):4. https://doi.org/10.3390/urbansci8010004
Chicago/Turabian StyleGowda, Sachin, Vaishakh Kunjar, Aakash Gupta, Govindaswamy Kavitha, Bishnu Kant Shukla, and Parveen Sihag. 2024. "Prediction of the Subgrade Soil California Bearing Ratio Using Machine Learning and Neuro-Fuzzy Inference System Techniques: A Sustainable Approach in Urban Infrastructure Development" Urban Science 8, no. 1: 4. https://doi.org/10.3390/urbansci8010004