Abstract
Maximum dry density (ρd(max)) and optimum moisture content (wopt) are two key parameters of embankment fill soil material using in transport construction. To obtain these parameters, Proctor test (ASTM D698/AASHTO) or Modified Proctor test (ASTM D1557/AASHTO T180 etc.) is traditionally performed in the laboratory. However, this test takes time and expenses. Moreover, the accuracy of the test depends significantly on the collection of samples, expertize of the testers and quality of the experimental apparatuses. In this study, the main aim is to propose two machine learning approaches named Multi-layer Perceptron Neural Network (ANN-MLP) and Random Forest (RF) for the prediction of ρd(max) and wopt. Input parameters include silt content(%), clay content (%), liquid limit (%), plastic limit (%), plasticity index (%), specific gravity which have strong correlations with ρd(max) and wopt were used in the model. Performance of the model was assessed by statistical methods, such as Mean Absolute Error (MAE), Root mean square error (RMSE), and Coefficient of determination (R2). Results of the models study indicate that the proposed models ANN-MLP and RF has the same predictive capability (R2average of ANN-MLP is 0.829 and R2average of RF is 0.827). The results of this study might help in quickly predicting ρd(max) and wopt of embankment fill soil material.
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References
2. B. M. Das and K. Sobhan, Principles of Geotechnical Engineering, 8th edition. Stamford, CT: Cengage Learning, 2013.
B. M. DAS, Principles of Foundation Engineering, 7th, INTERNATIONAL ECONOMY EDITION ed. Cengage India, 2013.
R. Whitlow, Basic Soil Mechanics, 4th edition. Harlow, England ; New York: Prentice Hall, 2000.
5. B. T. Pham, “A Novel Classifier Based on Composite Hyper-cubes on Iterated Random Projections for Assessment of Landslide Susceptibility,” J Geol Soc India, vol. 91, no. 3, pp. 355–362, Mar. 2018, doi: https://doi.org/10.1007/s12594-018-0862-5.
P. T. Nguyen et al., “Development of a Novel Hybrid Intelligence Approach for Landslide Spatial Prediction,” Applied Sciences, vol. 9, no. 14, Art. no. 14, Jan. 2019, doi: https://doi.org/10.3390/app9142824.
7. K. Khosravi et al., “A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran,” Science of The Total Environment, vol. 627, pp. 744–755, Jun. 2018, doi: https://doi.org/10.1016/j.scitotenv.2018.01.266.
B. T. Pham, A. Jaafari, I. Prakash, S. K. Singh, N. K. Quoc, and D. T. Bui, “Hybrid computational intelligence models for groundwater potential mapping,” CATENA, vol. 182, p. 104101, Nov. 2019, doi: https://doi.org/10.1016/j.catena.2019.104101.
9. S. Miraki et al., “Mapping Groundwater Potential Using a Novel Hybrid Intelligence Approach,” Water Resour Manage, vol. 33, no. 1, pp. 281–302, Jan. 2019, doi: https://doi.org/10.1007/s11269-018-2102-6.
B. T. Pham, L. H. Son, T.-A. Hoang, D.-M. Nguyen, and D. Tien Bui, “Prediction of shear strength of soft soil using machine learning methods,” CATENA, vol. 166, pp. 181–191, Jul. 2018, doi: https://doi.org/10.1016/j.catena.2018.04.004.
11. P. G. Asteris and K. G. Kolovos, “Self-compacting concrete strength prediction using surrogate models,” Neural Comput & Applic, vol. 31, no. 1, pp. 409–424, Jan. 2019, doi: https://doi.org/10.1007/s00521-017-3007-7.
12. B. T. Pham, M. D. Nguyen, K.-T. T. Bui, I. Prakash, K. Chapi, and D. T. Bui, “A novel artificial intelligence approach based on Multi-layer Perceptron Neural Network and Biogeography-based Optimization for predicting coefficient of consolidation of soil,” CATENA, vol. 173, pp. 302–311, Feb. 2019, doi: https://doi.org/10.1016/j.catena.2018.10.004.
13. K.-T. T. Bui, D. Tien Bui, J. Zou, C. Van Doan, and I. Revhaug, “A novel hybrid artificial intelligent approach based on neural fuzzy inference model and particle swarm optimization for horizontal displacement modeling of hydropower dam,” Neural Comput & Applic, vol. 29, no. 12, pp. 1495–1506, Jun. 2018, doi: https://doi.org/10.1007/s00521-016-2666-0.
D. Tien Bui, K.-T. T. Bui, Q.-T. Bui, C. V. Doan, and N.-D. Hoang, “Chapter 15 - Hybrid Intelligent Model Based on Least Squares Support Vector Regression and Artificial Bee Colony Optimization for Time-Series Modeling and Forecasting Horizontal Displacement of Hydropower Dam,” in Handbook of Neural Computation, P. Samui, S. Sekhar, and V. E. Balas, Eds. Academic Press, 2017, pp. 279–293.
D.-M. Bui, T. Huynh-The, Y. Yoon, S. Jun, and S. Lee, “EAP: Energy-Awareness Predictor in Multicore CPU,” in Advances in Computer Science and Ubiquitous Computing, Singapore, 2015, pp. 361–366, doi: https://doi.org/10.1007/978-981-10-0281-6_52.
16. T. Kavzoglu and P. M. Mather, “The use of backpropagating artificial neural networks in land cover classification,” International Journal of Remote Sensing, vol. 24, no. 23, pp. 4907–4938, Jan. 2003, doi: https://doi.org/10.1080/0143116031000114851.
M. H. Beale, M. T. Hagan, and H. B. Demuth, “Neural Network ToolboxTM User’s Guide,” p. 512.
18. L. Breiman, “Random Forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, Oct. 2001, doi: https://doi.org/10.1023/A:1010933404324.
19. V. Rodriguez-Galiano, M. Sanchez-Castillo, M. Chica-Olmo, and M. Chica-Rivas, “Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines,” Ore Geology Reviews, vol. 71, pp. 804–818, Dec. 2015, doi: https://doi.org/10.1016/j.oregeorev.2015.01.001.
M. D. Nguyen et al., “Development of an Artificial Intelligence Approach for Prediction of Consolidation Coefficient of Soft Soil: A Sensitivity Analysis,” The Open Construction & Building Technology Journal, vol. 13, no. 1, Aug. 2019, doi: https://doi.org/10.2174/1874836801913010178.
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Nguyen Duc, M., Ho Sy, A., Nguyen Ngoc, T., Hoang Thi, T.L. (2022). An Artificial Intelligence Approach Based on Multi-layer Perceptron Neural Network and Random Forest for Predicting Maximum Dry Density and Optimum Moisture Content of Soil Material in Quang Ninh Province, Vietnam. In: Ha-Minh, C., Tang, A.M., Bui, T.Q., Vu, X.H., Huynh, D.V.K. (eds) CIGOS 2021, Emerging Technologies and Applications for Green Infrastructure. Lecture Notes in Civil Engineering, vol 203. Springer, Singapore. https://doi.org/10.1007/978-981-16-7160-9_176
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