Machine Learning-Based Assessment of Soil Dispersibility and Erosion Types in Earthfill Dam: A Case Study from South Central Vietnam

International Journal of Civil Engineering

© 2025 by SSRG - IJCE Journal

Volume 12 Issue 9

Year of Publication : 2025

Authors : Tam Sy Ho, Quan Duy Tran, Huong Thi Pham, Hong Thi Pham, Vung Van Tran, Hien Thi Thu Dinh, Phi Quang Nguyen, Nga Thi Hang Nguyen

10.14445/23488352/IJCE-V12I9P111

How to Cite?

Tam Sy Ho, Quan Duy Tran, Huong Thi Pham, Hong Thi Pham, Vung Van Tran, Hien Thi Thu Dinh, Phi Quang Nguyen, Nga Thi Hang Nguyen, "Machine Learning-Based Assessment of Soil Dispersibility and Erosion Types in Earthfill Dam: A Case Study from South Central Vietnam," SSRG International Journal of Civil Engineering, vol. 12,  no. 9, pp. 120-128, 2025. Crossref, https://doi.org/10.14445/23488352/IJCE-V12I9P111

Abstract:

The stability of earthfill dam foundations is crucial for ensuring a reliable water supply and protecting downstream infrastructure. Currently, assessments of soil dispersibility and erosion in earthfill dams primarily rely on empirical formulas and expert judgment. However, traditional formulas often fail to cover the full range of real-world classification scenarios and require the continuous development of new thresholds for each specific case, making management challenging. We propose using machine learning combined with field survey data to offer a new approach for classifying soil dispersity and erosion types. This study focuses on comparing machine learning-based classifications with traditional expert-based methods to predict soil dispersity and erosion types across dams in South-Central Vietnam. Machine learning models, particularly Random Forests, outperformed traditional methods in both accuracy (R² = 0.92 for soil dispersity and R² = 0.92 for erosion type) and consistency, especially in complex cases. The classifications generated by the machine learning model were consistent with traditional models while better addressing complex scenarios. Key predictors such as quartz content, moisture, and mineralogy were identified as important factors influencing soil behavior.

Keywords:

Dam soil stability, Machine learning application, Dam structure safety, Soil erosion.

References:

[1] Julien Boulange et al., “Role of Dams in Reducing Global Flood Exposure under Climate Change,” Nature Communications, vol. 12, no. 1, pp. 1-7, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[2] S. Galicia et al., “‘Green’, Rammed Earth Check Dams: A Proposal to Restore Gullies under Low Rainfall Erosivity and Runoff Conditions,” Science of the Total Environment, vol. 676, pp. 584-594, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Jolanta Dąbrowska et al., “Mściwojów Reservoir – Study of a Small Retention Reservoir with an Innovative Water Self-Purification System,” Journal of Ecological Engineering, vol. 15, no. 2, pp. 7-16, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Shiying Zheng, “Quantitative Risk Assessment for Overtopping of Earth-Fill Dams in Japan Using Machine Learning Algorithms,” International Journal of Disaster Risk Reduction, vol. 113, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Stanisław Lach, “Analysis of Changes in the Trends Recorded in Piezometers of the Solina Dam in the Study Period 2010–2015,” Journal of Ecological Engineering, vol. 19, no. 1, pp. 150-155, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Marta Anna Łapuszek, “Analysis and Modeling the Streambed Evolution After the Check Dam Restoration: The Case of Krzczonówka Stream,” Journal of Ecological Engineering, vol. 20, no. 1, pp. 188-196, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Gabriel Villavicencio et al., “Failures of Sand Tailings Dams in a Highly Seismic Country,” Canadian Geotechnical Journal, vol. 51, no. 4, pp. 449-464, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[8] L.M. Zhang, Y. Xu, and J.S. Jia, “Analysis of Earth Dam Failures: A Database Approach,” Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, vol. 3, no. 3, pp. 184-189, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[9] F. Gutiérrez, G. Desir, and M. Gutiérrez, “Causes of the Catastrophic Failure of an Earth Dam Built on Gypsiferous Alluvium and Dispersive Clays (Altorricón, Huesca Province, NE Spain),” Environmental Geology, vol. 43, pp. 842-851, 2003.
[CrossRef] [Google Scholar] [Publisher Link]
[10] S.V. Sivapriya, and A. Anne Sherin, “Causes and Consequences of Dam Failures-Case Study,” Conference Proceedings Sustainable Practices and Innovations in Civil Engineering, pp. 155-159, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Krzysztof Radzicki, and Marek Stoliński, “Seepage Monitoring and Leaks Detection along an Earth Dam with a Multi-Sensor Thermal-Active System,” Bulletin of Engineering Geology and the Environment, vol. 83, pp. 1-15, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Mohammad Reza Boroomand, and Amirhossein Mohammadi, “Evaluation of Earth Dam Leakage Considering the Uncertainty in Soil "ŽHydraulic Parameters,” Civil Engineering Journal, vol. 5, no. 7, pp. 1543-1556, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Davood Salehi, and Ali Heidari, “Embankment Dam Design with Dispersive Soil: Solutions and Challenges,” Geotechnical and Geological Engineering, vol. 40, pp. 4289-4299, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Majdabad F. Askari, “Experimental Review of Dispersivity Phenomenon in Clay,” University of Tehran, 1992.
[Google Scholar]
[15] Sadettin Topçu, and Evren Seyrek, “A New Approach for Identification of Dispersivity Class of Soils by Combining Physical and Chemical Tests,” Physics and Chemistry of the Earth, Parts A/B/C, vol. 135, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[16] J. Bazargan, and S.D. Esmaeil, “Evaluation and Modification of Chemical Criteria to Detect Divergence Potential of Clay Soils,” Journal of Engineering Geology, vol. 4, no. 2, pp. 917-942, 2010.
[Google Scholar]
[17] Nader Abbasi, and Mohammad H. Nazifi, “Assessment and Modification of Sherard Chemical Method for Evaluation of Dispersion Potential of Soils,” Geotechnical and Geological Engineering, vol. 31, pp. 337-346, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Laxmi Kant Tiwari et al., “Forest Encroachment Mapping in Baratang Island, India, Using Maximum Likelihood and Support Vector Machine Classifiers,” Journal of Applied Remote Sensing, vol. 10, no. 1, pp. 1-21, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Behrang Beiranvand, Taher Rajaee, and Mehdi Komasi, “Comparison of K-Means and FCM Algorithms to Optimize Spatiotemporal Pore Pressure Prediction of Earth Dams,” Results in Engineering, vol. 24, pp. 1-11, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Jichen Tian et al., “Deep Transfer Learning-Based Time-Varying Model for Deformation Monitoring of High Earth-Rock Dams,” Engineering Applications of Artificial Intelligence, vol. 138, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Shengxiang Xu et al., “A Comparison of Machine Learning Algorithms for Mapping Soil Iron Parameters Indicative of Pedogenic Processes by Hyperspectral Imaging of Intact Soil Profiles,” European Journal of Soil Science, vol. 73, no. 1, pp. 1-49, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Seyma Akca, and Oguz Gungor, “Semantic Segmentation of Soil Salinity Using in-Situ EC Measurements and Deep Learning Based U-NET Architecture,” CATENA, vol. 218, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Nader Karballaeezadeh et al., “Machine Learning Approaches for Detection/Classification and Prediction Purposes in Pavement Engineering Studies: An Overview,” 2023 IEEE 17th International Symposium on Applied Computational Intelligence and Informatics (SACI), Timisoara, Romania, pp. 83-90, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Cuong Hung Pham, “Vietnam Dam Rehabilitation and Safety Improvement Project - P152309 - Implementation Status Results Report : Sequence 03,” The World Bank, pp. 1-9, 2017.
[Google Scholar] [Publisher Link]
[25] Doanh Pham et al., “Theoretical Framework for Dam Safety Risk Assessment for Vietnam,” Conference: RICS COBRA AUBEA 2015At: The University of Technology, Sydney, Australia, pp. 1-10, 2015.
[Publisher Link]
[26] Vi Pham, Anh Pham Nhat, and Anh Tuan-Nguyen Gia, “Soil Classification and Estimation of Potential Soil Erosion in Mainland Viet Nam Using Improved Rusle Model,” Proceedings of the 3rd Vietnam Symposium on Advances in Offshore Engineering, pp. 665-676, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[27] The Viet Tran et al., “Understanding Downstream Slope Failure in Earth Dams: Causes and Remedies - A Case Study of Trieu Thuong No.2 Dam, Quang Tri, Vietnam,” GEOMATE Journal, vol. 25, no. 108, pp. 163-171, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Shin-Ichir Wada, and Yuki Umegaki, “Major Ion and Electrical Potential Distribution in Soil under Electrokinetic Remediation,” Environmental Science & Technology, vol. 35, no. 11, pp. 2151-2155, 2001.
[CrossRef] [Google Scholar] [Publisher Link]
[29] M.E. Sumner, and W.P. Miller, Cation Exchange Capacity and Exchange Coefficients, Methods of Soil Analysis: Part 3 Chemical Methods, vol. 5, pp. 1201-1229, 1996.
[CrossRef] [Google Scholar] [Publisher Link]
[30] G.W. Thomas, Soil pH and Soil Acidity, Methods of Soil Analysis: Part 3 Chemical Methods, vol. 5, pp. 475-490, 1996.
[CrossRef] [Google Scholar] [Publisher Link]
[31] J.D. Rhoades, Salinity: Electrical Conductivity and Total Dissolved Solids, Methods of Soil Analysis: Part 3 Chemical Methods, vol. 5, pp. 417-435, 1996.
[CrossRef] [Google Scholar] [Publisher Link]
[32] L.A. Richards, Diagnosis and Improvement of Saline and Alkali Soils, U.S. Department of Agriculture, pp. 1-160, 1954.
[Google Scholar] [Publisher Link]
[33] S.G. Telkar et al., “Soil Erosion: Types and their Mechanism,” International Journal of Economic Plants, vol. 2, no. 4, pp. 178-180, 2015.
[Google Scholar] [Publisher Link]
[34] Leo Breiman, “Bagging Predictors,” Machine Learning, vol. 24, pp. 123-140, 1996.
[CrossRef] [Google Scholar] [Publisher Link]
[35] Tin Kam Ho, “The Random Subspace Method for Constructing Decision Forests,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 8, pp. 832-844, 1998.
[CrossRef] [Google Scholar] [Publisher Link]
[36] Petr Lánský, and Michael Weiss, “Classification of Dissolution Profiles in Terms of Fractional Dissolution Rate and a Novel Measure of Heterogeneity,” Journal of Pharmaceutical Sciences, vol. 92, no. 8, pp. 1632-1647, 2003.
[CrossRef] [Google Scholar] [Publisher Link]
[37] Yi-Fei Liu et al., “Disintegration Characteristics and Mechanism of Dispersive Soil Modified by Si/Ca Compound System,” Rock and Soil Mechanics, vol. 44, no. 5, pp. 1-14, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[38] Liwu Xin, “Study on Influencing Factors of Cohesive Soil Dispersion based on Gray Relational Analysis,” Chemical Engineering Transactions, vol. 59, pp. 403-408, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[39] B.L. McNeal, and N.T. Coleman, “Effect of Solution Composition on Soil Hydraulic Conductivity,” Soil Science Society of America Journal, vol. 30, no. 3, pp. 308-312, 1966.
[CrossRef] [Google Scholar] [Publisher Link]
[40] M.E. Sumner, “Sodic Soils-New Perspectives,” Soil Research, vol. 31, no. 6, pp. 683-750, 1993.
[CrossRef] [Google Scholar] [Publisher Link]
[41] H. Frenkel, J.O. Goertzen, and J.D. Rhoades, “Effects of Clay Type and Content, Exchangeable Sodium Percentage, and Electrolyte Concentration on Clay Dispersion and Soil Hydraulic Conductivity,” Soil Science Society of America Journal, vol. 42, no. 1, pp. 32-39, 1978.
[CrossRef] [Google Scholar] [Publisher Link]
[42] Essizewa Essowedeou Agate et al., “Performance of Expansive Soil Stabilized with Bamboo Charcoal, Quarry Dust, and Lime for Use as Road Subgrade Material,” arXiv Preprint, pp. 1-13, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[43] Mamoun A. Gharaibeh et al., “Estimation of Exchangeable Sodium Percentage from Sodium Adsorption Ratio of Salt-Affected Soils using Traditional and Dilution Extracts, Saturation Percentage, Electrical Conductivity, and Generalized Regression Neural Networks,” Catena, vol. 205, pp. 1-10, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[44] Liying Sun et al., “Comparing Surface Erosion Processes in Four Soils from the Loess Plateau under Extreme Rainfall Events,” International Soil and Water Conservation Research, vol. 9, no. 4, pp. 520-531, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[45] Junfei Lv et al., “Effects of Erosion and Deposition on the Extent and Characteristics of Organic Carbon Associated with Soil Minerals in Mollisol Landscape,” Catena, vol. 228, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[46] M. Lado, and M. Ben-Hur, “Soil Mineralogy Effects on Seal Formation, Runoff and Soil Loss,” Applied Clay Science, vol. 24, no. 3-4, pp. 209-224, 2004.
[CrossRef] [Google Scholar] [Publisher Link]
[47] Isabel Zentgraf et al., “Effect of Mineral and Organic Fertilizer on N Dynamics upon Erosion-Induced Topsoil Dilution,” Heliyon, vol. 10, no. 15, pp. 1-18, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[48] Rana Muhammad Adnan Ikram et al., “Application of Smart Technologies for Predicting Soil Erosion Patterns,” Scientific Reports, vol. 15, pp. 1-27, 2025.
[CrossRef] [Google Scholar] [Publisher Link]