Li Yangyang, Rangarajan Saranya, Rahardjo Harianto, Shen Yuanjie, Hamdany Abdul Halim, Satyanaga Alfrendo, Leong Eng Choon, Wong Swee Khian, Wang Chien Looi, Kew Huiling, Htoo Naing Tint, Poh Choon Hock, Ghosh Subhadip
Department of Civil Engineering, Monash University, 23 College Walk, Clayton, Victoria, 3800, Australia.
School of Civil and Environmental Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore.
Sci Rep. 2025 Jan 7;15(1):1066. doi: 10.1038/s41598-025-85250-5.
The effectiveness of using vegetation to reinforce slopes is influenced by the soil and vegetation characteristics. Hence, this study pioneers the construction of an extensive soil database using random forest machine learning and ordinary kriging methods, focusing on the influence of plant roots on the saturated and unsaturated properties of residual soils. Soil organic content, which includes contributions from both soil organisms and roots, functions as a key factor in estimating soil hydraulic and mechanical properties influenced by vegetation roots. This innovative approach of using organic content to estimate soil properties performs well when applied to machine learning models for soil database development. The results reveal that organic content markedly affects the hydraulic properties of soils, more than their mechanical properties. The finding illustrates the importance of exploring the hydraulic effects of vegetation on slope stability in addition to the traditional emphasis on mechanical reinforcement. This rooted soil database has practical applications in GIS-based analyses for mapping regional slope stability, incorporating the role of plant roots. A case study demonstrated the database's utility, showcasing that vegetation effectively limited rainwater infiltration and improved slope stability. Therefore, this research offers a valuable approach to improving slope stability through informed vegetation strategies.
利用植被加固边坡的有效性受土壤和植被特性的影响。因此，本研究率先运用随机森林机器学习和普通克里金法构建了一个广泛的土壤数据库，重点关注植物根系对残积土饱和与非饱和特性的影响。土壤有机含量包括土壤生物和根系的贡献，是估算受植被根系影响的土壤水力和力学性质的关键因素。这种利用有机含量估算土壤性质的创新方法在应用于土壤数据库开发的机器学习模型时表现良好。结果表明，有机含量对土壤水力性质的影响显著大于其对力学性质的影响。这一发现说明了除了传统上对机械加固的重视外，探索植被对边坡稳定性的水力效应的重要性。这个含根系土壤数据库在基于地理信息系统（GIS）的区域边坡稳定性制图分析中具有实际应用价值，其中纳入了植物根系的作用。一个案例研究展示了该数据库的实用性，表明植被有效地限制了雨水入渗并提高了边坡稳定性。因此，本研究为通过明智的植被策略提高边坡稳定性提供了一种有价值的方法。
