Lock-unlock landslides have thick sliding zones that store a lot of energy.This makes them start quickly,happen suddenly,and have serious consequences.Therefore,it becomes urgent to study the deformation and failure m...Lock-unlock landslides have thick sliding zones that store a lot of energy.This makes them start quickly,happen suddenly,and have serious consequences.Therefore,it becomes urgent to study the deformation and failure mechanisms of such landslides and develop rational predictive models.Taking the Jiuxianping landslide as an example,this study investigates the regularity of landslide displacement changes using multi-source data,focusing on the abrupt displacement patterns in the unlock phase.Furthermore,employing Transient Release and Inhalation Method tests combined with Geo-Studio’s SEEP/W and SIGMA/W modules for fluid-solid coupled simulation calculations,the evolution process of landslide failure mechanisms and deformation characteristics is analyzed and discussed.Lastly,utilizing data mining analysis of multi-source data,a hybrid optimized machine learning predictive model is established for model prediction comparison.The study reveals that:(1)The rise in infiltration line elevates pore water pressure,affecting the stability of the sliding zone,leading to“unlock effects”and step-like displacement deformation;(2)Simulation shows that YY208 is closer to the actual situation,located at the far bank position,while YY210 is greatly influenced by the“buoyancy effect”,resulting in a slowdown in deformation velocity;(3)After data preprocessing,overall actual displacement prediction performs better than simulation displacement prediction in terms of Mean Absolute Error,Mean Squared Error and Correlation Coefficient,but noise reduction processing can improve the periodic prediction effect of simulation displacement.展开更多
基金support from National Key Research and Development Program of China(2023YFC3007203).
文摘Lock-unlock landslides have thick sliding zones that store a lot of energy.This makes them start quickly,happen suddenly,and have serious consequences.Therefore,it becomes urgent to study the deformation and failure mechanisms of such landslides and develop rational predictive models.Taking the Jiuxianping landslide as an example,this study investigates the regularity of landslide displacement changes using multi-source data,focusing on the abrupt displacement patterns in the unlock phase.Furthermore,employing Transient Release and Inhalation Method tests combined with Geo-Studio’s SEEP/W and SIGMA/W modules for fluid-solid coupled simulation calculations,the evolution process of landslide failure mechanisms and deformation characteristics is analyzed and discussed.Lastly,utilizing data mining analysis of multi-source data,a hybrid optimized machine learning predictive model is established for model prediction comparison.The study reveals that:(1)The rise in infiltration line elevates pore water pressure,affecting the stability of the sliding zone,leading to“unlock effects”and step-like displacement deformation;(2)Simulation shows that YY208 is closer to the actual situation,located at the far bank position,while YY210 is greatly influenced by the“buoyancy effect”,resulting in a slowdown in deformation velocity;(3)After data preprocessing,overall actual displacement prediction performs better than simulation displacement prediction in terms of Mean Absolute Error,Mean Squared Error and Correlation Coefficient,but noise reduction processing can improve the periodic prediction effect of simulation displacement.
