Understandings of reinforcement mechanisms of landslide-stabilizing pile system are important for long-term safety of reservoir landslides installed piles. The paper proposes a framework to study deformation response and identify triggering factors for landslide–pile system by using geographic detector technology and uncertainty of monitoring data. Majiagou landslide, a representative reservoir landslide installed stabilizing and test piles, is selected as the case study. Firstly, monitoring data of monthly rainfall, variations of reservoir water level, deformation of landslide surface and piles’ head were preprocessed. The random deformation data were generated considering uncertainty of deformation monitoring data. Meanwhile, the deformation response of landslide–pile system is analyzed through studying the monitoring deformation data and random deformation data using the clustering algorithm. Finally, geographic detector technique was used to identify main triggering factors of landslide surface deformation and explore interaction types of any two factors. The uncertainty of monitoring data was used to identify the most important triggering factor. Influences of different error and uncertainty of monitoring data on the influence degrees of each factor were further discussed. The comparison with improved Apriori algorithm shows that the presented framework can intuitively measure influence degrees of each factor and analyze interaction types of any two factors. The main conclusions by studying Majiagou landslide indicate that the anti-slide performance and action range of piles gradually decrease with the increase of hydraulic cycle; the deterioration of geomaterials’ properties are the most important triggering factor leading to the deformation of landslide–pile system and the degradation of piles’ performance, supported by most random deformation groups.

了解滑坡稳定桩系统的加固机制对于水库滑坡安装桩的长期安全性很重要。本文提出了一个框架，利用地理探测器技术和监测数据的不确定性来研究滑坡桩系统的变形响应和识别触发因素。以马家沟滑坡为例，该滑坡是典型的水库滑坡，已安装了稳定桩和试验桩。首先，对月降雨量，水库水位变化，滑坡面变形和桩头的监测数据进行了预处理。考虑到变形监测数据的不确定性，生成了随机变形数据。与此同时，通过使用聚类算法研究监测变形数据和随机变形数据，分析了滑坡桩系统的变形响应。最后，利用地理探测技术确定了滑坡表面变形的主要触发因素，并探讨了这两个因素的相互作用类型。监测数据的不确定性被用来确定最重要的触发因素。进一步讨论了监测数据的误差和不确定性对各个因素影响程度的影响。与改进的Apriori算法的比较表明，所提出的框架可以直观地测量每个因素的影响程度并分析任何两个因素的相互作用类型。研究马家沟滑坡的主要结论表明，随着水力循环的增加，桩的抗滑性能和作用范围逐渐减小。在大多数随机变形组的支持下，岩土材料性能的下降是导致滑坡-桩系统变形和桩性能下降的最重要触发因素。