## 1 Introduction  
It is widely reported that high backfill slopes are damaged by rainfall and they significantly endanger people's lives and property [1; 2; 3; 4]. With the rapid growth of the economy and fast urbanization process of China in the past decades, the demand for the expansion and/or renewal of transportation infrastructures (e.g., highways, railways, and airports) has increasingly grown. To shorten the commuting distance, some transportation infrastructures have to be constructed in mountainous areas; as a result, a large number of high slopes due to excavation and backfill construction emerge. This gives rise to some critical geotechnical problems, e.g., excessive total and differential settlement, and slope failure. Meanwhile, rainfall is abundant in some regions; for example, it is commonly over 1500 mm a year in the east coast area of China. The seepage and infiltration of rainfall into the soil make the performance of high backfill slopes more complex. Under the influence of rainfall, the mechanical properties of the slope mass tend to be weak, such as the shear strength, matric suction, and modulus, which brings further challenges to engineers in predicting the stability of the slope [5; 6; 7; 8; 9; 10].  
Previous studies found that rainfall significantly affects the performance of the slope [11; 12; 13; 14]. [PERSON] et al. [15] conducted model tests to quantify the loess soil slopes undergoing failure due to rainfall, which may cause multi-sliding retrogressive landslides.  
[PERSON] et al. [16] carried out a hazard assessment of rainfall-induced debris landslides, and pointed out that the rainfall duration corresponding to the initiation of the debris landslide was exponentially related to the rainfall intensity. Meanwhile, some theoretical analyses, such as the limit equilibrium analysis and numerical simulation, were conducted to investigate the fracture evolution characteristics and failure mode of the slope [17; 18; 19; 20]. [PERSON] et al. [21] investigated the progressive development of rainfall-induced landslides using the random finite element and material point methods with hydro-mechanical coupling.  
In addition, machine learning techniques, such as neural networks, data mining techniques, BP algorithms, etc., have been increasingly adopted to assess the stability of slopes [22; 23; 24; 25; 26; 27]. Based on neural networks, [PERSON] et al. [28], [PERSON] et al. [29], and [PERSON] et al. [30] proposed a displacement prediction and early warning system for landslides. [PERSON] et al. [31] and [PERSON] et al. [32] applied data mining techniques to plot landslide susceptibility mapping so that more landslide-related parameters were considered, including the slope angle, slope aspect, plan curvature, rainfall threshold, etc. [PERSON] et al. [33] proposed a new BP algorithm for achieving a better prediction of the slope. However, machine learning has seldom been adopted for assessing high backfill slopes in practice.  
This study is aimed at investigating the stability of high backfill slopes moisturized by rainfall. A series of model tests were carried out under a plane strain condition considering the influences of rainfall duration and intensity. The slope displacements and moisture content in the backfill mass were monitored during the test. Based on the model tests, a numerical analysis was adopted to extend the analysis cases, and a machine learning method was used to develop a prediction method for the stability of high backfill slopes considering the effects of rainfall duration and intensity.