## 1 Introduction
In addition, numerical simulation technology based on the strength reduction method cannot reduce the slope strength parameters in real time, which represents a bottleneck in this technology [32; 33]. Dynamic changes in slope stability are often caused by factors such as rainfall, earthquakes and water level changes; as such, researchers in related fields have conducted dynamic evaluations of slope stability. [PERSON] combined the probability density evolution method to establish an evaluation method for determining the dynamic stability of three-dimensional slopes under earthquake action [34]; [PERSON] used monitoring data and a data model to make a dynamic prediction of slope displacement; and [PERSON] established a dynamic weight-changing model of slope stability for different rainfall conditions and analyzed the dynamic changes in slope index weights under different rainfall conditions [35; 36]. In addition, [PERSON] proposed a seismic slope stability calculation method combining vibration disturbance and progressive failure by introducing the filter softening model and vibration deterioration model to represent the attenuation law of rock strength parameters [37], and [PERSON] employed a combination of numerical simulation and microseismic monitoring technology to conduct a dynamic analysis of the stability of the Xilioudu Hydropower Station [38]. Furthermore, [PERSON] proposed a numerical manifold method based on the time domain coupled time integration algorithm to analyze the dynamic stability of rock slopes and improve the computational efficiency of the original algorithm [39]. In the field of transportation, some scholars have used intelligent algorithms to study dynamic traffic flow risks [40; 41], but few scholars have used intelligent algorithms to study the dynamic stability of slopes. Moreover, with the current methods used to evaluate dynamic slope stability, few studies have introduced multi-objective optimization methods and monitoring data, and most have employed numerical calculations combined with forward models for evaluation. Slope monitoring data, and particularly data monitoring displacement, directly reflect the macroscopic mechanical properties of a slope. In this respect, inversion research can be used to obtain relatively accurate macroscopic mechanical parameters of a slope, thus providing a reliable basis for conducting a dynamic stability analysis. This study is the first to conduct long-term monitoring of potentially dangerous slopes. After determining the inversion parameters, we used the BP neural network (improved by a genetic algorithm) to extract the nonlinear function between the inversion parameters and the monitored displacement, and ultimately establish the objective function. The multi-objective optimization algorithm (NSGA-II) was then improved to obtain the material properties of the rock and soil, and the dynamic mechanical parameters of the slope were obtained by inversion using the improved algorithm. Finally, the inversion parameters were applied to numerical simulation software (Flac 3D 6.0) to achieve a dynamic evaluation of slope stability, and its reliability was verified using actual monitoring curves.