Abstract: Safety monitoring and numerical simulation are two significant tools for assessing the slope stability. However, how to determine the slope critical failure state according to the monitoring data and numerical simulation results has always been the focus of the slope engineering. In this study, the Euclidean distances among the slope monitoring points of different types of state variables are calculated based on the hierarchical clustering method, and the effective monitoring points of slope are selected according to the distance. Then, the entropy weight of time series of effective monitoring points of the same type of state variables is calculated, and the entropy weight fusion method is used to perform data layer fusion for effective monitoring points of the same type of state variables, and the fusion monitoring index curves corresponding to different types of state variables are obtained. After that, principal component analysis method is adopted to perform feature level fusion for various fusion monitoring index curves, and a comprehensive monitoring information curve that can reflect the information characteristics of all state variables is obtained, and then an information mining and fusion framework for different monitoring variables in the process of slope progressive instability is constructed by using a variety of mathematical and statistical methods. Finally, a change point search method of graded curve is proposed to search the abrupt change point of slope state gradual evolution (i.e. slope critical failure state). The proposed method is applied to determining the critical instability of a highway slope. The results show that the cumulative value and change rate of a single monitoring point or a single fusion index are not unique as the criterion of slope instability. The comprehensive monitoring information sequence established by integrating multiple monitoring data can better reflect the evolution characteristics of slope state and avoid misjudgment of slope state by data of a single monitoring point with a single state variable, which verifies the feasibility and applicability of the proposed method.

Key words: critical failure state, monitoring index, data fusion, safety monitoring, numerical simulation