Topological mapping of complex networks from high slope deformation time series for landslide risk assessment

Abstract

With the continuous expansion of infrastructure coverage, construction is gradually shifting to remote mountainous areas, where high slopes are very prone to safety problems, such as landslides and collapses. Existing projects involving high slopes generally avoid engineering accidents by monitoring the deformation at discrete observation points on the slope. However, this approach tends to ignore the interaction between observation points and fails to assess the risk of excessive slope deformation from a systematic perspective. Existing methods rely mainly on the comparison between observed/predicted values and national standards/design requirements, which may lead to risk assessment bias and wrong decisions due to their subjective nature. In this paper, a method based on the analysis of observed data is proposed, by transforming high slope deformation time series into a complex network, which is then used to calculate and determine its network parameters, aiming to analyze the internal topology of the data and assess the potential risk of excessive slope deformation. China's Menghua Coal Transportation Railway was used as an example to verify the effectiveness of the method. The results showed that the method can reveal the systematic characteristics of the interaction between and influence of different observation points on the slope at the macro level and explore the topological characteristics within the deformation time series at the micro level. The relationship between the macroscopic and microscopic network characteristics and the environmental risk distribution across the high slope is established, which can accurately assess the deformation risk of high-slope construction from a systematic perspective and provide theoretical support for risk assessment and safety management in real projects.