Multi-means, dynamic and whole-process landslide prewarning method

Abstract

The invention provides a multi-means, dynamic and whole-process landslide prewarning method. The method includes: performing macroscopic observation, engineering exploration data monitoring and displacement monitoring on landslide; performing macro damage phenomenon analysis and influence factor analysis according to a result of macro observation; building a numerical value model and performing parameter indicator determination according to a result of engineering exploration data monitoring; performing displacement back analysis and displacement tendency analysis according to a result of displacement monitoring; building a correcting numerical value model according to influence factor analysis and the built numerical value model; determining calculating parameters through parameter indicator determination and displacement back analysis; performing numerical value analog calculation analysis according to the correcting numerical value calculating model and the calculating parameters; calculating a displacement-time curve under a series of different stability safe coefficients according to numerical value analog calculation analysis; drawing a monitoring displacement-time curve according to displacement tendency analysis; comparing the above two curves to determine stability state of the landslide. By the method, landslide disaster recognition, prewarning and control level can be improved effectively.

Description

technical field [0001] The invention belongs to the technical field of landslide early warning, and in particular relates to a multi-means, dynamic and whole-process landslide early warning method. Background technique [0002] Landslide prediction and early warning is a worldwide problem. For landslide projects under complex engineering geological conditions, such as reservoir landslides and earthquake landslides, the prediction and early warning is more complicated and has the characteristics of multidisciplinary and highly comprehensive integration. [0003] In terms of theoretical research, Saito Dixiao (1965) proposed a three-stage theory of landslide creep damage through a large number of experiments, and established a differential equation for accelerated creep; Yan Tongzhen (1998, 1988, 1985), Chen Changyan, etc. ( 2001) used various methods to study landslide law and its spatio-temporal prediction theory. In terms of prediction methods, Shi Bin et al. (2004, 2005) ...

AI Technical Summary

Problems solved by technology

However, for rock and soil materials with different properties, the daily displacement of landslides varies greatly, and there are a large number of sudden changes, breakpoints, and negative values, which are likely to cause misreporting and missing reporting. Simply rely on the monitoring results for mathematical deduction, and use the displacement rate, To predict the development trend of landslides based on relative changes in acceleration, etc., in the final analysis, it is still a re-understanding of the geometric shape of the displacement-time curve, and it is difficult to achieve accurate prediction results

Second, in terms of calculation and artificial intelligence forecasting, the dynamic process of landslide deformation, changes in factors affecting landslide stability, and changes in landslide intensity parameters over time have not been fully considered.

These influencing factors play different roles in different stages of landslide prediction, and the main factors affecting the stability of landslides are also different in different stages of landslide development. Most of the commonly used numerical analysis methods do not fully reflect the actual process of landslide deformation and failure , many predisposing factors

Third, there is currently a lack of unified standards for evaluating the stability of landslides

The evaluation criteria of macroscopic phenomena, monitoring data, and numerical analysis are difficult to correspond to each other, and the criteria for discrimination are single and insufficient.

It does not take into account the different characteristics, criteria, and time limits of landslide prediction at different stages of landslides.

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