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CN-120030825-B - Side slope remote video monitoring stability analysis system and method

CN120030825BCN 120030825 BCN120030825 BCN 120030825BCN-120030825-B

Abstract

The invention relates to a system and a method for analyzing remote video monitoring stability of a side slope, wherein the method comprises the following steps of S1, constructing a three-dimensional unsteady viscoelastic-plastic shearing creep model, obtaining side slope stability data under rainfall action, S2, calculating shearing creep rate on a sliding body, defining a main sliding line and a sliding part, determining a sliding-resistant section, S3, determining a nonlinear mapping relation between surface displacement or crack width of the side slope and shearing creep rate at the corresponding sliding-resistant section, S4, setting a sensor and a target based on the main sliding line, S5, obtaining displacement or crack width of the target, S6, outputting the displacement or crack width to a remote computer, S7, obtaining shearing creep rate at the position of the sliding-resistant section corresponding to the target, and S8, judging the side slope stability based on the shearing creep rate at the position of the sliding-resistant section corresponding to the target. Compared with the prior art, the method has the advantages of realizing dynamic evaluation of soil landslide stability, improving instability prediction precision and the like.

Inventors

  • XU JIANCONG
  • LI SHUANG
  • ZHENG JINHUO
  • WANG YONGSHUAI
  • SHEN MINGLONG
  • JIANG TAO

Assignees

  • 同济大学
  • 福建省建筑设计研究院有限公司

Dates

Publication Date
20260505
Application Date
20241231

Claims (8)

  1. 1. The method for analyzing the remote video monitoring stability of the side slope is characterized by comprising the following steps of: S1, constructing a three-dimensional unsteady viscoelastic-plastic shearing creep model, and acquiring slope stability data under the action of rainfall; S2, calculating the shear creep rate on the landslide body based on slope stability data under the action of rainfall, defining a potential main sliding line and a sliding part based on the shear creep rate, and defining a last through section in the sliding part as a sliding-resisting section; S3, determining a nonlinear mapping relation between slope surface displacement or crack width of the sliding-resistant section in the sliding part on the projection position of the surface and shear creep rate at the corresponding sliding-resistant section: S4, setting a sensor and a target based on a main sliding wire; s5, acquiring the displacement or crack width of the target; s6, outputting the displacement or the crack width to a remote computer; S7, the remote computer obtains the shear creep rate of the corresponding anti-slip section of the target based on the displacement or crack width of the target and the nonlinear mapping relation; S8, judging the slope stability based on the shear creep rate of the position of the target corresponding to the anti-slip section, and obtaining a slope stability analysis result; The specific steps of S2 are as follows: S2-1, calculating a shear creep rate based on stability data of a lower slope, and defining a longitudinal line with the fastest change of the shear creep rate on a landslide body as a potential main sliding line, wherein the potential main sliding line represents a main sliding direction of the whole sliding of the slope; S2-2, defining a through surface or a through belt with the change rate of the shear creep rate in the landslide body being greater than 0 as a sliding surface or a sliding belt; s2-3, defining a last through section in the sliding part as a slope sliding-resisting section; s2-4, determining the projection position of the sliding-resisting section in the sliding part on the ground surface and the possible instability damage range of the potential main sliding line and the side slope; The shear creep rate is: Wherein, in the formula, the chemical formula, In order to achieve a shear creep rate, As the current stress is to be applied, Is the yield stress of the soil body, As a viscosity coefficient of unsteady shear creep, 、 、 And Are all parameters of the calculation of the creep, Is the ultimate strength of the flowing state of the soil body; is the creep time history.
  2. 2. The method for analyzing the stability of remote video monitoring of a side slope according to claim 1, wherein the specific steps of S1 are as follows: S1-1, constructing a three-dimensional unsteady viscoelastic-plastic shearing creep model; s1-2, acquiring data required by a model through a triaxial creep test of soil body unsaturated/saturated soil; S1-3, carrying out three-dimensional finite element numerical simulation on the slope stability under the rainfall effect based on the model and parameters required by the model to obtain the slope stability data under the rainfall effect.
  3. 3. The method for analyzing the stability of the remote video monitoring of the side slope according to claim 2, wherein the data required by the model comprises permeability coefficient, suction force, binding force, internal friction angle, long-term strength, elastic modulus, viscosity coefficient and soil yield stress Ultimate strength of soil flow state And creep calculation parameters.
  4. 4. The method for analyzing the stability of remote video monitoring of a side slope according to claim 1, wherein the specific step of S4 is as follows: S4-1, mounting a non-contact intelligent sensing sensor and a machine vision deformation instrument in a certain range of a side slope; s4-2, installing targets on projection positions of the slope sliding-resisting sections on the ground surface in sliding parts in a certain range on and near the potential main sliding line.
  5. 5. The method for analyzing the stability of remote video monitoring of a side slope according to claim 1, wherein the specific step of S5 is as follows: s5-1, capturing a real-time preliminary frame image of the deformation of the slope body based on a non-contact intelligent sensing sensor; s5-2, establishing an optimization model of the Bayes-convolutional neural network, and inputting the preliminary frame image into the optimization model; s5-3, optimizing the model to obtain the optimal characteristic point sequence, and outputting a high-resolution image; S5-4, acquiring displacement or crack width of the target and video data from the high-resolution image based on the machine vision deformer.
  6. 6. The method for analyzing the stability of remote video monitoring of a side slope according to claim 1, wherein the specific step of S6 is as follows: S6-1, arranging a data acquisition station carrying solar power supply at a certain position of a machine vision deformation instrument, wherein the data acquisition station is connected with the machine vision deformation instrument through an optical fiber cable; And S6-2, transmitting the displacement or crack width of the target to a remote computer in a mode of a movement signal.
  7. 7. The method for analyzing the stability of remote video monitoring of a side slope according to claim 1, wherein the specific steps of S8 are as follows: S8-1, if the change rate of the shear creep rate of the target at the corresponding anti-slip section is smaller than 0, the slope is considered to be stable; S8-2, if the change rate of the shear creep rate of the target corresponding to the anti-slip section is equal to 0, the slope is considered to tend to be damaged; and S8-3, if the change rate of the shear creep rate of the target corresponding to the sliding-resisting section is greater than 0, the slope is regarded as the impending destabilization damage.
  8. 8. A slope remote video monitoring stability analysis system, comprising a memory, a processor, and a program stored in the memory, wherein the processor implements the method of any of claims 1-6 when executing the program.

Description

Side slope remote video monitoring stability analysis system and method Technical Field The invention relates to the field of soil landslide and slope engineering, in particular to a system and a method for analyzing remote video monitoring stability of a slope. Background At present, some related researches are carried out on rainfall landslide hazard risk early warning: (1) And building a landslide risk early warning model according to rainfall data. And establishing a macroscopic danger early warning and forecasting model of the regional landslide hazard based on the effective rainfall model. According to different scale risk division results and three rainfall critical values, early warning indexes and early warning grades are respectively set, and a regional loess landslide risk early warning calculation analysis model is developed. (2) And carrying out landslide risk early warning according to weather information data such as rainfall. Based on WEBGIS and based on landslide hazard risk prediction graphs, landslide hazard dynamic risk prediction is performed by combining regional real-time rainfall information. And calculating the rainfall landslide probability of the area according to the landslide space probability and the time probability of rainfall induced landslide, and carrying out risk early warning according to the risk subareas. And determining a landslide risk early-warning grade table by referring to geological disaster weather risk early-warning grades based on a matter element theory in the extension theory. (3) And carrying out landslide risk early warning according to the actually measured displacement data. And establishing a landslide early warning model based on dynamic prediction of the future speed state by using a Markov chain theory. And (3) merging the in-situ monitoring data into an engineering risk rate analysis method, and providing a real-time risk rate quantification model and an early warning method for the operation of the bank side slope. And establishing a landslide trend speed ratio early warning criterion, and judging landslide risk change by adopting a trend speed ratio and displacement speed ratio fusion method. (4) And carrying out landslide hazard risk early warning by adopting artificial intelligence, machine learning and deep learning algorithms. And establishing a landslide disaster weather risk early warning method based on the BP neural network. And establishing a risk identification model of the fuzzy neural network, and identifying the risk level of the soft rock slope based on meteorological conditions, topography and topography factors, rock-soil characteristics and actual measurement horizontal displacement. And the geological disaster early warning is realized by combining the technologies of a space convolution neural network, a fuzzy neural network and the like. At present, a plurality of related researches are carried out on intelligent early warning and forecasting technology of dangerous situations of a side slope at home and abroad, real-time monitoring is carried out by depending on a Beidou high-precision displacement deformation monitoring system, rainfall and soil moisture content are mainly monitored, deformation conditions of a landslide body are mainly monitored, a GPS one-machine multi-antenna monitoring system for monitoring the deformation of the side slope is established, high-precision, automatic and all-weather targets of monitoring the disaster of the side slope are achieved, all-weather automatic monitoring is achieved by a side slope disaster information processing system based on '3S' (remote sensing RS, geographic information system GIS and global positioning system GPS), research is carried out on the aspect of generating side slope disaster early warning information, a method for evaluating a reliability map of the side slope monitoring system based on expected deformation failure and critical reading frequency of the side slope is established, early warning effectiveness and reliability of the critical slope damage of the monitoring system are evaluated, early warning and early warning system for predicting soil texture and rock quality stability is established by applying rainfall indexes of radial basis functions, and a least square method is provided by combining genetic algorithm and a least square method. At present, on the basis of a fuzzy analytic hierarchy process, an estimated model of road cutting slope instability is established, corresponding slope instability prediction risk assessment software is developed, a gray correlation principle is adopted, the weight of factors influencing roadbed stability under rainfall conditions is analyzed, corresponding preventive measures are provided, key indexes such as surface displacement, rainfall, soil moisture content and the like of the slope are monitored in real time through a high-precision sensor, and once monitoring data exceeds a preset safety threshold, the system au