Slope monitoring system based on physical model

Abstract

The present invention provides a slope monitoring system based on a physical model, which relates to the technical field of slope physical testing and analysis, and its IPC classification number belongs to G01N. The present invention significantly improves the monitoring and assessment capabilities of risk areas by measuring the soil type, geological material characteristics, and plant root status data of ground materials; the system combines historical landslide fault data with risk area division to dynamically generate warning thresholds to ensure real-time prediction of slope instability; in addition, based on a comprehensive analysis of the state of plant roots, the system can quantify the impact of roots on slope stability by generating a root evaluation index; the threshold calibration unit provides a dynamic warning threshold adjustment strategy to ensure real-time reflection of the stability state of the slope; at the same time, by generating an unstable landslide evaluation index, the system can achieve more accurate risk assessment, thereby improving the efficiency and reliability of slope monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of slope physical testing and analysis, and its IPC classification number belongs to (G01N), in particular to a slope monitoring system based on a physical model. Background Art

[0002] Slope monitoring technology has been widely used in fields such as engineering geology, civil engineering, and environmental science. With the acceleration of urbanization and the frequent occurrence of natural disasters, the risk of slope landslides has become increasingly prominent, and the need for their monitoring and assessment has become more urgent. Traditional slope monitoring methods rely mostly on regular manual inspections and basic observation methods, which cannot reflect the structural changes and risk status of slopes in real time. In recent years, there has been an increasing number of studies using physical models to simulate slope behavior. These methods provide more accurate monitoring methods by combining laboratory models with actual geological conditions. For example, real-time monitoring technology based on strain and displacement data has been introduced to improve the early warning capability of slope instability. However, existing technologies still have shortcomings in real-time analysis of dynamic strain and displacement data, effective identification of risk areas, and comprehensive evaluation of plant root status, which limits the accuracy and operability of monitoring systems.

[0003] In the prior art, the publication number is CN116908413A, the IPC classification number belongs to (G01N33 / 24), and the name is an indoor twin slope physical model test system and method, the system includes: an on-site prototype system, an indoor model system, a monitoring method, analysis and evaluation, and mutual feedback adjustment; the on-site prototype system includes the on-site prototype slope, the hydrogeological conditions and environmental conditions in which it is located; the indoor model system includes the indoor model slope and the simulated environment in which it is located, and the material of the indoor model slope is determined by comparing and converting the rock and soil material parameters provided by the prototype slope survey; the indoor model slope is layered and filled in accordance with the prototype ratio of the prototype slope; the indoor environment simulation is to apply the prototype slope environmental indicators actually monitored on-site to the slope model after proportional conversion; adverse climate conditions such as rainfall and earthquakes in the future are applied to the model slope in advance to achieve advanced simulation of model slope disasters, thereby achieving prediction and prejudgment of the operating status of the prototype slope;

[0004] Xu Guomin's "A Special Type of Slope: Deformation and Instability Mechanisms of Fault Zone Slopes and Countermeasures for Prevention" describes the deformation and instability mechanisms of fault zone slopes. Slope deformation and failure are a combination of deformation, failure, and intermittent motion after failure, the former being a microscopic manifestation and the latter a macroscopic manifestation. In terms of mechanical mechanisms, collapse is mainly caused by tensile failure, and its formation mechanism can be classified as either sliding or tensile collapse. Landslides are mainly caused by shear failure, and their scale is related to the slope height, slope, and scale of the fault zone. Fault zones can be regarded as quasi-homogeneous slopes (soil, including broken or massive bodies), with creep-tensile cracking as the main deformation mode and upper-part sliding and lower-part rotation as the main failure modes. As a result of sliding, the upper part of the fault footwall is exposed, and an accumulation body forms in front of the slope.

[0005] Existing shortcomings: The existing slope monitoring system cannot effectively integrate historical landslide fault data with current monitoring data, resulting in a lack of continuity and accuracy in the assessment of risk areas; traditional monitoring methods often rely on single strain or displacement data, failing to fully consider the impact of fault characteristics (such as depth, angle, and activity frequency), resulting in a lag in the judgment of instability risk; in addition, the state of plant roots has a significant impact on slope stability, but the comprehensive analysis of root status by existing technologies is still insufficient, failing to effectively quantify its influence on strain and displacement; the lack of targeted evaluation indicators means that the dynamic adjustment of risk areas lacks a scientific basis, which can lead to errors in landslide risk assessment, thereby affecting the effectiveness of disaster prevention and mitigation;

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0007] The purpose of the present invention is to provide a slope monitoring system based on a physical model to solve the problems raised in the above background technology.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] The slope monitoring system based on physical model includes:

[0010] Risk area division unit: The historical landslide fault data of the prototype slope during the previous monitoring period is obtained and analyzed to generate a judgment analysis result of the current deformation and instability stage of the prototype slope. Based on the analysis result, the risk areas of the prototype slope are sequentially marked according to the risk level, thereby forming a risk area set sequence. The historical landslide fault data includes strain, displacement data and fault characteristics, and the fault characteristics are composed of fault depth, fault height, fault angle and activity frequency.

[0011] Data collection unit: used to collect geological material characteristics and plant root status data of each risk area on the prototype slope during the previous monitoring period, and conduct a comprehensive analysis of the plant root status data to generate a root evaluation index for comprehensive evaluation of the plant root status of each risk area;

[0012] Correlation coefficient generating unit: used to obtain the root system evaluation index of each risk area in the previous monitoring period, and perform correlation analysis on the root system evaluation index with the strain and displacement data of the corresponding risk area, thereby generating a first correlation coefficient for evaluating the degree of correlation between the root system evaluation index and the strain data of each risk area; and generating a second correlation coefficient for evaluating the degree of correlation between the root system evaluation index and the displacement data of each risk area;

[0013] Current data monitoring unit: used to monitor the strain and displacement data of each risk area in the risk area set sequence during the current monitoring period, as well as the plant root status data, and calculate the root evaluation index corresponding to the plant root status data in each risk area;

[0014] Indoor model slope construction unit: used to fill the indoor model slope in proportion and layer by layer by comparing and converting the geological material characteristics provided by the data collection unit; the strain and displacement data of each risk area in the monitoring risk area set sequence during the current monitoring period and the plant root status data are proportionally converted and applied to the indoor model slope;

[0015] Warning threshold setting unit: used to gradually apply different levels of earthquake levels to the indoor model slope and monitor the strain and displacement data of each risk area on the indoor model slope until the indoor model slope landslide occurs. If a landslide occurs, the strain and displacement data in the previous experimental simulation will be used as the initial warning threshold;

[0016] Threshold calibration unit: used to combine and analyze the first correlation coefficient and the second correlation coefficient of each risk area, and comprehensively generate a calibration index for providing a dynamic adjustment strategy for the initial warning threshold of each risk area in the current monitoring period, so as to obtain the final warning threshold of the strain and displacement data of each risk area in the current monitoring period;

[0017] Instability and landslide evaluation index generation unit: used to compare and analyze the strain data, displacement data and root system evaluation index of each risk area in the risk area set sequence during the current monitoring period with the corresponding final warning threshold, and generate an instability and landslide evaluation index for landslide evaluation of each risk area in the current deformation and instability stage.

[0018] Furthermore, the geological material characteristic data include soil type, soil density, soil water content, shear strength, soil permeability, and soil elastic modulus;

[0019] Plant root status data includes root depth, root density, and root strength. The root depth, root density, and root strength after normalization in the i-th risk area are uniformly scaled to the range of (0,1). At the same time, the normalized root depth, root density, and root strength are recorded as GXd i 、GXm i 、GXq i ;

[0020] For GXd i 、GXm i 、GXq i A comprehensive analysis is performed to generate a root evaluation index for comprehensive evaluation of the plant root status in the i-th risk area. The calculation formula is as follows:

[0021]

[0022] Among them, E1 i is the root system evaluation index of the i-th risk area; a1, a2, and a3 are the weight coefficients of the corresponding parameters; and a1+a2+a3=1; and the initial values ​​of a1, a2, and a3 are set to 0.4, 0.3, and 0.3 respectively;

[0023] E1 i The value range is (0,1), when E1 i The closer it is to 0, the weaker the plant roots’ grip on the slope soil.

[0024] When E1 i The closer it is to 1, the stronger the plant roots’ grip on the slope soil.

[0025] Furthermore, the generation of the calibration index specifically includes:

[0026] Define the calibration index of the i-th risk area as K i , the calculation formula is as follows:

[0027]

[0028] Among them, Q1 and Q2 are C1 i and C2 i Low threshold and high threshold;

[0029] When K i =0, indicating weak correlation, the initial warning threshold of strain and displacement data needs to be lowered to enhance monitoring and early warning of this risk area; the initial warning threshold adjustment formula is as follows:

[0030]

[0031] Among them, SD″′i,current,s,k-1 and V″ i,current,s,k-1 are the adjusted strain and displacement data, which are the final warning thresholds;

[0032] When 0<K i When <1, and the calibration index gradually increases in the range of (0,1), the correlation gradually increases;

[0033] The formula for adjusting the initial warning threshold is as follows:

[0034]

[0035] When K i =1, indicating strong correlation, and the initial warning threshold is set to the average value of strain and displacement data in the first two experimental simulations.

[0036] Furthermore, an instability landslide evaluation index is generated for evaluating the landslide of each risk area at the current deformation and instability stage, specifically including:

[0037] The instability landslide evaluation index is defined as I i , the calculation formula is as follows:

[0038]

[0039] Among them, b1, b2, and b3 are the weight coefficients of the corresponding parameters; the values ​​of b1, b2, and b3 are all in the range of (0, 1), and b1+b2+b3=1; η4 is the correction coefficient, which is used to ensure I i The value range is (0,1);

[0040] Through historical data analysis, the initial weight values ​​of b1, b2, and b3 are determined to be 0.4, 0.4, and 0.2 respectively;

[0041] When 0<I i When <0.36, the landslide evaluation of the i-th risk area at the current deformation and instability stage is as follows:

[0042]

[0043] The i-th risk area is in the risk score R in the current monitoring period. i The monitoring frequency is reduced by 30%;

[0044] When 0.36≤I i When <0.67, the landslide evaluation of the i-th risk area at the current deformation and instability stage is as follows:

[0045]

[0046] The i-th risk area is in the risk score R in the current monitoring period.i Critical stage; monitoring frequency increased by 50%;

[0047] When 0.67≤I i When <1, the landslide evaluation of the i-th risk area at the current deformation and instability stage is as follows:

[0048]

[0049] The i-th risk area is in an unstable state during the current monitoring period; immediate preventive measures are required and the monitoring frequency is increased by 100%.

[0050] Compared with the existing technology, the beneficial effects of the present invention are: by measuring the soil type, geological material characteristics, and plant root status data of ground materials, the monitoring and assessment capabilities of risk areas are significantly improved; the system combines historical landslide fault data with risk area divisions to dynamically generate warning thresholds to ensure real-time prediction of slope instability; in addition, based on the comprehensive analysis of plant root status, the system can quantify the impact of roots on slope stability by generating a root evaluation index; the threshold calibration unit provides a dynamic warning threshold adjustment strategy to ensure real-time reflection of the stability of the slope; at the same time, the system can achieve more accurate risk assessment by generating an unstable landslide evaluation index, thereby improving the efficiency and reliability of slope monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a schematic diagram of the overall system flow of the present invention. DETAILED DESCRIPTION

[0052] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to specific embodiments.

[0053] It should be noted that, unless otherwise defined, the technical or scientific terms used in the present invention should have the usual meanings understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0054] Example 1:

[0055] See also Figure 1 , the present invention provides a technical solution:

[0056] The slope monitoring system based on physical model includes:

[0057] Risk area division unit: The historical landslide fault data of the prototype slope during the previous monitoring period is obtained and analyzed to generate a judgment analysis result of the current deformation and instability stage of the prototype slope. Based on the analysis result, the risk areas of the prototype slope are sequentially marked according to the risk level, thereby forming a risk area set sequence. The historical landslide fault data includes strain, displacement data and fault characteristics, and the fault characteristics are composed of fault depth, fault height, fault angle and activity frequency.

[0058] Data collection unit: used to collect geological material characteristics and plant root status data of each risk area on the prototype slope during the previous monitoring period, and conduct a comprehensive analysis of the plant root status data to generate a root evaluation index for comprehensive evaluation of the plant root status of each risk area;

[0059] Correlation coefficient generating unit: used to obtain the root system evaluation index of each risk area in the previous monitoring period, and perform correlation analysis on the root system evaluation index with the strain and displacement data of the corresponding risk area, thereby generating a first correlation coefficient for evaluating the degree of correlation between the root system evaluation index and the strain data of each risk area; and generating a second correlation coefficient for evaluating the degree of correlation between the root system evaluation index and the displacement data of each risk area;

[0060] Current data monitoring unit: used to monitor the strain and displacement data of each risk area in the risk area set sequence during the current monitoring period, as well as the plant root status data, and calculate the root evaluation index corresponding to the plant root status data in each risk area;

[0061] Indoor model slope construction unit: used to fill the indoor model slope in proportion and layer by layer by comparing and converting the geological material characteristics provided by the data collection unit; the strain and displacement data of each risk area in the monitoring risk area set sequence during the current monitoring period, as well as the plant root status data, are proportionally converted and applied to the indoor model slope to ensure that the simulation conditions are consistent with the prototype slope environment;

[0062] Warning threshold setting unit: used to gradually apply different levels of earthquake levels to the indoor model slope and monitor the strain and displacement data of each risk area on the indoor model slope until the indoor model slope landslide occurs. If a landslide occurs, the strain and displacement data in the previous experimental simulation will be used as the initial warning threshold;

[0063] Threshold calibration unit: used to combine and analyze the first correlation coefficient and the second correlation coefficient of each risk area, and comprehensively generate a calibration index for providing a dynamic adjustment strategy for the initial warning threshold of each risk area in the current monitoring period, so as to obtain the final warning threshold of the strain and displacement data of each risk area in the current monitoring period;

[0064] Instability and landslide evaluation index generation unit: used to compare and analyze the strain data, displacement data and root system evaluation index of each risk area in the risk area set sequence during the current monitoring period with the corresponding final warning threshold, and generate an instability and landslide evaluation index for landslide evaluation of each risk area in the current deformation and instability stage.

[0065] Further explanation: the strain data is expressed as standard deviation, and the average value of the strain of the i-th risk area in the previous monitoring period is calculated. and standard deviation SD i ;

[0066]

[0067] in, is the average value of the strain of the i-th risk area during the previous monitoring period;

[0068] SD i is the standard deviation of the strain of the i-th risk area in the previous monitoring period; j represents the index of the number of monitoring times in the previous monitoring period, and m is the total number of monitoring times in the previous monitoring period;

[0069] The displacement data is expressed as displacement change rate, and the displacement change rate V of the i-th risk area is calculated i ;The calculation formula is as follows;

[0070]

[0071] Among them, V i is the displacement change rate of the i-th risk area, D i,j+1 、D i,j are the displacement values ​​of the i-th risk area during the j+1 and j-th monitoring periods within the previous monitoring period, and Δt is the time difference between the j+1 and j-th monitoring periods;

[0072] The normalized fault depth of the i-th risk area is recorded as Ds i ; It represents the depth of the fault from the surface to the fault plane;

[0073] The normalized fault height of the i-th risk area is recorded as Dh i ; It represents the vertical projection height of the fault on the slope;

[0074] The normalized fault angle of the i-th risk area is recorded as Dj i It represents the inclination angle of the fault plane relative to the horizontal plane. The smaller the fault angle, the higher the friction on the slope. The smaller the influence of gravity on the slope, making the slope less likely to become unstable or landslide.

[0075] The normalized historical fault activity frequency of the i-th risk area is recorded as Hfi, which represents the number of activities within a unit monitoring period.

[0076] The following risk scoring model is used to calculate the risk score of each risk area during the previous monitoring period. The calculation formula is as follows:

[0077]

[0078] Among them, R i is the risk score of the i-th risk area in the previous monitoring period, η1 is the adjustment coefficient;

[0079] w1, w2, w3, w4, w5, w6 are the weight coefficients of the corresponding parameters, And these weight coefficients and η1 are set to ensure R i The valid value range of is (0,1); w3, w4, w5, w6 and η1 are determined numerically based on experimental data by the expert group;

[0080] The following is a detailed adjustment strategy for the distribution ratio of weight coefficients w1, w2, w3, w4, w5, and w6, describing two different scenarios: a scenario with a large impact and a scenario with a small impact;

[0081] 1.1) Regarding the weight distribution of weight w1, when w1 has a greater influence in the distribution ratio:

[0082] When the deformation of the slope is significant and the change in strain data is the primary factor in determining the risk of instability, the value of w1 ranges from 0.3 to 0.4; the risk score R i The calculation of will mainly rely on strain data, and the high weight of strain will make the risk score more sensitive to fluctuations in strain;

[0083] When w1 has little influence on the distribution ratio:

[0084] When the strain data changes are small and other displacement change rates and fault characteristics begin to show larger fluctuations, the value range of w1 is reduced to 0.1 to 0.2; the change in risk score will be more influenced by other parameters, especially the weight of displacement change will be increased;

[0085] 1.2) Regarding the weight distribution of weight w2, when w2 has a greater influence in the distribution ratio:

[0086] In cases where displacement changes rapidly and has a direct impact on landslide risk, the value of w2 ranges from 0.25 to 0.35; the risk score will more strongly reflect the change in displacement;

[0087] When w2 has little influence on the distribution ratio:

[0088] When the displacement change rate V i When it is less than 0.2, the influence of other parameters becomes more significant, and the value of w2 is reduced to 0.1 to 0.2; the change in risk score is mainly determined by strain and fault characteristics, and the influence of displacement is weakened;

[0089] 1.3) Regarding the weight distribution of weight w3, when w3 has a greater influence in the distribution ratio:

[0090] When the fault depth changes in historical monitoring data by more than 30% of the initial value and its change directly affects the risk score, the value of w3 ranges from 0.15 to 0.25;

[0091] When w3 has less influence in the distribution ratio:

[0092] When the fault depth changes by less than 10%, the value of w3 drops to 0.05 to 0.1; in this case, the risk score is less affected by the depth and more dependent on other parameters.

[0093] 1.4) Regarding the weight distribution of weight w4, when w4 has a greater influence in the distribution ratio:

[0094] When the fault height change exceeds 20% of the initial value of the monitoring period and affects the landslide risk, the value of w4 ranges from 0.15 to 0.25; in this case, the risk rating will directly reflect the impact of the height change;

[0095] When w4 has little influence on the distribution ratio:

[0096] When the change in fault height is within 5% of the initial value of the monitoring period, the value of w4 is reduced to 0.05 to 0.1; at this point, the influence of height is reduced, and risk assessment depends on other parameters;

[0097] 1.5) Regarding the weight distribution of weight w5, when w5 has a greater influence in the distribution ratio:

[0098] When the change in fault angle directly leads to an increase in landslide risk of more than 15%, the value of w5 ranges from 0.15 to 0.25; the risk score will be sensitive to the angle change, reflecting a slope stability problem;

[0099] When w5 has less influence in the distribution ratio:

[0100] When the fault angle change is within 5% of the initial value of the monitoring period, the value of w5 is reduced to 0.05 to 0.1; at this time, the influence of the angle is weakened;

[0101] 1.6) Regarding the weight distribution of weight w6, when w6 has a greater influence in the distribution ratio:

[0102] When the historical activity frequency increases by more than 8% of the starting value of the monitoring period and indicates potential risk, the value of w6 ranges from 0.2 to 0.3; the risk rating will be closely dependent on the change in frequency.

[0103] When w6 has less influence in the distribution ratio:

[0104] When the change in activity frequency is within 10% of the starting value of the monitoring period, the value of w6 is reduced to 0.05 to 0.1; the influence of frequency is reduced, and the risk score is dominated by other factors;

[0105] In this embodiment,

[0106] According to the risk score R of the i-th risk area i , the deformation and instability stages of the prototype slope are set as stable stage, critical stage and instability stage;

[0107] 2.1) For the stable phase, the risk score R i The value is in the low range (0,0.36);

[0108] Strain standard deviation SD i ≤0.1%; means that the strain data changes no more than 0.1% during the monitoring period;

[0109] Displacement change rate V i ≤2; means that the displacement change rate does not exceed 2 mm per cycle; in this embodiment, each cycle is one week, i.e. one week;

[0110] Depth Ds i and height Dh i The range of change is within 10% of the starting value of the monitoring period;

[0111] Angle Dj i The variation range during the monitoring period is within 5°;

[0112] Historical activity frequency Hf i The change range is within 5% of the starting value of the monitoring period;

[0113] 2.2) In the critical stage, the strain and displacement changes begin to increase, the fault characteristics become abnormal, and the risk score R iThe value is in the medium range [0.36, 0.67];

[0114] Strain standard deviation 0.1%<SD i ≤0.5%, strain data begins to fluctuate significantly;

[0115] Displacement change rate 2<V i ≤5, displacement speed increases;

[0116] Depth Ds i and height Dh i The range of change is within the range of 10% to 30% of the starting value of the monitoring period, excluding the endpoint values ​​on both sides;

[0117] Angle Dj i The change range in the monitoring period is 5°<Dj i <15°;

[0118] Historical activity frequency Hf i The range of change is 5% to 15% of the starting value of the monitoring period, excluding the endpoint values ​​on both sides;

[0119] 2.3) In the unstable stage, the strain and displacement change significantly, the fault characteristics are obviously unstable, and the risk score R i The value is in the high range (0.67, 1), which means there is a risk of landslide;

[0120] Strain standard deviation SD i >0.5%, the strain data showed a significant increase;

[0121] Displacement change rate V i >5, the displacement speed increases significantly;

[0122] Depth Ds i and height Dh i The change range is more than 30% of the starting value of the monitoring period;

[0123] The angle Dji changes by more than 15° during the monitoring period;

[0124] The change in the historical activity frequency Hfi is more than 15% of the starting value of the monitoring period;

[0125] Sort all risk areas according to their risk scores to form a risk area set sequence {1, 2, …, i, …, n}, where i is the index of the risk area and n is the total number of risk areas;

[0126] The stable stage, critical stage and unstable stage are divided into low-risk area, medium-risk area and high-risk area in sequence, and marked in order according to the risk level to form a risk level sequence set {R1, R2, ..., R i ,…,R n}; where R i is the risk level of the ith risk area, R n is the risk level of the nth risk area;

[0127] The historical landslide fault data corresponding to each risk area, including strain, displacement data, fault depth, fault height, fault angle and activity frequency, are normalized to the same scale (0, 1) to ensure that the data are in the same dimension; the normalized strain, displacement data, fault depth, fault height, fault angle and activity frequency in the i-th risk area are marked as SD′ in sequence. i , V′ i , Ds′ i , Dh′ i , Dj′ i , Hf′ i .

[0128] It is further explained that the geological material characteristic data include soil type, soil density, soil water content, shear strength, soil permeability, and soil elastic modulus;

[0129] For soil type:

[0130] Data sources: soil survey, laboratory soil analysis;

[0131] Soil types are classified using the particle size of the soil;

[0132] For soil density:

[0133] Data sources: Field sampling and laboratory testing;

[0134] Soil density is measured using dry density (g / cm 3 ) or saturation density;

[0135] For soil moisture content:

[0136] Data source: laboratory drying method or field humidity sensor;

[0137] Soil moisture content is expressed as volumetric moisture content (%) or weight moisture content (%);

[0138] For shear strength:

[0139] Data source: laboratory triaxial shear test or direct shear test;

[0140] Shear strength is expressed in terms of internal friction angle and cohesion (kPa);

[0141] For soil infiltration rate:

[0142] Data source: Laboratory penetration test;

[0143] Soil permeability is expressed as permeability coefficient (m / s);

[0144] For the soil elastic modulus:

[0145] Data source: laboratory static pressure test or dynamic test;

[0146] The elastic modulus of soil is expressed in elastic modulus (kPa or MPa);

[0147] Plant root status data includes root depth, root density, and root strength. The root depth, root density, and root strength after normalization in the i-th risk area are uniformly scaled to the range of (0,1). At the same time, the normalized root depth, root density, and root strength are recorded as GXd i 、GXm i 、GXq i ;

[0148] For GXd i 、GXm i 、GXq i A comprehensive analysis is performed to generate a root evaluation index for comprehensive evaluation of the plant root status in the i-th risk area. The calculation formula is as follows:

[0149]

[0150] Among them, E1 i is the root system evaluation index of the i-th risk area; a1, a2, and a3 are the weight coefficients of the corresponding parameters; and a1+a2+a3=1; and the initial values ​​of a1, a2, and a3 are set to 0.4, 0.3, and 0.3 respectively;

[0151] E1 i The value range is (0,1), when E1 i The closer it is to 0, the weaker the plant roots’ grip on the slope soil.

[0152] When E1 i The closer it is to 1, the stronger the plant roots’ grip on the slope soil.

[0153] When the i-th risk area is a low-risk area, a medium-risk area, and a high-risk area, the initial values ​​of a1, a2, and a3 are adjusted as follows;

[0154] If the i-th risk area is a low-risk area, the weight of the root depth needs to be increased.

[0155] Root depth and soil stability: In low-risk areas, slope stability is relatively good, and a deeper root system can better anchor the soil and enhance the soil's shear strength; therefore, an increase in root depth can effectively resist potential landslide risks.

[0156] The role of plants: Deeper root systems increase their ability to anchor soil, helping to reduce topsoil erosion and enhance overall soil stability. Therefore, increasing the weight of root depth can better reflect its impact on low-risk areas.

[0157] If the i-th risk area is a medium-risk area, the weight of the root density needs to be increased.

[0158] Root density and landslide risk: In medium-risk areas, root density becomes more important because higher root density can provide additional support and enhance the structural integrity of the soil; increased root density can effectively enhance the shear strength of the soil, thereby reducing landslide risk.

[0159] Relative plant competition: In medium-risk areas, competition between plants will affect the distribution and density of roots. Increasing the weight of root density will make the evaluation more accurate in reflecting soil stability.

[0160] If the i-th risk area is a high-risk area, the weights of root depth and root strength need to be increased.

[0161] Comprehensive stability factors: In high-risk areas, slope stability is affected by multiple factors, and root depth or root strength alone is not sufficient to reflect landslide risk; therefore, increasing the weight of these two factors can more comprehensively assess the status of plant roots in risk areas.

[0162] Importance of root strength: Root strength is crucial for withstanding external forces (such as wind and rainfall) and soil slope changes. In high-risk areas, only deep and strong root systems can effectively stabilize the soil and prevent landslides.

[0163] For root depth:

[0164] Data sources: root sampling and measurements;

[0165] Root depth was expressed as the average root depth (cm) or the maximum root depth;

[0166] For root density:

[0167] Data source: Soil root distribution analysis;

[0168] Root density was expressed as the number of roots per unit volume of soil (roots / kg soil);

[0169] For root strength:

[0170] Data source: Laboratory root tensile strength test;

[0171] The root strength is expressed by the tensile strength of the root (N).

[0172] Further explanation: the first correlation coefficient of the i-th risk area is set to C1 i , the calculation formula is as follows:

[0173]

[0174] Wherein, η2 is a positive constant, and 0.01≤η2≤0.12; the specific value of η2 is determined by the expert group based on experimental data;

[0175] C1 i The value range is (0,1); when C1 i The closer it is to 0, and E1 i The closer to 0, the higher the SD′ i The closer it is to 1, the weaker the plant roots' grip on the slope soil, and the greater the strain variation during the previous monitoring period, which ultimately leads to a more unstable prototype slope. This indicates that the correlation between the root evaluation index and the strain data in the i-th risk area is weaker.

[0176] When C1 i The closer it is to 1, and E1 i The closer it is to 1, the higher the SD′ i The closer it is to 0, the stronger the plant roots' grip on the slope soil, and the smaller the strain variation during the previous monitoring period, which ultimately leads to a more stable prototype slope. This indicates that the correlation between the root evaluation index and the strain data in the i-th risk area is stronger.

[0177] Set the second correlation coefficient to C2 i , the calculation formula is as follows:

[0178]

[0179] Wherein, η3 is a positive constant, and 0.01≤η3≤0.11; the specific value of η3 is determined by the expert group based on experimental data;

[0180] C2 i The value range is (0,1); when C2 iThe closer it is to 0, the weaker the correlation between the root system evaluation index and displacement data of the i-th risk area; when C2 i The closer it is to 1, the stronger the correlation between the root system evaluation index and displacement data of the i-th risk area;

[0181] And set C1 i and C2 i The distinction thresholds are both a low threshold Q1 and a high threshold Q2, and Q1<Q2. The value ranges of Q1 and Q2 are both within the range of (0,1). The specific values ​​of Q1 and Q2 are determined by the expert group through experimental data. In this embodiment, Q1 and Q2 are 0.3 and 0.65 respectively.

[0182] If C1 i When the value interval is (0, Q1), it means that the root system evaluation index and strain data are in a weak correlation interval;

[0183] If C2 i When the value interval is (0, Q1), it means that the root system evaluation index and displacement data are in a weak correlation interval;

[0184] If C1 i When the value interval is [Q1, Q2], it means that the root evaluation index and the strain data are in a medium correlation interval;

[0185] If C2 i When the value interval is [Q1, Q2], it means that the root system evaluation index and displacement data are in a medium correlation interval;

[0186] If C1 i When the value interval is (Q2, 1), it means that the root system evaluation index and the strain data are in a strong correlation interval;

[0187] If C2 i When the value interval is (Q2, 1), it means that the root system evaluation index and displacement data are in a strong correlation interval.

[0188] Further explanation, based on SD′ i , V′ i The standardized processing method is to record the strain and displacement data of the i-th risk area in the current monitoring period as SD′ i,current , V′ i,current ;

[0189] The root system evaluation index of the i-th risk area in the current monitoring period is recorded as E1 i,current .

[0190] Further explanation: geological material characteristic data are collected from the site, including soil type, dry density, volumetric water content, shear strength, permeability coefficient, and elastic modulus; and the strain and displacement data SD′ of the i-th risk area in the current monitoring period are obtained. i,current , V′ i,current ; and plant root status data;

[0191] Data sources include soil survey reports, laboratory analysis, and field tests;

[0192] The collected data is converted into proportional values ​​for the indoor model slope to ensure that the indoor model slope is consistent with the on-site conditions. The proportional values ​​are determined by the expert group based on experimental data. The risk area set sequence on the indoor model slope is represented by {1′, 2′, ..., i′, ..., n′}. There is a one-to-one correspondence between the risk area set sequence {1′, 2′, ..., i′, ..., n′} of the indoor model slope and the risk area set sequence {1, 2, ..., i, ..., n} of the prototype slope.

[0193] According to the characteristics of the indoor model slope, the earthquake simulation level is set as low earthquake, moderate earthquake, and high earthquake; the specific value of each earthquake level is determined according to the maximum vibration value of the prototype slope in history, and the maximum vibration value in history is set to R1m / s 2 , set the vibration value intervals of low earthquake, medium earthquake and high earthquake to

[0194] The above-mentioned different earthquake waves are gradually applied to the model. Each vibration level is maintained for 5 minutes, and U vibration experiments are carried out for each vibration level. The number of experiments is represented by the k index value, and the strain and displacement data of each risk area are recorded. Low earthquake, moderate earthquake, and high earthquake are represented by s∈{1,2,3}, where 1, 2, and 3 represent low earthquake, moderate earthquake, and high earthquake, respectively.

[0195] The strain data of the kth experiment at the i′th risk area on the indoor model slope under the sth vibration level is recorded as SD′ i′,current,s,k ;

[0196] The displacement data of the kth experiment of the i′th risk area on the indoor model slope under the s vibration level is recorded as V′ i,current,s,k ;

[0197] If a landslide occurs, the strain SD′ in the previous experimental simulation i′,current,s,k-1 and displacement data V′ i,current,s,k-1 as the initial warning threshold.

[0198] Further explanation: the generation of the calibration index specifically includes:

[0199] Define the calibration index of the i-th risk area as K i, the calculation formula is as follows:

[0200]

[0201] Among them, Q1 and Q2 are C1 i and C2 i Low threshold and high threshold;

[0202] When K i =0, indicating weak correlation, the initial warning threshold of strain and displacement data needs to be lowered to enhance monitoring and early warning of this risk area; the initial warning threshold adjustment formula is as follows:

[0203]

[0204] Among them, SD i′,current,s,k-1 and V″ i,current,s,k-1 are the adjusted strain and displacement data, which are the final warning thresholds;

[0205] When 0<K i When <1, and the calibration index gradually increases in the range of (0,1), the correlation gradually increases;

[0206] The formula for adjusting the initial warning threshold is as follows:

[0207]

[0208] When K i =1, indicating strong correlation, and the initial warning threshold is set to the average value of strain and displacement data in the first two experimental simulations.

[0209] Further explanation: the generation of the instability landslide evaluation index for evaluating the landslide of each risk area at the current deformation and instability stage specifically includes:

[0210] The instability landslide evaluation index is defined as I i , the calculation formula is as follows:

[0211]

[0212] Among them, b1, b2, and b3 are the weight coefficients of the corresponding parameters; the values ​​of b1, b2, and b3 are all in the range of (0, 1), and b1+b2+b3=1; η4 is the correction coefficient, which is used to ensure I i The value range is (0,1); the specific value of η4 is determined by the expert group through experimental data;

[0213] Through historical data analysis, the initial weight values ​​of b1, b2, and b3 are determined to be 0.4, 0.4, and 0.2 respectively;

[0214] When 0<I iWhen <0.36, the landslide evaluation of the i-th risk area at the current deformation and instability stage is as follows:

[0215]

[0216] The i-th risk area is in the risk score R in the current monitoring period. i The monitoring frequency is reduced by 30%;

[0217] When 0.36≤I i When <0.67, the landslide evaluation of the i-th risk area at the current deformation and instability stage is as follows:

[0218]

[0219] The i-th risk area is in the risk score R in the current monitoring period. i Critical stage; monitoring frequency increased by 50%;

[0220] When 0.67≤I i When <1, the landslide evaluation of the i-th risk area at the current deformation and instability stage is as follows:

[0221]

[0222] The i-th risk area is in an unstable state during the current monitoring period; immediate preventive measures are required and the monitoring frequency is increased by 100%.

[0223] Example 2:

[0224] The experiment in this embodiment aims to use the warning threshold setting unit, the threshold calibration unit and the unstable landslide evaluation index generation unit to perform real-time monitoring and evaluation of various risk areas of the indoor model slope;

[0225] Preliminary preparations for the experiment included selecting materials for the indoor model slope to ensure it accurately reflects the characteristics of a natural slope; selecting multiple risk areas, each equipped with strain gauges and displacement sensors to record strain and displacement data; and setting three vibration levels: low, moderate, and high, to record in detail the impact of vibration on the model slope.

[0226] U vibration tests are applied for each vibration level, each lasting 5 minutes, and the strain and displacement data of each risk area are recorded. If a landslide occurs, the strain and displacement data of the previous test are used as the new initial warning threshold;

[0227] The threshold calibration unit analyzes the first and second correlation coefficients of each risk area to generate a calibration index. Based on the calibration index, the initial warning threshold of each area is dynamically adjusted to ensure the accuracy and reliability of the monitoring data.

[0228] The experimental data table is as follows:

[0229] Table 1

[0230]

[0231]

[0232] The above experimental data are analyzed as follows:

[0233] In risk zone i, when the vibration level increases from 1 to 3, the calibration index K i The increase from 0.3 to 0.7 reflects the increase in the risk level of unstable landslide with the increase of strain and displacement;

[0234] In risk zone i+1, the calibration index K i The increase from 0.25 to 0.6 indicates that the risk of unstable landslide gradually increases;

[0235] This trend shows that the improvement of the calibration index directly corresponds to the changes in the monitoring data of the risk area; by dynamically adjusting the strategy, the system can effectively reflect the actual risk situation of the area;

[0236] The initial warning threshold values ​​in the table also show the effect of dynamic adjustment of the calibration index:

[0237] In risk area i, the initial alert threshold increased from 0.35 for earthquake level 1 to 0.55 for earthquake level 3, an increase of 57%;

[0238] In risk area i+1, the initial alert threshold also increased from 0.25 to 0.45, an increase of 80%;

[0239] In risk area i, as the vibration level increases, the instability landslide evaluation index increases from 0.5 to 0.8, reflecting that the instability risk of the model is significantly improved;

[0240] In risk area i+1, the instability landslide evaluation index also shows an increase from 0.4 to 0.7, indicating that the landslide risk in this area is increasing;

[0241] This change indicates that it is possible to effectively conduct landslide evaluation in the region at the current stage of deformation and instability, provide timely risk assessment, and guide subsequent preventive measures.

[0242] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0243] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software depends on the specific application and design constraints of the technical solution.

[0244] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, and may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment as needed.

[0245] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. The slope monitoring system based on physical model is characterized by: Specifically include: Risk area division unit: The historical landslide fault data of the prototype slope during the previous monitoring period is obtained and analyzed to generate a judgment analysis result of the current deformation and instability stage of the prototype slope. Based on the analysis result, the risk areas of the prototype slope are sequentially marked according to the risk level, thereby forming a risk area set sequence. The historical landslide fault data includes strain, displacement data and fault characteristics, and the fault characteristics are composed of fault depth, fault height, fault angle and activity frequency. Data collection unit: used to collect geological material characteristics and plant root status data of each risk area on the prototype slope during the previous monitoring period, and conduct a comprehensive analysis of the plant root status data to generate a root evaluation index for comprehensive evaluation of the plant root status of each risk area; Correlation coefficient generating unit: used to obtain the root system evaluation index of each risk area in the previous monitoring period, and perform correlation analysis on the root system evaluation index with the strain and displacement data of the corresponding risk area, and generate a first correlation coefficient for evaluating the degree of correlation between the root system evaluation index and the strain data of each risk area; and generating a second correlation coefficient for evaluating the degree of correlation between the root evaluation index and the displacement data of each risk area; Current data monitoring unit: used to monitor the strain and displacement data of each risk area in the risk area set sequence during the current monitoring period, as well as the plant root status data, and calculate the root evaluation index corresponding to the plant root status data in each risk area; Indoor model slope construction unit: used to fill the indoor model slope in proportion and layer by layer by comparing and converting the geological material characteristics provided by the data collection unit; the strain and displacement data of each risk area in the monitoring risk area set sequence during the current monitoring period and the plant root status data are proportionally converted and applied to the indoor model slope; Warning threshold setting unit: used to gradually apply different levels of earthquake levels to the indoor model slope and monitor the strain and displacement data of each risk area on the indoor model slope until the indoor model slope landslide occurs. If a landslide occurs, the strain and displacement data in the previous experimental simulation will be used as the initial warning threshold; Threshold calibration unit: used to combine and analyze the first correlation coefficient and the second correlation coefficient of each risk area, and comprehensively generate a calibration index for providing a dynamic adjustment strategy for the initial warning threshold of each risk area in the current monitoring period, so as to obtain the final warning threshold of the strain and displacement data of each risk area in the current monitoring period; Instability and landslide evaluation index generation unit: used to compare and analyze the strain data, displacement data and root system evaluation index of each risk area in the risk area set sequence during the current monitoring period with the corresponding final warning threshold, and generate an instability and landslide evaluation index for landslide evaluation of each risk area in the current deformation and instability stage.

2. The physical model-based slope monitoring system according to claim 1, characterized in that: The strain data is expressed as standard deviation, and the average value S of the strain of the i-th risk area in the previous monitoring period is calculated. i and standard deviation SD i ; The displacement data is expressed as displacement change rate, and the displacement change rate V of the i-th risk area is calculated i ; The normalized fault depth of the i-th risk area is recorded as Ds i ; The normalized fault height of the i-th risk area is recorded as Dh i ; The normalized fault angle of the i-th risk area is recorded as Dj i ; The normalized historical fault activity frequency of the i-th risk area is recorded as Hf i .

3. The physical model-based slope monitoring system according to claim 2, characterized in that: The following risk scoring model is used to calculate the risk score of each risk area during the previous monitoring period. The calculation formula is as follows: Among them, R i is the risk score of the i-th risk area in the previous monitoring period, η1 is the adjustment coefficient; w1, w2, w3, w4, w5, w6 are the weight coefficients of the corresponding parameters, And these weight coefficients and η1 are set to ensure R i The valid value range of is (0,1); According to the risk score R of the i-th risk area i , the deformation and instability stages of the prototype slope are set as stable stage, critical stage and instability stage; 2.1) For the stable phase, the risk score R i The value is in the low range (0,0.36); 2.2) In the critical stage, the strain and displacement changes begin to increase, the fault characteristics become abnormal, and the risk score R i The value is in the medium range [0.36, 0.67]; 2.3) In the unstable stage, the strain and displacement change significantly, the fault characteristics are obviously unstable, and the risk score R i The value is in the high range (0.67, 1), which means there is a risk of landslide; Sort all risk areas according to their risk scores to form a risk area set sequence {1, 2, …, i, …, n}, where i is the index of the risk area and n is the total number of risk areas; The stable stage, critical stage and unstable stage are divided into low-risk area, medium-risk area and high-risk area in sequence, and marked in order according to the risk level to form a risk level sequence set {R1, R2, ..., R i ,…,R n }; where R i is the risk level of the ith risk area, R n is the risk level of the nth risk area; The standardized strain, displacement data, fault depth, fault height, fault angle, and activity frequency in the i-th risk area are marked as SD′ i , V′ i , Ds′ i , Dh′ i , Dj′ i , Hf′ i .

4. The physical model-based slope monitoring system according to claim 3, characterized in that: Geological material characteristic data include soil type, soil density, soil moisture content, shear strength, soil permeability, and soil elastic modulus; Plant root status data includes root depth, root density, and root strength. The root depth, root density, and root strength after normalization in the i-th risk area are uniformly scaled to the range of (0,1). At the same time, the normalized root depth, root density, and root strength are recorded as GXd i 、GXm i 、GXq i ; For GXd i 、GXm i 、GXq i A comprehensive analysis is performed to generate a root evaluation index for comprehensive evaluation of the plant root status in the i-th risk area. The calculation formula is as follows: Among them, E1 i is the root system evaluation index of the i-th risk area; a1, a2, and a3 are the weight coefficients of the corresponding parameters; and a1+a2+a3=1; and the initial values ​​of a1, a2, and a3 are set to 0.4, 0.3, and 0.3 respectively; E1 i The value range is (0,1), when E1 i The closer it is to 0, the weaker the plant roots’ grip on the slope soil. When E1 i The closer it is to 1, the stronger the plant roots’ grip on the slope soil. When the i-th risk area is a low-risk area, a medium-risk area, and a high-risk area, the initial values ​​of a1, a2, and a3 are adjusted as follows; If the i-th risk area is a low-risk area, the weight of the root depth needs to be increased. If the i-th risk area is a medium-risk area, the weight of the root density needs to be increased. If the i-th risk area is a high-risk area, the weights of root depth and root strength need to be increased.

5. The physical model-based slope monitoring system according to claim 4, characterized in that: Set the first correlation coefficient of the i-th risk area to C1 i , the calculation formula is as follows: Where η2 is a positive constant, and 0.01≤η2≤0.12; C1 i The value range is (0,1); when C1 i The closer it is to 0, the weaker the correlation between the root system evaluation index and the strain data of the i-th risk area; when C1 i The closer it is to 1, the stronger the correlation between the root system evaluation index and the strain data of the i-th risk area; Set the second correlation coefficient to C2 i , the calculation formula is as follows: Where η3 is a positive constant, and 0.01≤η3≤0.11; C2 i The value range is (0,1); when C2 i The closer it is to 0, the weaker the correlation between the root system evaluation index and displacement data of the i-th risk area; when C2 i The closer it is to 1, the stronger the correlation between the root system evaluation index and displacement data of the i-th risk area; And set C1 i and C2 i The distinction thresholds are all low threshold Q1 and high threshold Q2, and Q1<Q2, and the value ranges of Q1 and Q2 are both in the range of (0,1); If C1 i When the value interval is (0, Q1), it means that the root system evaluation index and strain data are in a weak correlation interval; If C2 i When the value interval is (0, Q1), it means that the root system evaluation index and displacement data are in a weak correlation interval; If C1 i When the value interval is [Q1, Q2], it means that the root evaluation index and the strain data are in a medium correlation interval; If C2 i When the value interval is [Q1, Q2], it means that the root system evaluation index and displacement data are in a medium correlation interval; If C1 i When the value interval is (Q2, 1), it means that the root system evaluation index and the strain data are in a strong correlation interval; If C2 i When the value interval is (Q2, 1), it means that the root system evaluation index and displacement data are in a strong correlation interval.

6. The physical model-based slope monitoring system according to claim 5, characterized in that: Based on SD′ i , V′ i The standardized processing method is to record the strain and displacement data of the i-th risk area in the current monitoring period as SD′ i,current , V′ i，current ; The root system evaluation index of the i-th risk area in the current monitoring period is recorded as E1 i,current .

7. The physical model-based slope monitoring system according to claim 6, characterized in that: The collected data is converted into proportional values ​​for the indoor model slope to ensure that the indoor model slope is consistent with the on-site conditions. The proportional values ​​are determined by the expert group based on experimental data. The risk area set sequence on the indoor model slope is represented by {1′, 2′, ..., i′, ..., n′}. There is a one-to-one correspondence between the risk area set sequence {1′, 2′, ..., i′, ..., n′} of the indoor model slope and the risk area set sequence {1, 2, ..., i, ..., n} of the prototype slope. According to the characteristics of the indoor model slope, the earthquake simulation level is set as low earthquake, moderate earthquake, and high earthquake; the specific value of each earthquake level is determined according to the maximum vibration value of the prototype slope in history, and the maximum vibration value in history is set to R1m / s 2 , set the vibration value intervals of low earthquake, medium earthquake and high earthquake to The above-mentioned different seismic waves are gradually applied to the indoor model slope. Each vibration level is maintained for 5 minutes, and U vibration experiments are carried out for each vibration level. The number of experiments is represented by the k index value, and the strain and displacement data of each risk area are recorded. Low earthquake, moderate earthquake, and high earthquake are represented by s∈{1,2,3}, where 1, 2, and 3 represent low earthquake, moderate earthquake, and high earthquake, respectively. The strain data of the kth experiment at the i′th risk area on the indoor model slope under the sth vibration level is recorded as SD′ i′,current,s,k ; The displacement data of the kth experiment of the i′th risk area on the indoor model slope under the s vibration level is recorded as V′ i,current,s,k ; If a landslide occurs, the strain SD′ in the previous experimental simulation i′,current,s,k-1 and displacement data V′ i,current,s,k-1 as the initial warning threshold.

8. The physical model-based slope monitoring system according to claim 7, characterized in that: The generation of the calibration index includes: Define the calibration index of the i-th risk area as K i , the calculation formula is as follows: Among them, Q1 and Q2 are C1 i and C2 i Low threshold and high threshold; When K i =0, indicating weak correlation, the initial warning threshold of strain and displacement data needs to be lowered to enhance monitoring and early warning of this risk area; the initial warning threshold adjustment formula is as follows: Among them, SD i′,current,s,k-1 and V″ i,current,s,k-1 are the adjusted strain and displacement data, which are the final warning thresholds; When 0<K i When <1, and the calibration index gradually increases in the range of (0,1), the correlation gradually increases; The formula for adjusting the initial warning threshold is as follows: When K i =1, indicating strong correlation, and the initial warning threshold is set to the average value of strain and displacement data in the first two experimental simulations.

9. The physical model-based slope monitoring system according to claim 8, characterized in that: Generate an instability landslide evaluation index for landslide evaluation in each risk area at the current deformation and instability stage, specifically including: The instability landslide evaluation index is defined as I i , the calculation formula is as follows: Among them, b1, b2, and b3 are the weight coefficients of the corresponding parameters; the values ​​of b1, b2, and b3 are all in the range of (0, 1), and b1+b2+b3=1; η4 is the correction coefficient, which is used to ensure I i The value range is (0,1); Through historical data analysis, the initial weight values ​​of b1, b2, and b3 are determined to be 0.4, 0.4, and 0.2 respectively; the basic monitoring frequency of the current monitoring system is determined; When 0<I i When <0.36, the landslide evaluation of the i-th risk area at the current deformation and instability stage is as follows: The i-th risk area is in the risk score R in the current monitoring period. i The monitoring frequency is reduced by 30%; When 0.36≤I i When <0.67, the landslide evaluation of the i-th risk area at the current deformation and instability stage is as follows: The i-th risk area is in the risk score R in the current monitoring period. i Critical stage; monitoring frequency increased by 50%; When 0.67≤I i When <1, the landslide evaluation of the i-th risk area at the current deformation and instability stage is as follows: The i-th risk area is in an unstable state during the current monitoring period; immediate preventive measures are required and the monitoring frequency is increased by 100%.

Images