Side slope safety evaluation method based on objective weighting of monitoring data

Abstract

The invention discloses a slope safety evaluation method based on objective weighting of monitoring data, and the method comprises the steps: carrying out the recognition of a slope safety risk, and finding out an influence factor; giving an evaluation index for evaluating each influence factor; providing a monitoring object according to each evaluation index, and installing a monitoring device on the slope; based on the influence factors, the evaluation indexes and the monitoring objects, constructing a fault tree about slope safety; based on the fault tree, an associated monitoring object is found, and the structural importance of the associated monitoring object is calculated; calculating a contribution correlation rate of the associated monitoring object; calculating a weight value of the associated monitoring object based on the structure importance and the contribution correlation rate; for each associated monitoring object, monitoring data at the same moment are obtained through a monitoring device, and the safety coefficient of the slope is calculated based on the normalized monitoring data and the weight value; and slope safety evaluation is completed based on the safety coefficient. The method is used for quantitative analysis, and is high in implementation efficiency, objective in evaluation result and high in accuracy.

Description

Technical Field

[0001] The invention relates to a slope safety evaluation method based on objective weighting of monitoring data, belonging to the technical field of slope safety evaluation. Background Art

[0002] Safety assessment technology can be traced back to the safety analysis of nuclear power plants in the United States in the 1970s. Several commonly used safety assessment methods are shown in Table 1. Each safety assessment method has its own characteristics and scope of application. When using it, it should be reasonably selected according to the characteristics of the evaluation object, specific conditions and needs.

[0003] Table 1

[0004]

[0005]

[0006] The safety evaluation methods listed in the table above are simple and easy to implement, and have been widely used in many fields. However, it can be found from actual implementation that most of the above existing safety evaluation methods are qualitative analysis of monitoring data, which have the disadvantages of poor accuracy and lack of objective basis. For the above quantitative / semi-quantitative analysis safety evaluation methods, the weighting method adopted is artificial weighting, that is, it is mainly based on expert evaluation, expert scoring, and empirical estimation, which has the disadvantages of poor objectivity, complex process and time-consuming. It can be seen that designing a technical solution that can improve the accuracy and objectivity of slope safety evaluation is an urgent problem to be solved. Summary of the invention

[0007] The purpose of the present invention is to provide a slope safety evaluation method based on objective weighting of monitoring data. The entire evaluation process is quantitative analysis with high implementation efficiency. The evaluation results are objective and accurate, providing a technical guarantee for risk evaluation for slope construction and operation.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] A slope safety evaluation method based on objective weighting of monitoring data includes the following steps:

[0010] 1) Identify the risks of slope safety and find out the direct causes that affect slope safety as influencing factors;

[0011] 2) providing an index capable of evaluating each of the influencing factors as an evaluation index;

[0012] 3) providing a monitoring object for each evaluation index, and installing a monitoring device on the slope for monitoring the monitoring object;

[0013] 4) constructing a fault tree for slope safety based on the influencing factors, the evaluation indicators and the monitoring objects;

[0014] 5) Based on the fault tree, find the monitoring objects associated with the slope safety and record them as associated monitoring objects, thereby calculating the structural importance of each of the associated monitoring objects;

[0015] 6) Calculating the contribution correlation rate of each of the associated monitoring objects;

[0016] 7) Calculating a weight value of each of the associated monitoring objects based on the structural importance and the contribution correlation rate;

[0017] 8) For each of the associated monitoring objects, the monitoring data at the same time is obtained through the monitoring device, and the monitoring data is normalized to obtain normalized monitoring data, so as to calculate the safety factor of the slope based on the normalized monitoring data and the weight value;

[0018] 9) Based on the safety factor of the slope, complete the slope safety evaluation.

[0019] The advantages of the present invention are:

[0020] The present invention provides evaluation indicators by analyzing the influencing factors found out, provides monitoring objects for each evaluation indicator, installs monitoring devices on the slope for the monitoring objects, finds related monitoring objects based on a fault tree constructed by the influencing factors, evaluation indicators and monitoring objects, calculates the structural importance of the related monitoring objects, calculates the contribution correlation rate of the monitoring data obtained by the monitoring device to the related monitoring objects, calculates the weight value for each related monitoring object, thereby realizing the calculation of the slope safety factor on the basis of objectively assigning weights to the monitoring data obtained by actual monitoring, and further completing the evaluation of the slope safety. The entire evaluation process is quantitative analysis with high implementation efficiency, and the evaluation results are objective and accurate, which provides a risk evaluation technical guarantee for slope construction and operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flow chart for realizing the slope safety evaluation method of the present invention.

[0022] Figure 2 It is a schematic diagram of the installation of the monitoring device.

[0023] Figure 3 This is an example of a fault tree. DETAILED DESCRIPTION

[0024] like Figure 1 As shown, the present invention proposes a slope safety evaluation method based on objective weighting of monitoring data, comprising the steps of:

[0025] 1) Identify the risks of slope safety and find out the direct causes that affect slope safety as influencing factors;

[0026] 2) Provide indicators that can evaluate each influencing factor as evaluation indicators;

[0027] 3) Provide monitoring objects for each evaluation index, and install monitoring devices on the slope for monitoring the monitoring objects;

[0028] 4) Based on the influencing factors, evaluation indicators and monitoring objects, a fault tree for slope safety is constructed for the slope;

[0029] 5) Based on the fault tree, find the monitoring objects associated with slope safety and record them as associated monitoring objects, so as to calculate the structural importance of each associated monitoring object;

[0030] 6) Calculate the contribution correlation rate of each associated monitoring object;

[0031] 7) Based on the structural importance and contribution correlation rate, calculate the weight value of each associated monitoring object;

[0032] 8) For each associated monitoring object, the monitoring data at the same time is obtained through the monitoring device, and the monitoring data is normalized to obtain normalized monitoring data, so as to calculate the safety factor of the slope based on the normalized monitoring data and the weight value;

[0033] 9) Complete slope safety evaluation based on the slope safety factor.

[0034] In step 1), the factors affecting slope safety are found to include environmental factors, slope self-influence and slope external influence. Of course, there are no limitations. Other influencing factors may exist according to the slope shape, environment, etc.

[0035] Due to the diversity and complexity of the environment in which the slope is located, geological disasters such as landslides and collapses are inevitable. Therefore, the basis of slope safety assessment is to identify risks and find out the direct causes (weak links) that affect slope safety, that is, to find out the influencing factors.

[0036] Risk identification, also known as risk identification, is based on the investigation and collection of data on slope geology, construction, operation, environment, human activities, etc., to judge, summarize and identify historical accidents, existing accidents and potential failure accidents that have not yet occurred, find out the factors that directly lead to slope risks, classify each factor according to the hazard type, and finally obtain the influencing factors that affect slope safety.

[0037] In this industry, the risk identification process for slope safety can be reasonably designed according to actual needs. This process is a well-known technology in the field and will not be described in detail here.

[0038] According to statistics, slope failure is mainly affected by sudden rise and fall of reservoir water level, heavy rainfall, earthquake, deterioration of slope geological conditions, lack of slope drainage capacity, excessive anchoring force load on slope, etc. Therefore, the present invention provides evaluation indicators for the above-mentioned three influencing factors: environmental quantity, slope self-influence and slope external influence. In the present invention, the evaluation indicators corresponding to environmental quantity include reservoir water level, rainfall and earthquake, the evaluation indicators corresponding to slope self-influence include rock and soil properties and geological structure, and the evaluation indicators corresponding to slope external influence include drainage facilities and reinforcement measures. Of course, in actual implementation, in addition to the above-mentioned evaluation indicators, other evaluation indicators can also be given for environmental quantity, slope self-influence and slope external influence, without being subject to the above-mentioned restrictions.

[0039] Furthermore, the monitoring devices installed on the slope include telemeters, small weather stations, strong earthquake meters, high-precision positioning systems (GNSS, Global Navigation Satellite System), flexible displacement meters, osmometers (also known as pore water pressure meters), anchor dynamometers, anchor cable dynamometers and strain gauges, among which:

[0040] The telemetered water level gauge is used to monitor the sudden rise / drop of reservoir water level, which is used as the monitoring object of reservoir water level evaluation index;

[0041] Small weather stations are used to monitor rainfall, which is used as the monitoring object of rainfall evaluation indicators;

[0042] Strong motion detectors are used to monitor earthquake intensity, which is the monitoring object of earthquake evaluation indicators;

[0043] The high-precision positioning system is used to monitor surface deformation, which is used as a monitoring target for evaluating rock and soil properties;

[0044] Flexible displacement meters are used to monitor surface subsidence, which is used as a monitoring object for geological structure evaluation indicators (in practice, each depth position can be set as a monitoring object);

[0045] The piezometer is used to monitor groundwater level, confined water, and surface drainage. Groundwater level, confined water, and surface drainage are the monitoring objects of drainage facility evaluation indicators.

[0046] Anchor dynamometers are used to monitor anchor stress, anchor cable dynamometers are used to monitor anchor cable stress, and strain gauges are used to monitor structural stress. Anchor stress, anchor cable stress and structural stress are monitored as evaluation indicators of reinforcement measures.

[0047] Table 2 below gives the matching design of influencing factors, evaluation indicators and monitoring devices.

[0048] Table 2

[0049]

[0050] In the present invention, the telemetered water level gauge, small weather station, strong earthquake instrument, high-precision positioning system, flexible displacement meter, piezometer, anchor dynamometer, anchor cable dynamometer and strain gauge are all existing equipment in the field, and they can be reasonably arranged and installed on the slope.

[0051] Figure 2 The figure shows the installation and layout of some monitoring devices on the slope, which is not limited. Figure 2 A groundwater level hole 30 is drilled on the slope. A piezometer 10 is set at the bottom of the groundwater level hole 30, and then a flexible displacement meter 50 is vertically placed in the groundwater level hole 30. Then, a collection device 40 is used to seal the hole. A column is set on the collection device 40, and a high-precision positioning system 70 is installed on the column. Anchor dynamometers 60 and anchor cable dynamometers 80 are inserted into the surface at appropriate positions on the slope according to monitoring needs. Figure 2 The installation of the strain gauge is not shown. The remote water level gauge 20 is usually installed at the lowest point of the slope, and the small weather station 90 is usually installed at the top 102 of the slope.

[0052] In the present invention, the slope generally refers to the top 102 and the slope surface 101. In the art, the slope generally includes the top, the slope surface and the bottom. The monitoring device involved in the present invention is generally installed on the top 102 and the slope surface 101, not on the bottom. The monitoring devices installed on the slope surface 101 should be reasonably arranged according to the shape of the slope, and do not need to be arranged regularly. The acquisition device 40 is a well-known electronic device in the art.

[0053] The present invention constructs a fault tree about slope safety for the slope to be evaluated based on the influencing factors, evaluation indicators and monitoring objects obtained above. The fault tree is composed of top events, intermediate events and basic events, wherein the slope safety is taken as the top event, the direct cause of the decline in slope safety performance, i.e., the environmental quantity, the slope's own influence, the slope's external influence as the intermediate events, and the corresponding evaluation indicators as the intermediate events, through the fault tree layer by layer, finally find all the basic events (monitoring objects). In addition, according to the actual engineering needs, unexpanded events can also be added.

[0054] Figure 3 An example of a fault tree is shown in Figure 1. Figure 3 In the fault tree, the fault tree consists of top events, intermediate events, basic events and unexpanded events. The events are shown in Table 3 below, and each event is represented by a corresponding symbol.

[0055] Table 3

[0056] symbol Event Type illustrate symbol Event Type illustrate A Top Events Slope safety C10 Basic Events earthquake intensity B1 Intermediate Events Environmental quantity C11 Basic Events Groundwater level B2 Intermediate Events Slope’s own influence C12 Basic Events Surface deformation B3 Intermediate Events External influence of slope C13 Basic Events Surface subsidence C1 Intermediate Events Reservoir water level C14 Basic Events Groundwater level C2 Intermediate Events Rainfall C15 Basic Events Surface drainage C3 Intermediate Events Rock and soil properties C16 Basic Events Pressurized water C4 Intermediate Events Geological structure C17 Basic Events Anchor stress C5 Basic Events Reservoir water level rises suddenly C18 Basic Events Anchor cable stress C6 Basic Events Rainfall C19 Basic Events Structural stress C7 Basic Events earthquake D1 Unexpanded events Human activities C8 Basic Events Reservoir water level drops sharply D2 Unexpanded events Regional Environment C9 Basic Events Rainfall D3 Unexpanded events Regional Geology

[0057] In actual implementation, the fault tree can be constructed in various forms and should be reasonably designed according to the actual requirements of the project. Preferably, multiple fault trees can be constructed, and finally a fault tree that best meets the actual project situation is selected.

[0058] In actual implementation, step 5) includes the following steps:

[0059] 5-1) Based on the fault tree, from the top event to the basic event, the minimum cut set is obtained through Boolean operations;

[0060] 5-2) Find all associated monitoring objects associated with slope safety through the obtained minimum cut set, where the associated monitoring objects are basic events;

[0061] 5-3) Based on the minimum cut set, the structural importance of each associated monitoring object is calculated by the following formula:

[0062]

[0063] In the above formula:

[0064] x(i) is the i-th associated monitoring object;

[0065] n p is the K where the pth associated monitoring object is located p The total number of basic events;

[0066] K p is the pth minimum cut set of the fault tree;

[0067] I x(i) is the structural importance of the i-th associated monitoring object.

[0068] In the present invention, the minimum cut set is the minimum set of basic events that lead to the occurrence of top events, which reflects the danger of the slope. Each minimum cut set is a possible way to cause the top event. The more minimum cut sets there are, the more accident inducements there are, the easier it is to cause accidents, and the worse the safety and stability.

[0069] For example: Figure 3 The fault tree for slope safety shown in the figure is subjected to Boolean operation to obtain the following formula:

[0070] A=B1+B2+B3

[0071] =C1C2+D1+C3C4+D2+C17C18C19

[0072] =(D1+D3+C5+C6+C7)(C8+C9+C10)+D1+D2+(C11+C12+C13)(C14+C15+C16)+C17C18C19

[0073] =D1+D2+D3C8+D3C9+D3C10+C5C8+C5C9+C5C10+C6C8+C6C9+C6C10+C7C8+C7C9+C7C10+ C11C14+C11C15+C11C16+C12C14+C12C15+C12C16+C13C14+C13C15+C13C16+C17C18C19

[0074] From the above formula, it can be seen that this fault tree consists of 2 first-order minimum cut sets, 21 second-order minimum cut sets and 1 third-order minimum cut set, which reflects that there are 24 ways to reduce the safety performance of the slope. Here, D1 has no association with C8, C9, and C10, so it is not considered as a minimum cut set.

[0075] Therefore, based on the actual situation, it can be concluded that the associated monitoring objects associated with the safety of the slope to be evaluated are the basic events shown in the above formula.

[0076] Then the structural importance of each associated monitoring object found is calculated as follows:

[0077] I C5 =1-(1-1 / 2)(1-1 / 2)(1-1 / 2)=0.875

[0078] I C6 =1-(1-1 / 2)(1-1*2)(1-1 / 2)=0.875

[0079] I C7 =1-(1-1 / 2)(1-1 / 2)(1-1 / 2)=0.875

[0080] I C8 =1-(1-1 / 2)(1-1 / 2)(1-1 / 2)(1-1 / 2)=0.9375

[0081] I C9 =1-(1-1 / 2)(1-1 / 2)(1-1 / 2)(1-1 / 2)=0.9375

[0082] I C10 =1-(1-1 / 2)(1-1 / 2)(1-1 / 2)(1-1 / 2)=0.9375

[0083] I C11 =1-(1-1 / 2)(1-1 / 2)(1-1 / 2)=0.875

[0084] I C12 =1-(1-1 / 2)(1-1 / 2)(1-1 / 2)=0.875

[0085] I C13 =1-(1-1 / 2)(1-1 / 2)(1-1 / 2)=0.875

[0086] I C14 =1-(1-1 / 2)(1-1 / 2)(1-1 / 2)=0.875

[0087] I C15 =1-(1-1 / 2)(1-1 / 2)(1-1 / 2)=0.875

[0088] I C16 =1-(1-1 / 2)(1-1 / 2)(1-1 / 2)=0.875

[0089] I C17 =1-(1-1 / 2 2 )=0.250

[0090] I C18 =1-(1-1 / 2 2 )=0.250

[0091] I C19 =1-(1-1 / 2 2 )=0.250

[0092] I D1 =1

[0093] I D2 =1

[0094] I D3 =1-(1-1 / 2)(1-1 / 2)(1-1 / 2)=0.875

[0095] From the perspective of practical implementation, calculating the structural importance of basic events can help identify and analyze the relative importance of basic events in affecting the occurrence of top events, and is a means to help improve the safety and stability of slopes.

[0096] In actual implementation, step 6) includes the following steps:

[0097] 6-1) Each associated monitoring object is monitored by a corresponding monitoring device at different times to obtain monitoring data;

[0098] 6-2) Normalize the monitoring data and establish a normalized matrix A, as shown in the following formula 1):

[0099]

[0100] In formula 1), the normalized matrix A has m rows and n columns, and the element a constituting the normalized matrix A is ij(1≤i≤m, 1≤j≤n) is the normalized data obtained after normalizing the monitoring data of the jth associated monitoring object at the i-th monitoring time, m is the number of monitoring times, and n is the number of associated monitoring objects;

[0101] 6-3) Calculate the correlation matrix R using formula 2) n×n :

[0102]

[0103] In formula 2), R n×n Indicates that the matrix is ​​an n×n matrix, Y m×n is the sample information matrix, obtained by the difference between the full matrix (m×n order matrix, each element is 1) and the normalized matrix A, T represents the transpose operation, I is the unit matrix, I = diag{1,1,…,1};

[0104] Here, the information in the correlation matrix R comes from the measured monitoring data, and the correlation matrix R can be used [r ij ] n×n To indicate that r ij Monitor the correlation between two different associated monitoring objects;

[0105] 6-4) Based on the correlation matrix R n×n , discard the associated monitoring objects whose correlation is greater than or equal to the set value and can be replaced by other associated monitoring objects, and retain the associated monitoring objects whose correlation is less than the set value, wherein the number of associated monitoring objects retained is set to q;

[0106] This step 6-4) is actually to calculate the correlation matrix R n×n Extract principal component information, and reduce the number of associated monitoring objects from n to q;

[0107] 6-5) Based on the correlation matrix R n×n , and get the new correlation matrix R q×q , that is, from the correlation matrix R n×n Extract the elements of the q associated monitoring objects to form a new correlation matrix R q×q ;

[0108] 6-6) Calculate the main factor loading matrix H by the following formula 3) q×q :

[0109]

[0110] In Equation 3), V is the eigenvector matrix, V = {θ1,θ2,…,θ q}, the element θ constituting V represents the evolution trend of the associated monitoring object retained (such as the rising and falling trend, which can be represented by 1, -1, or 0), is the eigenvalue matrix corresponding to V, as shown in equation 4),

[0111]

[0112] In formula 4), λ is the characteristic value of the retained associated monitoring object, which is obtained by quantitatively representing the evolution trend of the retained associated monitoring object, and is usually expressed in numerical values. For example, the numerical value 0.85 is used to represent the evolution trend of the rise, and the numerical value 0.9 can be used to represent a larger rise;

[0113] Here, an associated monitoring object can have one or more characteristic values. When there are multiple characteristic values, one is selected as the characteristic value of the associated monitoring object. The information of the V matrix comes from the original correlation matrix R n×n , therefore, the main factor loading matrix H q×q is unique;

[0114] 6-7) For the q associated monitoring objects retained, the variance contribution E of each eigenvalue is calculated by the following formula 5) j :

[0115]

[0116] In formula 5), ​​λ j is the characteristic value of the jth associated monitoring object, E j is the characteristic value λ of the jth associated monitoring object j The variance contribution of

[0117] 6-8) Main factor loading matrix H q×q It reflects the relationship between the original associated monitoring object and the associated monitoring object with small correlation. Therefore, the new correlation matrix R q×q With the main factor loading matrix H q×q The main factor load vector of each column in is used to calculate the contribution component vector by the following formula 6):

[0118]

[0119] In formula 6), z j is the contribution component vector of the jth associated monitoring object in the retained associated monitoring objects at a monitoring time, h j The main factor loading matrix H q×q The main factor loading vector of the j-th column in , 1≤j≤q;

[0120] 6-9) Based on the contribution component vector of the associated monitoring object at each monitoring moment obtained by the above formula 6), the initial contribution correlation rate is calculated by the following formula 7):

[0121]

[0122] In formula 7), u j is the initial contribution correlation rate of the jth associated monitoring object, z ij is the contribution component vector of the jth associated monitoring object at the i-th monitoring moment, E j is the characteristic value λ of the jth associated monitoring object j The variance contribution of , m is the number of monitoring moments;

[0123] 6-10) Normalizing the initial contribution correlation rate of each associated monitoring object to obtain the contribution correlation rate of each associated monitoring object.

[0124] In actual implementation, the fault tree may also include unexpanded events, such as Figure 3 The contribution correlation rate of the unexpanded event is generally between 0% and 3%. The unexpanded event is set according to the project requirements, and its contribution correlation rate is set according to the project experience.

[0125] In actual implementation, step 7) includes the following steps:

[0126] The weight value of each associated monitoring object is calculated by multiplying the structural importance of the associated monitoring object by the contribution correlation rate of the associated monitoring object.

[0127] In actual implementation, step 8) includes the following steps:

[0128] At the same monitoring time, each associated monitoring object is monitored by a corresponding monitoring device to obtain monitoring data;

[0129] The monitoring data is normalized to obtain normalized monitoring data;

[0130] The safety factor d of the slope is calculated based on the weighted algorithm using the following formula 8):

[0131]

[0132] In formula 8), c j is the normalized monitoring data obtained by normalizing the monitoring data obtained by monitoring the jth associated monitoring object, P j is the weight value of the jth associated monitoring object, and n is the number of associated monitoring objects.

[0133] In actual implementation, step 9) includes:

[0134] The safety of the slope is evaluated by checking the numerical range of the safety factor level, that is, evaluating whether the slope is safe. When the safety factors obtained at the previous and next monitoring moments are the same, the safety of the slope is evaluated by calculating the evaluation coefficients of each influencing factor at each monitoring moment through a weighted algorithm.

[0135] Table 4 is used below to illustrate the slope safety evaluation process.

[0136] Table 4 below lists the evaluation coefficients of the three major influencing factors that affect the slope (environmental variables, slope's own influence and slope's external influence), as well as the safety factor of the slope safety, and the numerical ranges of each level of comprehensive evaluation of slope safety. Unexpanded events are not considered here.

[0137] Table 4

[0138]

[0139] In this Table 4, the larger the grade value, the greater the slope risk and the lower the safety.

[0140] In the actual calculation process, if the safety factor d calculated by the slope safety evaluation method of the present invention is 1.8, it can be known from the table that the slope is at level 2, which is relatively safe.

[0141] However, if the safety factors obtained at the two previous monitoring moments are both 1.8, it is necessary to calculate the evaluation coefficients of the influencing factors at each monitoring moment through a weighted algorithm to evaluate the safety of the slope.

[0142] For example, the evaluation coefficient d1 of the environmental quantity calculated by the following formula 9) at the previous monitoring moment is 1.1, and the slope is relatively safe, but the evaluation coefficient d1 of the environmental quantity at the next monitoring moment is 1.6, and the slope is more dangerous. This shows that from the perspective of the influencing factor of environmental quantity, the slope tends to become dangerous.

[0143]

[0144] In formula 9), c1 j is the normalized monitoring data obtained by normalizing the monitoring data of the jth associated monitoring object in the environmental quantity, P j is the weight value of the jth associated monitoring object, and n is the number of associated monitoring objects.

[0145] Similarly, refer to the above formula 9) to calculate the evaluation coefficients of the slope's own influence and the slope's external influence at the two monitoring moments before and after. For example, the evaluation coefficient d2 of the slope's own influence at the previous monitoring moment is 0.2, and the slope is safe, but the evaluation coefficient d2 of the slope's own influence at the next monitoring moment is 0.3, and the slope is relatively safe, so this shows that from the perspective of the influencing factor of the slope's own influence, the slope is not in danger, but the safety tends to weaken. Similarly, the evaluation coefficient d3 of the slope's external influence at the previous monitoring moment is 0.06, and the slope is relatively safe, but the evaluation coefficient d3 of the slope's external influence at the next monitoring moment is 0.07, and the slope is relatively safe, so this shows that from the perspective of the influencing factor of the slope's external influence, the slope is relatively safe. Therefore, by comparing the evaluation coefficients of the environmental quantity, the slope's own influence, and the slope's external influence at the two monitoring moments before and after, it can be finally concluded that the slope is currently in a more dangerous state and tends to become dangerous, thereby completing the evaluation of the slope's safety.

[0146] The present invention has the following beneficial effects:

[0147] The present invention provides evaluation indicators by analyzing the influencing factors found out, provides monitoring objects for each evaluation indicator, installs monitoring devices on the slope for the monitoring objects, finds related monitoring objects based on a fault tree constructed by the influencing factors, evaluation indicators and monitoring objects, calculates the structural importance of the related monitoring objects, calculates the contribution correlation rate of the monitoring data obtained by the monitoring device to the related monitoring objects, calculates the weight value for each related monitoring object, thereby realizing the calculation of the slope safety factor on the basis of objectively assigning weights to the monitoring data obtained by actual monitoring, and further completing the evaluation of the slope safety. The entire evaluation process is quantitative analysis with high implementation efficiency, and the evaluation results are objective and accurate, which provides a risk evaluation technical guarantee for slope construction and operation.

[0148] The above are preferred embodiments of the present invention and the technical principles used therein. For those skilled in the art, any obvious changes such as equivalent transformations, simple replacements, etc. based on the technical solution of the present invention, without departing from the spirit and scope of the present invention, are within the scope of protection of the present invention.

Claims

1. A slope safety assessment method based on objective weighting of monitoring data, characterized in that: Includes steps: 1) Identify the risks of slope safety and find out the direct causes that affect slope safety as influencing factors; 2) providing an index capable of evaluating each of the influencing factors as an evaluation index; 3) providing a monitoring object for each evaluation index, and installing a monitoring device on the slope for monitoring the monitoring object; 4) constructing a fault tree for slope safety based on the influencing factors, the evaluation indicators and the monitoring objects; 5) Based on the fault tree, find the monitoring objects associated with the slope safety and record them as associated monitoring objects, thereby calculating the structural importance of each of the associated monitoring objects; 6) Calculating the contribution correlation rate of each of the associated monitoring objects; 7) Calculating a weight value of each of the associated monitoring objects based on the structural importance and the contribution correlation rate; 8) For each of the associated monitoring objects, the monitoring data at the same time is obtained through the monitoring device, and the monitoring data is normalized to obtain normalized monitoring data, so as to calculate the safety factor of the slope based on the normalized monitoring data and the weight value; 9) Based on the safety factor of the slope, complete the slope safety evaluation.

2. The slope safety assessment method based on objective weighting of monitoring data according to claim 1 is characterized in that: In the step 1), the influencing factors affecting the safety of the slope are found to include environmental factors, the influence of the slope itself and the external influence of the slope.

3. The slope safety assessment method based on objective weighting of monitoring data according to claim 2 is characterized in that: The evaluation indicators corresponding to the environmental quantities include reservoir water level, rainfall and earthquake, the evaluation indicators corresponding to the slope's own impact include rock and soil properties and geological structure, and the evaluation indicators corresponding to the slope's external impact include drainage facilities and reinforcement measures.

4. The slope safety assessment method based on objective weighting of monitoring data according to claim 3 is characterized in that: The monitoring device installed on the slope includes a telemetered water level gauge, a small meteorological station, a strong earthquake instrument, a high-precision positioning system, a flexible displacement meter, a piezometer, an anchor dynamometer, an anchor cable dynamometer and a strain gauge, wherein: the telemetered water level gauge is used to monitor the sudden rise / fall of the reservoir water level, and the sudden rise / fall of the reservoir water level is used as the monitoring object of the reservoir water level evaluation index; the small meteorological station is used to monitor the rainfall, and the rainfall is used as the monitoring object of the rainfall evaluation index; the strong earthquake instrument is used to monitor the earthquake intensity, and the earthquake intensity is used as the monitoring object of the earthquake evaluation index; the high-precision positioning system is used to monitor the surface The surface deformation is used as the monitoring object of the rock and soil property evaluation index; the flexible displacement meter is used to monitor the surface settlement, and the surface settlement is used as the monitoring object of the geological structure evaluation index; the piezometer is used to monitor the groundwater level, confined water, and surface drainage, and the groundwater level, confined water and surface drainage are used as the monitoring objects of the drainage facility evaluation index; the anchor dynamometer is used to monitor the anchor stress, the anchor cable dynamometer is used to monitor the anchor cable stress, and the strain gauge is used to monitor the structural stress, and the anchor stress, anchor cable stress and structural stress are used as the monitoring objects of the reinforcement measures evaluation index.

5. The slope safety assessment method based on objective weighting of monitoring data according to claim 4 is characterized in that: The step 5) comprises: 5-1) Based on the fault tree, from the top event to the basic event, the minimum cut set is obtained through Boolean operation; 5-2) finding all the associated monitoring objects associated with slope safety through the obtained minimum cut set, wherein: the associated monitoring objects are basic events; 5-3) Based on the minimum cut set, the structural importance of each of the associated monitoring objects is calculated by the following formula: In the above formula: x(i) is the i-th associated monitoring object; n p is the K where the pth associated monitoring object is located p The total number of basic events; K p is the pth minimum cut set of the fault tree; I x(i) is the structural importance of the i-th associated monitoring object.

6. The slope safety assessment method based on objective weighting of monitoring data according to claim 5 is characterized in that: The step 6) comprises: 6-1) Each of the associated monitoring objects is monitored by the corresponding monitoring device at different times to obtain monitoring data; 6-2) Normalize the monitoring data and establish a normalized matrix A, as shown in the following formula 1): In formula 1), the normalized matrix A has m rows and n columns, and the element a constituting the normalized matrix A is ij is the normalized data obtained after normalizing the monitoring data obtained by the jth associated monitoring object at the i-th monitoring moment, m is the number of monitoring moments, and n is the number of the associated monitoring objects; 6-3) Calculate the correlation matrix R using formula 2) n×n : In formula 2), Y m×n is the sample information matrix, obtained by the difference between the full matrix and the normalized matrix A, / is the unit matrix; 6-4) Based on the correlation matrix R n×n , discard the associated monitoring objects whose correlation is greater than or equal to the set value and can be replaced by other associated monitoring objects, and retain the associated monitoring objects whose correlation is less than the set value, wherein the number of the associated monitoring objects retained is set to q; 6-5) Based on the correlation matrix R n×n , and get the new correlation matrix R q×q ; 6-6) Calculate the main factor loading matrix H by the following formula 3) q×q : In Equation 3), V is the eigenvector matrix, V = {θ1, θ2, ..., θ q }, the element θ constituting V represents the evolution trend of the associated monitoring object retained, is the eigenvalue matrix corresponding to V, as shown in equation 4), In formula 4), λ is the characteristic value of the associated monitoring object retained, which is obtained by quantitatively representing the evolution trend of the associated monitoring object retained; 6-7) For the q associated monitoring objects retained, the variance contribution E of each eigenvalue is calculated by the following formula 5) j : In formula 5), ​​E j is the characteristic value λ of the jth associated monitoring object j The variance contribution of 6-8) Using the new correlation matrix R q×q With the main factor loading matrix H q×q The main factor load vector of each column in is used to calculate the contribution component vector by the following formula 6): In formula 6), z j is the contribution component vector of the jth associated monitoring object at a monitoring time, h j The main factor loading matrix H q×q The main factor loading vector of the jth column in ; 6-9) Based on the contribution component vector of the associated monitoring object at each monitoring moment obtained by the above formula 6), the initial contribution correlation rate is calculated by the following formula 7): In formula 7), u j is the initial contribution correlation rate of the jth associated monitoring object, z ij is the contribution component vector of the jth associated monitoring object at the i-th monitoring moment, E j is the characteristic value λ of the i-th associated monitoring object j The variance contribution of , m is the number of monitoring moments; 6-10) Normalizing the initial contribution correlation rate of each of the associated monitoring objects to obtain the contribution correlation rate of each of the associated monitoring objects.

7. The slope safety assessment method based on objective weighting of monitoring data according to claim 6 is characterized in that: The fault tree also includes unexpanded events, and the contribution correlation rate of the unexpanded events is between 0% and 3%.

8. The slope safety assessment method based on objective weighting of monitoring data according to claim 1 is characterized in that: The step 7) comprises: The weight value of each associated monitoring object is calculated by multiplying the structural importance of the associated monitoring object by the contribution correlation rate of the associated monitoring object.

9. The slope safety assessment method based on objective weighting of monitoring data according to claim 1 is characterized in that: The step 8) comprises: At the same monitoring time, each of the associated monitoring objects is monitored by the corresponding monitoring device to obtain monitoring data; The monitoring data is normalized to obtain normalized monitoring data; The safety factor d of the slope is calculated by the following formula (8): In formula 8), c j is the normalized monitoring data obtained by normalizing the monitoring data obtained by monitoring the jth associated monitoring object, P j is the weight value of the jth associated monitoring object, and n is the number of the associated monitoring objects.

10. The slope safety assessment method based on objective weighting of monitoring data according to claim 9, characterized in that: The step 9) includes: evaluating the safety of the slope by checking the numerical range of the level of the safety factor, wherein: when the safety factors obtained at the two previous monitoring moments are the same, the safety of the slope is evaluated by calculating the evaluation coefficient of each influencing factor at each monitoring moment through a weighted algorithm.

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