In-service slope durability and stability evaluation and analysis method

Abstract

The application discloses a kind of in-service side slope durability and stability evaluation analysis method, comprising the following steps: constructing the hierarchical index system of service side slope durability evaluation, based on the theory of improved analytic hierarchy process-fuzzy recognition correlation degree, establish the comprehensive evaluation analysis model of service side slope durability, form durability influence degree comprehensive score standard;Analysis of the evolution law of the durability weakening characteristics of slope protection engineering under the influence of different environmental factors, establish the slope durability decay factor model under the influence of single factor;Establish the comprehensive decay factor model of service side slope protection engineering durability weakening under the influence of multiple factors;Establish the numerical model of slope stability analysis under the influence of durability weakening, predict and evaluate the change of slope stability characteristics and the final effective service life of different service life.The application is applied to the durability design and construction maintenance of slope engineering, and fills the gap in the systematic analysis method of slope durability and stability at present.

Description

Technical Field

[0001] This invention relates to a method for evaluating and analyzing the durability and stability of in-service slopes. Background Technology

[0002] Slope protection engineering is an integral system consisting of a soil and rock cover layer and a corresponding protective structure system. The slope support structure includes rock anchoring structures such as anchor bolts and anchor cables, as well as slope protection structures such as masonry retaining walls, facing walls, and protective nets.

[0003] Under the influence of natural weathering and special corrosive environmental conditions, the long-term performance of slope rock and soil and support structures deteriorates, manifested as cracking, creep, and collapse of slope rock and soil layers, peeling and bulging of protective structures such as slope protection walls, cracking and damage of shotcrete surfaces, and corrosion and fracture of structures. This leads to a continuous decline in their protective capacity, thereby affecting the long-term safety and stability of the slope. Therefore, the stability problem under the influence of slope durability cannot be ignored.

[0004] Research on slope durability and stability requires establishing a slope durability index system and comprehensive evaluation level index that are subject to long-term environmental influences. It also necessitates testing and analyzing the degradation characteristics of slope geological bodies and structural materials, establishing numerical models for slope durability and stability analysis, and predicting changes in slope durability and stability over different service times. Currently, there are many theoretical methods for analyzing the overall stability of slopes under specific environmental conditions, such as continuous and discontinuous numerical methods, limit equilibrium methods, and various empirical methods, as well as nondeterministic analysis methods such as stochastic process analysis, fuzzy mathematics, reliability theory, and grey theory. However, a systematic method for assessing and analyzing slope durability and stability has not yet been established. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a simple algorithm and a highly accurate method for evaluating and analyzing the durability and stability of in-service slopes.

[0006] The technical solution of this invention to solve the above problems is: a method for evaluating and analyzing the durability and stability of in-service slopes, comprising the following steps:

[0007] S1: Construct a hierarchical index system for evaluating the durability of service slopes. Based on the improved analytic hierarchy process and fuzzy recognition correlation theory, establish a comprehensive evaluation and analysis model for the durability of service slopes, and form a comprehensive scoring standard for the degree of durability impact. This will provide a qualitative basis for subsequent evolution analysis of durability weakening characteristics and quantitative analysis of attenuation factors.

[0008] S2: Analyze the evolution of the durability weakening characteristics of slope protection projects under the influence of different environmental factors, and establish a slope durability attenuation factor model under the influence of single factors;

[0009] S3: Establish a comprehensive attenuation factor model for the weakening of durability of slope protection engineering under the influence of multiple factors;

[0010] S4: Establish a numerical model for slope stability analysis under the influence of durability weakening, and predict and evaluate the changes in slope stability characteristics and final effective service life at different service years.

[0011] The above-mentioned method for evaluating and analyzing the durability and stability of in-service slopes includes the following steps in step S1:

[0012] S11: An improved analytic hierarchy process (AHP) was used to build a hierarchical index system for durability evaluation, including a comprehensive durability evaluation target layer A, a criterion layer B, and a corresponding specific index layer C. Based on the criterion layer, the corresponding specific index layer C was further clarified to express the characteristics of durability function or influence effect, and the target layer level classification standard range was established.

[0013] S12: Calculate the weights of each level of indicators, construct a comparison matrix and an optimal transfer matrix, and transform them into a comprehensive judgment matrix. Finally, obtain the weight values ​​of each level of indicators according to the weight calculation formula.

[0014] S13: Sampling correlation analysis theory and method, calculate the correlation degree between each indicator and the target level and the weighted comprehensive correlation degree, and analyze to obtain the target level.

[0015] The above-mentioned method for evaluating and analyzing the durability and stability of in-service slopes, specifically step S1, is as follows:

[0016] S11: Establish a hierarchical index system for durability evaluation, consisting of a target layer, a criterion layer, and a specific index layer from top to bottom. The target layer is divided into five levels for comprehensive durability evaluation: Excellent, Good, Average, Poor, and Very Poor, i.e., {Ψ1,Ψ2,...,Ψ5}. The corresponding specific index parameters and their value ranges are determined based on the slope conditions of each region. The criterion layer consists of two layers, namely the I-th upper-level index B. i and the j-th lower-level indicator B under the i-th upper-level indicator IJ , I≤N A ,j≤n bi , where N a The total number of indicators at the upper criterion level, n bi The total number of lower-level criteria indicators corresponding to the i-th indicator in the upper-level criterion, i = 1 to n a The total number of indicators at the lower criterion level is set to n. b , Indicator layer C ijk C ijk This represents the k-th indicator in the indicator layer corresponding to the i-th upper-level indicator and the j-th lower-level indicator in the criterion layer, where k ≤ n. cij , where n cijLet n be the number of indicators in the indicator layer corresponding to the i-th upper-level indicator and the j-th lower-level indicator in the criterion layer, and let n be the total number of indicators in the indicator layer. c ,have

[0017] S12: Construct a comparison matrix between indicators at each level. Let there be m indicators at a certain level, where m is the number of indicators at the upper-level criteria layer. a Or the lower layer n b Or n of the index layer c Then the comparison matrix A is as follows, where the i-th element in A... m Line j m Column elements By comparing i m and j m Importance is determined by matrix A:

[0018]

[0019] The inverse matrix is ​​then determined as follows:

[0020]

[0021] Where r ij This represents the element in the i-th row and j-th column of R;

[0022] Finally, the consistency matrix, i.e. the comprehensive judgment matrix, is determined:

[0023]

[0024] Where d ij This represents the element in row i and column j of D;

[0025] The weights are calculated using the following formula:

[0026]

[0027] The criterion layer weights are obtained according to the above calculation process. Lower criterion layer weights C-level indicator weights The normalized C-layer index target weight w ijk for:

[0028]

[0029] S13: Calculate the correlation degree of a single indicator or the comprehensive correlation degree of multiple indicators using correlation theory. For quantitatively testable indicators, analyze and calculate the correlation degree based on fuzzy recognition theory; for a certain durability indicator c at the indicator layer... jjk Quantitative test sample values Conforms to a general normal distribution Parameters in parentheses These are the sample statistical mean and variance, respectively, for a certain target Ψ. l The value limit is set to {u l,min ,u l,max}, u l,min u l,max The target level Ψ l The minimum and maximum values ​​of the target level l = 1, ..., n d n d n represents the total number of target levels. d =5; Assuming the target level value is u v Follows a normal distribution Among them, intermediate quantity Then the single correlation degree of this indicator with respect to target level l can be calculated. as follows:

[0030]

[0031] Finally, the weighting formula (1-5) is introduced to calculate the comprehensive correlation η with the target level l. l As shown in the following formula:

[0032]

[0033] Finally, based on the principle of maximum similarity, the overall durability evaluation level is determined.

[0034] The above-mentioned method for evaluating and analyzing the durability and stability of in-service slopes, step S2 includes the following process:

[0035] S21: Analyze the degradation mechanism of carbonation and steel corrosion performance of slope concrete structure under the influence of various environmental factors on durability, and establish a decay factor model describing the weakening of the durability of slope protection structure with the change of steel corrosion rate as a parameter.

[0036] S22: Analyze the fatigue damage mechanism model of slope soil and rock layers under vibration load, and establish the durability attenuation factor under the influence of fatigue deterioration;

[0037] S23: Analyze the coupled effects of groundwater seepage and creep, and the effects of acid rain. Establish an analytical model for the weathering and weakening of slope soil and rock under the influence of groundwater physicochemical properties, and determine the corresponding durability attenuation factor.

[0038] The specific process of step S2 in the above-mentioned method for evaluating and analyzing the durability and stability of in-service slopes is as follows:

[0039] S21: Assume that the carbonization depth X is proportional to the square root of time t, i.e. The carbonization influence coefficient K reflects the combined influence of multiple factors and is calculated using the following formula (2-1), where Π represents the related multiple influencing factors k o The product of consecutive products, o = 1 to nk n k c0 represents the total number of relevant influencing factors, and c0 is a constant coefficient.

[0040]

[0041] The service life of the protective structure, also known as the durability failure time t cr Including the time t for the steel bars to begin corroding s and the time t from the start of corrosion to the expansion and cracking of the protective layer c The calculation formula is as follows:

[0042]

[0043] The above formula t c The calculation parameters include: protective layer thickness H c Concrete strength H f Rebar diameter H d Temperature coefficient H T Ambient temperature and humidity H RH And the influence coefficient H of the local environment on rust expansion and cracking. m , t r The number of years corresponding to a coefficient of 1.0; t s The calculation formula parameters include: x p The thickness of the protective layer is given by x0, which is the residual carbonization amount. The calculation of x0 is based on the following formula (2-3), where m... q λ is the local environmental coefficient. c According to x p A constant parameter that takes different values ​​from K;

[0044]

[0045] The reinforcement rate of the support structure is calculated as follows, and the corrosion rate v is given by equation (2-4), where χ c For position coefficients, θ and R H These represent ambient temperature and humidity, respectively, where κ is a constant and f is a variable. cu This refers to the standard value of concrete compressive strength.

[0046]

[0047] Then, within time t, the corrosion depth δ = vt. Furthermore, assuming d is the diameter of the reinforcing bar, the corrosion section loss rate λ... s :

[0048]

[0049] Finally, the mechanical parameters Q and corrosion rate λ were established. sThe functional relationship is used to describe the degree of degradation of the index parameter Q relative to the initial value Q0, as expressed by the linear fitting relationship in equation (2-5), where ξ is a constant fitting parameter, and the decay factor coefficient η is... c =Q / Q0=1-ξλ s ;

[0050] Q=Q0(1-ξλ s (2-5)

[0051] Considering the complexity of on-site influencing factors, if λ s The parameters have randomness and time-sensitivity, so the attenuation factor coefficient η c It is also a random time series, denoted as η. c =η c (t);

[0052] S22: First, through fatigue experiments on rock mass or structural materials, experimental curves such as stress-fatigue life (SN) curves are obtained; further, damage variables are established based on damage theory, and the coefficients of the damage variables include fatigue damage variables based on the cycle ratio:

[0053] D = N / N E (2-6)

[0054] Where N and N E These can be represented by the number of cycles consumed and the number of cycles leading to final failure, or by introducing a nonlinear fatigue damage variable:

[0055]

[0056] In the formula, D0 is the initial damage variable, and α is the material parameter;

[0057] The correction values ​​of material parameters are established based on damage variables. The material parameters include stress intensity S, elastic modulus E, and damage-type stress-strain ε constitutive relation model σ=f(ε,D), as shown in the following relation (2-8), where E0 is the initial elastic modulus, ζ is the correction factor of damage variables, and C is the elastic or elastoplastic matrix.

[0058]

[0059] That is, the attenuation factor η under the influence of vibration load. d The following equation reflects the degree of weakening in the durability of the elastic modulus or stress / strain, and is considered as a random time series, i.e., η. d =η d (t)

[0060]

[0061] S23: When establishing an analysis model for weathering and weakening under the influence of groundwater physicochemical properties, the effects of groundwater seepage creep coupling and acidic groundwater should be considered.

[0062] (1) The analysis of the effects of seepage-creep coupling is explained as follows:

[0063] First, the long-term creep effect of the completely weathered overburden or soil slope is analyzed, and the relaxation modulus parameters after creep adjustment are derived. A Burgers creep model, consisting of Maxwell and Kelvin assemblies connected in series, is adopted, and the constitutive equations are as follows:

[0064]

[0065] Let E M Let v be the elastic modulus of the Hookean elastomer. M E is the viscosity coefficient of a Newtonian viscous body. K Let v be the Kelvin bulk elastic modulus. K Here, Kelvin is the volume viscosity coefficient, and t is time; the three-dimensional Burgers model is in the form of:

[0066]

[0067] Among them, intermediate quantity intermediate quantity σ m ε v These are the stress and strain sphere tensors, S, respectively. ij e ij These are the corresponding stress and strain deviatoric tensors. By performing an inverse transformation of the shear modulus in the creep model using Laplace integrals, and combining this with the conversion relationship between the shear modulus and the relaxation modulus E(t), we obtain the expression for E(t):

[0068]

[0069] Substituting the relaxation modulus E(t) derived above into the shear modulus expression G(t)=E(t) / 2(1+μ), and setting the variable λ(t)=E(t)μ / {(1+μ)(1-2μ)}, we then substitute it into the following equilibrium equation under the influence of seepage:

[0070]

[0071]

[0072]

[0073]

[0074] In the above formula, Let k be the Laplace operator notation, where u, v, and w are the displacement variables in the left-right, front-back, and vertical directions, respectively. p γ is the permeability coefficient. w Let P be the specific weight of water, ε be the pore pressure, and ε be the specific weight of water. V The porosity coefficient ρ is adjusted by changing the volumetric strain, as shown in equation (2-17), where ρ0 is the initial porosity, to correct the permeability coefficient k. p To achieve seepage deformation coupling and correct the permeability coefficient k p The calculation is shown in equation (2-18), k p0 ρ and ρ0 are the initial permeability coefficient and initial porosity values, respectively. Combining equations (2-12) to (2-18), we can achieve a flow-creep coupled analysis:

[0075] ρ=(ρ0+ε v ) / (1+ε v (2-17)

[0076]

[0077] Attenuation factor η under the influence of seepage-creep coupling s This clearly reflects the relaxation modulus E(t) and the permeability coefficient k. p The durability changes of two relatively independent index parameters are established by the following function, which shows the relationship between the decay factor and the ratio of each index parameter to its initial value, where E0 refers to the initial relaxation modulus and the f function is linear or various fitting forms.

[0078] η s =f[E(t) / E0,k] p / k p0 (2-19)

[0079] (2) Construct a model to assess the weakening effect of acidic groundwater (mainly acid rain) on the durability of slope rock mass, as detailed below:

[0080] Acid rain immersion tests were conducted to examine the weakening effects of acid rain on the mechanical properties of slope structures, rock materials, and rock mass surfaces under different acid concentrations. and duration of action Under the conditions, the cohesive strength of the test sample performance index Friction angle and strength indicators To determine the relevant durability weakening factors, a fitting function relationship was established between acid treatment condition parameters and sample performance indicators to varying degrees of influence, as shown in the following formula:

[0081]

[0082] In the formula Initial compressive strength, The weakening factor related to the intensity index of the sample test was determined through the above experiments. value;

[0083] Increase the concentration of the simulated acid solution and the test duration. Equivalent to the actual action time t of acidic groundwater on the slope. a The experimental formula was modified to reflect the functional relationship between actual slope parameters and influencing environmental factors. Combined with on-site sampling and testing, the attenuation factor η under the influence of acid rain was determined. a .

[0084] The above-mentioned method for evaluating and analyzing the durability and stability of in-service slopes, step S3 includes the following process:

[0085] S31: Establish a theoretical model for predicting durability attenuation factors. Based on the theory of stochastic processes, analyze the time series of durability attenuation factors of a certain performance index of a slope engineering object under the influence of different environmental factors, perform differential transformation to a stationary series, determine the prediction model type and specific parameter values ​​of the stochastic series, and realize the prediction analysis of durability attenuation factors of the performance index.

[0086] S32: Describe the basic scheme for collecting sample data from indoor tests and field inspections, and establish a method for calculating the sample results of durability decay factor.

[0087] The specific process of step S3 in the above-mentioned method for evaluating and analyzing the durability and stability of in-service slopes is as follows:

[0088] S31: For different types of slope protection structures or soil and rock cover layers, construct a theoretical model for predicting the comprehensive factor of durability performance degradation.

[0089] Let I be the series of factors affecting the actual environmental durability of the slope. a a = 1, ..., n x n x The total number of influencing factors; the series of performance indicators affecting the durability of protective structures or geotechnical objects is X. b b = 1, ..., n y n y The total number of performance indicators of soil and rock objects related to slope stability analysis; the random variable η, which is the durability attenuation factor of a single factor a on a specific performance indicator b. a,b (t), where t is the durability impact time in years or quarters, and the prediction model is determined through stochastic analysis as follows:

[0090] First, single-factor attenuation factor samples were obtained through experimental testing. τ = 1, 2, ... represents the number of years or quarters in the sample test. The stationary sequence can be obtained by first-order or second-order differencing.

[0091]

[0092] Through stationary sequences Autocorrelation and partial correlation functions The figure is identified as one of the following linear model types: autoregressive model AR(p), sliding model MA(q), and mixed model ARIMA(p,q), where p and q are the order of the corresponding models, and the corresponding p and q parameter values ​​are determined. Predictive analysis is performed according to the recursive model, and the mixed model is used to predict the following formula;

[0093]

[0094] In the above formula, ω represents the increment of the forecast period; and The coefficients of the mixed model are calculated using the autocorrelation and partial correlation function values. To deduce; θ0 is a constant coefficient, derived from the sample mean and The coefficients were derived. For stationary sequences, white noise is used; utilizing the above By performing the calculations and combining them with equation (3-1), the single-factor durability decay factor prediction results for the year τ+ω or quarter can be obtained.

[0095] Considering the various durability-influencing factors on the slope, a comprehensive attenuation factor is used. It can be calculated using the following formula:

[0096]

[0097] Let the performance index of the slope object in year τ be... b = 1, ..., n y Then, the forecast results of the relevant indicators after the τ+ωth year are expressed as follows:

[0098] S32: Through indoor simulation tests and on-site testing, collect sample data of performance indicators of the index layer mentioned in step S1, and conduct prediction analysis and result verification of single-factor or comprehensive factors of durability degradation with reference to the above-mentioned stochastic theoretical model; design indoor accelerated durability tests including salt spray test, acid rain immersion test, wet-dry cycle test and vibration fatigue test, and collect the performance indicators of the durability weakening samples under the simulated test environment continuously or at set time intervals to determine the comprehensive factor of durability weakening; conduct on-site testing in year τ+ω, ω≥1 to obtain the actual change results of specific durability indicators of the slope, obtain the test value of the comprehensive factor of durability degradation, and verify the rationality of the above-mentioned factor prediction theoretical model.

[0099] The above-mentioned method for evaluating and analyzing the durability and stability of in-service slopes includes the following steps in step S4:

[0100] S41: Construct a numerical model for the stability analysis of a durable weakened slope by referring to the principles of traditional continuous and discontinuous numerical methods. The preprocessing includes geometric information input, attenuation and weakening of model parameters for different years, mesh cell or block division, and input of boundary conditions and load conditions.

[0101] S42: Using the above continuous and discontinuous numerical models, analyze the changes in the stability characteristics of service slopes with different service years. Based on the safety limit standards for slope displacement increment, structural ultimate bearing capacity, and stability safety factor, predict and judge the slope stability status and effective service life within a certain number of years in the future.

[0102] The above-mentioned method for evaluating and analyzing the durability and stability of in-service slopes, specifically step S4, is as follows:

[0103] S41: In terms of preprocessing, firstly, based on the attitude and geometric characteristics of the slope soil and rock layers and the support structure, a slope geometric model is established;

[0104] Secondly, input the material property parameters of the specific weakening treatment for the predicted analysis year τ+ω. This differs from the establishment of traditional analysis models. Let the basic index parameters of soil and rock stratification in year τ be... As follows, ∈=1,…,n r ;n r To calculate the total number of different soil and rock layers on the slope;

[0105]

[0106] Among them, natural heavy gamma ∈ internal friction angle Cohesion C ∈ Elastic modulus E ∈ Poisson's ratio μ ∈ Strength index σ ∈ The corresponding attenuation factors are respectively Composition of durability degradation comprehensive factor vector for:

[0107]

[0108] Use this vector to modify the parameter vector Perform durability weakening treatment, namely:

[0109]

[0110] The calculation and processing of basic parameters of the support structure are similar. Taking the anchor bolt parameters as an example, their corresponding index parameters are as follows:

[0111]

[0112] Among them, ultimate bearing capacity Initial prestress The meanings of other parameters are the same as those in equation (4-1), except for the superscript. The parameter vector after the durability weakening treatment in the (τ+ω)th year is as follows:

[0113]

[0114] Next, following traditional continuous and discontinuous numerical methods, mesh generation or discontinuous block discretization is performed to form mesh or block elements. Boundary conditions and load conditions are then added to form a stability analysis model for the weakened durability state of the slope.

[0115] S42: Using numerical models to predict and analyze the changes in stability characteristics of slopes in service for different years, specifically in year τ+ω, including the displacement change ΔU of the slope reference point. τ+ω Structural internal forces and deformation anchor axial force F N,τ+ω and the overall stability safety factor F of the slope s,τ+ω Based on the relevant criteria, the overall safety and instability failure mode are determined, and the slope displacement ΔU is analyzed. τ+ω ≤U cr U cr Displacement warning value; Anchor bolt axial force F N,τ+ω ≤F cr F cr The critical value of axial force; the overall stability safety factor F s,τ+ω ≥1.1~1.2; In terms of on-site monitoring and verification, since the longer the test cycle, the more sample data there are, which is more beneficial for the evaluation and statistical analysis of durability and stability characteristic indicators, a long-term automated monitoring platform for slopes is built, and monitoring and testing are carried out at intervals of quarters or years for a total of τ years. The sample data required by the above analysis method model are obtained, and the durability and stability changes in the τ+ωth year are predicted and analyzed.

[0116] The beneficial effects of this invention are as follows: This invention establishes a theoretical method for evaluating and analyzing the durability and stability of in-service slope engineering projects. It can establish appropriate durability index systems and corresponding durability evaluation standards based on the characteristics of slopes in different regions. Based on this, and combined with test data collection on slope durability, it forms an assessment result of the slope durability weakening level. Further analysis and calculation of the durability decay factor of slope structure and soil layer performance parameters can predict the degree of durability weakening of slope parameters in future years. Combined with continuous and discontinuous numerical calculation models, it can predict and analyze the stability state of the slope after future durability weakening. Attached Figure Description

[0117] Figure 1 This is a flowchart of the present invention.

[0118] Figure 2 This is a schematic diagram of the hierarchical index system for evaluating slope durability according to the present invention.

[0119] Figure 3 The diagram shows the continuous and discontinuous numerical models of the slope calculation example provided by this invention.

[0120] Figure 4 The figure shows some of the results of the slope durability and stability analysis example provided by this invention.

[0121] Figure 5 The diagram shows the results of the ultimate bearing capacity and axial force of anchor bolts of different ages in the slope calculation example provided by this invention. Detailed Implementation

[0122] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0123] like Figure 1 As shown, a method for evaluating and predicting the durability and stability of in-service slopes includes the following steps:

[0124] S1: Construct a hierarchical index system for evaluating the durability of service slopes. Based on the improved analytic hierarchy process and fuzzy recognition correlation theory, establish a comprehensive evaluation and analysis model for the durability of service slopes, and form a comprehensive scoring standard for the degree of durability impact. This will provide a qualitative basis for subsequent evolution analysis of durability weakening characteristics and quantitative analysis of attenuation factors.

[0125] Step S1 includes the following process:

[0126] S11: An improved analytic hierarchy process (AHP) was used to build a hierarchical index system for durability evaluation, including a comprehensive durability evaluation target layer A, a criterion layer B, and a corresponding specific index layer C. The criterion layer B is divided into two sub-layers. Based on the criterion layer, the corresponding specific index layer C is further clarified to express the characteristics of durability function or influence effect, and the target layer level classification standard range is established.

[0127] A hierarchical index system for durability evaluation was established, consisting of a target layer, a criterion layer, and a specific index layer from top to bottom. The target layer is divided into five levels for comprehensive durability evaluation: excellent, good, average, poor, and very poor, i.e., {Ψ1,Ψ2,…,Ψ5}. The basic scoring criteria for the comprehensive level of each layer of indicators are shown in Table 1. The corresponding specific index parameters and value ranges are determined according to the slope conditions of each region. Table 2 provides the suggested index value ranges for the case slope, where the chloride ion content in the slope support structure ranges as follows: 0–0.05 (excellent), 0.05–0.15 (good), 0.15–0.2 (average), 0.2–0.3 (poor), and 0.3–1.0 (very poor). The evaluation is based on the actual measured statistical information combined with Tables 1 and 2.

[0128] Table 1

[0129]

[0130] Table 2

[0131]

[0132] The criteria layer is divided into two layers, namely the i-th upper-layer index B. i and the j-th lower-level indicator B under the i-th upper-level indicator ij , i≤n a ,j≤n bi , where n a The total number of indicators at the upper criterion level, n bi The total number of lower-level criteria indicators corresponding to the i-th indicator in the upper-level criterion, i = 1 to n a The total number of indicators at the lower criterion level is set to n. b , Indicator layer C ijk C ijk This represents the k-th indicator in the indicator layer corresponding to the i-th upper-level indicator and the j-th lower-level indicator in the criterion layer, where k ≤ n. cij , where n cij Let n be the number of indicators in the indicator layer corresponding to the i-th upper-level indicator and the j-th lower-level indicator in the criterion layer, and let n be the total number of indicators in the indicator layer. c ,have It is worth noting that the specific indicators in the lower criterion layer and the specific indicator layer are optional and can be determined in combination with the actual project.

[0133] like Figure 2 As shown, the upper criterion layer includes the external environment B1, the soil and rock cover layer B2, the slope support structure B3, the construction method and quality B4, and the factors affecting the timeliness B5; the lower criterion layer B... ij This can be further refined to include the external environment, including atmospheric environment B. 11 B. Physical and mechanical effects of groundwater 12 and slope subjected to dynamic and static loads B 13 Impact of rock weathering B 21 Soil durability B 22 Anchor bolts and anchor cables B 31 Anti-slide pile B 32 Retaining wall B 33 Grouting projects B 34 Active Network B 35 1. Shotcrete surface of the facing wall B 36 Cast-in-place masonry B 41 Grouting B 42 Prefabricated assembly B 43 Service life or period of operation B 51 Frequency of influence B52 Based on the criteria layer, further clarify the corresponding specific indicator layer C to express the characteristics of durability action or impact effect. The specific indicator layer C includes CO2 concentration C. 111 Acid rain C 112 Salt spray C 113 Temperature difference C 114 Corrosion ions C 121 Scrubbing effect C 122 The effect of freeze-thaw cycles on C 123 Traffic load C 131 General overload C 132 Basic physical properties such as density and strength C 211 Weathering test index C 212 Structure and Construction C 213 Stress state C 214 Basic physical properties such as density and strength C 221 Acidity and alkalinity C 222 Conductivity C 223 Cracks and corrosion C 311 Ultimate bearing capacity C 312 Prestress relaxation C 313 Cracks and corrosion C 321 Flexural shear bearing capacity C 322 Stress level C 323 Material index C 331 Crack C 332 Stress level C 333 Material properties such as strength C 341 Crack C 342 Fullness C 343 Steel reinforcement corrosion C 351 C. Net cable breakage 352 Stress relaxation C 353 Surface smoothness C 361 Collapsed and exposed C 362 Material ratio C 411 Appearance quality C 412 , integrity C 413 Material ratio C 421 Indicators such as strength C 422 Fullness C 423 Transport protection C 431 , connect C 432 Grouting quality C 433 Years Limit C 511 Persistent or seasonal C 521 .

[0134] S12: Calculate the weights of each level of indicators, construct a comparison matrix and an optimal transfer matrix, and transform them into a comprehensive judgment matrix. Finally, obtain the weight values ​​of each level of indicators according to the weight calculation formula.

[0135] Construct a comparison matrix between indicators at each level. Suppose there are m indicators at a certain level, where m is the number of indicators at the upper-level criterion. a Or the lower layer n b Or n of the index layer c Then the comparison matrix A is as follows, where the i-th element in A... m Line j m Column elements By comparing i m and j m Importance can be determined by comparing indicators such as the physicochemical properties of groundwater. m Traffic load impact index j m ,Pick and This indicates that the effect of groundwater is more significant than that of traffic load, located on the diagonal. or Matrix A:

[0136]

[0137] The inverse matrix is ​​then determined as follows:

[0138]

[0139] Where r ij This represents the element in the i-th row and j-th column of R;

[0140] Finally, the consistency matrix, i.e. the comprehensive judgment matrix, is determined:

[0141]

[0142] Where d ij This represents the element in row i and column j of D;

[0143] The weights are calculated using the following formula:

[0144]

[0145] The criterion layer weights are obtained according to the above calculation process. Lower criterion layer weights C-level indicator weights The normalized C-layer index target weight w ijk for:

[0146]

[0147] S13: Sampling correlation analysis theory and method, calculate the correlation degree between each indicator and the target level and the weighted comprehensive correlation degree, and analyze to obtain the target level.

[0148] The correlation degree of a single indicator or the comprehensive correlation degree of multiple indicators is calculated using correlation theory. For quantitatively testable indicators, the correlation degree is analyzed and calculated based on fuzzy recognition theory. For a specific durability indicator c at the indicator layer... ijk Quantitative test sample values Conforms to a general normal distribution Parameters in parentheses These are the sample statistical mean and variance, respectively, for a certain target Ψ. l The value limit is set to {u l,min ,u l,max}, u l,min u l,max The target level Ψ l The minimum and maximum values ​​of the target level l = 1, ..., n d n d n represents the total number of target levels. d =5; Assuming the target level value is u v Follows a normal distribution Among them, intermediate quantity Then the single correlation degree of this indicator with respect to target level l can be calculated. as follows:

[0149]

[0150] Finally, the weighting formula (1-5) is introduced to calculate the comprehensive correlation η with the target level l. l As shown in the following formula:

[0151]

[0152] Finally, based on the principle of maximum similarity, the overall durability evaluation level is determined.

[0153] In practical applications, first, based on the specific circumstances of the slope case to be evaluated, refer to... Figure 2 Establish a clear and reasonable hierarchical framework for indicators, and collect characteristic data on slope structure and soil-rock layer durability indicators. Regarding the B2 layer of slope soil-rock overburden, methods for determining the degree of rock weathering include standard penetration test, elastic wave method, and rebound value method, to obtain the weathering impact test indicator B. 21 (C 211 C 212 (etc.), combined with the degree of variation of joints and fractures in the explored rock strata C 213 To determine the impact of rock (mass) weathering; basic physical and mechanical properties C of soil slopes can also be measured. 221 Acidity and alkalinity C 222For slope protection structures (B3), corresponding indicators for each structural type are included, such as the carbonation (neutralization) depth of concrete structures, the amount and depth of steel corrosion, crack width, and rebound or core sampling compressive strength. After investigating or measuring the values ​​of various indicators, the final comprehensive durability evaluation level is determined based on the improved AHP-fuzzy recognition and other correlation theory-based durability evaluation analysis methods suggested above.

[0154] S2: Analyze the evolution of the durability weakening characteristics of slope protection projects under the influence of different environmental factors, and establish a slope durability attenuation factor model under the influence of single factors.

[0155] To establish a durability analysis model for slope protection structures, this paper proposes to construct a theoretical model for carbonation, corrosion cracking, and durability degradation of reinforced concrete slope structures, based on the traditional durability design principles of concrete structures and partially referencing the "Standard for Durability Assessment of Existing Concrete Structures" (GB / T 51355-2019). The details are as follows:

[0156] S21: Analyze the degradation mechanism of carbonation and steel corrosion performance of slope concrete structures under the influence of various environmental factors on durability, and establish a decay factor model describing the weakening of the durability of slope protection structures using the change of steel corrosion rate as a parameter.

[0157] Assuming the carbonization depth X is proportional to the square root of time t, i.e. The carbonization influence coefficient K reflects the combined influence of multiple factors and is calculated using the following formula (2-1), where Π represents the related multiple influencing factors k o The product of consecutive products, o = 1 to n k n k c0 represents the total number of relevant influencing factors, and c0 is a constant coefficient.

[0158]

[0159] The service life of the protective structure, also known as the durability failure time t cr Including the time t for the steel bars to begin corroding s and the time t from the start of corrosion to the expansion and cracking of the protective layer c The calculation formula is as follows:

[0160]

[0161] The above formula t c The calculation parameters include: protective layer thickness H c Concrete strength H f Rebar diameter H d Temperature coefficient H T Ambient temperature and humidity H RH And the influence coefficient H of the local environment on rust expansion and cracking. m , tr The number of years corresponding to a coefficient of 1.0; t s The calculation formula parameters include: x p The thickness of the protective layer is given by x0, which is the residual carbonization amount. The calculation of x0 is based on the following formula (2-3), where m... q λ is the local environmental coefficient. c According to x p A constant parameter that takes different values ​​from K;

[0162]

[0163] The reinforcement rate of the support structure is calculated as follows, and the corrosion rate v is given by equation (2-4), where χ c For position coefficients, θ and R H These represent ambient temperature and humidity, respectively, where κ is a constant and f is a variable. cu This refers to the standard value of concrete compressive strength.

[0164]

[0165] Then, within time t, the corrosion depth δ = vt. Furthermore, assuming d is the diameter of the reinforcing bar, the corrosion section loss rate λ... s :

[0166]

[0167] Finally, the mechanical parameters Q and corrosion rate λ were established. s The functional relationship is used to describe the degree of degradation of the index parameter Q relative to the initial value Q0, as expressed by the linear fitting relationship in equation (2-5), where ξ is a constant fitting parameter, and the decay factor coefficient η is... c =Q / Q0=1-ξλ s ;

[0168] Q=Q0(1-ξλ s (2-5)

[0169] Considering the complexity of on-site influencing factors, if λ s The parameters have randomness and time-sensitivity, so the attenuation factor coefficient η c It is also a random time series, denoted as η. c =η c (t).

[0170] The mechanisms and analytical models for the effects of salt spray and chloride ion corrosion, stress coupling / temperature difference, and other durability factors can be found in relevant literature and are omitted here.

[0171] S22: Analyze the fatigue damage mechanism model of slope soil and rock layers under vibration load, and establish the durability attenuation factor under the influence of fatigue deterioration.

[0172] First, fatigue tests on rock mass or structural materials are conducted to obtain experimental curves such as stress-fatigue life (SN) curves. Then, damage variables are established based on damage theory, with coefficients including fatigue damage variables based on cycle ratios.

[0173] D = N / N E (2-6)

[0174] Where N and N E These can be represented by the number of cycles consumed and the number of cycles leading to final failure, or by introducing a nonlinear fatigue damage variable:

[0175]

[0176] In the formula, D0 is the initial damage variable, and α is the material parameter;

[0177] The correction values ​​of material parameters are established based on damage variables. The material parameters include stress intensity S, elastic modulus E, and damage-type stress-strain ε constitutive relation model σ=f(ε,D), as shown in the following relation (2-8), where E0 is the initial elastic modulus, ζ is the correction factor of damage variables, and C is the elastic or elastoplastic matrix.

[0178]

[0179] That is, the attenuation factor η under the influence of vibration load. d The following equation reflects the degree of weakening in the durability of the elastic modulus or stress / strain, and is considered as a random time series, i.e., η. d =η d (t)

[0180]

[0181] Finally, the above-mentioned fatigue damage and deterioration calculation model for slope rock materials is introduced into the slope stability analysis under the influence of long-term dynamic cyclic loading, which can predict the slope's durability, stability, and fatigue life.

[0182] S23: Analyze the coupled effects of groundwater seepage and creep, and the effects of acid rain. Establish an analytical model for the weathering and weakening of slope soil and rock under the influence of groundwater physicochemical properties, and determine the corresponding durability attenuation factor.

[0183] When establishing the weathering weakening analysis model under the influence of groundwater physicochemical properties, the main considerations are the coupling effect of groundwater seepage and creep, and the effect of acid rain. The analysis of the coupling effect of seepage and creep is explained as follows:

[0184] First, the long-term creep effect of the completely weathered overburden or soil slope is analyzed, and the relaxation modulus parameters after creep adjustment are derived. A Burgers creep model, consisting of Maxwell and Kelvin assemblies connected in series, is adopted, and the constitutive equations are as follows:

[0185]

[0186] Let E M Let v be the elastic modulus of the Hookean elastomer. M E is the viscosity coefficient of a Newtonian viscous body. K Let v be the Kelvin bulk elastic modulus. K Here, Kelvin is the volume viscosity coefficient, and t is time; the three-dimensional Burgers model is in the form of:

[0187]

[0188] Among them, intermediate quantity intermediate quantity σ m ε v These are the stress and strain sphere tensors, S, respectively. ij e ij These are the corresponding stress and strain deviatoric tensors. By performing an inverse transformation of the shear modulus in the creep model using Laplace integrals, and combining this with the conversion relationship between the shear modulus and the relaxation modulus E(t), we obtain the expression for E(t):

[0189]

[0190] Substituting the relaxation modulus E(t) derived above into the shear modulus expression G(t)=E(t) / 2(1+μ), and setting the variable λ(t)=E(t)μ / {(1+μ)(1-2μ)}, we then substitute it into the following equilibrium equation under the influence of seepage:

[0191]

[0192]

[0193]

[0194]

[0195] In the above formula, Let k be the Laplace operator notation, where u, v, and w are the displacement variables in the left-right, front-back, and vertical directions, respectively. p γ is the permeability coefficient. w Let P be the specific weight of water, ε be the pore pressure, and ε be the specific weight of water. VThe porosity coefficient ρ is adjusted by changing the volumetric strain, as shown in equation (2-17), where ρ0 is the initial porosity, to correct the permeability coefficient k. p To achieve seepage deformation coupling and correct the permeability coefficient k p The calculation is shown in equation (2-18), k p0 ρ and ρ0 are the initial permeability coefficient and initial porosity values, respectively. Combining equations (2-12) to (2-18), we can achieve a flow-creep coupled analysis:

[0196] ρ=(ρ0+ε v ) / (1+ε v (2-17)

[0197]

[0198] Attenuation factor η under the influence of seepage-creep coupling s This clearly reflects the relaxation modulus E(t) and the permeability coefficient k. p The durability changes of two relatively independent index parameters are established by the following function, which shows the relationship between the decay factor and the ratio of each index parameter to its initial value, where E0 refers to the initial relaxation modulus and the f function is linear or various fitting forms.

[0199] η s =f[E(t) / E0,k] p / k p0 (2-19)

[0200] The steps for constructing a model of the impact of acidic groundwater, i.e., acid rain, on the weakening of slope rock mass durability are as follows:

[0201] Acid rain immersion tests were conducted to examine the weakening effects of acid rain on the mechanical properties of slope structures, rock materials, and rock mass surfaces under different acid concentrations. and duration of action Under the conditions, the cohesive strength of the test sample performance index Friction angle and strength indicators To determine the relevant durability weakening factors, a fitting function relationship was established between acid treatment condition parameters and sample performance indicators to varying degrees of influence, as shown in the following formula:

[0202]

[0203] In the formula Initial compressive strength, The weakening factor related to the intensity index of the sample test was determined through the above experiments. value;

[0204] Increase the concentration of the simulated acid solution and the test duration. Equivalent to the actual action time t of acidic groundwater on the slope. a The experimental formula was modified to reflect the functional relationship between actual slope parameters and influencing environmental factors. Combined with on-site sampling and testing, the attenuation factor η under the influence of acid rain was determined. a .

[0205] Durability degradation factor η under the combined influence of the above environmental factors c η d η s η a These will be used for subsequent analysis.

[0206] S3: Establish a comprehensive attenuation factor model for the weakening of durability of slope protection engineering under the influence of multiple factors.

[0207] Step S3 includes the following processes:

[0208] S31: Establish a theoretical model for predicting durability degradation factors. Based on stochastic process theory, analyze the time series (in years or quarters) of the durability degradation factor of a certain performance index of a slope engineering object under the influence of different environmental factors, perform difference transformation to a stationary series, determine the prediction model type and specific parameter values ​​of the stochastic series, and realize the prediction analysis of the durability degradation factor of the performance index.

[0209] For different types of slope protection structures or soil and rock cover layers, respectively construct theoretical models for predicting comprehensive factors of durability attenuation;

[0210] For different types of slope protection structures or soil and rock cover layers, a comprehensive factor prediction theoretical model for the attenuation of their durability performance is constructed respectively.

[0211] Let I be the series of factors affecting the actual environmental durability of the slope. a a = 1, ..., n x n x The total number of influencing factors; the series of performance indicators for the durability impact on protective structures or geotechnical objects is X. b b = 1, ..., n y n y The total number of performance indicators of soil and rock objects related to slope stability analysis; the random variable η, which is the durability attenuation factor of a single factor a on a specific performance indicator b. a,b (t), where t is the durability impact time in years or quarters, and the prediction model is determined through stochastic analysis as follows:

[0212] First, single-factor attenuation factor samples were obtained through experimental testing. τ = 1, 2, ... represents the number of years or quarters in the sample test. The stationary sequence can be obtained by first-order or second-order differencing.

[0213]

[0214] Through stationary sequences Autocorrelation and partial correlation functions The figure is identified as one of the following linear model types: autoregressive model AR(p), sliding model MA(q), and mixed model ARIMA(p,q), where p and q are the order of the corresponding models, and the corresponding p and q parameter values ​​are determined. Predictive analysis is performed according to the recursive model, and the mixed model is used to predict the following formula;

[0215]

[0216] In the above formula, ω represents the increment of the forecast period; and The coefficients of the mixed model are calculated using the autocorrelation and partial correlation function values. To deduce; θ0 is a constant coefficient, derived from the sample mean and The coefficients were derived. For stationary sequences, white noise is used; utilizing the above By performing the calculations and combining them with equation (3-1), the single-factor durability decay factor prediction results for the year τ+ω or quarter can be obtained.

[0217] Considering the various durability-influencing factors on the slope, a comprehensive attenuation factor is used. It can be calculated using the following formula:

[0218]

[0219] Let the performance index of the slope object in year τ be... b = 1, ..., n y Then, the forecast results of the relevant indicators after the τ+ωth year are expressed as follows:

[0220] S32: Describe the basic scheme for collecting sample data from indoor tests and field inspections, and establish a method for calculating the sample results of durability decay factor.

[0221] Data was collected from the index layer through indoor simulation tests and on-site testing (specific indicators of durability weakening characteristics can be found in the appendix). Figure 2The durability degradation factor prediction results were verified and analyzed based on the above theoretical model. The indoor accelerated durability test included salt spray test, acid rain immersion test (mainly for structures), wet-dry cycle test and vibration fatigue test (mainly for slope soil). These included standard layer atmospheric influence factors B11 (corresponding indicators C111, C112, C113), water physicochemical effects B12 (corresponding indicators C121, C122), and vibration load (traffic load C131). The durability weakening sample performance indicators were collected continuously or at set time intervals under the simulated test environment to determine the comprehensive durability weakening factor. In the (i+1)th year, l≥1, on-site detection was carried out to obtain the specific durability index changes of the slope, and the test value of the comprehensive durability degradation factor was obtained to verify the rationality of the above factor prediction theoretical model.

[0222] S4: Establish a numerical model for slope stability analysis under the influence of durability weakening, and predict and evaluate the changes in slope stability characteristics and final effective service life at different service years.

[0223] Step S4 includes the following processes:

[0224] S41: Construct a numerical model for stability analysis of durable weakened slopes based on the principles of traditional continuous and discontinuous numerical methods. Preprocessing includes geometric information input, attenuation and weakening of model parameters for different ages, mesh cell or block division, and input of boundary conditions and load conditions.

[0225] In terms of preprocessing, the slope geometric model is first established based on the attitude and geometric characteristics of the slope soil and rock layers and the support structure.

[0226] Secondly, input the material property parameters of the specific weakening treatment for the predicted analysis year τ+ω. This differs from the establishment of traditional analysis models. Let the basic index parameters of soil and rock stratification in year τ be... As follows, ∈=1,…,n r ;n r To calculate the total number of different soil and rock layers on the slope;

[0227]

[0228] Among them, natural heavy gamma ∈ internal friction angle Cohesion C ∈ Elastic modulus E ∈ Poisson's ratio μ ∈ Strength index σ ∈ The corresponding attenuation factors are respectively Composition of durability degradation comprehensive factor vector for:

[0229]

[0230] Use this vector to modify the parameter vector Perform durability weakening treatment, namely:

[0231]

[0232] The calculation and processing of basic parameters of the support structure are similar. Taking the anchor bolt parameters as an example, their corresponding index parameters are as follows:

[0233]

[0234] Among them, ultimate bearing capacity Initial prestress The meanings of other parameters are the same as those in equation (4-1), except for the superscript. The parameter vector after the durability weakening treatment in the (τ+ω)th year is as follows:

[0235]

[0236] Next, following traditional continuous and discontinuous numerical methods, mesh generation or discontinuous block discretization is performed to form mesh or block elements. Boundary conditions and load conditions are then added to form a stability analysis model for the slope under weakened durability conditions, such as... Figure 3 Example numerical calculation models for continuous and discontinuous slope durability and stability analysis.

[0237] S42: Using the above continuous and discontinuous numerical models, analyze the changes in the stability characteristics of service slopes with different service years. Based on the safety limit standards for slope displacement increment, structural ultimate bearing capacity, and stability safety factor, predict and judge the slope stability status and effective service life within a certain number of years in the future.

[0238] S42: Using numerical models to predict and analyze the changes in stability characteristics of slopes in service for different years, specifically in year τ+ω, including the displacement change ΔU of the slope reference point. τ+ω Structural internal forces and deformation anchor axial force F N,τ+ω and the overall stability safety factor F of the slope s,τ+ω (Using the strength reduction method), the overall safety and instability failure mode are determined according to the relevant criteria, and the slope displacement ΔU is considered. τ+ω ≤U cr U cr Displacement warning value; Anchor bolt axial force F N,τ+ω ≤F cr F cr The critical value of axial force; the overall stability safety factor F s,τ+ω ≥1.1~1.2, as attached Figure 4-5Example slope durability stability analysis failure modes, initial anchor bolt axial force distribution, and anchor bolt failure after different service years, etc. (Note: In the figure, the solid line of anchor bolt axial force indicates that the anchor bolt axial force has not exceeded the ultimate bearing capacity after weakening, while the dashed line indicates that the ultimate bearing capacity limit is not considered). It shows that as the durability of the anchor bolt structure weakens with the increase of years, its ultimate bearing capacity decreases year by year. There may be some anchor bolts that fail when their actual bearing capacity reaches the ultimate bearing capacity, thus leading to a decrease in the overall stability of the slope.

[0239] In terms of on-site monitoring and verification, since the longer the test cycle, the more sample data there is, which is more beneficial for the evaluation and statistical analysis of durability and stability characteristic indicators, a long-term automated monitoring platform for slopes is built to implement monitoring and testing at intervals of quarters or years for a total of τ years. This will obtain the sample data required by the above analysis method model and predict and analyze the changes in durability and stability in the τ+ωth year.

Claims

1. A method for evaluating and analyzing the durability and stability of in-service slopes, characterized in that, Includes the following steps: S1: Construct a hierarchical index system for evaluating the durability of service slopes. Based on the improved analytic hierarchy process and fuzzy recognition correlation theory, establish a comprehensive evaluation and analysis model for the durability of service slopes, and form a comprehensive scoring standard for the degree of durability impact. This will provide a qualitative basis for subsequent evolution analysis of durability weakening characteristics and quantitative analysis of attenuation factors. S2: Analyze the evolution of the durability weakening characteristics of slope protection projects under the influence of different environmental factors, and establish a slope durability attenuation factor model under the influence of single factors; Step S2 includes the following process: S21: Analyze the degradation mechanism of carbonation and steel corrosion performance of slope concrete structure under the influence of various environmental factors on durability, and establish a decay factor model describing the weakening of the durability of slope protection structure with the change of steel corrosion rate as a parameter. S22: Analyze the fatigue damage mechanism model of slope soil and rock layers under vibration load, and establish the durability attenuation factor under the influence of fatigue deterioration; S23: Analyze the coupling effects of groundwater seepage and creep, and the effects of acid rain. Establish an analysis model for the weathering and weakening of slope soil and rock under the influence of groundwater physicochemical properties, and determine the corresponding durability attenuation factor. S3: Establish a comprehensive attenuation factor model for the weakening of durability of slope protection engineering under the influence of multiple factors; Step S3 includes the following process: S31: Establish a theoretical model for predicting durability attenuation factors. Based on stochastic process theory, analyze the time series of durability attenuation factors of a certain performance index of a slope engineering object under the influence of different environmental factors, perform difference transformation to a stationary series, determine the prediction model type and specific parameter values ​​of the stationary series, and realize the prediction analysis of durability attenuation factors of the performance index. For different types of slope protection structures or soil and rock cover layers, respectively construct theoretical models for predicting durability attenuation factors; Let the series of factors affecting the actual environmental durability of the slope be: , , The total number of influencing factors; the series of performance indicators affecting the durability of protective structures or geotechnical objects. , The total number of performance indicators of soil and rock objects related to slope stability analysis; single factor For specific performance indicators Durability decay factor random variable Where t is the durability impact time, in years or quarters, the prediction model is determined through stochastic analysis as follows: First, single-factor attenuation factor samples were obtained through experimental testing. , Given the number of years or quarters of the sample test, a stationary sequence can be obtained through first-order or second-order differencing: , (3-1)； Through stationary sequences The autocorrelation and partial correlation functions ( The graph is identified as one of the following linear model types: autoregressive model AR(p), sliding model MA(q), and mixed model ARIMA(p, q), where p and q are the order of the corresponding models. The corresponding p and q parameter values ​​are determined, and the predictive analysis is carried out according to the recursive model. The mixed model is used to predict the following formula. (3-2)； In the above formula, For the forecast of annual increments; and The coefficients of the mixture model are calculated using the autocorrelation and partial correlation function values. , To calculate; The constant coefficient is derived from the sample mean and The coefficients were derived. For stationary sequences, white noise is used; utilizing the above By performing the calculations and combining them with equation (3-1), we can obtain the result of the first step. Annual or quarterly single-factor durability degradation factor forecast results ; Considering the various durability-influencing factors on the slope, a comprehensive attenuation factor is used. It can be calculated using the following formula: (3-3)； Let the slope object be the first Annual performance indicators are , Then the first The post-holiday forecast results are expressed as follows: ; S32: Describe the basic scheme for collecting sample data from indoor tests and field inspections, and establish a method for calculating the sample results of the durability decay factor; Through indoor simulation tests and on-site testing, sample data of performance indicators of the index layer in step S31 are collected. The single-factor or comprehensive factor of durability attenuation is predicted and the results are verified by referring to the durability attenuation factor prediction theoretical model. Indoor durability accelerated tests are designed, including salt spray test, acid rain immersion test, dry and wet cycle test and vibration fatigue test. The performance indicators of the durability weakened sample under the simulated test environment are collected continuously or at set time intervals to determine the comprehensive attenuation factor. Proceed to the first Year, On-site testing was conducted to obtain the actual changes in specific durability indicators of the slope, obtain the test value of the comprehensive durability attenuation factor, and verify the rationality of the theoretical model for predicting the durability attenuation factor. S4: Establish a numerical model for slope stability analysis under the influence of durability weakening, and predict and evaluate the changes in slope stability characteristics and final effective service life at different service years.

2. The method for evaluating and analyzing the durability and stability of in-service slopes according to claim 1, characterized in that, Step S1 includes the following process: S11: An improved analytic hierarchy process (AHP) was used to build a hierarchical index system for durability evaluation, including a comprehensive durability evaluation target layer A, a criterion layer B, and a corresponding specific index layer C. Based on the criterion layer, the corresponding specific index layer C was further clarified to express the characteristics of durability function or influence effect, and the target layer level classification standard range was established. S12: Calculate the weights of each level of indicators, construct a comparison matrix and an optimal transfer matrix, and transform them into a comprehensive judgment matrix. Finally, obtain the weight values ​​of each level of indicators according to the weight calculation formula. S13: Sampling correlation analysis theory and method, calculate the correlation degree between each indicator and the target level and the weighted comprehensive correlation degree, and analyze to obtain the target level.

3. The method for evaluating and analyzing the durability and stability of in-service slopes according to claim 2, characterized in that, The specific process of step S1 is as follows: S11: Establish a hierarchical index system for durability evaluation, comprising, from top to bottom, a target layer, a criterion layer, and a specific indicator layer. The target layer is divided into five levels for comprehensive durability evaluation: Excellent, Good, Average, Poor, and Very Poor. , , , The specific index parameters and their value ranges are determined based on the slope conditions of each region; the criterion layer consists of two layers, namely the first... One upper-level indicator and the The first under the upper-level indicator Lower-level indicators , ,in The total number of indicators at the upper-level criteria level. For the upper level The total number of indicators in the lower-level criteria corresponding to the indicator. The total number of indicators at the lower criterion level is set to... , Indicator layer , Indicator level Upper-level indicators and the first The indicator level corresponding to the lower level indicator One indicator, ,in For the criterion level Upper-level indicators and the first The number of indicators in the indicator layer corresponding to the lower-level indicators, and the total number of indicators in the indicator layer are set to [value missing]. ,have ; S12: Construct a comparison matrix between indicators at each level. Let a certain level of indicators have a total of... indivual, Number of indicators at the upper criterion level or lower level Or perhaps the indicator layer Then compare the matrices As follows, among which The Middle Line number Column elements =1,0,-1, by comparison and To determine importance, matrix: (1-1)； The inverse matrix is ​​then determined as follows: ； (1-2)； In R, the first... Line number Column elements; Finally, the consistency matrix, i.e. the comprehensive judgment matrix, is determined: ， (1-3)； in Indicates the first in D Line number Column elements; The weights are calculated using the following formula: (1-4)； The weight of the criterion layer is obtained according to the above calculation process. Lower criterion layer weights Indicator layer C weight Then the normalized target weight of index layer C for: (1-5)； S13: Calculate the correlation degree of a single indicator or the comprehensive correlation degree of multiple indicators using correlation theory. For quantitatively testable indicators, analyze and calculate the correlation degree based on fuzzy recognition theory; for a specific durability indicator number at the indicator layer... Quantitative test sample values Conforms to a general normal distribution Parameters in parentheses These are the sample statistical mean and variance, respectively, for a certain target. The value limit is set to , Target levels The minimum and maximum values ​​of the target level. = The total number of target levels, =5; Assuming the target level value is 5. Follows a normal distribution Among them, intermediate quantity , Then the indicator can be calculated for the target level. single correlation as follows: (1-6)； Finally, the weighting formula (1-5) mentioned above is introduced to calculate the target level. Comprehensive correlation As shown in the following formula: (1-7)； Finally, based on the principle of maximum similarity, the overall durability evaluation level is determined.

4. The method for evaluating and analyzing the durability and stability of in-service slopes according to claim 3, characterized in that, The specific process of step S2 is as follows: S21: Assuming carbonization depth With time It is directly proportional to the square root, that is Among them, the carbonization influence coefficient The result is reflected by the combined influence of multiple factors, and is calculated according to the following formula (2-1). Where Π represents multiple related influencing factors The product of consecutive products, o=1~ , This represents the total number of relevant influencing factors. The coefficient is constant. (2-1)； The service life of the protective structure, also known as the durability failure time. Including the time when the steel bars begin to corrode Time from the onset of corrosion to the expansion and cracking of the protective layer The calculation formula is as follows: = (2-2)； The above formula The calculation parameters include: protective layer thickness. Concrete strength , diameter of reinforcing bars Temperature coefficient Ambient temperature and humidity and the influence coefficient of local environment on rust expansion and cracking , This refers to the number of years corresponding to a coefficient of 1.

0. The calculation parameters include: For the thickness of the protective layer, The carbonization residue is calculated using the following formula (2-3), where... For local environmental coefficients, According to and Constant parameters with different values; (2-3)； The reinforcement rate of the support structure is calculated as follows, and the corrosion rate is... As shown in equation (2-4), where For position coefficients, and These are ambient temperature and humidity, respectively. It is a constant. This refers to the standard value of concrete compressive strength. (2-4)； but Depth of rust over time Furthermore, let's set For the diameter of the reinforcing bar, the corrosion section loss rate is... : ； Finally, the mechanical parameter Q and the corrosion section loss rate were established. The functional relationship is used to describe the mechanical parameter Q relative to its initial value. The degree of degradation is expressed by the linear fitting relationship in the following equation (2-5), where To fit constant parameters, the attenuation factor coefficients are... = ; (2-5)； Considering the complexity of the influencing factors on site, if The parameters have randomness and time-sensitivity, so the attenuation factor coefficient It is also a random time series, denoted as ; S22: First, conduct fatigue tests on rock mass or structural materials to obtain experimental curves such as stress-fatigue life (SN) curves; then, establish damage variables based on damage theory, including fatigue damage variables based on cycle ratio: (2-6)； in and These can be represented by the number of cycles consumed and the number of cycles leading to eventual failure, or by introducing a nonlinear fatigue damage variable: (2-7)； In the formula As the initial damage variable, Material parameters; Corrected values ​​for material parameters are established based on damage variables. These parameters include stress intensity S, elastic modulus E, and damage-type stress. -strain constitutive relation model As shown in the following relation (2-8), where The initial elastic modulus, As a correction factor for the damage variable, It is an elastic or elastoplastic matrix; E= ； (2-8)； That is, the attenuation factor under the influence of vibration load. The following formula reflects the degree of weakening in the durability of the elastic modulus or stress / strain, and is considered as a random time series, i.e. or (2-9); S23: When establishing an analysis model for weathering and weakening under the influence of groundwater physicochemical properties, the effects of groundwater seepage creep coupling and acidic groundwater should be considered. (1) The analysis of the coupling effect of seepage and creep is explained as follows: First, the long-term creep effect of the completely weathered overburden or soil slope is analyzed, and the relaxation modulus parameters after creep adjustment are derived. The Burgers creep model is adopted, which is composed of Maxwell and Kelvin assemblies connected in series. The constitutive equation of the model is as follows: (2-10)； Let E M The elastic modulus of the Hookean elastomer. E is the viscosity coefficient of a Newtonian viscous body. K The elastic modulus of Kelvin is given. Here, Kelvin is the volume viscosity coefficient, and t is time; the three-dimensional Burgers model is in the form of: (2-11)； Among them, intermediate quantity Intermediate quantity ; These are the stress and strain sphere tensors, respectively. , These are the corresponding stress and strain deviatoric tensors. The shear modulus in the creep model is inversely transformed using Laplace integrals, and then the shear modulus is combined with the relaxation modulus. The transformation relationship is obtained. expression: (2-12)； The relaxation modulus derived above Substituting into the shear modulus expression And let variables be defined. Then, substitute these equations into the following equilibrium equation under the influence of seepage: (2-13)； (2-14)； (2-15)； (2-16)； In the above formula, For the Laplace operator notation, , , These represent displacement variables in the left-right, front-back, and vertical directions, respectively. Permeability coefficient, For water density, For pore pressure, For volumetric strain, the porosity coefficient is adjusted by changing the volumetric strain. As shown in equation (2-17), where The initial porosity is used to correct the permeability coefficient. Achieving seepage deformation coupling, permeability coefficient The calculation is shown in equation (2-18). and Given the initial permeability coefficient and initial porosity values, respectively, and combining equations (2-12) to (2-18), a flow-creep coupled analysis is achieved: (2-17)； (2-18)； Attenuation factor under the influence of seepage-creep coupling Clearly reflects the relaxation modulus and permeability coefficient The durability changes of two relatively independent index parameters are established using the following function, which shows the relationship between the decay factor and the ratio of each index parameter to its initial value: Refers to the initial relaxation modulus. The function can be linear or in various fitted forms; (2-19)； (2) Construct a model of the weakening effect of acidic groundwater on the durability of slope rock mass, as follows: Acid rain immersion tests were conducted to examine the weakening effects of acid rain on the mechanical properties of slope structures, rock materials, and rock mass surfaces under different acid concentrations. and duration of action Under the conditions, the cohesive strength of the test sample performance index Friction angle and strength indicators To determine the relevant durability weakening factors, a fitting function relationship was established between acid treatment condition parameters and sample performance indicators to varying degrees of influence, as shown in the following formula: (2-20)； In the formula Initial compressive strength, The relevant durability weakening factors were determined through the above experiments. value; Increase the concentration of the simulated acid solution and the test duration. Equivalent to the actual action time of acidic groundwater on the slope The experimental formula was modified to reflect the functional relationship between actual slope parameters and on-site environmental influencing factors. Combined with on-site sampling and testing, the relevant durability weakening factors under the influence of acid rain were determined.

5. The method for evaluating and analyzing the durability and stability of in-service slopes according to claim 1, characterized in that, Step S4 includes the following process: S41: Construct a numerical model for stability analysis of durable weakened slopes based on the principles of traditional continuous and discontinuous numerical methods. Preprocessing includes geometric information input, attenuation and weakening of model parameters for different years, mesh cell or block division, and input of boundary conditions and load conditions. S42: Using the above continuous and discontinuous numerical models, analyze the changes in the stability characteristics of service slopes with different service years. Based on the safety limit standards for slope displacement increment, structural ultimate bearing capacity, and stability safety factor, predict and judge the slope stability and effective service life within a certain number of years in the future.

6. The method for evaluating and analyzing the durability and stability of in-service slopes according to claim 1, characterized in that, The specific process of step S4 is as follows: S41: In terms of preprocessing, firstly, based on the attitude and geometric characteristics of the slope soil and rock layers and the support structure, a slope geometric model is established; Next, enter the predicted analysis period. The material property parameters of the year-specific weakening treatment are established, which is different from the establishment of traditional analysis models. Let the first year be... Year Basic parameters of soil and rock stratification as follows, ; To calculate the total number of different soil and rock layers on the slope; (4-1)； Among them, natural severe internal friction angle Cohesion Elastic modulus Poisson's ratio Intensity indicators The corresponding attenuation factors are respectively This constitutes the comprehensive factor vector of durability degradation. for: (4-2)； Using the durability decay composite factor vector to compare the parameter vector Perform durability weakening treatment, namely: (4-3)； The calculation and processing of basic parameters of the support structure are similar. Taking the anchor bolt parameters as an example, their corresponding index parameters are as follows: (4-4)； Among them, ultimate bearing capacity Initial prestress The meanings of the other parameters are the same as in equation (4-1), except for the superscript. The parameter vector after the annual durability reduction treatment is as follows: (4-5)； Next, following traditional continuous and discontinuous numerical methods, mesh generation or discontinuous block discretization is performed to form mesh or block elements. Boundary conditions and load conditions are then added to form a stability analysis model for the weakened durability state of the slope. S42: Using numerical models to predict and analyze the first year of different years Changes in the stability characteristics of slopes in service over the years, including changes in the displacement of slope reference points. Structural internal forces and deformation anchor bolt axial force and the overall stability safety factor of the slope Based on relevant criteria, the overall safety and instability failure mode are determined, and the displacement changes of the slope reference point are analyzed. , This is the displacement warning value; Axial force of deformed anchor bolt , The critical value of axial force; the safety factor for overall slope stability. Regarding on-site monitoring and verification, longer testing cycles yield more sample data, which is more beneficial for evaluating and statistically analyzing durability and stability characteristics. By establishing a long-term automated slope monitoring platform, and implementing monitoring at quarterly or annual intervals, a total of [number missing] tests can be conducted. Through monitoring and testing over the past year, the sample data required for the stability analysis model described above were obtained, and predictions and analyses were conducted for the next year. Results of annual durability and stability changes.

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