Method for evaluating and preventing secondary collapse disaster of mountainous highway side slope after earthquake

Abstract

The present application belongs to the technical field of geological disaster prevention, and relates to a method for evaluating and preventing secondary collapse disaster of mountainous highway slope after earthquake. The evaluation method comprises the following steps: obtaining disaster-pregnant factor data of the slope; identifying the main control interval of dangerous rock development according to the disaster-pregnant factor data; analyzing the evolution stage of the collapse disaster process, establishing a dangerous rock stability evaluation model considering the time-effect characteristics of water-induced deterioration of structural plane, and calculating the dangerous rock stability coefficient; and judging the instability risk grade of the dangerous rock according to the comparison result of the stability coefficient and the preset threshold value. The present application improves the evaluation of secondary collapse disaster after earthquake from subjective judgment relying on expert experience to a standard method which is repeatable and quantifiable. By introducing the time-effect characteristics of water-induced deterioration of structural plane, the present application makes up for the deficiency of traditional evaluation methods which cannot reflect the dynamic decline of dangerous rock stability in the rainfall process. The final output risk grade provides clear and intuitive quantitative basis for highway operation and disaster prevention decision-making.

Description

Technical Field

[0001] This invention belongs to the field of geological disaster prevention and control technology in highway engineering, specifically relating to a method for evaluating and preventing secondary landslide disasters on mountain highway slopes after an earthquake. Background Technology

[0002] Prevention and control of secondary landslides on mountain highway slopes after earthquakes is a crucial issue in engineering geology and transportation disaster prevention. Earthquakes not only directly induce landslides but also cause rock mass cracking and loosening on slopes, significantly reducing slope stability and leading to frequent secondary geological disasters in the years following strong earthquakes. Existing research indicates that crack propagation on rock mass structural surfaces, structural surface strength degradation due to rainfall infiltration, and topographic amplification effects are the main controlling factors for post-earthquake landslides. In response to this phenomenon, numerous studies have focused on statistical analysis of disaster-inducing factors, evaluation of unstable rock mass stability, and simulation of rockfall trajectories.

[0003] In the analysis of disaster-inducing factors, existing technologies mainly focus on the controlling effects of factors such as topography, lithology, geological structure, and seismic motion parameters on landslide distribution. For example, through field investigations and statistical methods, the correlation between the distribution of unstable rocks and factors such as slope, elevation difference, fault distance, and structural plane attitude has been identified. These studies provide important basis for regional disaster hazard zoning, but their conclusions are mostly based on specific earthquake events and geological environments, lacking a systematic characterization of the disaster-inducing mechanism of the coupling of multiple factors such as structure, climate, and geomorphology, making it difficult to directly generalize to other earthquake-prone and rainy mountainous areas.

[0004] In assessing the stability of unstable rock masses, existing technologies typically employ limit equilibrium methods or numerical simulations, considering loads such as self-weight, hydrostatic pressure, and seismic forces. However, research on the water-induced degradation effect on structural surfaces during post-earthquake secondary collapses is insufficient. Particularly for soluble rock masses such as limestone, the dissolution caused by rainfall infiltration significantly reduces the shear strength of structural surfaces, and this degradation process exhibits clear time-dependent characteristics. Existing stability assessment formulas often fail to consider the time-varying degradation patterns of structural surface strength, making it difficult to accurately reflect the rapid decline in rock mass stability under short-duration heavy rainfall conditions.

[0005] In the analysis of rockfall disasters, existing technologies mostly employ two-dimensional or three-dimensional kinematic simulation methods to calculate the velocity, impact energy, and bounce height of falling rocks, and then assess the effectiveness of protective structures accordingly. However, existing simulation methods often neglect the influence of the water content of the slope surface soil and rock on the coefficient of restitution, and pay little attention to the "channeling effect" of falling rocks during their movement, i.e., the pattern of falling rocks converging into negative topographic channels on the slope surface. In addition, the selection of existing protective structures relies heavily on experience, lacking quantitative decision-making basis based on simulation results, which makes passive nets, retaining walls, and other structures prone to fatigue damage under concentrated impact.

[0006] In summary, existing technologies still face the following technical challenges in assessing and preventing secondary landslides on mountain roads after earthquakes: a lack of systematic analysis of the coupled disaster-inducing mechanisms of multiple factors including tectonics, climate, and geomorphology; insufficient consideration of the time-dependent deterioration characteristics of limestone structural surfaces due to water in existing rock stability evaluation formulas; inadequate characterization of slope surface water content and channeling effects in rockfall kinematic simulations; and a lack of quantitative support based on simulation results for the optimal selection of prevention and control schemes. Therefore, there is an urgent need to propose a technical solution that can systematically evaluate the risk of secondary landslides on highway slopes in rainy mountainous areas after earthquakes and provide quantitative basis for the selection of active defense structures. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of existing technologies, such as the lack of systematic analysis of the multi-factor coupling disaster-causing mechanism of structure, climate and geomorphology, the lack of sufficient consideration of the time-dependent characteristics of water-induced deterioration of limestone structural surfaces in existing rock stability evaluation formulas, the insufficient characterization of slope surface water content and channeling effect in rockfall kinematic simulations, and the lack of quantitative support based on simulation results for the selection of prevention and control schemes. This invention provides a method for evaluating and preventing secondary collapse disasters on mountain highway slopes after earthquakes.

[0008] In a first aspect, the present invention provides a method for evaluating secondary landslide hazards on mountain road slopes after an earthquake, comprising the following steps:

[0009] Step 1: Obtain data on the disaster-prone factors of the slope; Step 2: Identify the main control interval for the development of dangerous rocks based on the disaster-prone factor data; Step 3: Analyze the evolution stages of the collapse and disaster process, establish a stable rock stability evaluation model that considers the time-dependent deterioration characteristics of the structural surface caused by water, and calculate the stable rock stability coefficient. Step 4: Determine the instability risk level of the unstable rock based on the comparison result between the stability coefficient and the preset threshold.

[0010] This method starts from the complete chain of post-earthquake secondary collapse disasters. First, it collects the key factors (disaster-causing factors) that control the distribution of collapses. Then, it identifies the dominant intervals of these factors, establishes a stability evaluation model that can reflect the post-earthquake structural surface damage and rainfall degradation effects, calculates the quantitative stability coefficient, and finally gives a clear level of instability risk by comparing the coefficient with engineering experience thresholds.

[0011] This invention elevates the assessment of post-earthquake secondary landslide disasters from subjective judgments relying on expert experience to a repeatable and quantifiable standard method. By introducing the time-dependent characteristics of water-induced degradation of structural surfaces, it overcomes the shortcomings of traditional assessment methods that fail to reflect the dynamic decline in the stability of unstable rocks during rainfall. The final risk level output provides a clear and intuitive quantitative basis for highway maintenance and disaster prevention decision-making.

[0012] Preferably, the disaster-causing factor data includes at least one of the following: the elevation difference between the dangerous rock and the highway, the slope gradient, the lithology, the horizontal distance from the fault, the slope aspect and the dip angle of the outward-dipping structural surface, and the maximum grain size of the dangerous rock.

[0013] These factors, such as elevation difference, slope, lithology, horizontal distance from fault, structural plane angle, and grain size, are indicators that have the most significant controlling effect on the distribution of post-earthquake collapses, selected through extensive field statistics and theoretical analysis. They cover multiple dimensions, including topography, geological structure, rock mass structure, and disaster scale.

[0014] Preferably, the main control interval specifically includes: the elevation difference interval, the slope interval, the horizontal distance interval from the fault, and the angle interval between structural planes where dangerous rocks are concentrated.

[0015] By statistically analyzing the frequency distribution of various disaster-causing factors—that is, counting the frequency of dangerous rock formations for each factor within different numerical ranges—the interval with the highest frequency is identified as the dominant control interval. For example, the number of dangerous rocks in slope ranges such as 40°-50° and 50°-60° is counted to identify the slope range with the highest proportion. This approach introduces large-sample statistical methods into disaster assessment, ensuring that the identification of dominant control intervals is data-driven rather than subjectively based. This method can extract regional commonalities from a large number of disaster sites, providing a reference for disaster prediction in similar geological environments. Furthermore, the statistical results of the frequency distribution can be directly used for weight assignment in subsequent evaluation models.

[0016] Preferably, the stability evaluation model for unstable rock formations is established for limestone strata, and the model includes a functional relationship between the shear strength of structural surfaces and the deterioration of dissolution time.

[0017] Preferably, the functional relationship expression is: τ=σ n tan[JRC·lg(JCS / σ n )+φ(t)], Where τ is the shear strength of the structural surface, σ n φ is the normal stress of the structural surface, JRC is the roughness coefficient of the structural surface, JCS is the compressive strength of the rock wall, φ(t) is the degradation function of the basic friction angle of the structural surface with the dissolution time t, and t is the duration of dissolution.

[0018] The physicochemical process of water-induced degradation of limestone structural surfaces is transformed into a calculable mathematical formula, enabling the stability assessment to be quantified and reproducible.

[0019] Preferably, the collapse and disaster process includes the following stages: wetting and weight gain of slope surface soil and rock, water-induced deterioration of structural surfaces, damage to the foundation of unstable rock and triggering of collapse disaster. The impact of each stage on the stability of unstable rock is quantified, and the quantification results are substituted into the unstable rock stability evaluation model to correct the calculation parameters of the unstable rock stability coefficient.

[0020] This approach breaks down the complex process of rainfall-induced landslides into four sub-processes that can be analyzed step-by-step, giving the evaluation model clear physical meaning and process orientation. Quantifying the impact of each stage and incorporating it into the model allows the stability coefficient to reflect the real-time changes in the state of unstable rocks during rainfall, rather than simply providing a static, initial-state evaluation result. This significantly improves the predictive ability for sudden landslides under heavy rainfall conditions.

[0021] Preferably, after determining the instability risk level of the unstable rock, the method further includes: Construct a three-dimensional terrain model of the slope, input the soil and rock mass restoration coefficient and the characteristic parameters of the dangerous rock, conduct rockfall kinematics simulation, and output the rockfall impact energy, movement speed, bounce height and trace density distribution. Based on the output results, identify high-risk sections of rockfall movement and evaluate the impact damage level of rockfalls on highways.

[0022] This approach extends hazard assessment from simply predicting whether a collapse will occur to estimating the extent of damage that would result from a collapse, thus achieving a comprehensive disaster assessment across the entire chain. The four output indicators provide quantitative data support for highway damage assessment, enabling engineers to accurately determine which sections of highway face the highest impact risk, rather than relying solely on experience to delineate danger zones.

[0023] Preferably, the coefficient of restitution of the rock and soil mass is adjusted according to the water content of the slope surface rock and soil mass, and the normal coefficient of restitution and the tangential coefficient of restitution under the water-saturated state are lower than those under the natural state.

[0024] This technical solution improves the accuracy of rockfall kinematics simulations under rainy season or rainfall conditions. Traditional simulations that do not consider water content underestimate the velocity and impact energy of falling rocks during rainfall, leading to conservative protective designs. This solution adjusts the water content of the coefficient of restitution, enabling simulation results to reflect the accelerating effect of rainfall on the rockfall disaster process, providing more reliable data for rainy season disaster early warning and protective structure verification.

[0025] Preferably, the method further includes: identifying the troughing effect of rockfall movement based on the output results, and quantitatively evaluating the impact of the troughing effect on the degree of rockfall impact concentration. By analyzing the trace density distribution and the spatial concentration of impact energy, the degree of impact concentration caused by the troughing effect is quantitatively evaluated. This solves the engineering problem of "where the risk of rockfall is concentrated." After identifying the troughing effect, engineers no longer need to apply average protection to the entire highway section, but can accurately locate the trough section with the highest impact concentration for key protection.

[0026] In a second aspect, the present invention provides a method for preventing secondary landslides on mountain road slopes after an earthquake, comprising: Using the above-mentioned post-earthquake assessment method for secondary landslides on mountain road slopes, high-risk rockfall zones were identified. In the high-risk section, a reinforced concrete tunnel is used to cover the road, and a buffer soil layer is placed on top of the tunnel. The buffer soil layer has a designed slope to guide falling rocks across the road. The structural strength of the tunnel and the thickness of the buffer soil layer are determined based on the impact energy and bounce height output by the rockfall kinematics simulation.

[0027] Compared to passive netting, the open-cut method offers higher impact resistance and lower maintenance requirements, making it particularly suitable for concentrated impact zones caused by the ground faulting effect. The guiding design of the buffer soil layer prevents falling rocks from directly impacting the structure itself, further enhancing the durability of the protection. The structural thickness is quantitatively designed based on simulation outputs, avoiding structural redundancy or insufficient strength caused by empirical design, thus achieving refined and reliable design of the protection project.

[0028] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention provides a method for assessing secondary landslide hazards on mountain highway slopes after earthquakes. By acquiring disaster-inducing factor data, it identifies the main control intervals for the development of unstable rocks, making the assessment no longer dependent on subjective experience but based on regional statistical patterns, significantly improving the objectivity and reproducibility of disaster identification. By analyzing the evolution stages of the landslide impact process and establishing a stability evaluation model that considers the time-dependent deterioration characteristics of structural surfaces caused by water, it is the first to incorporate the dynamic deterioration process of structural surface strength under post-earthquake rainfall conditions into quantitative calculations. This overcomes the technical shortcomings of existing methods that treat structural surface strength as a constant and cannot reflect the rapid decline in the stability of unstable rocks during short-term heavy rainfall. Finally, the method determines the instability risk level based on the comparison results of the stability coefficient and the preset threshold, transforming complex geotechnical problems into clear engineering decision indicators. This provides a systematic, quantitative, and operable risk assessment method for secondary landslide hazards on highway slopes in rainy mountainous areas after earthquakes, filling the gap in existing technologies that lack evaluation methods that take into account both the disaster-inducing mechanism of the tectonic-climate-geomorphic coupling and the time-dependent deterioration effect of structural surfaces. Attached Figure Description

[0029] Figure 1 : Engineering geological profile of typical work sites in the study area.

[0030] Figure 2 : Time-dependent characteristic curves of dissolution and deterioration of limestone structural surfaces.

[0031] Figure 3 : Stress analysis diagram of sliding unstable rock.

[0032] Figure 4 : 3D terrain mesh model of the slope.

[0033] Figure 5 Cloud map showing the energy distribution of falling rock impact.

[0034] Figure 6 : Curve of bouncing height and speed of falling rocks.

[0035] Figure 7 Schematic diagram of rockfall trace density distribution and troughing effect. Detailed Implementation

[0036] The present invention will now be described in further detail with reference to specific embodiments. However, this should not be construed as limiting the scope of the present invention to the following embodiments; all technologies implemented based on the content of the present invention fall within the scope of the present invention.

[0037] Example 1 This embodiment provides a method for evaluating secondary landslide hazards on mountain highway slopes after an earthquake, which specifically includes the following steps: Step 1: Obtain data on slope disaster-inducing factors First, on-site geological surveys and data collection were conducted on the slopes along the highways in the study area. For each of the 33 secondary landslide disaster sites that occurred and disrupted traffic between the 2022 LS earthquake and the end of 2025, the disaster-controlling factors were recorded.

[0038] The disaster-causing factor data includes at least one of the following: the elevation difference between the unstable rock and the highway, the slope gradient, the lithology, the horizontal distance from the fault, the slope aspect and the dip angle of the outward-dipping structural plane, and the maximum grain size of the unstable rock. In this embodiment, all six types of data were collected completely from all 33 disaster sites.

[0039] The specific data collection method is as follows: Elevation difference: The vertical distance from the source area of ​​the dangerous rock to the road surface is measured using drone aerial surveying or laser rangefinder.

[0040] Slope: The slope is extracted in the source area of ​​dangerous rock using a geological compass or digital elevation model (DEM).

[0041] Lithology: Determined through on-site geological surveys and regional geological maps, it is mainly divided into limestone, granite, and diorite.

[0042] Horizontal distance from the fault: Using the active fault distribution map V1.0 online system released by the China Earthquake Disaster Prevention Center, after importing the coordinates of the disaster point, the horizontal projection distance of the point from the Baoxing Fault is measured.

[0043] Angle between slope aspect and structural surface dip: The smallest angle between the slope orientation and the dip of the outward-dipping structural surface.

[0044] Maximum particle size of unstable rock: The maximum representative particle size of the rock after the collapse is measured in the collapse accumulation area.

[0045] Step 2: Identify the main control region for the development of dangerous rocks based on the disaster-prone factor data. Identifying the main control interval specifically includes: statistically analyzing the frequency distribution of each disaster-causing factor, and dividing the intervals into elevation difference intervals, slope intervals, horizontal distance intervals from faults, and structural plane angle intervals where dangerous rocks are concentrated.

[0046] In this embodiment, the disaster-causing factor data of 33 disaster sites were divided into intervals and frequency statistics were performed. The results are shown in Table 1.

[0047] Table 1. Frequency Distribution of Risk Factors for Rockfall Collapses in the Study Area

[0048] Based on the statistical results in Table 1, the main controlling interval for the development of unstable rocks in the study area was identified as follows: Elevation difference range: 30m~400m Slope range: 60°~90° Distance from fault: 200m~500m Structural plane angle range: less than 30° (i.e., developing outward-dip structural planes) These control intervals are the parameter ranges that will be the focus of subsequent hazard assessments.

[0049] Step 3: Analyze the evolution stages of the collapse and disaster process, establish a stability evaluation model for dangerous rocks that considers the time-dependent deterioration characteristics of structural surfaces caused by water, and calculate the stability coefficient of dangerous rocks.

[0050] Analysis of the evolution stages of the landslide disaster process The collapse process consists of four stages: wetting and weight gain of the slope surface soil and rock, water-induced deterioration of the structural surface, damage to the unstable rock base, and triggering of the collapse disaster.

[0051] In this embodiment, a typical work site from K3066+100 to K3066+681 in the study area is used as an example for illustration.

[0052] The geological profile of the work site is as follows: Figure 1 As shown. The slope aspect is approximately 124°, exposing Lower Permian limestone interbedded with carbonaceous shale, with a dip of 289°∠48°. Two main structural planes are developed: tectonic structural plane J1 (dipping 81°∠62°) and outward-dipping structural plane J2 (dipping 112°∠55°). The unstable rock mass is located in the upper middle part of the slope, with its back wall formed by cutting through J2 and its sidewalls formed by cutting through J1. The bottom slip surface is a nearly horizontal sedimentary bedding plane.

[0053] The specific analysis and quantification of the four stages are as follows: Phase 1: Increased Weight of Slope Surface Soil Due to Wetting: The top of the slope is covered by a layer of silty clay containing gravel, approximately 2-6 meters thick. According to the geological survey report, the natural unit weight of this soil layer is 19.6 kN / m³, and the saturated unit weight is 21.0 kN / m³. After rainfall, the soil layer becomes saturated, and the vertical load on the top of the unstable rock increases from 0.117 MPa to 0.126 MPa, an increase of approximately 7.7%. Substituting this increase into the stability model directly increases the sliding force component.

[0054] Phase Two: Water-Induced Deterioration of the Structural Surface: Rainfall infiltration occurs along the J2 structural surface, causing loss of infill material, limestone dissolution, and hydrostatic thrust. According to field monitoring, a single heavy rainfall event (24-hour rainfall > 50 mm) can infiltrate for more than 12 hours. The shear strength of the structural surface deteriorates with dissolution time; the specific quantification method is shown in the formula below.

[0055] Stage 3: Damage to the unstable rock base: The base is a carbonaceous shale layer, which softens and disintegrates upon contact with water. Its uniaxial compressive strength decreases from 12 MPa in its natural state to 3-5 MPa in its saturated state, with a softening coefficient of approximately 0.25-0.42. The weakening of the base's bearing capacity directly affects the anti-slip force of the bottom sliding surface.

[0056] Fourth stage: Collapse disaster triggering: When the cumulative effect of the previous three stages causes the sliding force to exceed the anti-sliding force, the dangerous rock will slide and collapse along the bottom sliding surface.

[0057] After quantifying the impact of each stage, the corrected parameters are substituted into the stability evaluation model. Specifically, this includes: replacing the natural density with the saturated density to correct the gravity W; substituting the dissolution time t into the friction angle degradation function to correct the shear strength; reducing the anti-slip force of the bottom sliding surface according to the base softening coefficient; and calculating the additional sliding force of the hydrostatic thrust of the rear edge crack.

[0058] Establish a stability evaluation model for unstable rocks that considers the time-dependent degradation characteristics of structural surfaces caused by water. This model is designed for limestone strata and includes a functional relationship between the shear strength of structural planes and the degradation over time due to dissolution. The expression for this functional relationship is: τ=σ n tan[JRC·lg(JCS / σ n )+φ(t)], Where τ is the shear strength of the structural surface, σ n φ is the normal stress of the structural surface, JRC is the roughness coefficient of the structural surface, JCS is the compressive strength of the rock wall, φ(t) is the degradation function of the basic friction angle of the structural surface with the dissolution time t, and t is the duration of dissolution.

[0059] φ(t) was obtained through indoor dissolution tests. In this embodiment, seepage dissolution tests of different durations were conducted on the limestone structural surfaces in the study area, and the results are as follows: Figure 2As shown. Experimental data indicate that φ(t) has a linear relationship with t: tanφ(t) = 0.446 - 0.02t, where t is in the unit of h, and R² = 0.96; When t=0, the friction angle tangent is 0.446 (φ=24.4°); when t=12h, the friction angle tangent deteriorates to 0.206 (φ=11.64°), with a deterioration of more than 50%.

[0060] Calculate the stability coefficient of unstable rock Taking a sliding unstable rock as an example, the stress analysis is as follows: Figure 3 As shown, the stability coefficient F s The calculation formula is:

[0061] in: W: Gravity of unstable rock and overlying load per unit width (kN / m) α: Inclination angle of the bottom sliding surface (°) θ: Dip angle of the steeply dipping fracture at the trailing edge (°) γ w : Specific weight of water (10 kN / m³) h: Water filling height of the fissure (m) In this embodiment, typical profile parameters are taken as follows: W=1568 kN / m, α=18°, θ=76°, σ n =414kPa, JRC=13, JCS=120 MPa, γ w =10 kN / m³, h=1.2m. Calculate the stability coefficients under the following conditions: natural state (t=0, no water) and 12 hours after heavy rainfall (t=12h, h=3m). Natural state: F s = 1.27>1.0, the dangerous rock is in a basically stable state.

[0062] 12 hours after heavy rainfall: F s =0.81<1.0, the dangerous rock is in an unstable state.

[0063] Step 4: Based on the comparison between the stability coefficient and the preset threshold, determine the instability risk level of the unstable rock. In this embodiment, the preset threshold is set with reference to conventional stability evaluation standards in the field and the relevant provisions of the "Code for Design of Landslide Prevention Engineering" (TCAGHP032-2018). According to this code, when F s ≥F st (F) st To ensure a stable safety factor, a value of 1.20 to 1.30 is taken based on the level of the prevention and control project, indicating a stable state. When F... sA value less than 1.00 indicates an unstable state. In this embodiment, to simplify the risk level classification, F is taken as... st =1.2 is used as the low-risk threshold, and 1.0 is used as the high-risk threshold, specifically: F s ≥1.2 corresponds to low risk (equivalent to a stable or basically stable state in the procedure), 1.0≤F s <1.2 corresponds to medium risk (equivalent to an understability state in the procedure), F s A value less than 1.0 corresponds to high risk (equivalent to an unstable state in the procedure). The above threshold setting is consistent with the procedure's judgment logic.

[0064] F s ≥1.2: Low risk (stable) 1.0≤F s <1.2: Medium risk (basically stable) F s <1.0: High risk (unstable) The above calculation results show that F in the natural state s =1.27, corresponding to low risk; F 12 hours after heavy rainfall s =0.81, corresponding to high risk. This judgment is consistent with the actual situation on site: the construction site experienced multiple collapses during periods of heavy rainfall following an earthquake in 2022, disrupting traffic.

[0065] After assessing the instability risk level of the unstable rock, rockfall kinematics simulation is also included. First, a three-dimensional terrain model of the slope is constructed. Based on high-precision terrain data obtained from UAV aerial surveys (point cloud density > 10 points / m²), the Deloni triangulation method is used to divide the terrain into a mesh, forming a three-dimensional slope model with 9485 nodes and 17640 surface elements, as shown below. Figure 4 As shown.

[0066] Then, input the coefficient of restitution of the soil and rock mass and the characteristic parameters of the unstable rock. Based on the geological survey report and trial calculations, the coefficient of restitution for each material is determined as shown in Table 2. The coefficient of restitution is adjusted according to the water content: the normal coefficient of restitution Rn and the tangential coefficient of restitution Rt under saturated conditions are both lower than those under natural conditions.

[0067] Table 2 Summary of Slope Surface Soil and Rock Mass Recovery Coefficient in the Study Area

[0068] Note: Rn is the normal restitution coefficient, and Rt is the tangential restitution coefficient.

[0069] The characteristic parameters of the dangerous rocks are shown in Table 3. A total of 5 dangerous rock zones (WYD1~WYD5) were set, all of which are spheres with a diameter of 1.5~2.0 m and a density of 2500 kg / m³.

[0070] Table 3 Summary of characteristic parameters of dangerous rocks

[0071] After performing the rockfall kinematics simulation, the output results are as follows: Figures 5 to 7 As shown: Impact energy: maximum value 13380 kJ, concentrated in the slope-foot road section.

[0072] Jump height: Maximum value 18.52 m, occurring at steep slopes.

[0073] Movement speed: Maximum value 63.38 m / s, reaching its peak in the straight section of the slope (after returning to the trench).

[0074] Trace density: Maximum value 29.10%, concentrated in negative topographic trenches.

[0075] Identify the rockfall accretion effect based on the output results. Figure 7 As can be seen from the trace density distribution map, the fallen rocks are not evenly distributed on the slope, but rather converge significantly into the three main gullies.

[0076] Example 2 This embodiment provides a prevention and control method, including the following steps: The first step is to use the post-earthquake mountain road slope secondary collapse disaster assessment method described in Example 1 to identify high-risk rockfall sections.

[0077] Based on the impact energy distribution and trace density distribution output from the rockfall kinematic simulation in Example 1, and combined with the quantitative evaluation results of the troughing effect, sections K3066+150 to K3066+250 and K3066+400 to K3066+500 in the study area were identified as high-risk rockfall zones. These two sections are located at the exit of the main trough, with a maximum impact energy exceeding 10,000 kJ and a trace density exceeding 20%.

[0078] The second step is to cover the highway with reinforced concrete tunnels in high-risk sections and then cover the tunnels with a buffer soil layer.

[0079] In this embodiment, the clearance height of the open tunnel is determined based on the bounce height at the tunnel entrance section output by simulation and in combination with the highway construction clearance requirements.

[0080] After the implementation of the prevention and control project, the judgment result was consistent with the field observation trend: statistics showed that landslides in the study area mostly occurred after heavy rainfall, which is consistent with the result that the post-rainfall stability coefficient calculated by this invention was below the threshold. The method described in this invention is mainly aimed at highway slopes in rainy mountainous areas with limestone development after earthquakes. For other lithologies, the deterioration function in the formula needs to be adjusted according to the test data of the corresponding lithology.

[0081] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for evaluating secondary landslide hazards on mountain highway slopes after an earthquake, characterized in that, Includes the following steps: Step 1: Obtain data on the disaster-prone factors of the slope; Step 2: Identify the main control interval for the development of dangerous rocks based on the disaster-prone factor data; Step 3: Analyze the evolution stages of the collapse and disaster process, establish a stable rock stability evaluation model that considers the time-dependent deterioration characteristics of the structural surface caused by water, and calculate the stable rock stability coefficient. Step 4: Determine the instability risk level of the unstable rock based on the comparison result between the stability coefficient and the preset threshold.

2. The method for evaluating secondary landslide hazards on mountain highway slopes after an earthquake, as described in claim 1, is characterized in that... The disaster-causing factor data includes at least one of the following: the elevation difference between the dangerous rock and the highway, the slope gradient, the lithology, the horizontal distance from the fault, the slope aspect and the dip angle of the outward-dipping structural surface, and the maximum particle size of the dangerous rock.

3. The method for evaluating secondary landslide hazards on mountain highway slopes after an earthquake, as described in claim 1, is characterized in that... The main control zone specifically includes: the elevation difference zone, slope zone, horizontal distance zone from fault, and angle zone of structural planes where dangerous rocks are concentrated.

4. The method for evaluating secondary landslide hazards on mountain highway slopes after an earthquake, as described in claim 1, is characterized in that... The stability evaluation model for the unstable rock formation is established for limestone strata, and the model includes a functional relationship between the shear strength of the structural plane and the deterioration of the dissolution time.

5. The method for evaluating secondary landslide hazards on mountain highway slopes after an earthquake, as described in claim 4, is characterized in that... The functional relationship expression is as follows: τ=σ n tan[JRC·lg(JCS / σ n )+φ(t)], Where τ is the shear strength of the structural surface, σ n φ is the normal stress of the structural surface, JRC is the roughness coefficient of the structural surface, JCS is the compressive strength of the rock wall, φ(t) is the degradation function of the basic friction angle of the structural surface with the dissolution time t, and t is the duration of dissolution.

6. The method for evaluating secondary landslide hazards on mountain highway slopes after an earthquake, as described in claim 1, is characterized in that... The collapse and disaster process includes the following stages: wetting and weight gain of slope surface soil and rock, water-induced deterioration of structural surfaces, damage to the foundation of unstable rock and triggering of collapse disaster. The impact of each stage on the stability of unstable rock is quantified, and the quantification results are substituted into the unstable rock stability evaluation model to correct the calculation parameters of the unstable rock stability coefficient.

7. The method for evaluating secondary landslide hazards on mountain highway slopes after an earthquake, as described in claim 1, is characterized in that... After determining the instability risk level of the unstable rock, the following steps are also included: Construct a three-dimensional terrain model of the slope, input the soil and rock mass restoration coefficient and the characteristic parameters of the dangerous rock, conduct rockfall kinematics simulation, and output the rockfall impact energy, movement speed, bounce height and trace density distribution. Based on the output results, identify high-risk sections of rockfall movement and evaluate the impact damage level of rockfalls on highways.

8. The method for evaluating secondary landslide hazards on mountain highway slopes after an earthquake, as described in claim 7, is characterized in that... The coefficient of restitution of the rock and soil mass is adjusted according to the water content of the slope surface rock and soil mass. The normal and tangential coefficients of restitution under the water-saturated state are lower than those under the natural state.

9. The method for evaluating secondary landslide hazards on mountain highway slopes after an earthquake, as described in claim 7, is characterized in that... Also includes: Based on the output results, the channeling effect of rockfall movement is identified, and the impact of the channeling effect on the concentration of rockfall impact is quantitatively evaluated.

10. A method for preventing secondary landslides on mountain road slopes after an earthquake, characterized in that, include: The method for assessing secondary landslide disasters on mountain road slopes after earthquakes, as described in any one of claims 1 to 9, is used to identify high-risk sections for rockfalls. In the high-risk section, a reinforced concrete tunnel is used to cover the road, and a buffer soil layer is placed on top of the tunnel. The buffer soil layer has a designed slope to guide falling rocks across the road. The structural strength of the tunnel and the thickness of the buffer soil layer are determined based on the impact energy and bounce height output by the rockfall kinematics simulation.

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