Method for analyzing building vulnerability of slow landslide based on FLAC3D and PS-INSAR

Abstract

The application is a slow landslide building vulnerability analysis method based on FLAC3D and PS-INSAR, including the following steps: S1, collecting the required research data; S2, obtaining the building disaster resistance capability evaluation index system and weight through literature research and expert scoring, and obtaining the building disaster resistance capability index calculation value R; S3, using PS-InSAR technology to calculate the cumulative displacement of the landslide at different locations, and according to the cumulative displacement value and the building disaster resistance capability index value R obtained in S2, the landslide disaster causing strength inversion value I is obtained by inversion p , and then the displacement-I p scatter plot fitting obtains the landslide disaster causing strength empirical curve; S4, using FLAC3D to simulate the cumulative displacement of the landslide under extreme conditions, and using the landslide disaster causing strength empirical curve to obtain the landslide disaster causing strength index calculation value I under extreme conditions; S5, using R and I to obtain the disaster body vulnerability under extreme conditions. The quantitative problems of building disaster resistance capability and landslide disaster causing strength are solved.

Description

Technical Field

[0001] This invention belongs to the field of engineering geology research, specifically involving a method for analyzing the vulnerability of structures in slow landslides based on FLAC3D and PS-INSAR. Background Technology

[0002] The stability of landslides in mountainous areas and the safety of residents living on landslide-prone areas are key concerns for local disaster prevention and mitigation departments. Vulnerability assessment of landslide-prone buildings is a core component of landslide risk management. During the slow movement of a landslide, surface deformation generates ground fissures or localized collapses, leading to uneven settlement or structural tilting of buildings. This has a significant impact on the safety and stability of buildings and seriously threatens the lives and property of residents. Therefore, to determine the most appropriate risk mitigation strategy, it is necessary to analyze the expected consequences of landslide activity. Risk control strategies typically have two objectives: reducing hazard or reducing vulnerability. In the second objective, vulnerability assessment is a crucial step in reducing the consequences associated with landslide disasters, and it is also a challenging aspect of risk analysis. Vulnerability assessment of buildings mainly involves two indicators: the hazard intensity of the landslide and the building's disaster resistance capacity. However, due to the large amount of information required, few methods can establish a completely quantitative relationship between these two indicators for vulnerability assessment.

[0003] Building resilience reflects a building's inherent ability to maintain its integrity, functionality, and anticipated resistance during interactions with landslide events. Existing research, particularly in the initial field investigations, rarely focuses on collecting and quantifying building attributes, resulting in a lack of systematicity and applicability in most building resilience assessments. Furthermore, various indicators are used to evaluate building resilience, each with varying importance; however, current research rarely prioritizes these indicators. For example, in the paper "The Impact of Set Formulas on Indicator-Based Assessment Methods for Building Susceptibility to Hydrometeorological Disasters," Agliata et al. selected five indicators (structural type, maintenance status, building orientation, number of floors, and number of openings) for direct building vulnerability assessment based on operability, applicability, and low variability, without calculating the weights of the building resilience indicators. This results in less important indicators having the same weight as those that might have a greater impact on vulnerability.

[0004] Landslide hazard intensity is one of the core contents of vulnerability assessment, which can be characterized by indicators such as landslide displacement, deformation rate, impact force, and impact velocity. Through summarizing previous relevant studies, it was found that there are two problems in the current research on landslide hazard intensity: (1) Due to the lack of historical data on intensity parameters, most studies use qualitative methods to evaluate the magnitude of hazard intensity, without considering the historical statistical relationship between the disaster-bearing body and the hazard intensity, which makes the vulnerability assessment inaccurate; (2) For large landslides, there is a lack of spatial characterization of hazard intensity under extreme conditions.

[0005] In conclusion, to address the existing problems in quantifying the disaster resistance capacity of landslide structures and the intensity of landslide disasters, this invention provides a vulnerability analysis method for slow-speed landslide structures based on FLAC3D and PS-INSAR. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a method for analyzing the vulnerability of structures in slow landslides based on FLAC3D numerical simulation and PS-INSAR, which solves the current problem of quantifying the disaster resistance capacity of landslide structures and the disaster intensity caused by landslides.

[0007] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: a method for analyzing the vulnerability of structures in slow landslides based on FLAC3D and PS-INSAR, characterized in that the analysis method includes the following steps:

[0008] S1. Collect the necessary research data, including: landslide overview, factors affecting landslides, Sentinel-1 data, basic information on buildings and damage data;

[0009] S2. Through literature review and expert scoring, obtain the disaster resistance capacity evaluation index system and weights of buildings, conduct disaster resistance capacity evaluation for buildings within the landslide area, and obtain the calculated value R of the building disaster resistance capacity index; the disaster resistance capacity evaluation index includes building materials (BST), renovation status (MC), ratio of service life to design life (RSD), foundation depth (FD), foundation type (FT), presence of ground beam (RB), number of floors (FL), and angle between the main sliding direction and the building axis (ANG);

[0010] S3. Using PS-InSAR technology, calculate the cumulative displacement at different locations of the landslide. Based on the cumulative displacement value and the building disaster resistance index value R obtained in S2, the landslide disaster intensity inversion value I is obtained. p Then through displacement -I p The empirical curve of landslide hazard intensity is obtained by fitting a scatter plot;

[0011] S4. Use FLAC3D to simulate the cumulative displacement of landslides under extreme conditions, and substitute the cumulative displacement obtained from the simulation under extreme conditions into the empirical curve of the landslide's disaster intensity in S3 to obtain the calculated value I of the disaster intensity index of the landslide under extreme conditions.

[0012] S5. Using the calculated value R of the building's disaster resistance capacity index obtained in S2 and the calculated value I of the disaster-causing intensity index of the landslide under extreme working conditions obtained in S4, the vulnerability V of the disaster-bearing body under extreme working conditions is obtained, and the vulnerability V of the disaster-bearing body under extreme working conditions is used to quantitatively evaluate the vulnerability status of the building.

[0013] The extreme working conditions described in this invention include heavy rainfall, earthquakes, reservoir water levels, etc., and the environment of the object under study is set according to the actual situation when conducting numerical simulation.

[0014] The vulnerability V of the disaster-bearing body under the extreme working conditions is obtained using formula (1).

[0015]

[0016] When V=1, it indicates that the disaster-bearing body is completely destroyed.

[0017] The specific steps of step S3 are as follows:

[0018] 1) PS-InSAR interpretation was performed on multiple Sentinel-1 data to obtain the multi-year average landslide surface deformation values;

[0019] 2) The maximum displacement of the building is obtained by using the inverse distance weighted (IDW) interpolation method;

[0020] 3) Extract the maximum displacement of each building within its footprint over several years, and use the maximum displacement as the cumulative displacement of the landslide at that location;

[0021] 4) Given the known observation vulnerability V p Under the premise of the building's disaster resistance index value R, the landslide disaster intensity inversion value I is obtained by inversion according to formula (12). p ;

[0022]

[0023] In the formula, R ranges from [0,1]; V p Obtained using methods for quickly assessing the extent of building damage;

[0024] 5) The maximum displacement and landslide-causing intensity inversion value I within the footprint of each building were investigated by fitting a power-law function. p Ultimately, an empirical curve of the landslide's disaster intensity was obtained.

[0025] The specific process of step S4 is as follows:

[0026] S41, FLA3D numerical simulation

[0027] When detailed topographic data, soil and rock material data, and plan and profile data are available, FLAC3D three-dimensional numerical simulation is used to calculate the cumulative displacement of landslides under extreme rainfall conditions. The calculation steps are as follows:

[0028] (1) Determine multiple landslide profiles:

[0029] Draw multiple cross-sections of the landslide in CAD, including at locations where the surface topography changes and at locations of different rock and soil masses within the slope.

[0030] (2) Determination of cross-sectional layering and basic parameters:

[0031] Based on the actual geological conditions of the landslide, different soil and rock layers were divided in the profile, and calculation conditions were set to determine the strength mechanical parameters of the soil and rock mass under each condition.

[0032] (3) Establish a three-dimensional model of the landslide

[0033] The 3dmesh function in CAD is used to generate a mesh, and the coordinates of the mesh nodes are output as a .dat file. After connecting different sections, interpolation is performed to obtain a complete 3D model of the landslide. Then, it is imported into FLAC3D to obtain the FLAC3D model. After setting the parameter values ​​under extreme working conditions, the landslide displacement is calculated to obtain the cumulative displacement at different locations of the landslide.

[0034] S42. Calculate the disaster-causing intensity value I of the landslide.

[0035] Using the cumulative displacement under extreme conditions in the FLAC3D model described above, the obtained cumulative displacement is substituted into the disaster intensity empirical curve in S3, and finally the disaster intensity index I of the landslide under extreme conditions is obtained.

[0036] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0037] (1) This invention combines FLAC numerical simulation and PS-INSAR technology to analyze the vulnerability of landslide structures. Using historical landslide displacement data collected by PS-InSAR, the landslide disaster intensity is inverted, and a quantitative empirical curve of landslide displacement and landslide disaster intensity is established. This fully considers the historical statistical relationship between the disaster-bearing body and the disaster intensity, and effectively quantitatively determines the landslide disaster intensity. At the same time, FLAC3D three-dimensional modeling is used to simulate the cumulative displacement of landslides under extreme conditions, and the landslide disaster intensity under extreme conditions is quantitatively determined through the quantitative empirical curve of landslide disaster intensity. Then, the vulnerability of different structures on the landslide body under extreme conditions is calculated, realizing the spatialized study of the landslide disaster intensity under extreme conditions.

[0038] (2) In the early stages of data collection, this invention focuses on collecting and quantifying the attributes of buildings, making the disaster resistance assessment more systematic and applicable. It also emphasizes the weighting of evaluation indicators, avoiding assigning equal importance to different indicators, thus making the assessment more consistent with reality.

[0039] (3) The analytical method of this invention helps decision-makers to quantitatively determine the impact of landslide intensity on buildings, and provides new ideas and methods for government departments to carry out risk assessment and management. Attached Figure Description

[0040] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0041] Table 1. Matrix for judging building resistance index.

[0042] Table 2. Indicator values ​​and weights.

[0043] Figure 1 Displacement-I p The scatter plot fitting result is shown in the figure. The horizontal axis represents the surface displacement D. tot The vertical axis represents the actual landslide-causing intensity I, and the scatter plot uses the inverted value of landslide-causing intensity I. p express. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. Of course, the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0045] This invention presents a vulnerability analysis method for structures in slow landslides based on FLAC3D and PS-INSAR, comprising the following steps:

[0046] S1. Collect the necessary research data, including: landslide overview, factors affecting landslides, Sentinel-1 data, basic information on buildings and damage data;

[0047] S2. Through literature review and expert scoring, obtain the disaster resistance capacity evaluation index system and weights of buildings, conduct disaster resistance capacity evaluation for buildings within the landslide area, and obtain the calculated value R of the building disaster resistance capacity index; the disaster resistance capacity evaluation index includes building materials (BST), renovation status (MC), ratio of service life to design life (RSD), foundation depth (FD), foundation type (FT), presence of ground beam (RB), number of floors (FL), and angle between the main sliding direction and the building axis (ANG);

[0048] S3. Using PS-InSAR technology, calculate the cumulative displacement at different locations of the landslide. Based on the cumulative displacement value and the building disaster resistance index value R obtained in S2, the landslide disaster intensity inversion value I is obtained. p Then through displacement -I p The empirical curve of landslide hazard intensity is obtained by fitting a scatter plot;

[0049] S4. Use FLAC3D to simulate the cumulative displacement of landslides under extreme conditions, and substitute the cumulative displacement obtained from the simulation under extreme conditions into the empirical curve of the landslide's disaster intensity in S3 to obtain the calculated value I of the disaster intensity index of the landslide under extreme conditions.

[0050] S5. Using the calculated value R of the building's disaster resistance capacity index obtained in S2 and the calculated value I of the disaster-causing intensity index of the landslide under extreme working conditions obtained in S4, the vulnerability V of the disaster-bearing body under extreme working conditions is obtained, and the vulnerability V of the disaster-bearing body under extreme working conditions is used to quantitatively evaluate the vulnerability status of the building.

[0051] In step S1, the landslide overview includes the landslide's location, boundaries, size, etc.; factors affecting the landslide include whether it is affected by rainfall, artificial excavation, earthquakes, etc.; basic building information includes the building's location, length, width, height, use, service life, building materials, foundation, etc.; and damage information includes whether there are cracks and their corresponding dimensions, whether it is tilted, whether it is damaged, and the degree of damage, etc.

[0052] In step S2, the disaster resistance capacity of a building is an important indicator of vulnerability analysis, reflecting the building's ability to withstand a specific landslide intensity. At the site scale, this invention proposes eight evaluation indicators to reflect the building's disaster resistance capacity: building materials (BST), renovation status (MC), ratio of service life to design life (RSD), foundation depth (FD), foundation type (FT), presence of a ground beam (RB), number of floors (FL), and angle between the main sliding direction and the building's axis (ANG). Each evaluation indicator is assigned a value based on the actual site conditions, and the weight of each indicator is set according to expert experience. The current disaster resistance capacity of the building is calculated according to equation (2):

[0053]

[0054] In the formula, R is the building's disaster resistance index value R, and n is the number of disaster resistance evaluation indicators. In this embodiment, n = 8; R i For the i-th indicator, w i The weights of the i-th disaster resilience evaluation index are obtained through the analytic hierarchy process (AHP). Then, based on literature review and expert scoring, an index judgment matrix is ​​established to obtain the final index value.

[0055] In step S3, the cumulative displacement at different locations of the landslide caused by rainfall in the mountainous area is calculated using PS-InSAR technology to obtain the empirical curve of the landslide's disaster intensity, including the following steps:

[0056] S31. Calculate cumulative displacement using PS-InSAR

[0057] InSAR technology can serve as an effective means to obtain spatiotemporal surface deformation data from landslides. In recent years, the rapid development of open-source Sentinel-1 imagery and data processing algorithms has led to the widespread application of InSAR technology. Buildings located on landslide bodies can serve as potential permanent scatterers, and InSAR technology can effectively monitor their deformation rates over many years. This method has proven capable of providing information on large-area ground deformation. It obtains displacement values ​​between different acquisition points by distinguishing the phase associated with ground motion from the phases of the atmosphere, topography, and noise. The calculation process is as follows:

[0058] (1) Form a time series of N+1 SAR images covering the same area. Select one image as the common master image based on the image quality, time and spatial baseline, and register the remaining images with a registration accuracy of not less than 1 / 8 pixel. Perform differential interferometry on each group of images to generate a time series differential interferogram.

[0059] (2) PS points are identified based on the discrete characteristics of the same pixel intensity values ​​in time-series SAR images. The calculation formula is as follows:

[0060]

[0061] In the formula σ ΔA With μ A Let D represent the standard deviation and mean of the interference amplitude at point A, respectively. ΔA This represents the amplitude value at that point. The smaller the amplitude value, the more stable the pixel is throughout the monitoring period. Point A in formula (3) is the PS point.

[0062] (3) The selected PS points are then spatially networked and unwrapped. The difference between the two vertices on each edge of the network is obtained as follows:

[0063]

[0064] In the formula These represent the phase differences for surface deformation, topographic error, atmospheric and noise terms, respectively. The surface deformation phase consists of linear and nonlinear deformation; the topographic error phase difference... Then, by applying the phase and spatial baseline B, the relationship estimate can be obtained, and finally, we can obtain:

[0065]

[0066]

[0067] In the formula is the phase difference caused by non-linear deformation; T is the time baseline; B is the spatial baseline (set value); R1 is the slant range between the antenna and the ground target point; θ is the incident angle; Δh is the elevation error increment between adjacent PS points; is the residual phase, and λ is the wavelength. When it is possible to perform phase unwrapping in space. PS-InSAR is a one-dimensional measurement technique, and the average velocity and displacement time series obtained are in the satellite's line-of-sight (LOS) direction. A positive value represents displacement towards the satellite, and a negative value indicates displacement away from the satellite. To overcome the differences in the deformation rate inversion results caused by the surface geometry and the satellite line-of-sight angle, the line-of-sight velocity v LOS is converted to the slope-direction velocity v SLOPE . Assuming that the displacement is completely parallel to the maximum slope direction, then:

[0068]

[0069] In the formula, C is the proportionality coefficient between the actual three-dimensional displacement of the surface and the measured displacement, and the calculation formula is as follows:

[0070] C = N·(cos(S)·sin(A - 90)) + E·(-1·(cos(S)·cos(A - 90)) + H·(sin(S)) (8)

[0071] In the formula, A is the surface slope direction, S is the slope, both of which can be obtained using DEM data; N, E, and H are the direction cosines in the line-of-sight direction, and are calculated through the following formulas:

[0072] N = cos(90 - θ)·cos(180 - α) (9)

[0073] E = cos(90 - θ)·cos(270 - α) (10)

[0074] H = cos(θ) (11)

[0075] In the formula, θ is the incident angle, and α is the satellite-ground orbital angle (about 15° for ascending orbit and about 165° for descending orbit) plus 90°. When the direction of the maximum slope of the slope is close to perpendicular to the satellite line-of-sight direction, the absolute value of the projection conversion coefficient F will be very small, and the slope-direction velocity V SLOPE tends to infinity. The threshold of F is set to 0.3, that is, when -0.3 < F < 0, F takes -0.3; when 0 < F < 0.3, F is set to 0.3, and V SLOPE cannot be greater than 3.33 times the line-of-sight velocity V LOS . When the target point V <Points with positive values ​​are discarded because they move upwards along the steepest direction. Although positive vertical displacement may occur at the bottom of the landslide, the horizontal displacement vector of the landslide should remain along the main sliding direction.

[0076] S32. Invert the landslide-causing intensity and obtain the empirical curve of the landslide-causing intensity.

[0077] Given the observation vulnerability V p Given the calculated value R of the building's disaster resistance index, the inversion value of the landslide-causing intensity is obtained using the following formula. The inversion of the disaster-causing intensity aims to obtain the functional relationship between the inversion and the maximum displacement of the building. The expression for the disaster-causing intensity is:

[0078]

[0079] In the formula I p This is for landslide disaster intensity inversion; R is the calculated value of the building's disaster resistance capacity index, ranging from [0,1], where the building is the disaster-bearing body; V p The vulnerability was observed using a method developed by Del Soldato et al. (2017) for rapidly assessing the extent of building damage.

[0080] In order to conduct vulnerability analysis on buildings, it is necessary to establish a quantitative relationship between cumulative displacement and disaster intensity.

[0081] (1) Multiple Sentinel-1 data were interpreted by PS-InSAR to obtain the average landslide surface deformation value over several years. In this embodiment, 111 scenes were used, but the number of scenes in the remote sensing images varies depending on the case.

[0082] (2) The maximum displacement of the building is obtained by using the inverse distance weighted (IDW) interpolation method. IDW is a spatial interpolation method based on geographical principles. It uses the principle that elements that are close to each other are more similar than elements that are far apart to predict the value of any unmeasured location by using the nearest measurement value. Therefore, when the IDW method generates the interpolation surface, the discrete values ​​are mainly affected by nearby points and less affected by distant points.

[0083] (3) Extract the maximum displacement of each building within its occupied area over several years, and use the maximum displacement as the cumulative displacement of the landslide at that location;

[0084] (4) Given the known observation vulnerability V p Under the premise of the building's disaster resistance index value R, the landslide disaster intensity inversion value I is obtained by inversion according to formula (12). p ;

[0085]

[0086] In the formula, R ranges from [0,1]; V p Obtained using methods for quickly assessing the extent of building damage;

[0087] (5) The maximum displacement and the landslide-causing intensity inversion value I within the footprint of each building were investigated by fitting a power-law function. p Ultimately, an empirical curve of the landslide's disaster intensity was obtained.

[0088] By comparing the landslide-induced ground displacement values ​​(cumulative displacement) and their impact on buildings (disaster intensity) over several years, a quantitative relationship between cumulative displacement and disaster intensity is established, and the empirical disaster intensity curve is used as an empirical disaster intensity model.

[0089] In step S4, the landslide disaster intensity under extreme conditions is simulated using FLAC3D, including the following steps:

[0090] S41, FLA3D numerical simulation

[0091] When detailed topographic data, soil and rock material data, and plan and profile data are available, FLAC3D three-dimensional numerical simulation can be used to calculate the cumulative displacement of landslides under extreme rainfall conditions. The calculation steps are as follows:

[0092] (1) Determine multiple landslide profiles.

[0093] Draw multiple cross-sections of the landslide in CAD, including at changes in surface topography and at different soil and rock masses within the slope.

[0094] (2) Determination of cross-sectional layering and basic parameters.

[0095] Based on the actual geological conditions of the landslide, different soil and rock layers were divided in the profile, and calculation conditions were set to determine the strength and mechanical parameters of the soil and rock mass under each condition.

[0096] (3) Establish a three-dimensional model of the landslide

[0097] The 3dmesh function in CAD was used to generate a mesh, and the coordinates of the mesh nodes were output as a .dat file. After connecting different sections, interpolation was performed to obtain a complete 3D model of the landslide. Then, it was imported into FLAC3D, and the landslide displacement was calculated after setting the parameter values ​​under extreme conditions (heavy rainfall).

[0098] S42. Calculate the disaster-causing intensity value I of the landslide.

[0099] Using the cumulative displacement caused by heavy rainfall in the FLAC3D model, the obtained cumulative displacement is substituted into the disaster intensity empirical curve in S3, and finally the actual disaster intensity index I of the landslide can be calculated.

[0100] Example 1

[0101] (1) Detailed building information survey

[0102] The Wumingshan landslide is located in Chengguan Town, Jizhou District, Tianjin. The landslide is 100-170m long and 50-90m wide, belonging to the depositional landslide category. It is roughly elongated in shape, with an average thickness of 5.7m and a volume of approximately 5 × 10⁻⁶ cubic meters per second. 4 m 3 The landslide's rear edge elevation is approximately 700-710m, while the front edge elevation is 630-640m, with a relatively wide plateau in the middle. The topsoil layer of the landslide is 5-10m thick, providing the material source for the landslide. The slope material consists of alluvial-diluvial silty clay mixed with broken boulders, with a boulder content of about 15%. The clay particles are highly hydrophilic, easily softened, and have low shear strength, making it prone to forming a sliding surface. In the past decade or so, under the combined effects of natural conditions and human activities, the Wumingshan landslide has caused significant surface deformation, and houses on the landslide site have suffered some damage. The landslide threatened a total of 8 houses, covering a total area of ​​834.2 square meters, affecting 29 people. In 2020, a field investigation of the surface deformation and building damage at the Wumingshan landslide was conducted, revealing that some buildings were severely affected, with obvious cracks appearing in the walls and on the ground in the yard, ranging in length from 2 to 15m, opening width from 5 to 37mm, and measured vertical displacement from 7 to 38mm. All these characteristics indicate that the landslide-affected area is still deforming, and it is likely to pose a serious threat to the safety of buildings and the personal safety of residents in the future.

[0103] (2) Constructing an evaluation index system for the disaster resistance capacity of buildings

[0104] This example constructs a disaster resistance evaluation system for buildings using eight indicators: building materials (BST), renovation status (MC), ratio of service life to design life (RSD), foundation depth (FD), foundation type (FT), presence of ground beam (RB), number of floors (FL), and angle between the main sliding direction and the building axis (ANG). The weight of each indicator is obtained using the analytic hierarchy process (AHP). Based on literature review and expert scoring, an indicator judgment matrix is ​​established (Table 1), and the final calculated indicator values ​​and weights are shown in Table 2. The disaster resistance of buildings on the Wumingshan landslide is then evaluated, yielding a resistance value between 0.214 and 0.675.

[0105] Table 1. Building Resistance Index Judgment Matrix

[0106]

[0107]

[0108] Table 2. Indicator Values ​​and Weights

[0109]

[0110] (3) Based on PS-InSAR remote sensing technology, the cumulative displacement of the landslide is obtained, and the landslide disaster intensity inversion value is obtained by inverting the observed vulnerability value of the building. Based on the maximum displacement of the building on the landslide body and the scatter plot of the landslide disaster intensity, the empirical curve of the landslide disaster intensity is fitted.

[0111] The Wumingshan landslide is a slow landslide. According to field surveys and on-site visits, the landslide displacement mainly occurred in the last ten years. Using Sentinel-1 data from April 2011 to March 2020, PS-InSAR interpretation was performed to obtain the annual average deformation value of the landslide surface during this period. Then, the inverse distance weighted interpolation method was used to generate an interpolation surface to obtain the maximum annual average displacement within the footprint of each building. Multiplying this value by 10 yields the total displacement of each of the eight buildings over these ten years, i.e., the maximum displacement within the footprint of the buildings over several years. For buildings within the landslide area, vulnerability observation values ​​were assessed in the field to obtain the observed vulnerability. Then, using formula (12), the disaster resistance index value of the buildings and the observed vulnerability were used to obtain the landslide disaster intensity inversion value. Using the scatter plot of the landslide disaster intensity inversion value and the maximum displacement of each building, the landslide disaster intensity empirical curve was fitted. Figure 1 The horizontal axis of the curve represents displacement (i.e., surface displacement D). tot Therefore, when the displacement value of the landslide is known, the actual landslide's destructive intensity can be determined using this curve. Finally, the empirical formula fitting the relationship between the destructive intensity and displacement of the Wumingshan landslide is:

[0112] I = -2.32 × 0.97 Dtot +0.29 (14)

[0113] In the formula D tot I represents the displacement of the building (mm) and I represents the actual landslide intensity.

[0114] (4) Use FLAC3D to simulate the cumulative displacement of landslides under extreme conditions, and combine the landslide disaster intensity and the disaster resistance of buildings to evaluate the vulnerability of buildings on landslides under extreme rainfall conditions.

[0115] Over the past decade, heavy rainfall during the annual flood season has caused slow deformation of the Wumingshan landslide. Rainfall data from 2011 to 2020 in this area were collected, and the 100-year return period value of the cumulative rainfall over three consecutive days was found to be 280 mm. FLAC3D was used to simulate the cumulative displacement of the landslide under this rainfall condition. The maximum cumulative displacement of the Wumingshan landslide under the 100-year return period rainfall condition was calculated to be 278.8 mm. The landslide hazard intensity corresponding to the cumulative displacement value of different buildings was found on the disaster intensity empirical curve. Then, the landslide hazard intensity and the disaster resistance capacity of the buildings were combined using formula (14) to obtain the vulnerability of different buildings under this condition, and they were divided into five levels according to their magnitude: extremely low vulnerability (V = 0-0.2), low vulnerability (0.2-0.4), medium vulnerability (0.4-0.6), high vulnerability (0.6-0.8), and extremely high vulnerability (0.8-1). The final results showed that among the eight houses on the landslide, one house was in a highly vulnerable state, two houses were in a moderately vulnerable state, three houses were in a low vulnerable state, and two houses were in a very low vulnerable state.

[0116] Any aspects not covered in this invention are applicable to existing technologies.

Claims

1. A method for vulnerability analysis of structures in slow landslides based on FLAC3D and PS-INSAR, characterized in that, The analytical method includes the following steps: S1. Collect the necessary research data, including: landslide overview, factors affecting landslides, Sentinel-1 data, basic information on buildings and damage data; S2. Through literature review and expert scoring, obtain the disaster resistance capacity evaluation index system and weights of buildings, conduct disaster resistance capacity evaluation for buildings within the landslide area, and obtain the calculated value R of the building disaster resistance capacity index; the disaster resistance capacity evaluation index includes building materials (BST), renovation status (MC), ratio of service life to design life (RSD), foundation depth (FD), foundation type (FT), presence of ground beam (RB), number of floors (FL), and angle between the main sliding direction and the building axis (ANG); S3. Using PS-InSAR technology, calculate the cumulative displacement at different locations of the landslide. Based on the cumulative displacement value and the building disaster resistance index value R obtained in S2, the landslide disaster intensity inversion value I is obtained. p Then through displacement -I p The empirical curve of landslide hazard intensity is obtained by fitting a scatter plot; S4. Use FLAC3D to simulate the cumulative displacement of landslides under extreme conditions, and substitute the cumulative displacement obtained from the simulation under extreme conditions into the empirical curve of the landslide's disaster intensity in S3 to obtain the calculated value I of the disaster intensity index of the landslide under extreme conditions. S5. Using the calculated value R of the building's disaster resistance capacity index obtained in S2 and the calculated value I of the disaster-causing intensity index of the landslide under extreme working conditions obtained in S4, the vulnerability V of the disaster-bearing body under extreme working conditions is obtained, and the vulnerability V of the disaster-bearing body under extreme working conditions is used to quantitatively evaluate the vulnerability status of the building.

2. The method for vulnerability analysis of structures in slow landslides based on FLAC3D and PS-INSAR according to claim 1, characterized in that, The extreme conditions mentioned include heavy rainfall, earthquakes, and reservoir water levels.

3. The method for vulnerability analysis of structures in slow landslides based on FLAC3D and PS-INSAR according to claim 1, characterized in that, The vulnerability V of the disaster-bearing body under the extreme working conditions is obtained using formula (1). When V=1, it indicates that the disaster-bearing body is completely destroyed.

4. The method for vulnerability analysis of structures in slow landslides based on FLAC3D and PS-INSAR according to claim 1, characterized in that, The specific steps of step S3 are as follows: 1) PS-InSAR interpretation was performed on multiple Sentinel-1 data to obtain the multi-year average landslide surface deformation values; 2) The maximum displacement of the building is obtained by using the inverse distance weighted (IDW) interpolation method; 3) Extract the maximum displacement of each building within its footprint over several years, and use the maximum displacement as the cumulative displacement of the landslide at that location; 4) Given the known observation vulnerability V p Under the premise of the building's disaster resistance index value R, the landslide disaster intensity inversion value I is obtained by inversion according to formula (12). p ; In the formula, R ranges from [0,1]; V p Obtained using methods for quickly assessing the extent of building damage; 5) The maximum displacement and landslide-causing intensity inversion value I within the footprint of each building were investigated by fitting a power-law function. p Ultimately, an empirical curve of the landslide's disaster intensity was obtained.

5. The method for vulnerability analysis of structures in slow landslides based on FLAC3D and PS-INSAR according to claim 1, characterized in that, The specific process of step S4 is as follows: S41, FLA3D numerical simulation When detailed topographic data, soil and rock material data, and plan and profile data are available, FLAC3D three-dimensional numerical simulation is used to calculate the cumulative displacement of landslides under extreme rainfall conditions. The calculation steps are as follows: (1) Determine multiple landslide profiles: Draw multiple cross-sections of the landslide in CAD, including at locations where the surface topography changes and at locations of different rock and soil masses within the slope. (2) Determination of cross-sectional layering and basic parameters: Based on the actual geological conditions of the landslide, different soil and rock layers were divided in the profile, and calculation conditions were set to determine the strength mechanical parameters of the soil and rock mass under each condition. (3) Establish a three-dimensional model of the landslide The 3dmesh function in CAD is used to generate a mesh, and the coordinates of the mesh nodes are output as a .dat file. After connecting different sections, interpolation is performed to obtain a complete 3D model of the landslide. Then, it is imported into FLAC3D to obtain the FLAC3D model. After setting the parameter values ​​under extreme working conditions, the landslide displacement is calculated to obtain the cumulative displacement at different locations of the landslide. S42. Calculate the disaster-causing intensity value I of the landslide. Using the cumulative displacement under extreme conditions in the FLAC3D model described above, the obtained cumulative displacement is substituted into the disaster intensity empirical curve in S3, and finally the disaster intensity index I of the landslide under extreme conditions is obtained.

6. The analytical method according to claim 1, characterized in that, The evaluation index system and weights for the disaster resistance capacity of buildings are shown in the table below: 。

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