Water-rich depth foundation pit ground surface settlement prediction method

Abstract

The invention discloses a water-rich depth foundation pit ground surface settlement prediction method, which comprises the steps of obtaining a point cloud data set of a to-be-constructed part of a water-rich area through three-dimensional scanning, and inputting parameters of a plurality of foundation pit construction schemes to generate corresponding construction simulation results; randomly generating sampling points based on the construction simulation result, and acquiring geological parameters of the sampling points; and foundation pit settlement simulation is carried out according to the geological parameters, and finally the evaluation indexes are output. Therefore, in combination with a three-dimensional scanning technology, three-dimensional point cloud data of the to-be-constructed area is obtained, so that before construction, according to a preliminary design scheme of foundation pit excavation input by an engineer, a plurality of random parameter fields are generated through a Monte Carlo method to complement lacked data, and according to actual sampling data and a simulation result, a simulation result is obtained; and generating a reliable comparison structure of foundation pit settlement prediction among the foundation pit schemes so as to assist personnel in selecting a construction scheme which is less prone to serious settlement in the future.

Description

Technical Field

[0001] This invention relates to the field of foundation pit settlement simulation technology, and in particular to a method for predicting surface settlement of deep water-rich foundation pits. Background Technology

[0002] In existing technologies, settlement prediction of foundation pits is mostly achieved by collecting a series of geological parameters from various areas of the excavated foundation pit, such as the slope or bottom area, and then using various mathematical models to establish a coupled model of the corresponding area. Through mathematical calculations on the coupled model, the possible settlement situation can be simulated to predict the settlement direction of the foundation pit, and then targeted support or waterproofing treatment can be carried out.

[0003] However, if targeted support is carried out after the foundation pit construction is basically completed, on the one hand, the support methods that can be used are limited, and on the other hand, since the basic construction framework has been completed, the effect of passive support or water-proofing is limited. In the end, even if possible settlement is predicted, it is impossible to intervene sufficiently. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for predicting and controlling the direction of surface settlement in deep water-rich foundation pits. This method predicts the settlement situation of various construction schemes during construction and then carries out targeted construction to improve the human control effect on settlement.

[0005] The objective of this invention is achieved through the following technical solution: In a first aspect, this application discloses a method for predicting surface settlement of deep foundation pits in water-rich areas, comprising: acquiring a point cloud dataset of the water-rich area to be constructed through 3D scanning, transmitting it to a terminal for visualization processing, and inputting parameters of several foundation pit construction schemes to generate corresponding construction simulation results; randomly generating sampling points based on the construction simulation results and obtaining the geological parameters of the sampling points; performing foundation pit settlement simulation based on the geological parameters, generating multiple random parameter fields using the Monte Carlo method, and calculating the settlement response of each construction scheme in multiple simulations; for each construction scheme, calculating evaluation indicators based on multiple Monte Carlo simulation results, including the expected maximum settlement and the probability of settlement exceeding the standard, and finally outputting the evaluation indicators.

[0006] Its beneficial effects are as follows: By combining 3D scanning technology, 3D point cloud data of the area to be constructed can be obtained. This allows for the selection of corresponding sampling points based on the preliminary design scheme of the foundation pit excavation input by the engineer before construction, thereby reducing the number of sampling points while increasing their relevance. It also eliminates the need for grid-based sampling for geological analysis. Furthermore, by generating multiple random parameter fields using the Monte Carlo method to fill in missing data, a reliable comparative structure for foundation pit settlement prediction between various foundation pit schemes can be generated based on actual sampling data and simulation results. This helps personnel select the construction scheme less likely to experience severe settlement in the future.

[0007] Furthermore, the step of acquiring point cloud datasets of the water-rich area to be constructed through 3D scanning, transmitting them to the terminal for visualization processing, and inputting several parameters of the foundation pit construction scheme to generate corresponding construction simulation results includes: acquiring point cloud data using a 3D laser scanner, including the 3D coordinates of each point; filtering and denoising the point cloud data; rendering the processed point cloud data into a 3D model and visualizing it; receiving construction parameters input by the user, including the top boundary, bottom boundary, bottom elevation, slope gradient, and support structure parameters of the foundation pit, and generating construction simulation results.

[0008] Furthermore, the process of receiving user-inputted construction parameters, including the top boundary, bottom boundary, bottom elevation, slope gradient, and support structure parameters of the foundation pit, and generating construction simulation results includes: generating a dense discrete point set based on the top and bottom boundaries of the foundation pit using linear interpolation; calculating the elevation of each point based on the bottom elevation and slope gradient to form a first point cloud set P; generating a redundant bounding box based on the first point cloud set P, traversing the original point cloud data, determining whether each point is within the redundant bounding box, and updating the elevation value according to its positional relationship with the first point cloud dataset P to generate a modified point cloud dataset; wherein, for points located within the redundant bounding box: for each point Pi(xi, yi, zi) within the redundant bounding box, comparing it with the first point cloud dataset P: determining whether it is located inside the bottom polygon P2 of the foundation pit; and using the ray method or the number of windings method to determine the two-dimensional projection (xi, yi) Whether it is located inside the polygon P2 at the bottom boundary of the foundation pit; if so, then the point is located below the bottom of the foundation pit to be excavated, and it is necessary to further determine the relationship between the elevation zi of the point and the design bottom elevation zb of the foundation pit, including: if zi ≤ zb b Retain it, and set a first threshold q, when z b -If zi > q, remove it from P; if zi > z b Remove it; if point Pi is not inside the bottom polygon P2, calculate its horizontal distance di to the top boundary P1 of the pit; based on the slope k and the bottom elevation z of the pit. bCalculate the design slope elevation at the location (xi, yi) of point Pi, compare the actual elevation zi of the point with the design slope elevation, and retain the point if it is within the allowable tolerance range; otherwise, remove it.

[0009] Furthermore, the step of simulating foundation pit settlement based on the geological parameters, generating multiple random parameter fields using the Monte Carlo method, and calculating the settlement response of each construction scheme in multiple simulations includes: dividing the soil into several soil block units based on the first point cloud set P, and assigning geological parameters to each unit; retrieving the corresponding support structure parameters according to the input support type to establish a support unit, and coupling it with adjacent soil block units; generating a random parameter field that conforms to the statistical characteristics of geological parameters and spatial structure through Monte Carlo simulation, and simulating the excavation unloading process to calculate the effective stress change and settlement response to obtain the evaluation index.

[0010] Furthermore, the process of dividing the soil mass into several soil block units based on the first point cloud set P and assigning geological parameters to each unit also includes: based on the shortest distance d between the center point of each soil block unit and the water-rich area. w The hydraulic influence factor is calculated using the exponential decay function: ω = ω0·exp(-α·d w In the formula, ω0 is the baseline influence coefficient retrieved from the database, α is the attenuation constant used to reflect the weakening effect of water-rich effect with distance; different soil block units are assigned a hydraulic influence factor ω.

[0011] Furthermore, the step of retrieving the corresponding support structure parameters based on the input support type to establish a support unit and coupling it with adjacent soil block units includes: adding a corresponding support unit to the model based on the support type input by the user; the parameters corresponding to the support type can be parameters pre-stored in the terminal database; the support unit is regarded as an elastic body, and its stiffness matrix is ​​calculated based on material parameters, such as the elastic modulus of concrete Es and the moment of inertia of the section I; identifying soil block units adjacent to the support structure unit; for each support structure unit, such as a beam unit, pile unit, or slab unit, traversing all soil block units, calculating the horizontal and vertical distances between the support unit node and the center point of the soil block unit, and setting a coupling distance threshold δ; if the three-dimensional Euclidean distance between the center point of a soil block unit and a node of a support unit is less than or equal to δ, it is considered that there is a coupling effect between the soil block unit and the support unit node, and a connection needs to be established; the step of establishing a coupling spring unit between the support structure node and the adjacent soil block unit includes a normal spring Kn and a tangential spring Kt; the normal spring Kn is estimated based on the formula of the geological parameters of the adjacent soil block unit: Kn = (EN * A) / L where EN is the elastic modulus of adjacent soil block elements, which can be a weighted average; A is the effective area of ​​the coupling effect; for pile elements, A is approximately taken as d * L, where d is the diameter or equivalent width of the pile, and L is the effective length of the element represented by the node; for continuous wall elements, A is taken as t * L, where t is the wall thickness, and L is the effective length of the element; the tangential spring Kt is expressed as: Kt = (G * A) / Ls where G is the shear modulus of adjacent soil block elements, G = EN / (2*(1+ν)), ν is Poisson's ratio; Ls is the shear characteristic length, which can also be retrieved based on pre-stored data; based on the degrees of freedom of the spring element connecting the support structure node and the degrees of freedom of the center node of the adjacent soil block element; the hydraulic influence factor ω of the soil block element near the support element is corrected, including: ω' = ω * β where ω' is the updated hydraulic influence factor, and β is the reduction coefficient β; 0 < β ≤ 1, determined according to the support type.

[0012] Furthermore, the generation of a random parameter field conforming to the statistical characteristics and spatial structure of geological parameters through Monte Carlo simulation includes: using a variogram model to quantify the spatial correlation of geological parameters, including: calculating the experimental variogram based on the sampling point coordinates and parameter values. :

[0013] Where h is the lag distance vector, N(h) is the number of point pairs separated by a distance h, and z(x) is the distance between points. i () is point x i The parameter values ​​at the specified points; a theoretical variogram model is used to fit the experimental variogram:

[0014] Where c0 is the nugget value, representing small-scale variation or measurement error, c is the sill value, and a is the correlation distance; for any parameter E among the measured geological parameters of the sampling point, its set of observations is {E1, E2, ..., En}; the mean μ and standard deviation σ are calculated for each key calculation parameter E; and they follow a specific probability distribution F(z); a large number of geological condition replicas are generated through Monte Carlo simulation, where the number of Monte Carlo simulations is set to N; for each simulation cycle mc, mc = 1, 2, ..., N, a three-dimensional random parameter field is generated using sequential Gaussian simulation; and at the sampling point location x i The generated parameter value must be equal to the measured value z(x) i ); At non-sampling points, the generated values ​​follow the specified distribution F(z) and spatial structure γ(h); For the random parameter field calculation of the mc-th simulation, the settlement response that construction scheme j may cause under the geological conditions is calculated. The first point cloud dataset P and various parameters are loaded, and the random parameter field generated under the current Monte Carlo cycle mc is used to interpolate each soil block unit through spatial interpolation method. Unit center point x c The parameter value is .

[0015] Furthermore, the simulation of the excavation and unloading process, and the calculation of effective stress changes and settlement responses, include: simulating excavation and unloading and calculating the effective stress change Δσ′; calculating the initial release stress caused by excavation, which is applied in the opposite direction as an equivalent nodal force {Fexcav} to the remaining soil element nodes on the pit sidewalls and bottom; and solving the system equilibrium equations. [K]{u}={Fexcav} Where [K] is the overall stiffness matrix of the system, including the stiffness matrix of the soil elements. Stiffness matrix of support structure element The soil-structure coupling spring stiffnesses kn and kt, defined in the preceding steps, are also included; where {u} is the unknown nodal displacement vector; based on the obtained displacement {u}, the strain increment {Δϵ} of each soil element is calculated. For each soil element, the relationship between the strain increment and the displacement increment is as follows:

[0016] Where [B] is the strain-displacement matrix of the element, which depends on the element type and shape function; {ue} is the displacement vector of all nodes of the element, extracted from the global displacement vector {u}; the effective stress change Δσ′ of each element is calculated through the soil constitutive relation; for each soil element, the relationship between its effective stress increment {Δσ′} and strain increment {Δϵ} is as follows:

[0017] Where [D] is the elasticity matrix for an isotropic linear elastic material, expressed as:

[0018] Calculating the principal consolidation settlement Sc includes: determining the calculation unit and extracting the vertical component from Δσ′ as the effective vertical stress increment. Estimate the increment of excess pore water pressure Δu: Δu=B[Δσ3+C(Δσ1−Δσ3)] Where B and C are pore pressure coefficients, and Δσ1 and Δσ3 are the major and minor principal stress increments, respectively. The final principal consolidation settlement Sc is calculated as follows: For each affected upper layer, according to the one-dimensional consolidation theory, the relationship between its final consolidation settlement and the effective stress increment can be expressed as: For normally consolidated soil ; For overconsolidated soils ; Among them, C c It is the compression index, C r It is the rebound index, e0 is the initial void ratio, and H is the soil layer thickness. It is the initial effective stress.

[0019] Furthermore, the evaluation indicators obtained include: Calculate the expected maximum settlement ; For the mc-th Monte Carlo simulation of the j-th construction scheme, the total settlement Stotal(x,mc,j) at any point x on the ground surface is:

[0020] Iterate through all points in set P and find the maximum total settlement value in this simulation:

[0021] Calculate the expected value by averaging the maximum settlement values ​​after corrections from N Monte Carlo simulations, and obtain the expected maximum settlement of scheme j:

[0022] At the same time, its standard deviation and confidence interval are calculated to measure the uncertainty of the indicator; S42. Calculate the probability of settlement exceeding the standard. This indicator measures the risk probability of the scheme failing. Based on the protection level of surrounding buildings, pipeline requirements, etc., set or retrieve a maximum allowable settlement limit value Slim from the database; For each simulation of mc and scheme j, the maximum settlement obtained in that simulation needs to be determined. To determine if the limit Slim has been exceeded, set an indicator function I(mc,j):

[0023] The probability of settlement exceeding the standard for scheme j, Pexceed(j), is the proportion of the number of times the standard is exceeded in N simulations:

[0024] Output the corresponding indicators to assist users in selecting a solution.

[0025] Furthermore, the method also includes: setting a unified set of hydraulic parameter assumptions for use in Monte Carlo simulations of all construction schemes, including: hydraulic gradient i, and setting a unified permeability coefficient reference value k in the geological parameters. ref Uniform aquifer thickness H aquifer A unified initial pore water pressure distribution u0(x,y,z), and a unified initial pore water pressure field under future water-rich conditions; the effective permeability coefficient k of the unit. eff This can be represented as k eff =k ref ⋅ω; The hydraulic gradient i or the change in pore water pressure Δu can also be modulated by ω; consolidation coefficient E oed It is the confined compressive modulus, γ w Here, e is the water unit weight, and e0 is the initial void ratio. In each Monte Carlo simulation cycle, in addition to generating a random field of soil mechanical parameters, uniform assumptions are used for hydraulic parameters. Then, the above corrections are applied to the consolidation settlement calculation to calculate the expected maximum settlement and the probability of settlement exceeding the standard for each scheme j under simulated water-rich conditions. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of the method for predicting surface settlement of water-rich deep foundation pits according to some embodiments of this application; Figure 2 This is a schematic diagram illustrating another representation of the method flow for predicting surface settlement of water-rich deep foundation pits according to some embodiments of this application. Detailed Implementation

[0027] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] In existing technologies, predicting the settlement of excavated or under-excavation foundation pits requires extensive data collection and input of relevant parameters. This method cannot effectively assist personnel during the construction plan design phase; it can only provide predictions after construction is completed or nearly completed.

[0029] For this purpose, please refer to Figures 1-2 This invention provides a method for predicting surface settlement in water-rich deep foundation pits, comprising the following steps: S1. A point cloud dataset of the water-rich area to be constructed is obtained using 3D scanning technology and sent to the terminal. The terminal visualizes the 3D scanning results and then inputs several foundation pit construction schemes to form corresponding construction simulation results. Specifically, S1 includes: S11. Use a 3D laser scanner to scan the water-rich area to be constructed, obtaining a high-precision point cloud dataset. The point cloud data includes the 3D coordinates (x, y, z) of each point. The point cloud data is saved in a standard format (such as LAS, PLY, or XYZ) and transmitted to a computer terminal via a network or storage device.

[0030] S12. After receiving the point cloud dataset, the terminal first performs filtering and noise reduction processing to remove abnormal points and noise. Statistical filtering algorithms can be used.

[0031] S13. Render the filtered point cloud dataset into a 3D model and visualize it.

[0032] The visualization process includes coordinate transformation, which converts the point cloud data from the world coordinate system to the screen coordinate system. Users can interactively view the point cloud model through the terminal, and rotate, scale, and translate it to check the details of the area to be constructed.

[0033] S14. The user inputs construction parameters for several construction schemes into the terminal, and the construction simulation results are calculated based on the construction parameters, specifically including: S141. The user inputs several foundation pit construction schemes. The construction parameters for each scheme include: the top boundary of the foundation pit, specifically a set of polygon vertex coordinates; the bottom boundary of the foundation pit, specifically another set of polygon vertex coordinates; in some examples, the user can select a region on the interface, and the coordinates of the selected area are mapped to a point cloud model formed by the point cloud dataset in the terminal; and the input bottom elevation z... b The slope gradient k (slope ratio 1:k, meaning a vertical drop of 1 unit and a horizontal distance of k units) and the support structure parameters, including the support type, such as pile wall, diaphragm wall, etc.; the support location, i.e., a set of vertex coordinates and support height. The vertex coordinates can also be selected by a box, the specific process of which will not be elaborated.

[0034] S142. For the top boundary and bottom boundary of the foundation pit, a dense discrete point set is generated using a linear interpolation method. The resulting boundary point set includes: the top boundary P1 and the bottom boundary P2 of the foundation pit.

[0035] S143, Based on the bottom elevation z b The slope gradient k is used to calculate the elevation zi of each coordinate point, specifically including: Calculate the elevation zi = z at each point based on the slope gradient k. b + di / k. Where di / k represents the vertical elevation difference caused by the slope gradient.

[0036] Specifically, for a point located on the polygon at the bottom boundary of the foundation pit, di = 0, therefore zi = z b For points on the boundary of the foundation pit, di is the horizontal distance from the boundary of the foundation pit to the bottom boundary, and zi corresponds to the ground elevation. For other points, zi is calculated according to the proportion of the aforementioned formula based on its horizontal distance di, which will not be elaborated here.

[0037] S144. The approximate outline of the foundation pit, determined by the top boundary P1 and the bottom boundary P2, is defined as the first point cloud set P. The specific process is as follows: As mentioned above, the elevation zi = z at each point is calculated based on the slope gradient k. b + di / k; thus forming the foundation pit slope surface, and forming the foundation pit bottom surface based on the foundation pit bottom boundary, generating the corresponding first point cloud set P. Furthermore, in some embodiments, to increase some redundant data, the minimum bounding box corresponding to the first point cloud set P is calculated, and then for each point Pi(xi,yi,zi) in the point cloud dataset, the following steps are performed: Calculate the minimum two-dimensional rectangular bounding box defined by the polygon P1 at the top boundary of the foundation pit.

[0038] Considering the impact of the excavation on the surrounding soil, a horizontal buffer distance L is set based on engineering experience. This parameter can be adjusted as needed. This buffer distance is applied to the minimum bounding box, and its four sides are extended outward by a distance L to form a larger redundant bounding box. This operation aims to ensure that all surface points that may be affected by construction are included.

[0039] Therefore, the original filtered point cloud dataset stored in the terminal can be traversed. First, it is determined whether the two-dimensional planar coordinates (xi, yi) of each point Pi are within the generated redundant bounding box. All points outside the redundant bounding box are marked as unaffected points in the far field, and their elevation values ​​are kept unchanged, and they are directly included in the final simulation result set.

[0040] For points located within the redundant bounding box, further processing is performed: For each point Pi(xi,yi, zi) within the redundant bounding box, it is compared with the first point cloud dataset P: Determine if it is located inside polygon P2 at the bottom of the foundation pit: Use the ray method or the number of windings method to determine whether the two-dimensional projection (xi, yi) of point Pi is located inside the polygon P2 at the bottom boundary of the foundation pit.

[0041] If so, then the point is located below the bottom of the proposed excavation pit. In this case, it is necessary to determine the relationship between the elevation zi of this point and the designed bottom elevation zb of the pit: if zi ≤ zb... b This indicates that the point is a soil point below the bottom of the foundation pit, and it is retained, but its properties have changed to soil at the bottom of the pit. Furthermore, a first threshold q is set, when z... b - If zi > q, remove it from P. If zi > z b This indicates that the point is located within the area to be excavated and should be removed during the simulated excavation.

[0042] Determine if point Pi is located above or below the slope of the foundation pit: If point Pi is not located inside the bottom polygon P2, calculate its horizontal distance di to the top boundary P1 of the foundation pit. This is based on the slope gradient k and the bottom elevation z of the foundation pit. b Calculate the design slope elevation at the location (xi, yi) of point Pi, compare the actual elevation zi of the point with the design slope elevation, and retain the point if it is within the allowable tolerance range; otherwise, remove it.

[0043] Therefore, a modified point cloud dataset is generated to form the construction simulation result. This allows for the traversal of each point in the original point cloud, locating the original point cloud data based on the x and y coordinates of the first point cloud set P, and then updating its corresponding elevation by replacing the elevation value z with z. new The construction simulation results were obtained.

[0044] In some examples, the modified point cloud can be saved in a standard format (such as LAS or PLY) containing the 3D coordinates (x, y, z) of each point. new This serves as the result of the construction simulation. The modified point cloud is rendered on the terminal, and the color mapping can be displayed according to elevation changes or excavation depth gradients to visually represent the terrain after excavation.

[0045] S2. Randomly generate sampling points based on the construction simulation results, and perform actual sampling according to the actual required sampling points to reduce the construction cycle and the amount of data processing in subsequent simulation processes. In other words, for each construction scheme i, sample part of the coordinate positions of the corresponding first point cloud set P to obtain the geological parameters of the sampling points.

[0046] S3. Based on the various geological information obtained from the collected sampling points, the data is transmitted back to the computer terminal for foundation pit settlement simulation, specifically including: S31. The geological parameters mentioned include soil elastic modulus EN, Poisson's ratio ν, compression index C, permeability coefficient, etc. Based on the first point cloud dataset P in the construction simulation results, the soil in the entire foundation pit and its surrounding area is divided into several soil block units.

[0047] Furthermore, based on the horizontal and vertical distances of each point from water-rich areas (such as groundwater bodies and aquifer boundaries), different soil block units are assigned a hydraulic influence factor ω; simultaneously, the constraint effect of the support structure on soil deformation is introduced, and a preliminary mathematical model simulating the foundation pit is established, specifically including: S311. Based on the first point cloud dataset P, the point cloud is aggregated into continuous soil block units according to the spatial location relationship and geological uniformity. Each unit is assigned the average value of the actual geological parameters obtained from any one or more sampling points in the unit as its geological parameters. The units are connected by nodes.

[0048] Among them, based on the shortest distance d between the center point of each soil block unit and the water-rich area w The hydraulic influence factor is calculated using an exponential decay function: ω = ω0·exp(-α·d w ) In the formula, ω0 is the baseline influence coefficient, retrieved from the database, and α is the attenuation constant, used to reflect the weakening effect of water-rich effect with distance. Both ω0 and α can be retrieved from the database.

[0049] S312. Based on the support type input by the user, such as pile wall, diaphragm wall, and its location and height parameters, add corresponding support units to the model. The parameters corresponding to the support type can be pre-stored parameters in the terminal database. The support unit is treated as an elastic body, and its stiffness matrix is ​​calculated based on material parameters, such as the elastic modulus of concrete Es and the moment of inertia of the section I, specifically including: Based on the user-defined location of the support structure, i.e., a set of polygon vertex coordinates and height parameters, the support structure is discretized, depending on the different types of user input, including: For pile banks, each pile is discretized along its depth direction into a series of one-dimensional beam elements or pile elements. The coordinates of each element node are determined by the pile center plane position, pile top elevation, and pile bottom elevation (or calculated based on the support height). For diaphragm walls, the diaphragm wall is discretized along its length and depth directions into a series of two-dimensional plate elements (or considered as a series of closely arranged one-dimensional beam elements). The node coordinates of each element are generated by interpolation of the wall's vertex coordinates and height parameters.

[0050] Furthermore, each support element is assigned material properties: elastic modulus Es, Poisson's ratio ν, cross-sectional area A (for beam / pile elements), moment of inertia I (for beam / pile elements, about their bending axis), and thickness t (for plate elements).

[0051] S313. Couple the support structure with adjacent soil block units, specifically including: Identify soil blocks adjacent to the supporting structure unit. For each supporting structure unit, such as a beam, pile, or slab unit, traverse all soil block units and calculate the horizontal and vertical distances between the nodes of the supporting unit and the center point of the soil block unit. Set a coupling distance threshold δ; for example, it can be taken as 1.5 times the characteristic size of the supporting structure, such as the pile diameter or wall thickness. If the three-dimensional Euclidean distance between the center point of a soil block unit and a node of the supporting unit is less than or equal to δ, it is considered that there is a coupling effect between the soil block unit and the node of the supporting unit, and a connection needs to be established.

[0052] Coupled spring elements are established between the support structure nodes and adjacent soil block elements to simulate their interactions. These interactions typically include normal forces (such as compressive and tensile forces) and tangential forces (friction). Therefore, each coupled node pair usually requires two springs: a normal spring Kn and a tangential spring Kt.

[0053] Specifically, the normal spring Kn is estimated based on the formula of geological parameters of the adjacent soil block unit: Kn = (EN *A) / L.

[0054] Where EN is the elastic modulus of adjacent soil elements, which can be a weighted average; A is the effective area of ​​the coupling effect.

[0055] For a pile element, we can approximate it as A = d * L, where d is the diameter or equivalent width of the pile, and L is the effective length of the element represented by the node. For a continuous wall element, we can approximate it as A = t * L, where t is the wall thickness, and L is the effective length of the element. L is a characteristic length, which is usually taken as the initial distance between the two nodes connected by the coupling spring, and can be retrieved according to the parameters pre-stored in the computer terminal.

[0056] The tangential spring Kt can simulate the frictional shear behavior at the interface between the soil and the support structure; it can be expressed as: Kt = (G * A) / Ls Where G is the shear modulus of adjacent soil block elements, G = EN / (2*(1+ν)), where ν is Poisson's ratio; A is also the effective shear area, calculated in the same way as the normal spring; Ls is the shear characteristic length, which can also be retrieved from pre-stored data; kt is the tangential subgrade coefficient per unit area.

[0057] In more detail, the cohesion and internal friction angle of the soil can also be considered.

[0058] For example, the ultimate shear force Fsmax = c' * A + σn * A * tan(φ'), where c' is the effective cohesion, φ' is the effective internal friction angle, and σn is the normal stress. Some of these parameters are calculated based on the positional relationships of various points, while others are pre-stored on the computer terminal; details will not be elaborated further.

[0059] Based on the degrees of freedom of the spring unit connecting the support structure node and the degrees of freedom of the center node of the adjacent soil block unit.

[0060] Then, the hydraulic influence factor ω needs special consideration in the coupling region. The presence of the support structure may alter the seepage path and pore water pressure distribution of the nearby soil. As a simplification, the hydraulic influence factor ω of the soil block units near the support unit (distance less than the threshold δ) can be corrected, for example, by multiplying it by a reduction factor β (0 < β ≤ 1), to simulate the blocking effect of the support structure on groundwater. The correction formula can be: ω' = ω * β Wherein, ω' is the updated hydraulic influence factor, and β can be determined based on parameters such as the permeability and sealing of the support structure; for example, for impermeable continuous walls, β can take a smaller value such as 0.1~0.3; for pile walls, β can take a larger value such as 0.6~0.8.

[0061] After completing the coupling connection definition of all support unit nodes and adjacent soil block units, the spring stiffness calculation and hydraulic factor correction, the coupling between the support structure and the soil is realized.

[0062] Furthermore, for settlement prediction, traditional settlement calculation methods primarily rely on indoor soil sample tests to calculate compressive settlement. These methods fail to reflect the nonlinear effects of stress or load levels and the in-situ characteristics of natural soil, leading to significant discrepancies between calculations and reality. Moreover, they cannot calculate the true nonlinear settlement process of the foundation. It is evident that because the constitutive model of soil and its parameters are typically based on indoor tests, the significant differences between indoor soil test parameters and in-situ soil parameters make it difficult to accurately calculate foundation settlement using constitutive models established through indoor tests.

[0063] In this application, since it is in the construction planning stage, it is impossible to obtain the parameters of the soil samples under the actual construction conditions, making it very difficult to calculate the accurate settlement situation. Therefore, the core purpose of the subsequent steps is to obtain the settlement risk and settlement area in each construction plan, select the optimal construction plan after comprehensive comparison, and plan the corresponding support structure, thereby obtaining reliable plan comparison results, rather than settlement prediction results.

[0064] In other words, compared to traditional methods that aim to predict an absolute and precise settlement value, the method in this application assesses multi-dimensional settlement risk to recommend the optimal solution; furthermore, the method also includes: S32. Under the same assumptions and uncertainties, conduct a multi-dimensional and relative quantitative assessment and ranking of the settlement risk of different construction schemes j, where j = 1, 2, ..., n, and n is the total number of schemes; the final output can assist decision-makers in identifying the optimal scheme; specifically including: S321. Since geological parameters are not completely randomly distributed in space, but rather correlated (i.e., neighboring points have more similar values), a variogram model is used to quantify this spatial correlation, including: Calculate the experimental variation function based on the sampling point coordinates and parameter values. :

[0065] Where h is the lag distance vector, N(h) is the number of point pairs separated by a distance h, and z(x) is the distance between points. i () is point x i The parameter value at that location.

[0066] The theoretical variation function model is used to fit the experimental variation function:

[0067] Where c0 is the nugget value, representing small-scale variation or measurement error, c is the sill value, and a is the correlation distance. The correlation distance 'a' of the parameters is used to define the range of influence, that is, within this distance, the parameter values ​​of two points are significantly correlated.

[0068] S322. For any parameter E among the measured geological parameters at the sampling points, its set of observed values ​​is {E1, E2, ..., En}; calculate the mean μ and standard deviation σ for each key calculation parameter E. Furthermore, based on the KS test, assume that this parameter follows a specific probability distribution F(z), such as a log-normal distribution, to ensure that its value is always positive; that is... .

[0069] S323. Generate a large number of copies of geological conditions through Monte Carlo simulation, wherein the number of Monte Carlo simulations is set to N.

[0070] For each simulation cycle mc, where mc = 1, 2, ..., N, a three-dimensional random parameter field covering the entire foundation pit area and conforming to the statistical characteristics and spatial structure in S1 and S2 is generated using sequential Gaussian simulation:

[0071] At sampling point location x i The generated parameter value must be equal to the measured value z(x) i At non-sampling points, the generated values ​​follow a specified distribution F(z) and spatial structure γ(h).

[0072] S324. Calculate the settlement response that construction scheme j may induce under the geological conditions in the mc-th simulation using the random parameter field. Load the first point cloud dataset P and its parameters, and assign the random parameter field generated in the current Monte Carlo cycle mc to each soil cell defined in S311 using spatial interpolation. Cell center point x c The parameter value is .

[0073] Understandably, the random parameters generated by Monte Carlo simulation cannot represent the actual parameters after the foundation pit is excavated, but there is no difference between the various schemes; therefore, the simulation results after multiple iterations can be used to compare the schemes and output accurate comparison results. Next, we will continue to explain the method.

[0074] S325. Simulate excavation unloading and calculate effective stress change Δσ′. Calculate the initial released stress caused by excavation. This released load is applied in the opposite direction as the equivalent nodal force {Fexcav} to the remaining soil element nodes on the sidewall and bottom of the pit.

[0075] Solve the system equilibrium equations: [K]{u}={Fexcav} Where [K] is the overall stiffness matrix of the system, including the stiffness matrix of the soil element and the stiffness matrix of the support structure element. And the soil-structure coupling spring stiffnesses kn and kt defined in the preceding steps. Here, {u} is the unknown nodal displacement vector.

[0076] Based on the obtained displacement {u}, the strain increment {Δϵ} of each soil element is calculated. For each soil element, the strain increment can be obtained through the displacement field and the strain-displacement relationship. Taking linear elastic small deformation in three dimensions as an example, the relationship between the strain increment and the displacement increment is as follows:

[0077] Where [B] is the strain-displacement matrix of the element, which depends on the element type and shape function, and {ue} is the displacement vector of all nodes of the element, extracted from the global displacement vector {u}. Furthermore, the effective stress change Δσ′ of each element is calculated using the soil constitutive relation. In this application, to balance computational efficiency and engineering practicality, a nonlinear elastic model, such as the Duncan-Chang model, or a simple elastoplastic model, such as the Mohr-Coulomb model, can be used to simulate the stress-strain behavior of the soil; details are not elaborated here.

[0078] Here, we take the generalized Hooke's law in incremental form, i.e., isotropic linear elasticity, as an example for explanation. However, its elastic parameters E and ν can be considered as variables that vary with stress level, such as using secant modulus or tangent modulus to approximate the nonlinearity of soil.

[0079] For each soil element, the relationship between its effective stress increment {Δσ′} and strain increment {Δϵ} is as follows:

[0080] Where [D] is the elasticity matrix (or more generally, the constitutive matrix). For isotropic linear elastic materials, for example, the expression for [D] is:

[0081] S326. The elastic or elastoplastic deformation caused by unloading is directly reflected in the solved displacement field {u}. Therefore, the instantaneous settlement St at a certain point on the ground surface is its vertical displacement u. z Then, the principal consolidation settlement Sc is calculated, including: The calculation unit is determined primarily for cohesive soil units with low permeability affected by excavation. For example, for sandy soil, settlement is assumed to be instantaneous. The vertical effective stress increment is calculated. This is the extraction of the vertical component from Δσ′. Estimate the increment of excess pore water pressure Δu: Δu=B[Δσ3+C(Δσ1−Δσ3)] Where B and C are pore pressure coefficients, and Δσ1 and Δσ3 are the major and minor principal stress increments. Under excavation unloading conditions, Δσ1 and Δσ3 are usually negative (reduced load). The final principal consolidation settlement S is calculated as follows: For each affected upper layer, according to the one-dimensional consolidation theory, the relationship between its final consolidation settlement and the effective stress increment can be expressed as: For normally consolidated soil ; For overconsolidated soils ; Among them, C c It is the compression index, C r It is the rebound index, e0 is the initial void ratio, and H is the soil layer thickness. It is the initial effective stress.

[0082] S4. For each construction scheme j (j=1,2,...,n), based on the results of N Monte Carlo simulations, calculate the evaluation indicators, including: expected maximum settlement and probability of settlement exceeding the standard. This includes the following steps: S41. Calculate the expected maximum settlement. .

[0083] For the mc-th Monte Carlo simulation of the j-th construction scheme; mc=1,2,...,N, after solving the system equilibrium equations to obtain the displacement field {u} of the entire site, the displacement of each node has been obtained, including the vertical displacement uz. The instantaneous settlement St is this vertical displacement uz. Furthermore, the principal consolidation settlement Sc has been calculated.

[0084] Therefore, the total settlement Stotal(x,mc,j) at any point x on the Earth's surface is:

[0085] Iterate through all points in set P and find the maximum total settlement value in this simulation:

[0086] This maximum value This represents the maximum total surface subsidence that may occur under the mc-th simulation and the j-th scheme.

[0087] Then, the expected value is calculated by averaging the maximum settlement values ​​after N Monte Carlo simulations to obtain the expected maximum settlement of scheme j:

[0088] At the same time, its standard deviation and confidence interval are calculated to measure the uncertainty of this indicator:

[0089] Calculate the confidence interval for the expected maximum settlement, for example, at a 95% confidence level, assuming... If it approximately follows a normal distribution, then the confidence interval is:

[0090] Where, z α / 2 It is the two-tailed quantile of the standard normal distribution; for example, for a 95% confidence level, z 0.025 ≈1.96.

[0091] S42. Calculate the probability of settlement exceeding the standard. This indicator measures the risk probability of the scheme failing.

[0092] Based on the protection level of surrounding buildings, pipeline requirements, etc., set or retrieve a maximum allowable settlement limit value Slim from the database.

[0093] For each simulation of mc and scheme j, the maximum settlement obtained in that simulation needs to be determined. Has the limit Slim been exceeded? Set an indicator function I(mc,j):

[0094] This indicator function takes a value of 1 when the limit is exceeded, and 0 otherwise.

[0095] Then, the probability of settlement exceeding the standard for scheme j, Pexceed(j), is the proportion of the number of times the standard is exceeded in N simulations:

[0096] This probability value directly reflects the degree of excessive settlement risk faced by scheme j. The closer Pexceed(j) is to 1, the higher the risk of the scheme; the closer it is to 0, the safer the scheme.

[0097] S43. Output the corresponding indicators to assist users in selecting a solution.

[0098] Furthermore, the method in this application embodiment also has an advantage, namely, in some construction cases, a foundation pit may be excavated next to an artificial lake. When the foundation pit is excavated, the artificial lake may not yet be filled with water. After the artificial lake is filled with water, the foundation pit will be in a water-rich environment. In particular, for artificial lakes that draw water into the lake, which are directly connected to rivers, the large volume of water may lead to serious subsidence.

[0099] Conventional methods in existing technologies require testing the excavated foundation pit to obtain necessary parameters, such as installing sensors within the pit to detect seepage. This makes it impossible to predict the water-rich environment that has not yet formed. However, in this embodiment, since accurate settlement prediction is not required, various construction schemes can be compared in the water-rich environment that has not yet formed, such as... Figure 2 As shown, the following steps can be performed: By introducing unified seepage simulation values ​​and combining them with the aforementioned hydraulic influence factor ω and support barrier coefficient β, water-rich conditions are simulated. That is, although accurate settlement prediction results cannot be obtained, scheme comparisons can be achieved by inputting simulation values; specifically including: To simulate future water-rich environments, a unified set of hydraulic parameter assumptions needs to be established for use in Monte Carlo simulations of all construction schemes, including: The hydraulic gradient i is estimated as a typical or possible maximum value based on the relative water level difference and distance between the future water source (such as an artificial lake or river) and the foundation pit.

[0100] Set a uniform reference value k for the permeability coefficient in the geological parameters. ref Based on regional geological data and experience, a uniform representative value (or distribution) of the saturated permeability coefficient is assigned to the main soil layers. A uniform aquifer thickness H is also assigned. aquifer : Define the aquifer thickness that may be affected in the future. Define a uniform initial pore water pressure distribution u0(x,y,z) and define a uniform initial pore water pressure field under future water-rich conditions (e.g., based on hydrostatic pressure assumptions).

[0101] Effective permeability coefficient k of the unit eff This can be represented as k eff =k ref ⋅ω. Thus, for units located close to a water source and without any supporting barriers, ω is large, and k... eff Closer to k ref For units that are far apart or have support barriers, ω is small, k eff Decrease.

[0102] Similarly, the hydraulic gradient *i* or the change in pore water pressure *Δu* can also be modulated by *ω* to reflect spatial differences. The recalculated consolidation coefficient is expressed as *c*. v : E oed It is the confined compressive modulus, γ w It is the specific gravity of water. Use k. eff =k ref After ⋅ω, the consolidation coefficient also reflects spatial variation and the influence of support; thus, the primary consolidation settlement can be obtained.

[0103] In each Monte Carlo simulation cycle, in addition to generating a random field of soil mechanical parameters, uniform assumptions are used for hydraulic parameters. These corrections are then applied to the consolidation settlement calculation; based on the corrected settlement results, steps S41 and S42 are re-executed to calculate the expected maximum settlement and the probability of exceeding the settlement limit for each scheme j under simulated water-rich conditions.

[0104] The above description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the concept described herein through the above teachings or related technologies or knowledge. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. A method for predicting surface settlement of deep foundation pits with abundant water, characterized in that, include: The point cloud dataset of the water-rich area to be constructed is obtained by 3D scanning, transmitted to the terminal for visualization processing, and several parameters of the foundation pit construction scheme are input to generate the corresponding construction simulation results. Sampling points are randomly generated based on the construction simulation results to obtain the geological parameters of the sampling points; Based on the geological parameters, the settlement of the foundation pit was simulated. Multiple random parameter fields were generated using the Monte Carlo method, and the settlement response of each construction scheme was calculated in multiple simulations. For each construction scheme, evaluation indicators are calculated based on the results of multiple Monte Carlo simulations, including the expected maximum settlement and the probability of settlement exceeding the standard, and finally the evaluation indicators are output.

2. The method for predicting surface settlement of water-rich deep foundation pits according to claim 1, characterized in that, The process of acquiring point cloud datasets of the water-rich area to be constructed through 3D scanning, transmitting them to the terminal for visualization processing, and inputting parameters of several foundation pit construction schemes to generate corresponding construction simulation results includes: Point cloud data, including the three-dimensional coordinates of each point, is acquired using a 3D laser scanner. Filter and denoise the point cloud data; The processed point cloud data is rendered into a 3D model and then visualized. It receives construction parameters input by the user, including the top boundary, bottom boundary, bottom elevation, slope gradient, and support structure parameters of the foundation pit, and generates construction simulation results.

3. The method for predicting surface settlement of water-rich deep foundation pits according to claim 2, characterized in that: The construction parameters input by the user are received, including the top boundary, bottom boundary, bottom elevation, slope gradient, and support structure parameters of the foundation pit. The generated construction simulation results include: Based on the top and bottom boundaries of the foundation pit, a dense discrete point set is generated through linear interpolation; Calculate the elevation of each point based on the bottom elevation and the slope gradient to form the first point cloud set P; A redundant bounding box is generated based on the first point cloud set P. The original point cloud data is traversed, and it is determined whether each point is within the redundant bounding box. The elevation value is updated according to its positional relationship with the first point cloud set P, generating a modified point cloud set. Among them, for points located within the redundant bounding box, the following steps are performed: For each point Pi(xi, yi, zi) within the redundant bounding box, compare it with the first point cloud dataset P: Determine if it is located inside polygon P2 at the bottom of the foundation pit: Use the ray method or the number of windings method to determine whether the two-dimensional projection (xi, yi) of point Pi is located inside the polygon P2 at the bottom boundary of the foundation pit; If so, then the point is located below the bottom of the proposed excavation pit. Further analysis is needed to determine the relationship between the elevation zi of this point and the designed bottom elevation zb of the pit, including: if zi ≤ zb b Retain it, and set a first threshold q, when z b -If zi > q, remove it from P; if zi > z b Then remove; If point Pi is not located inside the bottom polygon P2, calculate its horizontal distance di to the top boundary P1 of the foundation pit; based on the slope k and the bottom elevation z of the foundation pit... b Calculate the design slope elevation at the location (xi, yi) of point Pi, compare the actual elevation zi of the point with the design slope elevation, and retain the point if it is within the allowable tolerance range; otherwise, remove it.

4. The method for predicting surface settlement of water-rich deep foundation pits according to claim 1, characterized in that: The process of simulating foundation pit settlement based on the geological parameters involves generating multiple random parameter fields using the Monte Carlo method and calculating the settlement response of each construction scheme in multiple simulations, including: Based on the first point cloud set P, the soil body is divided into several soil block units, and geological parameters are assigned to each unit; Based on the input support type, retrieve the corresponding support structure parameters to establish a support unit, and couple it with the adjacent soil block unit. The evaluation index is obtained by generating a random parameter field that conforms to the statistical characteristics of geological parameters and spatial structure through Monte Carlo simulation, and by simulating the excavation and unloading process, calculating the effective stress change and settlement response.

5. The method for predicting surface settlement of water-rich deep foundation pits according to claim 4, characterized in that, The process of dividing the soil mass into several soil block units based on the first point cloud set P and assigning geological parameters to each unit also includes: Based on the shortest distance d between the center point of each soil unit and the water-rich area w The hydraulic influence factor is calculated using an exponential decay function: ω = ω0·exp(-α·d w ) In the formula, ω0 is the baseline influence coefficient, retrieved from the database, and α is the attenuation constant, used to reflect the weakening effect of water-rich effect with distance; Different soil block units are assigned a hydraulic influence factor ω.

6. The method for predicting surface settlement of water-rich deep foundation pits according to claim 5, characterized in that: The step of retrieving the corresponding support structure parameters based on the input support type to establish a support unit, and coupling it with adjacent soil block units, includes: Based on the support type input by the user, corresponding support units are added to the model. The parameters corresponding to the support type can be the parameters pre-stored in the terminal database. The support unit is regarded as an elastic body, and its stiffness matrix is ​​calculated based on material parameters, such as the elastic modulus Es of concrete and the moment of inertia I of the section. Identify soil blocks adjacent to the support structure unit. For each support structure unit, such as a beam unit, pile unit, or slab unit, traverse all soil block units and calculate the horizontal and vertical distances between the support unit nodes and the center point of the soil block unit. Set a coupling distance threshold δ. If the three-dimensional Euclidean distance between the center point of a soil block unit and a node of a support unit is less than or equal to δ, it is considered that there is a coupling effect between the soil block unit and the node of the support unit, and a connection needs to be established. The coupling spring unit established between the support structure node and the adjacent soil block unit includes a normal spring Kn and a tangential spring Kt; The normal spring Kn is estimated based on a formula for the geological parameters of adjacent soil units: Kn = (EN * A) / L Where EN is the elastic modulus of adjacent soil elements, which can be a weighted average; A is the effective area of ​​the coupling effect. For a pile element, we approximate A = d * L, where d is the diameter or equivalent width of the pile, and L is the effective length of the element represented by the node; for a continuous wall element, we approximate A = t * L, where t is the wall thickness, and L is the effective length of the element. The tangential spring Kt is represented as: Kt = (G * A) / Ls Where G is the shear modulus of adjacent soil elements, G = EN / (2*(1+ν)), where ν is Poisson's ratio; Ls is the shear characteristic length, which can also be retrieved from pre-stored data; The degrees of freedom of the support structure nodes are based on the spring element connection and the degrees of freedom of the center nodes of the adjacent soil block elements. The hydraulic influence factor ω of the soil block units near the support unit is corrected, including: ω' = ω * β Where ω' is the updated hydraulic influence factor, and β is the reduction coefficient β; 0 < β ≤ 1, determined according to the support type.

7. The method for predicting surface settlement of water-rich deep foundation pits according to claim 5, characterized in that, The generation of a random parameter field that conforms to the statistical characteristics and spatial structure of geological parameters through Monte Carlo simulation includes: The use of variogram models to quantify the spatial correlation of geological parameters includes: Calculate the experimental variation function based on the sampling point coordinates and parameter values. : Where h is the lag distance vector, N(h) is the number of point pairs separated by a distance h, and z(x) is the distance between points. i () is point x i The parameter value at that location; The theoretical variation function model is used to fit the experimental variation function: Where c0 is the nugget value, representing small-scale variation or measurement error, c is the sill value, and a is the correlation distance; For any parameter E among the measured geological parameters at the sampling point, its set of observed values ​​is {E1,E2,...,En}; calculate the mean μ and standard deviation σ for each key calculation parameter E; and follow a specific probability distribution F(z); A large number of geological simulations are generated through Monte Carlo simulation, wherein the number of Monte Carlo simulations is set to N; for each simulation cycle mc, mc=1,2,...,N, a three-dimensional random parameter field is generated using sequential Gaussian simulation. Furthermore, at sampling point location x i The generated parameter value must be equal to the measured value z(x) i At non-sampling points, the generated values ​​follow a specified distribution F(z) and spatial structure γ(h). For the random parameter field calculation of the mc-th simulation, the settlement response that construction scheme j may cause under the geological conditions is calculated. The first point cloud dataset P and various parameters are loaded. The random parameter field generated under the current Monte Carlo cycle mc is used to calculate each soil block unit through spatial interpolation. Unit center point x c The parameter value is .

8. The method for predicting surface settlement of water-rich deep foundation pits according to claim 7 or 5, characterized in that, The simulation of the excavation and unloading process, and the calculation of effective stress changes and settlement response include: Simulate excavation unloading and calculate effective stress change Δσ′. Calculate the initial release stress caused by excavation. This release load is applied in the opposite direction as an equivalent nodal force {Fexcav} to the remaining soil element nodes on the pit sidewalls and bottom. Solve the system equilibrium equations: [K]{u}={Fexcav} Where [K] is the overall stiffness matrix of the system, including the stiffness matrix of the soil elements. Stiffness matrix of support structure element And the soil-structure coupling spring stiffness kn and kt defined in the preceding steps; where {u} is the unknown nodal displacement vector; Based on the obtained displacement {u}, calculate the strain increment {Δϵ} for each soil element. The relationship between the strain increment and the displacement increment for each soil element is as follows: Where [B] is the strain-displacement matrix of the element, which depends on the element type and shape function, and {ue} is the displacement vector of all nodes of the element, extracted from the global displacement vector {u}. The effective stress variation Δσ′ of each element is calculated using the soil constitutive relation. For each soil element, the relationship between its effective stress increment {Δσ′} and strain increment {Δϵ} is as follows: Where, [D] is the elasticity matrix for an isotropic linear elastic material, expressed as: ; Calculate the principal consolidation settlement Sc, including: Determine the calculation unit and extract the vertical component from Δσ′ as the vertical effective stress increment. ; Estimate the increment of excess pore water pressure Δu: Δu=B[Δσ3+C(Δσ1−Δσ3)] Where B and C are pore pressure coefficients, and Δσ1 and Δσ3 are the major and minor principal stress increments, respectively, the final principal consolidation settlement Sc is calculated as follows: For each affected upper layer, according to the one-dimensional consolidation theory, the relationship between its final consolidation settlement and the effective stress increment can be expressed as: For normally consolidated soil ; For overconsolidated soils ; Among them, C c It is the compression index, C r It is the rebound index, e0 is the initial void ratio, and H is the soil layer thickness. It is the initial effective stress.

9. The method for predicting surface settlement of water-rich deep foundation pits according to claim 8, characterized in that: The evaluation indicators obtained include: Calculate the expected maximum settlement ; For the mc-th Monte Carlo simulation of the j-th construction scheme, the total settlement Stotal(x,mc,j) at any point x on the ground surface is: ; Traverse all points in set P and find the maximum total settlement value in this simulation: Calculate the expected value by averaging the maximum settlement values ​​after corrections from N Monte Carlo simulations to obtain the expected maximum settlement of scheme j: Simultaneously, its standard deviation and confidence interval are calculated to measure the uncertainty of the indicator. Calculate the probability of settlement exceeding the standard; this indicator measures the risk of the plan failing. Based on the protection level of surrounding buildings, pipeline requirements, etc., set or retrieve a maximum allowable settlement limit value Slim from the database; For each simulation of mc and scheme j, the maximum settlement obtained in that simulation needs to be determined. To determine if the limit Slim has been exceeded, set an indicator function I(mc,j): The probability of settlement exceeding the standard in scheme j, Pexceed(j), is the proportion of the number of times the standard is exceeded in N simulations. Output the corresponding indicators to assist users in selecting a solution.

10. The method for predicting surface settlement of water-rich deep foundation pits according to claim 9, characterized in that, The method also includes: A set of uniform hydraulic parameter assumptions is established for use in Monte Carlo simulations of all construction schemes, including: The hydraulic gradient i is set with a uniform permeability coefficient reference value k in the geological parameters. ref Uniform aquifer thickness H aquifer A unified initial pore water pressure distribution u0(x,y,z), and a unified initial pore water pressure field under future water-rich conditions; Effective permeability coefficient k of the unit eff This can be represented as k eff =k ref ⋅ω; The hydraulic gradient i or the change in pore water pressure Δu can also be modulated by ω; consolidation coefficient E oed It is the confined compressive modulus, γ w e0 is the water specific weight, and e0 is the initial void ratio; In each Monte Carlo simulation cycle, in addition to generating a random field of soil mechanical parameters, uniform assumptions are used for hydraulic parameters. The above corrections are then applied to the consolidation settlement calculation to calculate the expected maximum settlement and the probability of settlement exceeding the standard for each scheme j under simulated water-rich conditions.

Images