Foundation pit support structure design method, system and cloud platform

Abstract

The application provides a foundation pit supporting structure design method, system and cloud platform, and belongs to the technical field of intelligent geotechnical control. The method comprises the following steps: constructing a coupling model, outputting multi-field interaction results and intelligently partitioning, generating a customized reinforcement scheme based on a risk working condition. The transition layer is designed by quantifying the transmission relationship between the partitions, and a fusion supporting system is formed. The fusion supporting system combines the failure differences to construct a life cycle model and an early warning model, realizes dynamic monitoring throughout the life cycle, improves the monitoring efficiency, combines environmental monitoring data with supporting structure design, fully utilizes environmental factors, helps to promote the transformation of geotechnical engineering to intelligence and green, realizes functional safety and ecological protection, and prolongs the service life of the foundation pit supporting structure.

Description

Technical Field

[0001] This invention relates to the field of intelligent geotechnical control technology, and in particular to a design method, system and cloud platform for foundation pit support structures. Background Technology

[0002] Foundation pit support is a crucial aspect of geotechnical engineering. Its design requires comprehensive consideration of geological conditions, groundwater, surrounding loads, and other factors, thus it is linked to environmental monitoring. However, traditional foundation pit support schemes often neglect the beneficial or harmful effects of environmental factors on the site of the foundation pit to be constructed, resulting in insufficient utilization of environmental factors, inadequate development of the potential of biological support, and inadequate environmental monitoring. Furthermore, the accelerated biochemical corrosion leads to a reduction in the service life of the foundation pit support structure.

[0003] Therefore, the present invention provides a design method, system and cloud platform for foundation pit support structure. Summary of the Invention

[0004] This invention provides a design method, system, and cloud platform for foundation pit support structures. By constructing a coupled model, it outputs multi-field interaction results and intelligently partitions the data, generating customized reinforcement schemes based on risk conditions. It quantifies the inter-regional transmission relationships to design transition layers, forming a fusion support system. This fusion support system, combined with failure differences, constructs a lifecycle model and an early warning model, achieving dynamic monitoring throughout the entire lifecycle, improving monitoring efficiency. By integrating environmental monitoring data with support structure design, it fully utilizes environmental factors, contributing to the transformation of geotechnical engineering towards intelligence and green practices, achieving functional safety and ecological protection, and simultaneously extending the service life of foundation pit support structures.

[0005] This invention provides a method for designing a foundation pit support structure, comprising:

[0006] S1: Obtain relevant data on the foundation pit to be constructed, construct a first model, a second model, and a third model based on the relevant data, and couple the first model, the second model, and the third model to obtain a coupled model;

[0007] S2: Output the multi-field coupling results of the foundation pit structure according to the coupling model, divide the foundation pit structure into partitions based on the multi-field coupling results, sample parameters according to the partitions, calculate the high-risk working conditions of the partitions according to the partition parameter sampling results, and generate the partition reinforcement scheme of the foundation pit structure according to the high-risk working conditions of the partitions.

[0008] S3: Based on the multi-field coupling results, quantify the transmission relationship between each partition, determine the transition layer according to the transmission relationship, and fuse all partition reinforcement schemes with the coupling model according to the transition layer to obtain the fused support structure;

[0009] S4: Based on the fusion support structure, predict the failure differences of the foundation pit, develop a life cycle model based on the failure differences, construct an early warning model by combining the fusion support structure and the life cycle model, and obtain a full life cycle early warning system by integrating the life cycle model and the early warning model.

[0010] This invention provides a design method for foundation pit support structure, S1 including:

[0011] S11: Perform LiDAR scanning and borehole sampling on the foundation pit to be constructed, and then obtain relevant stratigraphic data and groundwater layer data to generate the first model;

[0012] S12: Determine the relevant data of the above-ground buildings based on the location information of the foundation pit to be constructed, and generate the second model;

[0013] S13: Perform a CT scan on the foundation pit to be constructed, obtain root system-related data, and generate a third model;

[0014] S14: Establish a three-way index table for the first model, the second model, and the third model. Synchronize the first model, the second model, and the third model in time using a time scaling factor to obtain a time synchronization result. Based on the time synchronization result and the three-way index table, couple the first model, the second model, and the third model to obtain a coupled model.

[0015] This invention provides a design method for foundation pit support structure, S11 including:

[0016] S111: Based on the stratigraphic data, a three-dimensional geological model is generated by Kriging interpolation. Each voxel in the three-dimensional geological model is directly mapped to an FEM mesh node. The interpolated stratigraphic data is assigned as the unit material property, thereby forming an eco-elastoplastic constitutive equation.

[0017] S112: A hierarchical contact system is established based on the stratigraphic interfaces in the three-dimensional geological model. Constraints are set based on the hierarchical contact system and groundwater layer data. The ecological-elastoplastic constitutive equation is dynamically adjusted based on the constraints to obtain the first model.

[0018] This invention provides a design method for foundation pit support structure, S2 including:

[0019] S21: Based on the multi-field coupling results of the foundation pit structure output by the coupling model, extract multiple key field variables from the multi-field coupling results, calculate the distribution of the key field variables, and establish a field variable collaborative failure matrix by combining the key field variables and their corresponding distribution.

[0020] S22: Based on the eigenvalues ​​of the field variable co-failure matrix, formulate partitioning indicators, partition the foundation pit structure according to the partitioning indicators, sample parameters for each partition, calculate the multi-field coupling risk index for each sampling point, and extract the risk gradient and field variable co-factors from the multi-field coupling risk index.

[0021] S23: Based on the multi-field coupling risk index, risk gradient and field variable synergy coefficient, the corresponding partition is judged from multiple aspects, and the high-risk working conditions of the partition are determined based on the results of the multi-faceted judgment. Based on the high-risk working conditions of the partition, the partition reinforcement scheme of the corresponding partition is determined.

[0022] This invention provides a design method for foundation pit support structure, S3 including:

[0023] S31: Based on the multi-field coupling results, quantify the transmission relationship between each partition, determine adjacent partitions, extract the boundary field variables of the interface between adjacent partitions from the multi-field coupling results, and generate the transmission intensity matrix based on the boundary field variables;

[0024] S32: Calculate the energy transfer efficiency and dominant transfer direction at the interface of adjacent partitions based on the transfer intensity matrix, determine the transition layer based on the energy transfer efficiency and dominant transfer direction, and map the transfer intensity matrix into support parameters based on the transition layer;

[0025] S33: Determine the connection between the partition reinforcement schemes based on the support parameters of the transition layer, and fuse all partition reinforcement schemes with the coupling model according to the connection, to obtain the fused support structure.

[0026] This invention provides a design method for foundation pit support structure, S4 including:

[0027] S41: Obtain the vegetation root system-time intensity curve of the foundation pit to be constructed, determine the root-concrete friction coefficient based on the vegetation root system-time intensity curve, and determine the ecological mechanics-time disturbance index of the foundation pit to be constructed.

[0028] S42: Obtain the material-time strength curve of the support material in the fusion support structure, and determine the material-aging index based on the material-time strength curve;

[0029] S43: Combine the ecological mechanics-time disturbance index and the material-aging index to determine the comprehensive failure risk index of the support-vegetation system of the integrated support structure, and determine the failure difference of the foundation pit based on the comprehensive failure risk index of the support-vegetation system.

[0030] S44: Based on the failure difference and coupling model, construct a life cycle model, combine the integrated support structure with the life cycle model to construct an early warning model, and combine the life cycle model and the early warning model to obtain a full life cycle early warning system.

[0031] This invention provides a foundation pit support structure design system, comprising:

[0032] Coupled model construction module: Obtain relevant data of the foundation pit to be constructed, construct a first model, a second model and a third model based on the relevant data, and couple the first model, the second model and the third model to obtain the coupled model;

[0033] Zonal reinforcement module: Based on the multi-field coupling results of the foundation pit structure output by the coupling model, the foundation pit structure is divided into zonal sections based on the multi-field coupling results. Parameters are sampled according to the zonal sections. High-risk working conditions of the zonal sections are calculated based on the zonal parameter sampling results. Zonal reinforcement schemes for the foundation pit structure are generated based on the high-risk working conditions of the zonal sections.

[0034] Fusion support structure module: Based on the multi-field coupling results, the transmission relationship between each partition is quantified, the transition layer is determined according to the transmission relationship, and the reinforcement schemes of all partitions are fused with the coupling model according to the transition layer to obtain the fusion support structure;

[0035] Full life cycle early warning module: Based on the fusion support structure, predict the failure differences of the foundation pit, develop a life cycle model according to the failure differences, combine the fusion support structure and the life cycle model to construct an early warning model, and combine the life cycle model and the early warning model to obtain a full life cycle early warning system.

[0036] This invention provides a cloud platform for the design of foundation pit support structures, including a coupled model calculation engine, a dynamic partitioning optimization tool, a fusion support structure generator, and a full life cycle early warning system.

[0037] Compared with existing technologies, the beneficial effects of this application are as follows: By constructing a coupled model, outputting multi-field interaction results and intelligently partitioning them, customized reinforcement schemes are generated based on risk conditions. A transition layer is designed by quantifying the inter-regional transmission relationship, forming an integrated support system. This integrated support system, combined with failure differences, constructs a life cycle model and an early warning model, achieving dynamic monitoring throughout the entire life cycle, improving monitoring efficiency. Integrating environmental monitoring data with support structure design, and fully utilizing environmental factors, helps promote the transformation of geotechnical engineering towards intelligence and greening, achieving functional safety and ecological protection, while simultaneously extending the service life of foundation pit support structures.

[0038] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0039] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

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

[0041] Figure 1 This is a flowchart illustrating a design method for a foundation pit support structure provided in an embodiment of the present invention;

[0042] Figure 2 This is a structural schematic diagram of a foundation pit support structure design system provided in an embodiment of the present invention;

[0043] Figure 3 This is a collaborative flowchart of a cloud platform for the design of foundation pit support structures provided in an embodiment of the present invention. Detailed Implementation

[0044] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention. Example 1:

[0045] This invention provides a design method for foundation pit support structures, such as... Figure 1 As shown, it includes:

[0046] S1: Obtain relevant data on the foundation pit to be constructed, construct a first model, a second model, and a third model based on the relevant data, and couple the first model, the second model, and the third model to obtain a coupled model;

[0047] S2: Output the multi-field coupling results of the foundation pit structure according to the coupling model, divide the foundation pit structure into partitions based on the multi-field coupling results, sample parameters according to the partitions, calculate the high-risk working conditions of the partitions according to the partition parameter sampling results, and generate the partition reinforcement scheme of the foundation pit structure according to the high-risk working conditions of the partitions.

[0048] S3: Based on the multi-field coupling results, quantify the transmission relationship between each partition, determine the transition layer according to the transmission relationship, and fuse all partition reinforcement schemes with the coupling model according to the transition layer to obtain the fused support structure;

[0049] S4: Based on the fusion support structure, predict the failure differences of the foundation pit, develop a life cycle model based on the failure differences, construct an early warning model by combining the fusion support structure and the life cycle model, and obtain a full life cycle early warning system by integrating the life cycle model and the early warning model.

[0050] In this embodiment, the relevant data includes stratigraphic data, groundwater layer data, above-ground building data, and root system data.

[0051] In this embodiment, the first model is a geological-hydrological model. The corresponding inputs are stratigraphic data (borehole SPT value, soil c, φ, k) and groundwater layer data (water level, water pressure, aquifer thickness). The corresponding outputs are a three-dimensional geological model (including soil layer distribution and groundwater isosurface), initial stress field, permeability coefficient field, and a sand layer liquefaction risk zone predicted by a certain foundation pit model (safety factor Fs=0.8), which is then improved to Fs=1.3 after dewatering.

[0052] In this embodiment, the second model is a building-foundation pit interaction model. The corresponding input is building geometry, including outline, foundation type, and structural stiffness, including concrete elastic modulus E=30GPa. The corresponding output is the building additional settlement cloud map, such as the maximum settlement of 12mm, and the additional load that the support structure needs to bear. For example, if a project predicts that the settlement of a commercial complex will exceed the limit, it will be optimized to a "pile + isolation ditch" scheme, and the settlement will be controlled within 8mm.

[0053] In this embodiment, the third model is a root biomechanical model. The corresponding inputs are root topology, including diameter, orientation, and branching, and biochemical parameters, including water absorption rate and exudate composition. The corresponding outputs are the increase in shear strength of the root-reinforced zone and the concrete aging rate caused by root corrosion.

[0054] In this embodiment, the input of the coupled model is the parameter field after the three models are synchronized, such as soil c(x,y,z,t), building load p(t), and time scaling factor. The output is the multi-field coupling result, such as the spatiotemporal evolution of stress-seepage-displacement, and the comprehensive risk index, such as the failure probability Pf=0.25 of region A after 5 years.

[0055] In this embodiment, the multi-field coupling result is the comprehensive field data output by the coupling model, which includes the interaction results of stress field, seepage field, displacement field, etc. For example, a simulation of a deep foundation pit in Shanghai shows that the rise in groundwater level caused the pore water pressure of the clay layer to increase to 120 kPa, resulting in a lateral displacement of 25 mm in the support piles.

[0056] In this embodiment, parameter sampling is a technical process based on multi-field coupling results, selectively acquiring key mechanical-hydrological-deformation parameters within the pit partition. Its core is to optimize the support design through spatially differentiated data acquisition, including stress field variables, seepage field variables, displacement field variables, and material degradation parameters. Parameter sampling also includes the pit's own characteristics, such as pit depth, pit plan shape and size, and excavation method and sequence.

[0057] In this embodiment, partitioning divides the foundation pit into sub-regions with similar mechanical behavior; adaptive sampling dynamically adjusts the sampling density according to the risk level. For example, the sampling density in area A is 1 point / ㎡, and in area C it is 1 point / 10㎡, saving 40% of computing resources.

[0058] In this embodiment, the high-risk working condition of a zone is the state of a specific zone under the most unfavorable load combination. For example, under the condition of heavy rain + excavation, the pore water pressure in zone B suddenly rises to 150 kPa, and the displacement exceeds the limit.

[0059] In this embodiment, the zoned reinforcement scheme is a customized measure for high-risk areas. For example, in Zone A: high-pressure jet grouting piles (15m deep, 1.2m spacing); in Zone B: light wellpoint dewatering (water level drops to -5m).

[0060] In this embodiment, the transmission relationship is a quantitative representation of the mechanical and hydraulic interaction between zones, reflecting the cross-zone transmission characteristics of load, deformation, and seepage. For example, in a deep foundation pit, the seepage pressure of the sand layer (zone A) is transmitted to the clay layer (zone B), causing the pore water pressure at the interface to increase by 15 kPa, which induces local heave in zone B.

[0061] In this embodiment, the transition layer is a buffer structure designed to coordinate the differences in stiffness / permeability between adjacent zones. For example, the sand-clay interface is a 1m thick graded crushed stone layer, and the pile-soil interface is a 0.5m wide cement-soil mixing pile.

[0062] In this embodiment, the integrated support structure is an overall support system formed by coordinating the various zoning schemes. For example, in a certain foundation pit, the high-risk area uses a 1m thick underground continuous wall; the transition layer uses triple-tube jet grouting piles; and the low-risk area uses a soil nailing wall. The three are rigidly connected by a cap beam and a waist beam to form a unified force system.

[0063] In this embodiment, the failure difference is the time difference between the damage to the vegetation root system in the foundation pit and the failure of the material. For example, a project predicts that the roots will penetrate the support in 2035, while the material will fail in 2040. The difference of 60 months requires intervention.

[0064] In this embodiment, the inputs to the life cycle model are the vegetation root-time intensity curve, the material-time intensity curve, and environmental data. The outputs are the remaining lifespan (years) of the support structure and the critical failure time node. For example, after inputting local climate data, the model outputs that the lifespan of the support piles is shortened by 3 years under rainwater acidification.

[0065] In this embodiment, the input of the early warning model is the risk index, failure difference, and real-time monitoring data, and the output is the early warning level (red / orange / yellow) and handling suggestions. For example, the system automatically pushes the following for a project: Area A will be at high risk in 2 years, and it is recommended to pre-bury root barrier membrane.

[0066] In this embodiment, the full lifecycle early warning system is a closed-loop management system that integrates monitoring, analysis, and decision-making.

[0067] The working principle and beneficial effects of the above technical solution are as follows: By constructing a coupled model, the results of multi-field interactions are output and intelligently partitioned, generating customized reinforcement schemes based on risk conditions. A transition layer is designed by quantifying the inter-regional transmission relationships, forming an integrated support system. This integrated support system, combined with failure differences, constructs a life cycle model and an early warning model, achieving dynamic monitoring throughout the entire life cycle, improving monitoring efficiency. Integrating environmental monitoring data with support structure design, and fully utilizing environmental factors, helps promote the transformation of geotechnical engineering towards intelligence and green practices, achieving functional safety and ecological protection, while simultaneously extending the service life of foundation pit support structures. Example 2:

[0068] This invention provides a method for designing a foundation pit support structure, S1 including:

[0069] S11: Perform LiDAR scanning and borehole sampling on the foundation pit to be constructed, and then obtain relevant stratigraphic data and groundwater layer data to generate the first model;

[0070] S12: Determine the relevant data of the above-ground buildings based on the location information of the foundation pit to be constructed, and generate the second model;

[0071] S13: Perform a CT scan on the foundation pit to be constructed, obtain root system-related data, and generate a third model;

[0072] S14: Establish a three-way index table for the first model, the second model, and the third model. Synchronize the first model, the second model, and the third model in time using a time scaling factor to obtain a time synchronization result. Based on the time synchronization result and the three-way index table, couple the first model, the second model, and the third model to obtain a coupled model.

[0073] In this embodiment, the stratigraphic data is a set of parameters describing the spatial distribution and mechanical properties of soil / rock strata. For example, in a certain foundation pit: clay layer (burial depth 2-5m, c=25kPa, φ=12°) and sand layer (5-8m, ...) are obtained through drilling. Layered data (m / s).

[0074] In this embodiment, the groundwater-related data are parameters characterizing the location and hydraulic properties of the aquifer. For example, in a subway project, the monitoring well measured a confined water head of -8m and a permeability coefficient... The radius of influence is 150m.

[0075] In this embodiment, the data related to above-ground buildings are the geometric, structural and current deformation data of the buildings around the foundation pit. For example, for high-rise buildings: laser scanning shows that the building is 15m away from the foundation pit, has a raft foundation (thickness 1.2m), and has a current tilt rate of 0.05%.

[0076] In this embodiment, root-related data refers to the distribution, mechanical, and biochemical characteristics of the plant root system. For example, for banyan tree roots: μCT scans show that the taproot diameter is 8-15 mm, the extension depth is 3.5 m, and the pH of the exudate is 4.2.

[0077] In this embodiment, the three-way index table is a cross-model retrieval database that links three key model parameters. For example, the table header is the coordinates, corresponding to the data (100, 50, -5); the table header is the address parameter derived from the stratigraphic data and the groundwater layer data, corresponding to the data clay, c=30kPa; the table header is the building influence, corresponding to the data additional load 15kPa; and the table header is the root effect, corresponding to the data principal root, Δc=8kPa.

[0078] In this embodiment, the time scaling factor is used to coordinate different time scales of geological evolution and root growth, and the corresponding specific formula is as follows: , Indicates the time scaling factor. Indicates root system characteristics over time; Indicates geological features over time; for example, the silt layer of a certain project. With willow root system During synchronization, use That is: 1 year in the first model ≈ 0.5 years in the second model.

[0079] In this embodiment, the time synchronization result is a unified spatiotemporal reference generated by coordinating the time scales of the first model and the second model through a time scaling factor. Its core is to align two systems with different evolution rates onto the same time axis.

[0080] In this embodiment, a CT scan is performed on the site to be detected to obtain the topological parameters of the root system. The topological parameters are classified to obtain the coordinates of the main root path and the coordinates of the fibrous root path. Rigid cluster particle chains are determined based on the coordinates of the main root path, and flexible cluster particle chains are determined based on the coordinates of the fibrous root path. The main root-soil contact interaction is determined based on the rigid cluster particle chains, and the fibrous root-soil contact interaction is determined based on the flexible cluster particle chains. The root-root connection interaction is determined based on the connection relationship between the rigid and flexible cluster particle chains. A second model is constructed by combining the main root-soil contact interaction, the fibrous root-soil contact interaction, and the root-root connection interaction. The topological parameters are determined by the CT scan data and include: root diameter distribution, branching angle, and spatial density. For example, the fractal dimension D of the banyan tree root system at a depth of 2m is 2.3.

[0081] In this embodiment, the topological parameters are the three-dimensional geometry and connectivity data of the root system obtained through CT scans, including diameter, orientation, and branching points. For example, a CT scan of a sycamore tree shows that the main root diameter is 8-15 mm, the fibrous root diameter is 0.5-2 mm, and the branching angle is 55°±10°.

[0082] In this embodiment, the main root path coordinates are a continuous spatial coordinate sequence of the main root system obtained by CT scanning. This sequence is used to describe the three-dimensional spatial orientation and geometric shape of the main root from the base to the tip. The main characteristics include robustness: the diameter is significantly larger than that of the fibrous roots (the threshold is usually 5 mm), continuity: it presents a coherent spatial curve (not discrete points), and mechanical dominance: it bears more than 80% of the tensile / shear resistance of the root system.

[0083] In this embodiment, the root path coordinates are spatial distribution data of fine roots (diameter ≤ 5 mm) extracted by CT scan, used to describe the three-dimensional topological structure of the root network and its microscopic interaction with the soil. The main characteristics include: fineness: the diameter is usually 0.1~5 mm, distributed in a hair-like manner; high density: hundreds to thousands of roots per unit volume, such as 300 roots / cm³; dominant function: regulating soil permeability, microbial activity and shallow soil stabilization.

[0084] In this embodiment, the rigid cluster particle chain models the mechanical behavior of the taproot as a series of rigidly connected spherical particle chains using the discrete element method. Each particle represents a segment of the taproot, and its diameter and mechanical parameters are directly mapped from CT scan data. This chain structure can accurately simulate the tensile and bending characteristics of the taproot. For example, in slope engineering, after modeling a 10mm diameter poplar taproot using the rigid cluster particle chain, its effect of increasing the soil shear strength by 15kPa can be quantified. The contact between particles adopts the Hertz-Mindlin model, with the normal stiffness set to 1e8 N / m to simulate the rigid characteristics of the root xylem.

[0085] In this embodiment, flexible cluster particle chains are used to characterize the permeability and micromechanical effects of the rootlets, employing a combination of porous media theory and discrete fiber networks. Each flexible particle represents a micro-segment of the rootlet, and its permeability characteristics are corrected using Darcy's law (e.g., permeability coefficient k = 1.5k_soil), while its mechanical behavior is simulated using low-stiffness spring elements (e.g., stiffness 500 N / m). For example, after modeling the bermudagrass rootlet network using flexible cluster particle chains, it can be predicted that it will reduce the permeability coefficient of surface sand by 60%, while simultaneously increasing the apparent cohesion of the soil by 3 kPa through the fiber pull-out effect.

[0086] In this embodiment, the main root-soil contact interaction focuses on the macroscopic mechanical interaction between the main root and the surrounding soil, and is described by a combination of the contact mechanics model and the bonding particle model.

[0087] In this embodiment, the root-soil contact interaction focuses on microscopic changes in soil structure and hydraulic coupling. Roots bind soil particles through secretions and form tortuous paths within soil pores.

[0088] In this embodiment, the root-root connection is modeled through two dimensions: mechanical binding and biochemical coupling. Mechanically, a spring-damping system is used to simulate force transmission at root bifurcation points. For example, in the mangrove root network model, the bifurcation nodes are set with a radial stiffness of 800 N / m and a tangential damping of 50 Ns / m, which can reproduce the cooperative deformation characteristics of the root system under typhoon loads, improving the overall overturning resistance by 40%. Biochemically, the diffusion-reaction equation is used to simulate the material exchange between roots. For example, the nutrient transport rate at the poplar root connection is set to 0.2 mmol / (m·s), explaining its symbiotic reinforcement phenomenon.

[0089] In this embodiment, the connection relationship encompasses both topological and functional levels. The topological level defines the geometric relationship between root branches through parent-child node IDs; for example, in a pine tree model, this indicates the connection between the main root ID=101 and the lateral roots IDs=201-215. The functional level defines the failure criteria for the connection points, such as triggering spring breakage at the bifurcation point when the bending moment exceeds 2 N·m, corresponding to the critical condition for mechanical damage to the root system in reality.

[0090] The working principle and beneficial effects of this invention include: acquiring multi-source data on geology, architecture, and root systems; constructing three models and establishing a three-way index table to associate key parameters; using a time scaling factor to unify the time scale of different processes; and finally coupling to generate a dynamic model that comprehensively reflects geological deformation, building load, and root system effects, thereby improving prediction accuracy and realizing dynamic optimization of ecological engineering. Example 3:

[0091] This invention provides a method for designing a foundation pit support structure, S11 including:

[0092] S111: Based on the stratigraphic data, a three-dimensional geological model is generated by Kriging interpolation. Each voxel in the three-dimensional geological model is directly mapped to an FEM mesh node. The interpolated stratigraphic data is assigned as the unit material property, thereby forming an eco-elastoplastic constitutive equation.

[0093] S112: A hierarchical contact system is established based on the stratigraphic interfaces in the three-dimensional geological model. Constraints are set based on the hierarchical contact system and groundwater layer data. The ecological-elastoplastic constitutive equation is dynamically adjusted based on the constraints to obtain the first model.

[0094] In this embodiment, Kriging interpolation is based on geostatistical principles, utilizing variograms to quantify spatial correlation and predict parameters at unsampled points. For example, for a financial center foundation pit, the input is the SPT-N values ​​of 42 boreholes (50m spacing), and the output is a three-dimensional soil stiffness field (Es=12-58MPa) with a resolution of 0.25m³. The corresponding effect is an interpolation error of ±8%, which is 35% more accurate than the traditional inverse distance weighted method.

[0095] In this embodiment, the voxel unit is the smallest volumetric unit (typically a 0.5-2m cube) of 3D digital soil and rock modeling, storing multiple parameter attributes. The voxel dimensions corresponding to a subway foundation pit are 1m × 1m × 0.5m, storing parameters such as soil layer type, c (15-35kPa), and φ (8-30°). (cm / s), special treatment: voxel markings in the cave area are designated as "high-risk units".

[0096] In this embodiment, the material properties of the elements are assigned physical parameters to the finite element mesh, supporting anisotropy definitions. For example, the silt layer element corresponding to the financial center foundation pit has E=15MPa, ν=0.35, and c=18kPa (considering creep), while the rock layer element has E=5GPa, ν=0.2, and a tensile strength of 2MPa.

[0097] In this embodiment, the FEM grid nodes are geometrically discrete points for numerical calculations and need to be precisely matched with geological voxels.

[0098] In this embodiment, the constraints are mathematical representations of boundary mechanics and hydraulics. For example, for an airport foundation pit, the mechanical boundary is: base spring support (stiffness 50MN / m³), the seepage boundary is: dynamic head boundary (varies by ±3m with the seasons), and the special constraint is: displacement limit of 8mm for adjacent high-speed rail tracks.

[0099] In this embodiment, the graded contact system is a contact algorithm based on the importance of geological interfaces. For example, if the contact level is level one, the corresponding interface type is a sand-clay interface, the processing method is the interface element method, and the parameters are set to kn=1e8kPa / m, μ=0.4. If the contact level is level two, the corresponding interface type is a fractured zone, the processing method is equivalent continuum, and the parameters are set to reduce E to 70%.

[0100] In this embodiment, the eco-elastoplastic constitutive equation is: Where F represents the yield function value; The norm of the deviatoric stress tensor; Represents the tensor norm; This represents the spatial enhancement factor of cohesion as a function of position x; This represents the stress caused by geological structural effects, which varies with location x. represents pore water pressure; s represents deviatoric stress tensor; p represents mean stress. This represents the coefficient of confining pressure friction effect as a function of position x; This represents the soil cohesion as a function of position x. The internal friction angle varies with position x; γ represents the geological structure effect coefficient. Indicates the pore water pressure softening coefficient; This represents the pore water pressure as a function of position x. Indicates the specific gravity of water; Indicates the water head height; The saturation indicator function for position x is 1, and 0 is 0.

[0101] The working principle and beneficial effects of this invention include: generating a three-dimensional geological model from discrete geological parameters through Kriging interpolation and mapping it to FEM grid nodes as material properties, thereby forming an eco-elastoplastic constitutive equation. The discontinuous boundary conditions are automatically set according to the stratigraphic interface, and the parameters of the eco-elastoplastic constitutive equation are dynamically corrected in combination with the root distribution area. Finally, the first model coupled with the root effect is output, realizing the quantitative characterization of the root-soil interaction and optimizing the support design. Example 4:

[0102] This invention provides a method for designing a foundation pit support structure, wherein S2 includes:

[0103] S21: Based on the multi-field coupling results of the foundation pit structure output by the coupling model, extract multiple key field variables from the multi-field coupling results, calculate the distribution of the key field variables, and establish a field variable collaborative failure matrix by combining the key field variables and their corresponding distribution.

[0104] S22: Based on the eigenvalues ​​of the field variable co-failure matrix, formulate partitioning indicators, partition the foundation pit structure according to the partitioning indicators, sample parameters for each partition, calculate the multi-field coupling risk index for each sampling point, and extract the risk gradient and field variable co-factors from the multi-field coupling risk index.

[0105] S23: Based on the multi-field coupling risk index, risk gradient and field variable synergy coefficient, the corresponding partition is judged from multiple aspects, and the high-risk working conditions of the partition are determined based on the results of the multi-faceted judgment. Based on the high-risk working conditions of the partition, the partition reinforcement scheme of the corresponding partition is determined.

[0106] In this embodiment, the key field variables are physical quantities that play a dominant role in the stability of the foundation pit, such as maximum shear stress, pore water pressure, and plastic strain. For example, in a silty clay foundation pit, the combined excess of maximum shear stress and pore water pressure can lead to local slippage.

[0107] In this embodiment, the distribution is the spatial variation law of the key field variable, which is expressed by contour maps or three-dimensional cloud maps. For example, the BIM platform of a certain project shows that the pore water pressure forms a high-pressure zone in the northwest corner of the foundation pit.

[0108] In this embodiment, the field variable co-failure matrix is ​​a symmetric matrix that reflects the joint failure probability of multiple field variables. The elements represent the failure correlation between row variables and column variables. For example, the co-failure coefficient of 0.65 in the matrix indicates that the combined effect of the two increases the failure probability to 0.28.

[0109] In this embodiment, the zoning index is a quantitative standard constructed based on the eigenvalues ​​of the failure matrix, used to classify risk levels. For example, a certain eigenvalue corresponds to the seepage-stress coupling dominant zone, which needs to be processed separately.

[0110] In this embodiment, the multi-field coupling risk index is a weighted risk score that integrates various field variables; the risk gradient is the spatial rate of change of the risk index; and the field variable synergy coefficient is a parameter that quantifies the strength of multi-field coupling.

[0111] In this embodiment, the multi-faceted judgment is a comprehensive evaluation combining the multi-field coupling risk index, risk gradient, and field variable synergy coefficient. For example, if a certain partition simultaneously satisfies the multi-field coupling risk index > 0.8, risk gradient > 5, and field variable synergy coefficient > 0.6, it is judged as extremely high risk.

[0112] In this embodiment, the results of multiple judgments are the final conclusion of the risk assessment, including the risk level and the dominant factors. For example, in Area A: high risk due to seepage, it is recommended to carry out precipitation and grouting.

[0113] The working principle and beneficial effects of this invention include: the coupling model outputs the multi-field coupling results of the foundation pit structure, extracts key field variables and constructs a collaborative failure matrix; the region is divided by matrix eigenvalues, the risk gradient and field variable synergy coefficient of each partition are calculated based on the parameter sampling results, high-risk conditions are identified by combining the multi-field coupling risk index, and finally a targeted partition reinforcement scheme is generated. Example 5:

[0114] This invention provides a method for designing a foundation pit support structure, wherein step S3 includes:

[0115] S31: Based on the multi-field coupling results, quantify the transmission relationship between each partition, determine adjacent partitions, extract the boundary field variables of the interface between adjacent partitions from the multi-field coupling results, and generate the transmission intensity matrix based on the boundary field variables;

[0116] S32: Calculate the energy transfer efficiency and dominant transfer direction at the interface of adjacent partitions based on the transfer intensity matrix, determine the transition layer based on the energy transfer efficiency and dominant transfer direction, and map the transfer intensity matrix into support parameters based on the transition layer;

[0117] S33: Determine the connection between the partition reinforcement schemes based on the support parameters of the transition layer, and fuse all partition reinforcement schemes with the coupling model according to the connection, to obtain the fused support structure.

[0118] In this embodiment, adjacent zones are sub-regions with different risk levels that have direct physical contact. For example, a subway foundation pit is divided into three zones: a high-risk zone with support pile displacement > 20 mm, a medium-risk zone with support pile displacement 10-20 mm, and a low-risk zone with support pile displacement < 10 mm. The boundary between the three zones needs to be specially treated.

[0119] In this embodiment, the boundary field variables are key physical quantities at the interface between adjacent zones, such as shear stress, displacement difference, and hydraulic gradient. For example, a project monitored that the shear stress at the clay-sand interface was 32 kPa and the displacement difference was 8 mm, indicating that a transition structure needs to be set up.

[0120] In this embodiment, the transfer intensity matrix is ​​a square matrix that describes the coupling relationship between boundary field variables, and the elements represent the transfer contribution of row variables to column variables.

[0121] In this embodiment, the energy transfer efficiency is the ratio of the effectively transferred energy to the total energy. For example, the energy transfer efficiency at the interface of the silt layer is 0.4, which is inefficient and requires the addition of geotextile; the energy transfer efficiency of the sand layer is 0.9, which is highly efficient.

[0122] In this embodiment, the dominant transmission direction is the spatial azimuth angle corresponding to the maximum energy transmission efficiency. For example, if the dominant transmission direction of a certain foundation pit is 42°, a 45° inclined micropile transition layer is adopted.

[0123] In this embodiment, the support parameters are the technical indicators of the transition layer, such as pile diameter, spacing, inclination angle, material strength, etc. For example, the transition layer parameters of a certain project are: jet grouting pile diameter of 800mm, spacing of 1.2m, inclination angle of 30°, and compressive strength of 15MPa.

[0124] In this embodiment, the mapping process is a technical process that converts the physical quantities of the transfer matrix into support parameters. For example, an energy transfer efficiency of <0.5 is mapped to a 50% reduction in pile spacing, and a dominant transfer direction of >30° is mapped to the installation of inclined piles.

[0125] In this embodiment, the connection situation is the overlapping method and mechanical compatibility of different zone support structures. For example, a certain project requires that the bored pile (zone A) and the mixing pile (zone B) overlap by 1.5m, and the lap length of the steel cage is 35d (d is the diameter of the steel bar).

[0126] The working principle and beneficial effects of this invention include: extracting the interface field variables of adjacent partitions based on multi-field coupling data, constructing a transmission intensity matrix to quantify energy transmission efficiency and dominant direction, mapping the transmission characteristics to support parameters through transition layer design, and finally integrating the reinforcement schemes of each partition to form an overall support system, eliminating support faults, accurately controlling risks, and dynamically adapting to the construction progress. Example 6:

[0127] This invention provides a method for designing a foundation pit support structure, wherein step S4 includes:

[0128] S41: Obtain the vegetation root system-time intensity curve of the foundation pit to be constructed, determine the root-concrete friction coefficient based on the vegetation root system-time intensity curve, and determine the ecological mechanics-time disturbance index of the foundation pit to be constructed.

[0129] S42: Obtain the material-time strength curve of the support material in the fusion support structure, and determine the material-aging index based on the material-time strength curve;

[0130] S43: Combine the ecological mechanics-time disturbance index and the material-aging index to determine the comprehensive failure risk index of the support-vegetation system of the integrated support structure, and determine the failure difference of the foundation pit based on the comprehensive failure risk index of the support-vegetation system.

[0131] S44: Based on the failure difference and coupling model, construct a life cycle model, combine the integrated support structure with the life cycle model to construct an early warning model, and combine the life cycle model and the early warning model to obtain a full life cycle early warning system.

[0132] In this embodiment, the vegetation root-time strength curve is a curve that describes the change of root mechanical properties over time, reflecting the tensile strength, diameter expansion and other characteristics of the root system during the growth process. For example, the monitoring of the root system of sycamore trees at a construction site showed that the tensile strength of the main root of 3-year-old trees increased from 8MPa to 15MPa, and stabilized at 12MPa after 5 years.

[0133] In this embodiment, the root-concrete friction coefficient is a friction characteristic parameter of the contact surface between the root system and the concrete. It varies with the root secretions and surface roughness. For example, a test of a certain foundation pit showed that the initial friction coefficient between the camphor root system and C30 concrete was 0.6, which dropped to 0.4 after 10 years due to corrosion from secretions.

[0134] In this embodiment, the Ecomechanics-Time Disturbance Index is a time-varying index that quantifies the influence of the root system on the mechanical performance of the support structure. It combines the root growth force and the friction coefficient decay. For example, in a certain slope project, the banyan tree root system causes the index to increase by 0.15 per year, indicating that the lateral pressure of the support piles needs to increase by 20% after 5 years.

[0135] In this embodiment, the material-time strength curve is the curve of the strength of the support material degrading over time. For example, 28 years of data on C40 concrete piles in the foundation pit shows that the compressive strength decreases by 0.8 MPa per year.

[0136] In this embodiment, the support material is the engineering material used in the support structure, such as concrete, steel support, geosynthetics, etc. For example, a project uses glass fiber reinforcement instead of steel reinforcement to avoid root corrosion problems.

[0137] In this embodiment, the material-aging index is a parameter characterizing the rate of material performance degradation, based on environmental exposure conditions and material properties. For example, chloride ion erosion in coastal areas causes the concrete aging index to reach 0.12 / year, while in inland areas it is only 0.05 / year.

[0138] In this embodiment, the comprehensive failure risk index of the support-vegetation system is a risk score that combines ecological disturbance and material aging. The comprehensive failure risk index of the support-vegetation system = ecological disturbance index × 0.6 + aging index × 0.4. For example, when the index of a certain project exceeds 0.8, root cutting and grouting reinforcement are triggered.

[0139] The working principle and beneficial effects of this invention include: quantifying the decay of the root-concrete friction coefficient through the vegetation root-time intensity curve, quantifying the material-aging index through the material-time intensity curve, determining the comprehensive failure risk index of the support-vegetation system by combining the ecological mechanics-time disturbance index and the material-aging index, and finally integrating the coupled model data to form a full life cycle early warning system, realizing dynamic prediction of the failure risk of the support-vegetation system, balancing vegetation protection and engineering safety, and effectively extending the service life of the support structure. Example 7:

[0140] This invention provides a foundation pit support structure design system, such as... Figure 2 As shown, it includes:

[0141] Coupled model construction module: Obtain relevant data of the foundation pit to be constructed, construct a first model, a second model and a third model based on the relevant data, and couple the first model, the second model and the third model to obtain the coupled model;

[0142] Zonal reinforcement module: Based on the multi-field coupling results of the foundation pit structure output by the coupling model, the foundation pit structure is divided into zonal sections based on the multi-field coupling results. Parameters are sampled according to the zonal sections. High-risk working conditions of the zonal sections are calculated based on the zonal parameter sampling results. Zonal reinforcement schemes for the foundation pit structure are generated based on the high-risk working conditions of the zonal sections.

[0143] Fusion support structure module: Based on the multi-field coupling results, the transmission relationship between each partition is quantified, the transition layer is determined according to the transmission relationship, and the reinforcement schemes of all partitions are fused with the coupling model according to the transition layer to obtain the fusion support structure;

[0144] Full life cycle early warning module: Based on the fusion support structure, predict the failure differences of the foundation pit, develop a life cycle model according to the failure differences, combine the fusion support structure and the life cycle model to construct an early warning model, and combine the life cycle model and the early warning model to obtain a full life cycle early warning system.

[0145] The working principle and beneficial effects of the above technical solution are as follows: By constructing a coupled model, the results of multi-field interactions are output and intelligently partitioned, generating customized reinforcement schemes based on risk conditions. A transition layer is designed by quantifying the inter-regional transmission relationships, forming an integrated support system. This integrated support system, combined with failure differences, constructs a life cycle model and an early warning model, achieving dynamic monitoring throughout the entire life cycle, improving monitoring efficiency. Integrating environmental monitoring data with support structure design, and fully utilizing environmental factors, helps promote the transformation of geotechnical engineering towards intelligence and green practices, achieving functional safety and ecological protection, while simultaneously extending the service life of foundation pit support structures. Example 8:

[0146] This invention provides a cloud platform for the design of foundation pit support structures, such as... Figure 3 As shown, it includes a coupled model calculation engine, a dynamic partitioning optimization tool, a fusion support structure generator, and a full life cycle early warning system.

[0147] In this embodiment, the coupled model calculation engine is the core of the calculation that integrates geomechanics-root biomechanics-environment multiphysics coupling algorithms, and supports parallel solving and real-time data-driven updates.

[0148] In this embodiment, the dynamic zoning optimization tool is an intelligent zoning system based on machine learning and topology analysis, which can dynamically adjust the risk area division according to the construction progress.

[0149] In this embodiment, the fusion support structure generator is a parametric design module that intelligently integrates the partitioned support scheme with the transition layer design.

[0150] In this embodiment, the full life cycle early warning system is a monitoring-early warning-decision platform that embeds a time-varying model of material aging and root growth.

[0151] The working principle and beneficial effects of this invention include: multi-field coupling calculation improves the reliability of support schemes and reduces the risk of structural failure; real-time zoning adjustment reduces material waste; automated fusion improves support design efficiency; and the full-cycle early warning system identifies risks 6-12 months in advance, improving maintenance efficiency.

[0152] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A design method for foundation pit support structure, characterized in that, include: S1: Obtain relevant data on the foundation pit to be constructed, construct a first model, a second model, and a third model based on the relevant data, and couple the first model, the second model, and the third model to obtain a coupled model; S2: Output the multi-field coupling results of the foundation pit structure according to the coupling model, divide the foundation pit structure into partitions based on the multi-field coupling results, sample parameters according to the partitions, calculate the high-risk working conditions of the partitions according to the partition parameter sampling results, and generate the partition reinforcement scheme of the foundation pit structure according to the high-risk working conditions of the partitions. S3: Based on the multi-field coupling results, quantify the transmission relationship between each partition, determine the transition layer according to the transmission relationship, and fuse all partition reinforcement schemes with the coupling model according to the transition layer to obtain the fused support structure; S4: Based on the fusion support structure, predict the failure differences of the foundation pit, develop a life cycle model based on the failure differences, construct an early warning model by combining the fusion support structure and the life cycle model, and obtain a full life cycle early warning system by integrating the life cycle model and the early warning model.

2. The design method for a foundation pit support structure according to claim 1, characterized in that, S1 includes: S11: Perform LiDAR scanning and borehole sampling on the foundation pit to be constructed, and then obtain relevant stratigraphic data and groundwater layer data to generate the first model; S12: Determine the relevant data of the above-ground buildings based on the location information of the foundation pit to be constructed, and generate the second model; S13: Perform a CT scan on the foundation pit to be constructed, obtain root system-related data, and generate a third model; S14: Establish a three-way index table for the first model, the second model, and the third model. Synchronize the first model, the second model, and the third model in time using a time scaling factor to obtain a time synchronization result. Based on the time synchronization result and the three-way index table, couple the first model, the second model, and the third model to obtain a coupled model.

3. The design method for a foundation pit support structure according to claim 2, characterized in that, S11 includes: S111: Based on the stratigraphic data, a three-dimensional geological model is generated by Kriging interpolation. Each voxel in the three-dimensional geological model is directly mapped to an FEM mesh node. The interpolated stratigraphic data is assigned as the unit material property, thereby forming an eco-elastoplastic constitutive equation. S112: A hierarchical contact system is established based on the stratigraphic interfaces in the three-dimensional geological model. Constraints are set based on the hierarchical contact system and groundwater layer data. The ecological-elastoplastic constitutive equation is dynamically adjusted based on the constraints to obtain the first model.

4. The design method for a foundation pit support structure according to claim 1, characterized in that, S2 includes: S21: Based on the multi-field coupling results of the foundation pit structure output by the coupling model, extract multiple key field variables from the multi-field coupling results, calculate the distribution of the key field variables, and establish a field variable collaborative failure matrix by combining the key field variables and their corresponding distribution. S22: Based on the eigenvalues ​​of the field variable co-failure matrix, formulate partitioning indicators, partition the foundation pit structure according to the partitioning indicators, sample parameters for each partition, calculate the multi-field coupling risk index for each sampling point, and extract the risk gradient and field variable co-factors from the multi-field coupling risk index. S23: Based on the multi-field coupling risk index, risk gradient and field variable synergy coefficient, the corresponding partition is judged from multiple aspects, and the high-risk working conditions of the partition are determined based on the results of the multi-faceted judgment. Based on the high-risk working conditions of the partition, the partition reinforcement scheme of the corresponding partition is determined.

5. The design method for a foundation pit support structure according to claim 1, characterized in that, S3 includes: S31: Based on the multi-field coupling results, quantify the transmission relationship between each partition, determine adjacent partitions, extract the boundary field variables of the interface between adjacent partitions from the multi-field coupling results, and generate the transmission intensity matrix based on the boundary field variables; S32: Calculate the energy transfer efficiency and dominant transfer direction at the interface of adjacent partitions based on the transfer intensity matrix, determine the transition layer based on the energy transfer efficiency and dominant transfer direction, and map the transfer intensity matrix into support parameters based on the transition layer; S33: Determine the connection between the partition reinforcement schemes based on the support parameters of the transition layer, and fuse all partition reinforcement schemes with the coupling model according to the connection, to obtain the fused support structure.

6. The design method for a foundation pit support structure according to claim 1, characterized in that, S4 include: S41: Obtain the vegetation root system-time intensity curve of the foundation pit to be constructed, determine the root-concrete friction coefficient based on the vegetation root system-time intensity curve, and determine the ecological mechanics-time disturbance index of the foundation pit to be constructed. S42: Obtain the material-time strength curve of the support material in the fusion support structure, and determine the material-aging index based on the material-time strength curve; S43: Combine the ecological mechanics-time disturbance index and the material-aging index to determine the comprehensive failure risk index of the support-vegetation system of the integrated support structure, and determine the failure difference of the foundation pit based on the comprehensive failure risk index of the support-vegetation system. S44: Based on the failure difference and coupling model, construct a life cycle model, combine the integrated support structure with the life cycle model to construct an early warning model, and combine the life cycle model and the early warning model to obtain a full life cycle early warning system.

7. A foundation pit support structure design system, characterized in that, include: Coupled model construction module: Obtain relevant data of the foundation pit to be constructed, construct a first model, a second model and a third model based on the relevant data, and couple the first model, the second model and the third model to obtain the coupled model; Zonal reinforcement module: Based on the multi-field coupling results of the foundation pit structure output by the coupling model, the foundation pit structure is divided into zonal sections based on the multi-field coupling results. Parameters are sampled according to the zonal sections. High-risk working conditions of the zonal sections are calculated based on the zonal parameter sampling results. Zonal reinforcement schemes for the foundation pit structure are generated based on the high-risk working conditions of the zonal sections. Fusion support structure module: Based on the multi-field coupling results, the transmission relationship between each partition is quantified, the transition layer is determined according to the transmission relationship, and the reinforcement schemes of all partitions are fused with the coupling model according to the transition layer to obtain the fusion support structure; Full life cycle early warning module: Based on the fusion support structure, predict the failure differences of the foundation pit, develop a life cycle model according to the failure differences, combine the fusion support structure and the life cycle model to construct an early warning model, and combine the life cycle model and the early warning model to obtain a full life cycle early warning system.

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