Foundation pit supporting structure design method and system and cloud platform

Abstract

The invention provides a foundation pit supporting structure design method and system and a cloud platform, and belongs to the technical field of intelligent rock-soil control, and the method comprises the steps: building a coupling model, outputting a multi-field interaction result, carrying out intelligent partitioning, and generating a customized reinforcement scheme based on a risk working condition. A transition layer is designed according to the quantitative partition transfer relation, a fusion supporting system is formed, the fusion supporting system is combined with failure differences to construct a life cycle model and an early warning model, full-life-cycle dynamic monitoring is achieved, the monitoring efficiency is improved, environment monitoring data and supporting structure design are combined, and the reliability of the system is improved. Environmental factors are fully utilized, geotechnical engineering is promoted to be transformed to intelligence and greenization, functional safety and ecological protection are achieved, and meanwhile the service life of the foundation pit supporting structure is prolonged.

Description

Technical Field

[0001] The present invention relates to the field of intelligent geotechnical control technology, and in particular to a foundation pit support structure design method, system and cloud platform. Background Art

[0002] Foundation pit support is a key link in geotechnical engineering. Its design needs to comprehensively consider factors such as geological conditions, groundwater, and surrounding loads. Therefore, it is linked to environmental monitoring. However, traditional foundation pit support schemes often ignore the beneficial or harmful effects of environmental factors in the foundation pit to be constructed, resulting in insufficient utilization of environmental factors and insufficient development of biological support potential. At the same time, environmental monitoring is not in place, and biochemical corrosion is accelerated, resulting in a decrease in the service life of the foundation pit support structure.

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

[0004] The present invention provides a foundation pit support structure design method, system, and cloud platform. By constructing a coupling model, the system outputs multi-field interaction results and intelligently partitions the structure, generating customized reinforcement solutions based on risk conditions. The transfer relationship between partitions is quantified to design transition layers, forming a fused support system. The fused support system combines failure differences to construct a lifecycle model and early warning model, enabling dynamic monitoring throughout the entire lifecycle and improving monitoring efficiency. The integration of environmental monitoring data with support structure design and the full utilization of environmental factors will help promote the intelligent and green transformation of geotechnical engineering, achieve functional safety and ecological protection, and simultaneously extend the service life of foundation pit support structures.

[0005] The present invention provides a foundation pit support structure design method, comprising: S1: 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 perform model coupling on the first model, the second model, and the third model to obtain a coupled model; S2: Outputting multi-field coupling results of the foundation pit structure according to the coupling model, partitioning the foundation pit structure based on the multi-field coupling results, performing parameter sampling according to the partitions, calculating high-risk working conditions of the partitions according to the partition parameter sampling results, and generating a partition reinforcement plan for the foundation pit structure based on the high-risk working conditions of the partitions; S3: Quantify the transfer relationship between each partition based on the multi-field coupling results, determine the transition layer according to the transfer relationship, and fuse all partition reinforcement schemes with the coupling model according to the transition layer to obtain a fused support structure; S4: Predict the failure differences of the foundation pit based on the fusion support structure, develop a life cycle model based on the failure differences, build an early warning model by combining the fusion support structure and the life cycle model, and derive a full life cycle early warning system by integrating the life cycle model and the early warning model.

[0006] The present invention provides a foundation pit support structure design method, S1 comprising: S11: Perform LiDAR scanning and borehole sampling on the construction pit to obtain stratum-related data and groundwater layer-related data to generate a first model; S12: Determine the relevant data of the above-ground building according to the location information of the foundation pit to be constructed, and generate a second model; S13: Perform CT scanning 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 through a time scaling factor to obtain a time synchronization result, and couple the first model, the second model, and the third model according to the time synchronization result and the three-way index table to obtain a coupling model.

[0007] The present invention provides a foundation pit support structure design method, S11 comprising: S111: generating a three-dimensional geological model through Kriging interpolation based on the stratum-related data, directly mapping each voxel unit in the three-dimensional geological model to a FEM grid node, assigning the interpolated stratum-related data to the unit material properties, and thus forming an eco-elastoplastic constitutive equation; S112: Establishing a hierarchical contact system based on the stratum interfaces in the three-dimensional geological model, setting constraints based on the hierarchical contact system in combination with groundwater layer-related data, and dynamically adjusting the ecological-elastoplastic constitutive equation based on the constraints to obtain a first model.

[0008] The present invention provides a foundation pit support structure design method, S2 includes: S21: Outputting a multi-field coupling result of the foundation pit structure according to the coupling model, extracting a plurality of key field variables from the multi-field coupling result, calculating the distribution of the key field variables, and establishing a field variable collaborative failure matrix by combining the key field variables and the corresponding distribution of the key field variables; S22: formulating a partition index based on the eigenvalue of the field variable synergy failure matrix, partitioning the foundation pit structure according to the partition index, sampling parameters for each partition, calculating a multi-field coupling risk index for each sampling point, and extracting a risk gradient and a field variable synergy coefficient from the multi-field coupling risk index; S23: Perform multi-faceted judgments on the corresponding partitions based on the multi-field coupling risk index, risk gradient, and field variable synergy coefficient, determine high-risk working conditions for the partitions based on the multi-faceted judgment results, and determine partition reinforcement plans for the corresponding partitions based on the high-risk working conditions for the partitions.

[0009] The application provides a foundation pit supporting structure design method, S3 comprises: S31: quantifying the transmission relationship between each partition based on the multi-field coupling result, determining adjacent partitions, extracting the interface field variable of the interface between adjacent partitions from the multi-field coupling result, and generating a transmission strength matrix according to the interface field variable; S32: calculating the energy transmission efficiency and the dominant transmission direction of the interface between adjacent partitions according to the transmission strength matrix, and determining a transition layer according to the energy transmission efficiency and the dominant transmission direction, and mapping the transmission strength matrix into supporting parameters based on the transition layer; S33: determining the connection between partition reinforcement schemes based on the supporting parameters of the transition layer, fusing all partition reinforcement schemes and the coupling model according to the connection, and obtaining a fused supporting structure.

[0010] The application provides a foundation pit supporting structure design method, S4 comprises: S41: obtaining a vegetation root-time strength curve of a foundation pit to be constructed, determining a root-concrete friction coefficient based on the vegetation root-time strength curve, and determining an ecological mechanics-time interference index of the foundation pit to be constructed; S42: obtaining a material-time strength curve of a supporting material in the fused supporting structure, and determining a material-aging index based on the material-time strength curve; S43: determining a supporting-vegetation system comprehensive failure risk index of the fused supporting structure in combination with the ecological mechanics-time interference index and the material-aging index, and determining a failure difference of the foundation pit according to the supporting-vegetation system comprehensive failure risk index; S44: constructing a life cycle model based on the failure difference and the coupling model, combining the fused supporting structure and the life cycle model to construct an early warning model, and obtaining a whole life cycle early warning system by comprehensively considering the life cycle model and the early warning model.

[0011] The application provides a foundation pit supporting structure design system, comprising: A coupling model construction module: obtaining related data of a foundation pit to be constructed, constructing a first model, a second model and a third model based on the related data, coupling the first model, the second model and the third model, and obtaining a coupling model; A partition reinforcement module: outputting a multi-field coupling result of a foundation pit structure according to the coupling model, partitioning the foundation pit structure based on the multi-field coupling result, sampling parameters according to the partition, calculating a partition high-risk working condition according to the partition parameter sampling result, and generating a partition reinforcement scheme of the foundation pit structure according to the partition high-risk working condition; Fusion support structure module: quantifying the transmission relationship between each partition based on the multi-field coupling result, determining the transition layer according to the transmission relationship, and fusing all partition reinforcement schemes and coupling models according to the transition layer to obtain the fusion support structure; Full life cycle warning module: predicting the failure difference of the foundation pit based on the fusion support structure, developing a life cycle model according to the failure difference, combining the fusion support structure and the life cycle model to construct a warning model, and obtaining a full life cycle warning system by comprehensively considering the life cycle model and the warning model.

[0012] The application provides a foundation pit support structure design cloud platform, which comprises a coupling model calculation engine, a dynamic partition optimization tool, a fusion support structure generator and a full life cycle warning system.

[0013] Compared with the prior art, the application has the following beneficial effects: by constructing a coupling model, outputting multi-field interaction results and intelligently partitioning, generating customized reinforcement schemes based on risk working conditions. The transition layer is designed by quantifying the transmission relationship between partitions, and the fusion support system is formed. The fusion support system combines the failure difference to construct a life cycle model and a warning model, realizes full life cycle dynamic monitoring, improves the monitoring efficiency, combines environmental monitoring data with support structure design, fully utilizes environmental factors, helps to promote the intelligent and green transformation of geotechnical engineering, realizes functional safety and ecological protection, and prolongs the service life of the foundation pit support structure.

[0014] Other features and advantages of the present application will be described in the following description, and some will become apparent from the description, or will be understood by those skilled in the art. The purpose and other advantages of the present application can be achieved and obtained by the structure specifically pointed out in the written description and the accompanying drawings.

[0015] The technical solutions of the present application will be further described in detail below with the help of the accompanying drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0016] The accompanying drawings are used to provide a further understanding of the present application, and constitute a part of the specification, together with the embodiments of the present application, to explain the present application, and do not constitute a limitation on the present application. In the drawings: Figure 1 is a flowchart of a foundation pit support structure design method provided by an embodiment of the present application; Figure 2 is a structural schematic diagram of a foundation pit support structure design system provided by an embodiment of the present application; Figure 3 is a collaborative flowchart of a foundation pit support structure design cloud platform provided by an embodiment of the present application. DETAILED DESCRIPTION

[0017] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention. Example 1:

[0018] The embodiment of the present invention provides a foundation pit support structure design method, such as Figure 1 Shown, including: S1: 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 perform model coupling on the first model, the second model, and the third model to obtain a coupled model; S2: Outputting multi-field coupling results of the foundation pit structure according to the coupling model, partitioning the foundation pit structure based on the multi-field coupling results, performing parameter sampling according to the partitions, calculating high-risk working conditions of the partitions according to the partition parameter sampling results, and generating a partition reinforcement plan for the foundation pit structure based on the high-risk working conditions of the partitions; S3: Quantify the transfer relationship between each partition based on the multi-field coupling results, determine the transition layer according to the transfer relationship, and fuse all partition reinforcement schemes with the coupling model according to the transition layer to obtain a fused support structure; S4: Predict the failure differences of the foundation pit based on the fusion support structure, develop a life cycle model based on the failure differences, build an early warning model by combining the fusion support structure and the life cycle model, and derive a full life cycle early warning system by integrating the life cycle model and the early warning model.

[0019] In this embodiment, the relevant data includes stratum-related data, groundwater layer-related data, above-ground building-related data, and root system-related data.

[0020] In this embodiment, the first model is a geological-hydrological model. The corresponding input is the stratum-related data (borehole SPT value, soil c, φ, k) and the groundwater layer-related data (water level, water pressure, aquifer thickness). The corresponding output is a three-dimensional geological body model (including soil layer distribution, groundwater isosurface), initial stress field, and permeability coefficient field. A certain foundation pit model predicts the sand layer liquefaction risk area (safety factor Fs = 0.8), which is then increased to Fs = 1.3 after dewatering.

[0021] In this embodiment, the second model is a building-foundation pit interaction model. The corresponding input is the building geometry, including the outline, foundation type, structural stiffness, including the 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, a project predicts that the settlement of a commercial complex exceeds the limit, and it is optimized to a "pile row + isolation ditch" scheme, and the settlement is controlled within 8mm.

[0022] In this embodiment, the third model is a root biomechanics model, the corresponding input is root topology, including diameter, orientation, branching, biochemical parameters, including water absorption rate, secretion composition, and the corresponding output is root reinforcement zone shear strength increment and concrete aging rate caused by root corrosion.

[0023] In this embodiment, the coupling model input is the parameter field after synchronization of the three models, such as soil c(x, y, z, t), building load p(t), time scaling factor, and the output is the multi-field coupling result, such as stress-seepage-displacement spatiotemporal evolution and comprehensive risk index, such as the failure probability Pf=0.25 of a certain area A after 5 years.

[0024] In this embodiment, the multi-field coupling result is the comprehensive field data output by the coupling model, including the interaction results of stress field, seepage field, and displacement field, etc. For example, the simulation of a deep foundation pit in Shanghai shows that the rise of groundwater level causes the increase of pore water pressure in the clay layer to 120 kPa, triggering the lateral displacement of the supporting pile to 25 mm.

[0025] In this embodiment, parameter sampling is a technical process of selectively obtaining key mechanical-hydrological-deformation parameters in the foundation pit partition based on multi-field coupling results, the core of which is to optimize the support design through spatially differentiated data collection, including stress field variables, seepage field variables, displacement field variables, and material degradation parameters. Parameter sampling also includes the characteristics of the foundation pit itself, such as foundation pit depth, foundation pit plan shape and size, excavation method and sequence.

[0026] In this embodiment, partitioning is to divide the foundation pit into sub-regions with similar mechanical behavior; adaptive sampling is to dynamically adjust the sampling density according to the risk level, for example, the sampling density in area A is 1 point / ㎡, and in area C is 1 point / 10㎡, saving 40% of computing resources.

[0027] In this embodiment, the high-risk working condition of the partition is the state of a specific partition under the most unfavorable load combination, for example, under the working condition of heavy rain + excavation, the pore water pressure in area B rises to 150 kPa, and the displacement exceeds the limit.

[0028] In this embodiment, the partition reinforcement scheme is a customized measure for high-risk areas, for example, in area A: high-pressure jet grouting pile (depth 15 m, spacing 1.2 m); in area B: light well point dewatering (water level drops to -5 m).

[0029] In this embodiment, the transfer relationship is a quantitative representation of the mechanical and hydraulic interaction between partitions, reflecting the cross-zone transmission characteristics of load, deformation, and seepage, for example, in a certain deep foundation pit, the seepage pressure of the sand layer (area A) is transmitted to the clay layer (area B), causing the pore water pressure at the junction to rise by 15 kPa, inducing local uplift in area B.

[0030] In this embodiment, the transition layer is a buffer structure designed to coordinate the differences in stiffness / permeability between adjacent partitions. For example, the sand-clay interface is a 1m thick graded gravel layer, and the pile-soil interface is a 0.5m wide cement-soil mixing pile.

[0031] In this embodiment, the integrated support structure is an overall support system formed by coordinating various zoning schemes. For example, a foundation pit adopts an underground continuous wall with a thickness of 1m in the high-risk area; the transition layer is triple-tube rotary jet piles; and the low-risk area is a soil nail wall. The three are rigidly connected by the crown beam and the waist beam to form a unified force system.

[0032] In this embodiment, the failure difference is the time difference between the destruction of vegetation roots in the foundation pit and the failure of materials. For example, a project predicts that the roots will penetrate the support in 2035, while the materials will fail in 2040. The difference is 60 months and intervention is required.

[0033] In this embodiment, the input of the life cycle model is the vegetation root-time strength curve, the material-time strength curve, and environmental data, and the output is the remaining life (years) of the support structure and the critical failure time node. For example, after inputting the climate data of a certain place, the model output shows that the life of the support pile is shortened by 3 years due to rainwater acidification.

[0034] 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 warning level (red / orange / yellow) and disposal suggestions. For example, the system of a certain project automatically pushes: Area A will be at high risk in two years, and it is recommended to pre-embed a root barrier membrane.

[0035] In this embodiment, the full life cycle early warning system is a closed-loop management system that integrates monitoring, analysis, and decision-making.

[0036] The working principle and beneficial effects of the above technical solution are as follows: by constructing a coupled model, outputting multi-field interaction results and intelligently zoning, a customized reinforcement plan is generated based on risk conditions. The transfer relationship between zones is quantified to design transition layers, forming a fused support system. The fused support system combines failure differences to construct a lifecycle model and early warning model, enabling dynamic monitoring throughout the entire lifecycle and improving monitoring efficiency. Integrating environmental monitoring data with support structure design and fully utilizing environmental factors will help promote the transformation of geotechnical engineering towards intelligent and green development, achieve functional safety and ecological protection, and simultaneously extend the service life of foundation pit support structures. Example 2:

[0037] An embodiment of the present invention provides a foundation pit support structure design method, S1 comprising: S11: Perform LiDAR scanning and borehole sampling on the construction pit to obtain stratum-related data and groundwater layer-related data to generate a first model; S12: Determine the relevant data of the above-ground building according to the location information of the foundation pit to be constructed, and generate a second model; S13: Perform CT scanning 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 through a time scaling factor to obtain a time synchronization result, and couple the first model, the second model, and the third model according to the time synchronization result and the three-way index table to obtain a coupling model.

[0038] In this embodiment, the stratum-related data is a set of parameters that describe the spatial distribution and mechanical properties of soil / rock layers. For example, in a foundation pit, the clay layer (buried depth 2-5m, c=25kPa, φ=12°), sand layer (5-8m, m / s) of layered data.

[0039] In this embodiment, the groundwater layer related data are parameters that characterize the location and hydraulic characteristics of the aquifer. For example, in a subway project, the monitoring well measured the pressure head of -8m and the permeability coefficient of , impact radius 150m.

[0040] In this embodiment, the data related to the above-ground buildings are the geometry, structure, and current deformation data of the buildings around the foundation pit. For example, for a high-rise building, laser scanning measures a distance of 15 m from the foundation pit, a raft foundation (thickness 1.2 m), and a current inclination rate of 0.05%.

[0041] In this embodiment, the root system-related data are the distribution, mechanical and biochemical parameters of the plant root system. For example, for the banyan tree root system, μCT scanning shows that the taproot diameter is 8-15 mm, the extension depth is 3.5 m, and the secretion pH is 4.2.

[0042] In this embodiment, the three-way index table is a cross-model retrieval database that associates three key model parameters. For example, the table header is "Coordinates," and the corresponding data is (100, 50, -5). The table header is "Address Parameters" derived from stratum and groundwater data, and the corresponding data is clay, c = 30 kPa. The table header is "Building Impact," and the corresponding data is an additional load of 15 kPa. The table header is "Root Effect," and the corresponding data is the primary root, Δc = 8 kPa.

[0043] In this embodiment, the time scaling factor is used to coordinate the different time scales of geological evolution and root growth. The corresponding specific formula is: , represents the time scaling factor, Indicates the characteristic time of the root system; Indicates the time of geological characteristics; for example, the silt layer of a project Willow root system When synchronizing, use That is: the first model 1 year ≈ the second model 0.5 year.

[0044] In this embodiment, the time synchronization result is a unified space-time reference generated after the time scales of the first model and the second model are coordinated by the time scaling factor, and the core is to align the two systems with different evolution rates to the same time axis.

[0045] In this embodiment, CT scanning is performed on the to-be-detected land to obtain topological parameters of root systems, the topological parameters are classified to obtain main root path coordinates and hair root path coordinates, rigid cluster particle chains are determined based on the main root path coordinates, and flexible cluster particle chains are determined according to the hair root path coordinates; main root-soil contact action is determined according to the rigid cluster particle chains, hair root-soil contact action is determined according to the flexible cluster particle chains, and root-root connection action is determined according to the connection relationship between the rigid cluster particle chains and the flexible cluster particle chains, and the main root-soil contact action, the hair root-soil contact action and the root-root connection action are comprehensively constructed to construct the second model; the topological parameters are determined by CT scanning data, including root system diameter distribution, branch angle and spatial density, for example, the fractal dimension D of the root system of a banyan tree at a depth of 2 m is 2.3.

[0046] In this embodiment, the topological parameters are three-dimensional geometric and connectivity data of root systems obtained by CT scanning, including diameter, direction, bifurcation point and the like. For example, the CT scan of a sycamore tree shows that the main root diameter is 8-15 mm, the hair root diameter is 0.5-2 mm, and the bifurcation angle is 55°±10°.

[0047] In this embodiment, the main root path coordinates are the spatial continuous coordinate sequences of the main root system obtained by CT scanning, which are used to describe the three-dimensional spatial direction and geometric morphology of the main root from the root base to the tip, and the main characteristics include robustness: the diameter is significantly larger than that of the hair root (common threshold 5 mm), continuity: presents a coherent spatial curve (non-discrete points), and mechanical dominance: bears more than 80% of the root system tensile / shear resistance.

[0048] In this embodiment, the hair root path coordinates are spatial distribution data of fine root systems (diameter ≤5 mm) extracted by CT scanning, which are used to describe the three-dimensional topological structure of the hair root network and its micro interaction with the soil, and the main characteristics include: fineness: diameter is usually 0.1-5 mm, and is distributed in a hair-like manner, high density: up to hundreds to thousands of roots per unit volume, such as 300 roots / cm³, and functional dominance: regulates soil permeability, microbial activity and shallow soil fixation.

[0049] In this example, the rigid cluster particle chain is used to model the mechanical behavior of the main root as a series of rigidly connected spherical particle chains. Each particle represents a segment of the main root, with its diameter and mechanical parameters directly mapped from CT scan data. This chain structure can accurately simulate the tensile and bending properties of the main root, for example, a 10 mm diameter poplar main root modeled by a rigid cluster particle chain can quantify its effect on increasing the soil shear strength by 15 kPa in slope engineering. The contact between particles uses the Hertz-Mindlin model, with a normal stiffness of 1e8 N / m to simulate the rigid characteristics of the root xylem.

[0050] In this example, the flexible cluster particle chain is used to represent the penetration and micro-mechanical effects of the fibrous root. The method combines porous media theory and discrete fiber networks. Each flexible particle represents a micro-section of the fibrous root, with its penetration properties modified by Darcy's law (e.g., permeability k = 1.5k_soil), and its mechanical behavior simulated by low-stiffness spring elements (e.g., stiffness 500 N / m). For example, a dog grass fibrous root network modeled by a flexible cluster particle chain can predict a 60% reduction in the surface sand permeability, while increasing the apparent cohesion of the soil by 3 kPa through the fiber pullout effect.

[0051] In this example, the main root-soil contact effect focuses on the macro-mechanical interaction between the main root and the surrounding soil, which is described by a combination of contact mechanics and bonded particle models.

[0052] In this example, the fibrous root-soil contact effect focuses on the micro-scale changes in soil structure and hydraulic coupling. Fibrous roots cement soil particles through exudates and form tortuous paths in soil pores.

[0053] In this example, the root-root connection effect is modeled through two dimensions of mechanical binding and biochemical coupling. Mechanically, a spring-damper system is used to simulate force transmission at root branching points, for example, in a mangrove root network model, a radial stiffness of 800 N / m and a tangential damping of 50 Ns / m at the branching nodes can reproduce the cooperative deformation characteristics of the root system under typhoon loading, increasing the overall anti-overturning capacity by 40%. Biochemically, a diffusion-reaction equation is used to simulate the exchange of substances between roots, such as setting the nutrient transport rate at the junction of poplar roots to 0.2 mmol / (m·s), which explains the symbiotic reinforcement phenomenon.

[0054] In this example, the connection relationship includes two aspects of topology and function. The topology aspect explicitly defines the geometric association of root branches through parent-child node IDs, for example, in a certain pine model, the connection relationship between the main root ID=101 and the fibrous root ID=201-215 is marked. The functional aspect defines the failure criteria for the connection points, such as triggering the spring fracture at the branching point when the bending moment exceeds 2 N·m, which corresponds to the critical condition of mechanical damage to the root system in reality.

[0055] The working principle and beneficial effects of the embodiments of the present application include: acquiring geological-architectural-root system multi-source data, constructing three models and establishing three-way index table to associate key parameters, using time scaling factor to unify the time scales of different processes, and finally coupling to generate a dynamic model that comprehensively reflects geological deformation, architectural load and root system action, thereby improving prediction accuracy and realizing dynamic optimization of ecological engineering. Embodiment 3:

[0056] The embodiment of the present application provides a foundation pit support structure design method, S11 includes: S111: generating a three-dimensional geological model based on the stratum-related data through Kriging interpolation, directly corresponding each voxel unit in the three-dimensional geological model to a FEM grid node, assigning the interpolated stratum-related data as the unit material attribute, and then forming an ecological-elastic-plastic constitutive equation; S112: establishing a hierarchical contact system based on the stratum boundary surface in the three-dimensional geological model, setting constraint conditions based on the hierarchical contact system and groundwater layer-related data, and dynamically adjusting the ecological-elastic-plastic constitutive equation based on the constraint conditions, and then obtaining the first model.

[0057] In this embodiment, Kriging interpolation is based on the principle of geostatistics, uses a variogram to quantify spatial correlation, and predicts parameters of unsampled points. For example, for a financial center foundation pit, the input is the SPT-N value of 42 drill holes (spacing 50 m), and the output is a three-dimensional soil stiffness field (Es=12-58 MPa) with a resolution of 0.25 m³. The corresponding effect is that the interpolation error is ±8%, and the accuracy is improved by 35% compared with the traditional inverse distance weighted method.

[0058] In this embodiment, the voxel unit is the smallest volume unit of three-dimensional digital rock (usually 0.5-2m cube), which stores multiple parameter attributes. The voxel size corresponding to a subway foundation pit is 1m x 1m x 0.5m, and the stored parameters are: soil type, c (15-35kPa), φ (8-30°), and cm / s), special processing: cave area voxel is marked as "high-risk unit".

[0059] In this embodiment, the unit material attribute is the physical parameter assignment of the finite element grid, which supports anisotropic definition. For example, the muck layer unit corresponding to the financial center foundation pit: E=15MPa, v=0.35, c=18kPa (considering creep), rock layer unit: E=5GPa, v=0.2, tensile strength 2MPa.

[0060] In this embodiment, the FEM grid node is a geometric discrete point for numerical calculation, which needs to be accurately matched with the geological voxel.

[0061] In this embodiment, the constraint condition is a mathematical representation of the boundary mechanics and hydrology. For example, for an airport foundation pit, the mechanical boundary is a foundation spring support (stiffness 50 MN / m³), the seepage boundary is a dynamic water head boundary (± 3 m changes with seasons), and the special constraint is a displacement limit of 8 mm adjacent to a high-speed rail track.

[0062] In this embodiment, the hierarchical contact system is a contact algorithm classified according to the importance of the geological interface. For example, the contact level is one, the corresponding interface type is a sand-clay interface, the processing method is an interface element method, and the parameter setting is kn=1e8 kPa / m, μ=0.4. If the contact level is two, the corresponding interface type is a fracture development zone, the processing method is an equivalent continuum, and the parameter setting is a reduction E to 70%.

[0063] In this embodiment, the ecological-elastoplastic constitutive equation is where F represents the yield function value; represents the norm of the deviatoric stress tensor; represents the tensor norm; represents the spatial enhancement coefficient of cohesion force varying with position x; represents the geological structure effect stress varying with position x; represents the pore water pressure; s represents the deviatoric stress tensor; p represents the average stress; represents the confining pressure friction effect coefficient varying with position x; represents the soil cohesion varying with position x; represents the internal friction angle varying with position x; γ represents the geological structure effect coefficient; represents the pore water pressure softening coefficient; represents the pore water pressure varying with position x; represents the water density; represents the water head height; represents the saturation indicator function of position x, and saturation is equal to 1 and unsaturation is equal to 0.

[0064] The working principle and beneficial effects of the embodiment of the application include: generating a three-dimensional geological model by Kriging interpolation of discrete geological parameters, and mapping the three-dimensional geological model to FEM grid nodes as material properties, and then forming an ecological-elastoplastic constitutive equation, automatically setting discontinuous boundary conditions according to stratigraphic interfaces, dynamically correcting ecological-elastoplastic constitutive equation parameters in combination with root distribution areas, and finally outputting a first model coupling root effect, realizing quantitative representation of root-soil interaction, and optimizing support design. Embodiment 4:

[0065] The embodiment of the application provides a foundation pit support structure design method, and S2 comprises: S21: outputting a multi-field coupling result of the foundation pit structure according to the coupling model, extracting a plurality of key field variables from the multi-field coupling result, and calculating a distribution of the key field variables; and establishing a field variable synergistic failure matrix based on the key field variables and the distribution of the key field variables corresponding to the key field variables; S22: formulating a partition index based on an eigenvalue of the field variable synergistic failure matrix, partitioning the foundation pit structure according to the partition index, sampling parameters for each partition, calculating a multi-field coupling risk index of each sampling point, and extracting a risk gradient and a field variable synergistic coefficient from the multi-field coupling risk index; S23: making a multi-aspect judgment on a corresponding partition according to the multi-field coupling risk index, the risk gradient and the field variable synergistic coefficient, and determining a partition high-risk working condition according to a result of the multi-aspect judgment, and determining a partition reinforcement scheme of the corresponding partition based on the partition high-risk working condition.

[0066] In this embodiment, the key field variable is a physical quantity that plays a leading role in the stability of the foundation pit, such as the maximum shear stress, pore water pressure, and plastic strain. For example, in a mucky clay foundation pit, joint over-standard of the maximum shear stress and the pore water pressure leads to local slip.

[0067] In this embodiment, the distribution is the spatial variation law of the key field variable, which is expressed by an isogram or a three-dimensional cloud chart. For example, a BIM platform visual display of a certain project shows that the pore water pressure forms a high-pressure area in the northwest corner of the foundation pit.

[0068] In this embodiment, the field variable synergistic failure matrix is a symmetric matrix reflecting the joint failure probability of multiple field variables, and the elements represent the failure correlation of the row variable and the column variable. For example, a synergistic coefficient of 0.65 in the matrix indicates that the joint action of the two increases the failure probability to 0.28.

[0069] In this embodiment, the partition index is a quantitative standard constructed based on the eigenvalue of the failure matrix, which is used to divide the risk level. For example, a certain eigenvalue corresponds to a seepage-stress coupling dominant area, which needs to be handled separately.

[0070] In this embodiment, the multi-field coupling risk index is a weighted risk score that comprehensively considers each field variable; the risk gradient is the spatial variation rate of the risk index; and the field variable synergistic coefficient is a parameter quantifying the multi-field coupling strength.

[0071] In this embodiment, the multi-aspect judgment is a comprehensive evaluation combining the multi-field coupling risk index, the risk gradient and the field variable synergistic coefficient. For example, a certain partition satisfies the conditions of multi-field coupling risk index > 0.8, risk gradient > 5, and field variable synergistic coefficient > 0.6 at the same time, and is determined as an extremely high risk.

[0072] In this embodiment, the multi-faceted judgment results are the final conclusion of the risk assessment, including risk level and dominant factors. For example, in area A, the risk is high due to seepage dominance, and precipitation + grouting is recommended.

[0073] The working principle and beneficial effects of the embodiment of the present 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; divides the area by the matrix eigenvalues, calculates the risk gradient and field variable collaborative coefficient of each partition based on the results of parameter sampling, combines the multi-field coupling risk index to identify high-risk working conditions, and finally generates a targeted partition reinforcement plan. Example 5:

[0074] An embodiment of the present invention provides a method for designing a foundation pit support structure, wherein S3 includes: S31: quantifying the transfer relationship between each partition based on the multi-field coupling results, determining adjacent partitions, extracting interface field variables of adjacent partitions from the multi-field coupling results, and generating a transfer intensity matrix based on the interface field variables; S32: Calculating the energy transfer efficiency and dominant transfer direction of the interface between adjacent partitions according to the transfer strength matrix, determining a transition layer according to the energy transfer efficiency and dominant transfer direction, and mapping the transfer strength 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 the partition reinforcement schemes with the coupling model according to the connection to obtain a fused support structure.

[0075] In this embodiment, adjacent zones are sub-areas with different risk levels that have direct physical contact. For example, a subway foundation pit is divided into: a high-risk zone with support pile displacement >20mm, a medium-risk zone with support pile displacement 10-20mm, and a low-risk zone with support pile displacement <10mm. The boundary between the three zones requires special treatment.

[0076] In this embodiment, the interface field variables are key physical quantities at the interface between adjacent partitions, such as shear stress, displacement difference, and hydraulic gradient. For example, a project monitors the shear stress of the clay-sand interface = 32 kPa and the displacement difference = 8 mm, indicating that a transition structure needs to be set up.

[0077] In this embodiment, the transfer intensity matrix is ​​a square matrix that describes the coupling relationship between interface field variables, and the elements represent the transfer contribution of row variables to column variables.

[0078] In this embodiment, the energy transfer efficiency is the ratio of the effective 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 geotextiles; the energy transfer efficiency of the sand layer is 0.9, which is efficient.

[0079] In this embodiment, the dominant transmission direction is the spatial azimuth corresponding to the maximum energy transmission efficiency, for example, the dominant transmission direction of a certain foundation pit is 42°, so a 45° inclined micro pile transition layer is used.

[0080] In this embodiment, the support parameter is a technical index of the transition layer, such as pile diameter, spacing, inclination, material strength, etc., for example, the transition layer parameters of a certain project are rotary jet pile diameter 800mm, spacing 1.2m, inclination 30°, and compressive strength 15MPa.

[0081] In this embodiment, the mapping process is a technical process of converting physical quantities of the transmission matrix into support parameters, for example, energy transmission efficiency <0.5 is mapped to a 50% reduction in pile spacing, and dominant transmission direction >30° is mapped to setting an inclined pile.

[0082] In this embodiment, the connection condition is the lapping method and mechanical compatibility of different partition support structures, for example, a certain project requires that the bored pile (A zone) overlaps the mixing pile (B zone) by 1.5m, and the reinforcement cage lapping length is 35d (d is the diameter of the reinforcement).

[0083] The working principle and beneficial effects of the embodiments of the present application include: based on the adjacent partition interface field variable extracted from the multi-field coupling data, the transmission strength matrix is constructed to quantify the energy transmission efficiency and the dominant direction, the transmission characteristics are mapped to the support parameters through the transition layer design, and finally the reinforcement schemes of each partition are fused to form a whole support system, which prevents the support from being broken, accurately prevents and controls the risk, and dynamically adapts to the construction progress. Embodiment 6:

[0084] The embodiment of the present application provides a foundation pit support structure design method, and S4 comprises: S41: obtaining a vegetation root-time strength curve of a to-be-constructed foundation pit, determining a root-concrete friction coefficient based on the vegetation root-time strength curve, and determining an ecological mechanics-time interference index of the to-be-constructed foundation pit; S42: obtaining a material-time strength curve of a support material in a fused support structure, and determining a material-aging index based on the material-time strength curve; S43: determining a support-vegetation system comprehensive failure risk index of the fused support structure in combination with the ecological mechanics-time interference index and the material-aging index, and determining a failure difference of the foundation pit according to the support-vegetation system comprehensive failure risk index; S44: constructing a life cycle model based on the failure difference and the coupling model, combining the fused support structure with the life cycle model to construct an early warning model, and obtaining a whole life cycle early warning system by comprehensively considering the life cycle model and the early warning model.

[0085] In this embodiment, the vegetation root-time strength curve is a curve describing the change of the mechanical properties of the roots over time, reflecting the tensile strength, diameter expansion and other characteristics during the growth of the roots. For example, the monitoring of the roots of a certain site of a sycamore tree showed that the tensile strength of the main roots increased from 8 MPa to 15 MPa in 3 years, and stabilized at 12 MPa after 5 years.

[0086] In this embodiment, the root-concrete friction coefficient is a friction characteristic parameter of the contact surface between the roots and the concrete, which changes with the secretion of the roots and the surface roughness. For example, a test of a foundation pit showed that the initial friction coefficient between the roots of a camphor tree and C30 concrete was 0.6, and decreased to 0.4 after 10 years due to corrosion of the secretion.

[0087] In this embodiment, the ecological mechanics-time interference index is a time-varying index quantifying the influence of the roots on the mechanical properties of the supporting structure, which comprehensively considers the growth force of the roots and the attenuation of the friction coefficient. For example, in a certain slope engineering, the roots of a banyan tree caused the index to increase by 0.15 per year, indicating that the lateral pressure of the supporting pile needed to be increased by 20% after 5 years.

[0088] In this embodiment, the material-time strength curve is a curve showing the degradation of the strength of the supporting material over time. For example, 28-year data of a C40 concrete pile of a foundation pit showed that the compressive strength decreased by 0.8 MPa per year.

[0089] In this embodiment, the supporting material is an engineering material used in the supporting structure, such as concrete, steel support, geosynthetic material, etc. For example, a certain project used glass fiber reinforcement instead of steel reinforcement to avoid the problem of root corrosion.

[0090] In this embodiment, the material-aging index is a parameter representing the degradation rate of the material performance, which is based on the environmental exposure conditions and the material characteristics. For example, the chloride ion corrosion in coastal areas made the aging index of the concrete reach 0.12 / year, while in inland areas, the index was only 0.05 / year.

[0091] In this embodiment, the supporting-vegetation system comprehensive failure risk index is a risk score that comprehensively considers the ecological interference and the material aging. The supporting-vegetation system comprehensive failure risk index = ecological interference index x 0.6 + aging index x 0.4. For example, when the index of a certain project exceeds 0.8, root cutting and grouting reinforcement are triggered.

[0092] The working principle and beneficial effects of the embodiments of the present application include: quantifying the decay of the root-concrete friction coefficient through the vegetation root-time strength curve, quantifying the material-aging index through the material-time strength curve, determining the supporting-vegetation system comprehensive failure risk index by combining the ecological mechanics-time interference index and the material-aging index, and finally integrating the coupling model data to form a whole life cycle early warning system, which realizes the dynamic prediction of the failure risk of the supporting-vegetation system, balances the vegetation protection and the engineering safety, and effectively prolongs the service life of the supporting structure. Embodiment 7:

[0093] The embodiment of the present invention provides a foundation pit support structure design system, such as Figure 2 Shown, including: A coupling model construction module: obtaining relevant data of the foundation pit to be constructed, constructing a first model, a second model, and a third model based on the relevant data, and performing model coupling on the first model, the second model, and the third model to obtain a coupling model; Partition reinforcement module: Outputs the multi-field coupling results of the foundation pit structure according to the coupling model, partitions the foundation pit structure based on the multi-field coupling results, samples parameters according to the partitions, calculates the high-risk working conditions of the partitions according to the partition parameter sampling results, and generates a partition reinforcement plan for the foundation pit structure based on the high-risk working conditions of the partitions; Fusion support structure module: quantifies the transfer relationship between each partition based on the multi-field coupling results, determines the transition layer based on the transfer relationship, and fuses all partition reinforcement schemes with the coupling model based on the transition layer to obtain a fusion support structure; Full life cycle warning module: Based on the fusion support structure, the failure differences of the foundation pit are predicted, and a life cycle model is developed according to the failure differences. The warning model is constructed by combining the fusion support structure and the life cycle model. The full life cycle warning system is obtained by integrating the life cycle model and the warning model.

[0094] The working principle and beneficial effects of the above technical solution are as follows: by constructing a coupled model, outputting multi-field interaction results and intelligently zoning, a customized reinforcement plan is generated based on risk conditions. The transfer relationship between zones is quantified to design transition layers, forming a fused support system. The fused support system combines failure differences to construct a lifecycle model and early warning model, enabling dynamic monitoring throughout the entire lifecycle and improving monitoring efficiency. Integrating environmental monitoring data with support structure design and fully utilizing environmental factors will help promote the transformation of geotechnical engineering towards intelligent and green development, achieve functional safety and ecological protection, and simultaneously extend the service life of foundation pit support structures. Example 8:

[0095] The embodiment of the present invention provides a foundation pit support structure design cloud platform, such as Figure 3 As shown, it includes a coupled model calculation engine, a dynamic partition optimization tool, a fused support structure generator, and a full life cycle early warning system.

[0096] In this embodiment, the coupled model calculation engine is the computing core of the integrated geomechanics-root biomechanics-environmental multi-physics field coupling algorithm, supporting parallel solution and real-time data-driven update.

[0097] In this embodiment, the dynamic zoning optimization tool is an intelligent zoning system based on machine learning and topological analysis, which can dynamically adjust the risk area division as the construction progresses.

[0098] In this embodiment, the fusion support structure generator is a parameterized design module that intelligently fuses the partition support scheme with the transition layer design.

[0099] In this embodiment, the full life cycle early warning system is a monitoring-early warning-decision platform embedded with time-varying models of material aging and root growth.

[0100] The working principle and beneficial effects of the embodiments of the present application include: multi-field coupling calculation improves the reliability of the support scheme, reduces the risk of structural failure, real-time partition adjustment reduces material waste, automatic fusion improves support design efficiency, the full-cycle early warning system identifies risks 6-12 months in advance, and improves maintenance efficiency.

[0101] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A foundation pit support structure design method, characterized in that: include: S1: 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 perform model coupling on the first model, the second model, and the third model to obtain a coupled model; S2: Outputting multi-field coupling results of the foundation pit structure according to the coupling model, partitioning the foundation pit structure based on the multi-field coupling results, performing parameter sampling according to the partitions, calculating high-risk working conditions of the partitions according to the partition parameter sampling results, and generating a partition reinforcement plan for the foundation pit structure based on the high-risk working conditions of the partitions; S3: Quantify the transfer relationship between each partition based on the multi-field coupling results, determine the transition layer according to the transfer relationship, and fuse all partition reinforcement schemes with the coupling model according to the transition layer to obtain a fused support structure; S4: Predict the failure differences of the foundation pit based on the fusion support structure, develop a life cycle model based on the failure differences, build an early warning model by combining the fusion support structure and the life cycle model, and derive a full life cycle early warning system by integrating the life cycle model and the early warning model.

2. A foundation pit support structure design method according to claim 1, characterized in that: S1 includes: S11: Perform LiDAR scanning and borehole sampling on the construction pit to obtain stratum-related data and groundwater layer-related data to generate a first model; S12: Determine the relevant data of the above-ground building according to the location information of the foundation pit to be constructed, and generate a second model; S13: Perform CT scanning 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 through a time scaling factor to obtain a time synchronization result, and couple the first model, the second model, and the third model according to the time synchronization result and the three-way index table to obtain a coupling model.

3. A foundation pit support structure design method according to claim 2, characterized in that: The S11 includes: S111: generating a three-dimensional geological model through Kriging interpolation based on the stratum-related data, directly mapping each voxel unit in the three-dimensional geological model to a FEM grid node, assigning the interpolated stratum-related data to the unit material properties, and thus forming an eco-elastoplastic constitutive equation; S112: Establishing a hierarchical contact system based on the stratum interfaces in the three-dimensional geological model, setting constraints based on the hierarchical contact system in combination with groundwater layer-related data, and dynamically adjusting the ecological-elastoplastic constitutive equation based on the constraints to obtain a first model.

4. A foundation pit support structure design method according to claim 1, characterized in that: S2 includes: S21: Outputting a multi-field coupling result of the foundation pit structure according to the coupling model, extracting a plurality of key field variables from the multi-field coupling result, calculating the distribution of the key field variables, and establishing a field variable collaborative failure matrix by combining the key field variables and the corresponding distribution of the key field variables; S22: formulating a partition index based on the eigenvalue of the field variable synergy failure matrix, partitioning the foundation pit structure according to the partition index, sampling parameters for each partition, calculating a multi-field coupling risk index for each sampling point, and extracting a risk gradient and a field variable synergy coefficient from the multi-field coupling risk index; S23: Perform multi-faceted judgments on the corresponding partitions based on the multi-field coupling risk index, risk gradient, and field variable synergy coefficient, determine high-risk working conditions for the partitions based on the multi-faceted judgment results, and determine partition reinforcement plans for the corresponding partitions based on the high-risk working conditions for the partitions.

5. The method for designing a foundation pit support structure according to claim 1, wherein: S3 includes: S31: quantifying the transfer relationship between each partition based on the multi-field coupling results, determining adjacent partitions, extracting interface field variables of adjacent partitions from the multi-field coupling results, and generating a transfer intensity matrix based on the interface field variables; S32: Calculating the energy transfer efficiency and dominant transfer direction of the interface between adjacent partitions according to the transfer strength matrix, determining a transition layer according to the energy transfer efficiency and dominant transfer direction, and mapping the transfer strength 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 the partition reinforcement schemes with the coupling model according to the connection to obtain a fused support structure.

6. A foundation pit support structure design method according to claim 1, characterized in that S4 include: S41: obtaining a vegetation root system-time intensity curve of the foundation pit to be constructed, measuring a root-concrete friction coefficient based on the vegetation root system-time intensity curve, and determining an ecomechanics-time interference index of the foundation pit to be constructed; S42: Obtaining a material-time strength curve of a support material in the fusion support structure, and determining a material-aging index based on the material-time strength curve; S43: Determine a comprehensive failure risk index of the support-vegetation system of the fused support structure by combining the ecomechanics-time interference index and the material-aging index, and determine a failure difference of the foundation pit based on the comprehensive failure risk index of the support-vegetation system; S44: Construct a life cycle model based on the failure difference and coupling model, combine the fusion support structure with the life cycle model to construct an early warning model, and integrate the life cycle model and the early warning model to derive a full life cycle early warning system.

7. A foundation pit support structure design system, characterized in that: include: A coupling model construction module: obtaining relevant data of the foundation pit to be constructed, constructing a first model, a second model, and a third model based on the relevant data, and performing model coupling on the first model, the second model, and the third model to obtain a coupling model; Partition reinforcement module: Outputs the multi-field coupling results of the foundation pit structure according to the coupling model, partitions the foundation pit structure based on the multi-field coupling results, samples parameters according to the partitions, calculates the high-risk working conditions of the partitions according to the partition parameter sampling results, and generates a partition reinforcement plan for the foundation pit structure based on the high-risk working conditions of the partitions; Fusion support structure module: quantifies the transfer relationship between each partition based on the multi-field coupling results, determines the transition layer based on the transfer relationship, and fuses all partition reinforcement schemes with the coupling model based on the transition layer to obtain a fusion support structure; Full life cycle warning module: Based on the fusion support structure, the failure differences of the foundation pit are predicted, and a life cycle model is developed according to the failure differences. The warning model is constructed by combining the fusion support structure and the life cycle model. The full life cycle warning system is obtained by integrating the life cycle model and the warning model.

8. A foundation pit support structure design cloud platform, including a coupled model calculation engine, a dynamic partition optimization tool, a fusion support structure generator, and a full life cycle early warning system.

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