Parameterized design and automatic checking method and system for underground pipe jacking working well
By generating a risk resonance potential field and iteratively adjusting the threshold, the key risk areas of underground pipe jacking working shafts are identified, solving the problem of inaccurate risk area identification in traditional design methods and improving the reliability and safety of the design.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- BEIJING HUANQIU ENERGY & CHEMICAL ENGINEERING PROJECT MANAGEMENT CO LTD
- Filing Date
- 2026-03-20
- Publication Date
- 2026-06-19
Smart Images
Figure CN122241826A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of underground engineering pipe jacking construction, more particularly, the present application relates to a parameterized design and automatic calculation method and system for a working well of underground pipe jacking. BACKGROUND
[0002] In the development of urban underground space, pipe jacking construction is widely used due to its small disturbance to the ground surface and low environmental impact. The stability of the working well, which is the core structure, is directly related to the safety of the project. Traditional design methods usually base on static equilibrium theory, considering the well structure as an independent bearing system, and reinforcing and checking according to the specification experience, supplemented by monitoring feedback during the construction stage. This approach implies a basic consensus that the safety risk of the structure mainly comes from its response to static water and soil load, while the construction disturbance is regarded as an isolated or simplified external factor.
[0003] However, as pipe jacking advances to deeper and more complex strata, the influence of construction disturbance is significantly enhanced, leading to complex dynamic coupling between soil stress redistribution and structural stress state. Especially when the pipe jacking direction, soil mechanical properties, and the weak area of the well body form a specific spatial correlation, local and dynamic construction disturbance will interact with the inherent static vulnerability of the structure. Although existing methods can perform static force calculation and construction settlement prediction separately, they lack a mechanism for dynamic coupling analysis at the micro-mechanical level. This leads to a design that is based on the simple superposition of "the most unfavorable static working condition" and "the average disturbance effect", failing to effectively identify the local high-risk area where the structure is most vulnerable in a specific direction of construction disturbance.
[0004] Therefore, how to accurately locate the local damage risk area that is dramatically amplified by the high coupling of construction dynamic disturbance and structural static vulnerability in space and direction, so as to avoid the sudden instability triggered by "resonance effect" that may be hidden by relying on the overall safety factor. Solving this problem is the key to realizing the fine and anti-disturbance design of the working well, therefore, the parameterized design and automatic calculation method and system for the working well of underground pipe jacking are proposed to solve this problem. SUMMARY
[0005] To solve the above technical problems, the parameterized design and automatic calculation method and system for the working well of underground pipe jacking are provided, which solve the problems raised in the background technology.
[0006] In order to achieve the above purpose, the technical scheme of the present application is as follows:
[0007] In the first aspect, the present application provides a parameterized design and automatic calculation method for the working well of underground pipe jacking, comprising:
[0008] Obtain the cross-sectional geometric parameters, pipe jacking construction disturbance parameters, and engineering geological parameters of the target working well;
[0009] Based on the cross-sectional geometric definition parameters, pipe jacking construction disturbance parameters, and engineering geological parameters, initial modeling, static analysis, and disturbance prediction are performed, and structural fragility mode characteristics and construction disturbance mode characteristics are output.
[0010] Perform feature coupling calculations on the structural vulnerability mode features and construction disturbance mode features to output the risk resonance potential field;
[0011] The region in the risk resonance potential field whose risk resonance potential value is greater than the preset initial threshold is output as the initial analysis domain;
[0012] In the initial analysis domain and its neighborhood, the accuracy of repeated feature coupling calculations is improved, and the updated risk resonance potential field is updated and output.
[0013] Based on the updated risk resonance potential field, the initial threshold is adjusted and output. The region where the risk resonance potential value is greater than the adjusted initial threshold is output as the updated analysis domain.
[0014] Calculate the spatial overlap between the initial and updated analysis domains and determine whether the first convergence threshold has been reached:
[0015] If this is achieved, the output update analysis domain becomes the final focused analysis domain;
[0016] If not, the output update analysis domain is set to the new initial analysis domain and iteratively updated until the spatial overlap reaches the first convergence threshold or the number of iterations reaches the preset value.
[0017] When the number of iterations reaches the maximum number of iterations and still has not converged, the update analysis domain with the minimum spatial overlap during the output iteration process is the final focus analysis domain.
[0018] A refined mechanical analysis is performed within the final focused analysis domain, and the design results are output based on the analysis results.
[0019] Secondly, this application provides a parametric design and automatic structural calculation system for underground pipe jacking shafts, used to implement the parametric design and automatic structural calculation method for underground pipe jacking shafts described above, including:
[0020] The data acquisition module is used to acquire the cross-sectional geometric definition parameters, pipe jacking construction disturbance parameters, and engineering geological parameters of the target working well.
[0021] The pattern feature generation module is used to perform initial modeling, static analysis and disturbance prediction based on the cross-sectional geometric definition parameters, pipe jacking construction disturbance parameters and engineering geological parameters, and output structural vulnerability pattern features and construction disturbance pattern features.
[0022] The risk resonance potential field construction module is used to perform feature coupling calculations on the structural vulnerability mode features and construction disturbance mode features, and output the risk resonance potential field.
[0023] The risk resonance potential field update module is used to output the region in the risk resonance potential field where the risk resonance potential value is greater than the preset initial threshold as the initial analysis domain;
[0024] It is also used to improve the accuracy of repeated feature coupling calculations in the initial analysis domain and its neighborhood, update and output the updated risk resonance potential field;
[0025] The analysis domain update module is used to adjust and output the initial threshold based on the updated risk resonance potential field, and output the region where the risk resonance potential value is greater than the adjusted initial threshold as the updated analysis domain.
[0026] The convergence judgment module is used to calculate the spatial overlap between the initial analysis domain and the updated analysis domain and determine whether the first convergence threshold has been reached.
[0027] It is also used to update the output analysis domain to the final focused analysis domain if the desired result is achieved.
[0028] It is also used to update the analysis domain to a new initial analysis domain and iterate until the spatial overlap reaches the first convergence threshold or the number of iterations reaches a preset value if the target is not reached.
[0029] It is also used to output the update analysis domain with the minimum spatial overlap during the iteration process as the final focus analysis domain when the number of iterations reaches the maximum number of iterations and still has not converged;
[0030] The design output module is used to perform refined mechanical analysis within the final focused analysis domain and output design results based on the analysis results.
[0031] Compared with the prior art, the beneficial effects of the present invention are as follows:
[0032] This application generates a risk resonance potential field by coupling structural vulnerability mode characteristics and construction disturbance mode characteristics, which solves the problems of separate assessment of structural risk and construction risk and difficulty in explicit quantification of coupling effect in traditional methods. It realizes early warning and spatial positioning of complex and dynamic engineering risks, and provides accurate guidance for subsequent refined analysis.
[0033] This application determines the focused analysis domain by defining an initial analysis domain, adjusting the threshold based on the updated risk resonance potential field, and iteratively calculating the spatial overlap. This solves the problem of mismatched computational resources in risk area identification in traditional methods, and achieves intelligent locking of key risk areas and adaptive focusing of computational resources, ensuring a balance between analysis efficiency and accuracy. Attached Figure Description
[0034] The disclosure of this invention is illustrated with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention. Wherein:
[0035] Figure 1 This is a flowchart of the parametric design and automatic structural verification method for underground pipe jacking working shafts proposed in this invention.
[0036] Figure 2 This is a schematic diagram of the parametric design and automatic structural verification system for underground pipe jacking working shafts proposed in this invention. Detailed Implementation
[0037] It is readily understood that, based on the technical solution of this invention, those skilled in the art can propose various interchangeable structural methods and implementations without altering the essential spirit of the invention. Therefore, the following detailed embodiments and accompanying drawings are merely illustrative examples of the technical solution of this invention and should not be considered as the entirety of the invention or as limitations or restrictions on the technical solution of this invention.
[0038] In existing technologies, the design of underground pipe jacking shafts suffers from the problem of disconnect between structural static verification and construction disturbance prediction, and the difficulty in explicitly quantifying and spatially locating the coupling effect. Traditional methods, based on static equilibrium theory and code experience, simplify construction disturbances as superimposed external loads, failing to dynamically identify local high-risk areas formed by the spatial interaction of specific directions of construction disturbances and inherent structural vulnerabilities. This leads to designs relying on overall safety factors, potentially masking sudden instability triggered by "risk resonance."
[0039] To address the aforementioned issues, this application generates a risk resonance potential field by coupling structural vulnerability mode characteristics and construction disturbance mode characteristics, thereby quantifying and spatially mapping the intensity of coupled risks. By defining an initial analysis domain, updating the potential field with improved accuracy, and iteratively adjusting the threshold and computational space overlap, the application adaptively determines the focused analysis domain. Finally, refined mechanical analysis is performed within the focused analysis domain, and design results are output. This application solves the problems of missing risk coupling analysis and the imbalance between accuracy and efficiency caused by the global uniform distribution of computational resources in traditional methods. It achieves early warning, precise location, and intelligent focusing of computational resources for dynamic coupling risks, ensuring ultra-high precision analysis of key areas under limited resources, thereby improving the anti-disturbance safety and design reliability of the working well.
[0040] Example 1:
[0041] like Figure 1 As shown, this application introduces a parametric design and automatic structural verification method for underground pipe jacking shafts, including:
[0042] S1. Obtain the cross-sectional geometric definition parameters, pipe jacking construction disturbance parameters, and engineering geological parameters of the target working well;
[0043] Obtaining and processing the geometric parameters of the cross-section: Extract these parameters from the design drawings of the target working well. They must include: the inner diameter, outer diameter (or wall thickness), depth, and the location and dimensions of the pre-reserved opening for the jacking pipe. Organize these parameters into structured data and associate them with structural material properties (such as concrete grade, elastic modulus, and Poisson's ratio). Output the geometric parameter set.
[0044] Acquisition and processing of disturbance parameters during pipe jacking construction: Extracted from the project's "Construction Organization Design" document. This must include: the outer diameter of the pipe jacking machine, the designed jacking thrust range, the predetermined three-dimensional coordinates of the jacking axis, and the grouting pressure. Convert the jacking axis coordinates into spatial vectors for subsequent calculations. Output the set of construction disturbance parameters.
[0045] Acquisition and processing of engineering geological parameters: Extracted from the project's Geotechnical Engineering Investigation Report. This must include: the distribution of each soil layer, the depth of each layer's bottom, and key mechanical parameters such as soil weight, cohesion, and internal friction angle. Based on the depth and location of the working well, establish a sequence of soil layer distribution and their mechanical parameters along the well depth direction. Output the geological parameter set.
[0046] The three parameter sets mentioned above are integrated into a unified target working well input dataset for use in step S2.
[0047] Example: For a circular working well:
[0048] Geometric parameter set (from design drawings): Inner diameter of well wall 6.0 meters, outer diameter 6.8 meters, well depth 12.0 meters, reserved opening center located at depth -3.5 meters, diameter 2.0 meters, material is C30 concrete.
[0049] Construction disturbance parameter set (from construction organization design): pipe jacking machine outer diameter 2.04 meters, jacking thrust 8000-12000 kN, jacking axis coordinates (starting point X=1000.000, Y=2000.000, Z=-3.500; ending point X=1150.000, Y=2000.000, Z=-3.480), grouting pressure 0.3-0.4 MPa.
[0050] Geological parameter set (from the exploration report): 0 to -2.0 meters is miscellaneous fill, -2.0 to -10.0 meters is silty clay, and below -10.0 meters is silty clay. The unit weight, cohesion, and internal friction angle of each soil layer are also included. Output the integrated dataset.
[0051] S2. Based on the cross-sectional geometric definition parameters, pipe jacking construction disturbance parameters and engineering geological parameters, perform initial modeling, static analysis and disturbance prediction, and output structural vulnerability mode characteristics and construction disturbance mode characteristics;
[0052] The process of obtaining structurally fragile pattern features specifically includes:
[0053] Based on the cross-sectional geometric parameters and engineering geological parameters, a parameterized structural model is constructed and initial soil and water loads are applied.
[0054] Finite element analysis was performed on the structural model bearing the initial soil and water load to obtain the initial stress field;
[0055] Based on the stress state at each analysis point in the initial stress field, calculate the structural fragility mode characteristics of that analysis point;
[0056] The structurally fragile mode feature is a second-order tensor, which is constructed as follows: a direction tensor is built based on the principal stress direction at the analysis point, and the direction tensor is weighted and summed using a weight function that is positively correlated with the magnitude of the principal stress at the analysis point;
[0057] Among them, the feature vector of the structural vulnerability mode feature is used to characterize the potential vulnerability direction of the analysis point, and its feature value is used to characterize the vulnerability intensity of the analysis point.
[0058] The formula for calculating the structural fragility mode characteristics is as follows:
[0059] ;
[0060] in, Indicates structural fragility pattern characteristics, and These are the first and second principal stresses at this analysis point. and These are the corresponding first and second principal direction vectors. and The weighting function is positively correlated with the magnitudes of the first and second principal stresses, satisfying... ,symbol This represents the vector tensor product.
[0061] The process of obtaining the characteristics of construction disturbance modes specifically includes:
[0062] Based on the disturbance parameters and engineering geological parameters of pipe jacking construction, predict the additional shear strain field of the soil caused by pipe jacking construction;
[0063] The additional shear strain field is mapped onto the surface of the target working well wall to obtain the initial soil constraint reduction coefficient at each mapping point of the well wall.
[0064] For each mapping point, the dominant direction vector of construction disturbance at that mapping point is determined based on its spatial location and geometric relationship with the pipe jacking axis.
[0065] Calculate the spatial rate of change of the initial soil constraint reduction coefficient at each mapping point along the dominant direction vector of construction disturbance, and use it as the disturbance intensity gradient.
[0066] Construction disturbance mode features are constructed based on the initial soil constraint reduction coefficient, the dominant direction vector of construction disturbance, and the disturbance intensity gradient.
[0067] S2. Based on the obtained cross-sectional geometric definition parameters, pipe jacking construction disturbance parameters and engineering geological parameters, initial modeling, static analysis and construction disturbance prediction are performed. Structural vulnerability mode features and construction disturbance mode features are output respectively. Based on the structural vulnerability mode features and construction disturbance mode features, feature coupling calculation is performed to generate and output the risk resonance potential field used to quantify the coupling risk intensity.
[0068] The process of obtaining structurally fragile pattern features specifically includes:
[0069] Based on the cross-sectional geometric parameters and engineering geological parameters, a parameterized structural model is constructed and initial soil and water loads are applied.
[0070] Finite element analysis was performed on the structural model bearing the initial soil and water load to obtain the initial stress field;
[0071] Based on the stress state of each analysis point in the initial stress field, the structural vulnerability mode characteristics of the analysis point are calculated. The structural vulnerability mode characteristics are a second-order tensor, which is constructed by: constructing a direction tensor based on the principal stress direction of the analysis point, and using a weight function that is positively correlated with the magnitude of the principal stress of the analysis point to perform weighted summation of the direction tensor.
[0072] Based on the target working well input dataset output by S1, a risk resonance potential field is generated to quantify the coupling risk.
[0073] Obtaining structurally fragile mode characteristics: Input S1 outputs the geometric parameter set and geological parameter set. Processing: (1) Based on the geometric and geological parameters, establish a three-dimensional finite element model of the working well and apply water and soil loads, perform static analysis, and obtain the initial stress field. (2) For each analysis point in the initial stress field: extract its stress tensor, calculate the first and second principal stresses ( , ) and the corresponding principal direction unit vector ( , ); calculate weights, for example: ; Calculate the structural vulnerability mode characteristics at this point. (Second-order tensor): Output structurally fragile mode feature field (at each analysis point) ).
[0074] Acquisition of construction disturbance mode features: Input the construction disturbance parameter set and geological parameter set output by S1. Processing: (1) Based on the pipe jacking parameters (outer diameter, thrust, grouting pressure) and soil parameters, predict the additional shear strain field of the soil caused by pipe jacking construction. (2) Map the additional shear strain field to each node on the well wall surface. According to the shear strain value γ of the mapped point, calculate the initial soil constraint reduction coefficient k of the point, for example: k=exp(-8·γ), where exp represents exponential operation. (3) For each well wall node, determine its dominant construction disturbance direction vector d (horizontal direction, away from the pipe jacking axis), and calculate the disturbance intensity gradient g (g=|Δk / Δs|) of its k value along the d direction, where Δk represents the difference of k values between two adjacent points along the d direction, and Δs represents the spatial distance between two adjacent points along the d direction. Output the construction disturbance mode features, that is, the triplet {k, d, g} corresponding to each well wall node.
[0075] Example: For the working well in example S1 (inner diameter 6.0m, depth 12.0m), the pipe jacking is carried out in the positive direction of the X-axis.
[0076] Structural side: After static analysis, at a point located at a well depth of -5.0m on the jacking side, the calculated values are... =1.8MPa (tensile). Horizontal outwards, =0.5MPa. Calculated... It is approximately 0.78 at this point. Maximum eigenvalue It is approximately 0.78.
[0077] On the disturbed side: the additional shear strain γ corresponding to the wellbore node mapping is 0.0012, and the calculated k = exp(-8 × 0.0012) is approximately 0.990. Its disturbance direction d is... Consistent, the gradient g is approximately 0.0067. .
[0078] Through the above technical solution, this application solves the problem of difficulty in quantifying the dynamic coupling risk between construction disturbance and inherent structural vulnerability in complex engineering environments. By tensorizing the structural stress state into vulnerability mode features with directional and strength information, and performing directional coupling calculation with disturbance mode features reflecting the directionality, strength and constraint degradation gradient of construction disturbance, a risk resonance potential field that can accurately quantify the risk intensity and spatial distribution is constructed, providing accurate and reliable quantitative basis for subsequent risk area focusing analysis and dynamic iteration.
[0079] S3. Perform feature coupling calculation on the structural vulnerability mode features and construction disturbance mode features, and output the risk resonance potential field;
[0080] The process of performing feature coupling calculations on the structural vulnerability mode features and construction disturbance mode features to output the risk resonance potential field specifically includes:
[0081] For each analysis point corresponding to the structural vulnerability mode characteristics, obtain its structural vulnerability mode characteristics and the dominant direction vector of construction disturbance and the disturbance intensity gradient corresponding to the mapping point of the analysis point on the surface of the target working well wall;
[0082] The calculation and analysis point is used to quantify the consistency between the main structural vulnerability direction and the main construction disturbance direction at that point as a mode coupling factor.
[0083] Based on the maximum eigenvalue of the structural vulnerability mode characteristics of the analysis point, the mode coupling factor, and the disturbance intensity gradient and initial soil constraint reduction coefficient of the mapping point, the risk resonance potential value of the point is calculated.
[0084] The risk resonance potential field is constituted by the risk resonance potential values of all analysis points.
[0085] The formula for calculating the mode coupling factor is: ;
[0086] in, Represents the mode coupling factor. The first principal direction vector representing the structurally fragile pattern characteristics. This represents the dominant direction vector of construction disturbance. To adjust the preset coefficient of gradient influence intensity, Indicates the gradient of disturbance intensity;
[0087] The formula for calculating the risk resonance potential is: ;
[0088] in, Indicates the risk resonance potential value. Characteristic of structural fragility patterns The largest eigenvalue, This is the initial soil constraint reduction factor.
[0089] Feature coupling calculation generates risk resonance potential field: input structural vulnerability mode feature field and construction disturbance mode feature. Processing: (1) Associate the construction disturbance feature {k, d, g} of the nearest wellbore node with each structural analysis point. (2) Calculate the mode coupling factor of the point. : Calculate the risk resonance potential value at this point. : The Φ values of all points are collected to form a spatial distribution field.
[0090] Output the risk resonance potential field. This field is a scalar field, and the higher the Φ value, the higher the risk of damage caused by the coupling of "structural fragility" and "construction disturbance" at that location.
[0091] Example: Continuing from S2, coupling calculation: α = |1| × (1 + 0.5 × 0.0067) is approximately 1.0034. Φ = 0.78 × 1.0034 × 0.990 is approximately 0.775.
[0092] After performing this calculation on all points, the system generates a complete risk resonance potential field. Subsequent steps will use this field to identify high-risk areas.
[0093] S4. Output the region in the risk resonance potential field whose risk resonance potential value is greater than the preset initial threshold as the initial analysis domain;
[0094] In the initial analysis domain and its neighborhood, the accuracy of repeated feature coupling calculations is improved, and the updated risk resonance potential field is updated and output.
[0095] Based on the acquired risk resonance potential field, high-risk areas are identified and recalculated with higher accuracy in these areas, outputting an updated risk resonance potential field.
[0096] (1) Define the initial analysis domain: Set a preset initial threshold (for example, take the 90th percentile of all Φ values in the risk resonance potential field). Traverse the risk resonance potential field and collect all analysis points with Φ values greater than the preset initial threshold. Output the spatial region formed by these points as the initial analysis domain.
[0097] (2) Local refinement and recalculation: Extend the boundary of the initial analysis domain outward by a fixed distance (e.g., 0.5 meters) to form a neighborhood, collectively called the extended region. Refine the finite element mesh within this region (e.g., halve the element size). Only within this extended region, call the characteristic coupling calculation method in S2 to recalculate the risk resonance potential value Φ' for each new analysis point based on the refined mesh. Replace the original risk resonance potential field Φ value within the extended region with the recalculated Φ' value to form the updated risk resonance potential field.
[0098] Output the updated risk resonance potential field. This field has higher accuracy in high-risk regions.
[0099] Example: Suppose S2 outputs a risk resonance potential field with 100,000 points, with Φ values ranging from 0.2 to 1.5, and Φ greater than 2.0 in a few regions.
[0100] (1) Define the initial analysis domain: The preset initial threshold is calculated to be 1.8 (90th percentile). About 1500 points with Φ greater than 1.8 are selected. They are concentrated in the local area of the well wall from -4.5 meters to -5.5 meters. This area is defined as the initial analysis domain.
[0101] (2) Update the potential field: Expand the region by 0.5 meters and refine the mesh (size from 0.2 meters to 0.1 meters). Re-execute the coupling calculation of S2 in this refined region. For example, if the original value of a certain point is Φ=0.775, after refinement, there are 4 new points, and the recalculated values of Φ' are 0.81, 0.79, 0.83, and 0.80, respectively. Replace the old values in the original region with these new values to obtain the updated risk resonance potential field.
[0102] Through the above technical solution, this application solves the contradiction between low computational efficiency and insufficient analysis accuracy of key areas when using single precision for calculations in the global scope. By quickly locking the initial high-risk analysis domain based on a preset threshold, and focusing computational resources and improving computational accuracy in this core area and its neighborhood, it achieves targeted and refined updates of the risk resonance potential field, thereby improving the accuracy and reliability of key risk area identification while ensuring overall computational efficiency.
[0103] S5. Adjust and output the initial threshold based on the updated risk resonance potential field, and output the region where the risk resonance potential value is greater than the adjusted initial threshold as the updated analysis domain.
[0104] Based on the updated risk resonance potential field, a more focused high-risk region is generated, i.e., the updated analysis domain.
[0105] (1) Adjust the initial threshold: Calculate the 95th percentile of the Φ values of all analysis points in the updated risk resonance potential field. Use this percentile as the adjusted initial threshold.
[0106] (2) Define the update analysis domain: Traverse the updated risk resonance potential field and select all analysis points whose Φ values are greater than the adjusted initial threshold. Define the spatial region formed by these points as the update analysis domain.
[0107] Example: The updated risk resonance potential field output by S3 contains approximately 100,000 points of Φ value. Its 95th percentile is calculated to be 2.1, so the adjusted initial threshold is 2.1.
[0108] Using this threshold for filtering, approximately 800 points with Φ greater than 2.1 were obtained. These points are concentrated in the central region of the original initial analysis domain, which is located near a well depth of -5.0 meters. This region is defined as the updated analysis domain.
[0109] Through the above technical solution, this application solves the problem that the use of static thresholds in risk assessment cannot adapt to the dynamic evolution of the risk field, resulting in inaccurate identification of the analysis domain range. By dynamically adjusting the initial threshold according to the updated risk resonance potential field, and accurately defining the high resonance potential region accordingly, sensitive capture and adaptive definition of the risk aggregation range are achieved, thereby improving the identification accuracy of the risk analysis domain and its adaptability to the dynamic evolution process.
[0110] S6. Calculate the spatial overlap between the initial and updated analysis domains and determine whether the first convergence threshold has been reached:
[0111] If this is achieved, the output update analysis domain becomes the final focused analysis domain;
[0112] If not, the output update analysis domain is set to the new initial analysis domain and iteratively updated until the spatial overlap reaches the first convergence threshold or the number of iterations reaches the preset value.
[0113] When the number of iterations reaches the maximum number of iterations and still has not converged, the update analysis domain with the minimum spatial overlap during the output iteration process is the final focus analysis domain.
[0114] This step is based on the initial analysis domain and the updated analysis domain. By calculating their spatial overlap, it determines whether the iteration has converged, so as to determine the focus analysis domain or to proceed to the next round of iteration.
[0115] (1) Calculate spatial overlap: Calculate the volume intersection and volume union of the initial analysis domain and the updated analysis domain. Spatial overlap is the ratio of volume intersection to volume union. Output scalar value of spatial overlap R (0≤R≤1).
[0116] (2) Determine convergence and execute branch logic: Compare the spatial overlap with the preset first convergence threshold (e.g., the first convergence threshold is 0.85).
[0117] If the spatial overlap is greater than or equal to the first convergence threshold, convergence is determined. The current update analysis domain is output as the final focused analysis domain.
[0118] If the spatial overlap is less than the first convergence threshold, then convergence is considered non-converged. The current updated analysis domain is assigned to the initial analysis domain, and the process returns to step S3 to begin the next iteration.
[0119] When the maximum number of iterations is reached (e.g., 10), the update analysis domain with the lowest spatial overlap during the iteration process is taken as the final focus analysis domain.
[0120] Example: Suppose the initial analysis domain volume is 0.15 m³, and the updated analysis domain volume is 0.08 m³. The calculated intersection volume is 0.07 m³, and the union volume is 0.16 m³. Therefore, R = 0.07 / 0.16 = 0.4375.
[0121] Since R(0.4375) is less than the first convergence threshold (0.85), convergence is not achieved. The system updates the analysis domain to the new initial analysis domain and returns to S3 for the next iteration.
[0122] Repeat the iteration 10 times. During this process, R is always less than the first convergence threshold. The update analysis domain with the maximum spatial overlap during the iteration is selected as the final focus analysis domain.
[0123] Through the above technical solution, this application solves the problem that relying on a single calculation or subjective judgment in the process of determining the analysis domain may lead to unstable and inaccurate results. By introducing an iterative convergence mechanism based on spatial overlap, the updated analysis domain is fed back as a new iteration starting point when the standard is not met. The update and calculation are repeated until the preset threshold is met or the maximum number of iterations is reached. This achieves the automated and quantitative accurate determination of the focused analysis domain, effectively improving the reliability and repeatability of the final analysis results.
[0124] The process of calculating the spatial overlap between the initial and updated analysis domains also includes trend constraints on the iterative process, specifically including:
[0125] During multiple iterations, the geometric features of multiple updated analysis domains generated in the previous iterations are obtained, including the centroid location and volume.
[0126] Based on the geometric characteristics of multiple update analysis domains, the spatial evolution trend of the update analysis domains during the iteration process is calculated;
[0127] Based on the characteristics of construction disturbance patterns, the expected disturbance trends for the corresponding time periods in spatial evolution are determined.
[0128] Determine whether the spatial evolution trend deviates from the expected perturbation trend; if a deviation occurs, then smooth the boundary of the updated analysis domain obtained in the current iteration based on the expected perturbation trend.
[0129] In each iteration, this step constrains the evolution trend of the updated analysis domain to ensure that it conforms to the expected trend of construction disturbance.
[0130] (1) Obtain historical data and disturbance characteristics: Input the update analysis domain of the previous iteration, the update analysis domain of the current iteration, and the construction disturbance mode characteristics output by S2.
[0131] Calculate the centroid of the update analysis domain of the previous iteration and the update analysis domain of the current iteration. , ) and volume ( , ).for Match its corresponding dominant construction disturbance direction vector .
[0132] (2) Calculate and determine the trend: Calculate the centroid displacement vector ,calculate and The dot product is calculated. If the dot product is less than or equal to 0, a trend divergence is determined, and the divergence judgment result (yes / no) is output.
[0133] (3) Perform correction (if deviation): If a deviation is determined, the boundary points of the updated analysis domain in this iteration are adjusted along the direction. Translate by a smooth step size δ (e.g., δ = 0.5| |), and perform geometric smoothing (such as Laplace smoothing) on the translated boundary.
[0134] Output the corrected update analysis domain, which is used to replace the original update analysis domain of the previous iteration in subsequent calculations.
[0135] Example: Centroid of the historical domain =(1000.0, 2000.0, -5.0), current domain centroid =(1000.2, 2000.0, -5.0), calculated as follows =(0.2, 0.0, 0.0). Corresponding dominant disturbance direction =(-1.0, 0.0, 0.0). Calculate the dot product: · =-0.2 is less than 0, indicating a divergence. Calculate the step size δ = 0.5 + 0.2 = 0.1 meters, and move all points in the current domain along... The new boundary is shifted by 0.1 meters (negative X-axis direction) and smoothed to obtain the corrected updated analysis domain.
[0136] Through the above technical solution, this application solves the problem that the spatial evolution trend may deviate from the expected disturbance mode in iterative analysis, leading to distortion of the analysis domain or unstable convergence. By dynamically monitoring the geometric characteristics of historical iterations and calculating the spatial evolution trend, it compares it with the expected disturbance trend determined based on the characteristics of the construction disturbance mode, and smoothly corrects the boundary of the updated analysis domain when deviation occurs, thereby ensuring that the iterative process always conforms to the dynamic law of construction, effectively improving the accuracy of the analysis domain and the robustness of the iterative process.
[0137] In each iteration, while calculating the spatial overlap between the initial and updated analysis domains and determining the first convergence threshold, a parallel assessment of the stability of risk intensity is also included, specifically:
[0138] In each iteration, obtain the updated risk resonance potential field generated in the current iteration, as well as the risk resonance potential field generated in the previous iteration;
[0139] The average rate of change and peak value change of the updated risk resonance potential field and the previous risk resonance potential field within the updated analysis domain are calculated to generate intensity change characteristic data; at the same time, the spatial overlap between the initial analysis domain and the updated analysis domain is calculated.
[0140] Determine whether the spatial overlap reaches the first convergence threshold and whether the intensity change feature reaches the second convergence threshold; if both reach the threshold, the system is considered converged and a convergence status signal is output; if either fails to reach the threshold, the system is considered non-converged and a non-converged status signal is output.
[0141] After receiving the convergence status signal, the updated analysis domain is determined as the final focused analysis domain.
[0142] In each iteration, this step simultaneously evaluates the stability of spatial overlap and risk resonance potential. Convergence is determined only when both conditions are met.
[0143] (1) Calculate the intensity change characteristics: Input the updated risk resonance potential field of the current iteration Risk resonance potential field of the previous iteration The current update analysis domain.
[0144] Within the update analysis domain:
[0145] Calculate the average relative rate of change of the risk resonance potential: , This indicates that the arithmetic mean of all elements within the parentheses is calculated. This represents a very small constant used to avoid numerical stability issues when the denominator is zero. For example, if its value is... .
[0146] Calculate the change in the peak value of the risk resonance potential: .
[0147] Output intensity change characteristic data ( , ).
[0148] (2) Parallel convergence judgment: The spatial overlap degree R and intensity change characteristics calculated by input S5 are used to determine the parallel convergence degree. , ).
[0149] Simultaneously, two conditions are judged: the spatial overlap is greater than or equal to the first convergence threshold (0.85).
[0150] Less than or equal to and Less than or equal to (Second convergence threshold, such as) =0.02, =0.05), , This represents the second convergence threshold.
[0151] Judgment and output: If both conditions are met, the system is judged to be converged and a "converged" signal is output; otherwise, the system is judged to be non-converged and a "non-converged" signal is output.
[0152] (3) Control the final decision:
[0153] In S5, the operation of "determining the updated analysis domain as the final focused analysis domain" is performed only when a "convergence" signal is received.
[0154] Example: After the current iteration, S5 calculates R=0.88.
[0155] Within the current update analysis domain, the following is calculated: =0.015, =0.03.
[0156] Judgment: R(0.88) is greater than or equal to 0.85. (0.015) is less than or equal to 0.02 and (0.03) is less than or equal to 0.05.
[0157] Therefore, the system determines that convergence has occurred and outputs a "convergence" signal. Based on this, S5 determines the current update analysis domain as the final focus analysis domain.
[0158] Through the above technical solution, this application solves the problem that relying solely on the geometrical overlap degree in iterative optimization may lead to false convergence and ignore the inherent risk volatility. By introducing a risk intensity stability judgment in parallel after the spatial overlap degree reaches the standard, and comprehensively evaluating the potential field change rate and peak change, a dual convergence criterion is constructed. This ensures that the iterative process is only judged as true convergence when both the spatial domain and the intensity field tend to stabilize, which significantly improves the reliability and robustness of the final determination result of the focused analysis domain.
[0159] S7. Perform refined mechanical analysis within the final focused analysis domain and output design results based on the analysis results.
[0160] (1) Constructing and solving the refined model: Input the final focused analysis domain, the initial parametric structural model, the initial stress field, and the construction disturbance mode characteristics of the corresponding region (especially the reduction factor k). Within the focused domain, the structural model is further meshed (e.g., the element size is refined to 0.05 meters), and a nonlinear material constitutive model (e.g., a concrete damage plasticity model) is adopted. Based on the reduction factor k, the soil boundary constraints of the model in this region are updated. The refined analysis results are output, including detailed stress / strain contour maps, safety factor distribution, and identified over-limit stress / strain zones.
[0161] (2) Generate design results: Based on the detailed analysis results, for the stress / strain zone exceeding the limit, perform reinforcement calculations (such as reinforcement calculation and section reinforcement) in accordance with the structural design code.
[0162] The output design results include: reinforcement design drawings: marking the location of the focal area and detailed reinforcement measures within the area (such as the specifications and arrangement of additional steel bars) on the original structural drawings; design parameter table: listing the specific parameters of all reinforced components; analysis and explanation: briefly describing the causes of risks and the expected effects after reinforcement.
[0163] Example: Refined analysis: Within a focused domain of approximately 0.05 m³, after model refinement and nonlinear analysis, a tensile stress over-limit zone was identified (e.g., the maximum tensile stress of 3.5 MPa is greater than the tensile strength of C30 concrete of 2.0 MPa).
[0164] Design output: Calculations indicate the need to add 8 HRB400 grade, 16mm diameter longitudinal reinforcing bars to the inner side of the well wall in this area. The design deliverables include drawings illustrating this reinforcement measure and a detailed table of reinforcing bar parameters, explaining that this reinforcement can increase the local safety factor to over 1.2.
[0165] After outputting the design results, the process also includes stress transfer updating of the design results, specifically including:
[0166] After generating the design results, the boundary constraints of the initial structural model are globally updated;
[0167] Finite element analysis was performed on the updated structural model to obtain the overall stress field after constraint degradation.
[0168] Calculate the stress variation field between the overall stress field and the initial stress field;
[0169] The region in the stress variation field where the stress variation is greater than a preset variation threshold and is located outside the focused analysis domain is defined as the stress migration risk zone.
[0170] The design deliverables should include reinforcement design parameters and / or construction monitoring and control recommendations for stress migration risk areas, and an updated design deliverable should be output.
[0171] This step assesses the redistribution of structural stress after construction disturbances lead to degradation of overall constraints, identifies new risk areas outside the original focus area, and supplements the design results.
[0172] (1) Global constraint degradation analysis: Input the initial parametric structural model, construction disturbance mode characteristics (reduction coefficient k for each analysis point), and initial stress field. Based on the reduction coefficient k of all well wall nodes, globally update the soil boundary constraints in the model (e.g., multiply the soil spring stiffness by k). Perform static analysis on the updated model. Output the overall stress field after constraint degradation.
[0173] (2) Calculate stress changes and identify new risk areas: Calculate the stress changes (e.g., equivalent stress changes) of the new and old stress fields point by point. Set a preset change threshold (e.g., 0.2 MPa). Filter out all points that simultaneously satisfy the condition that the equivalent stress change is greater than the preset change threshold and are located outside the final focused analysis domain. Output the spatial region formed by these points, i.e., the stress migration risk area.
[0174] (3) Update the design results: In the original design results, add annotations, risk assessments and monitoring or construction suggestions (such as enhanced monitoring, preparation of surface bonding reinforcement) for stress migration risk areas. Output the updated design results, which include dual considerations for the focal area and stress migration risk areas.
[0175] Example: Based on the reduction factor k (0.6-1.0) of the entire well wall, update the soil constraints of the initial model and reanalyze to obtain the new stress field.
[0176] Calculations revealed that in a certain area at a well depth of -7.0m, facing away from the top, the equivalent stress increased by 0.25MPa, which is greater than the threshold of 0.2MPa and is not within the original focusing area. Therefore, this area was identified as a stress migration risk zone.
[0177] Mark this new area on the design drawings and add the following explanation: "It is recommended to strengthen crack monitoring in this area during construction and prepare an emergency plan for surface bonding of carbon fiber cloth." Output the updated design results.
[0178] Through the above technical solution, this application solves the problem of insufficient identification of stress redistribution risk caused by changes in constraints during the design process. By performing global updates and finite element analysis on the structural boundary constraints, the overall stress field after constraint degradation is accurately obtained. By comparing with the initial stress field, stress migration risk areas located outside the focused analysis domain are identified. Finally, targeted improvement suggestions are provided in the design results, thereby improving the reliability and safety of the structural design.
[0179] Example 2:
[0180] like Figure 2 As shown, this application provides a parametric design and automatic structural calculation system for underground pipe jacking shafts, used to implement the aforementioned parametric design and automatic structural calculation method for underground pipe jacking shafts, including:
[0181] The data acquisition module 100 is used to acquire the cross-sectional geometric definition parameters, pipe jacking construction disturbance parameters, and engineering geological parameters of the target working well;
[0182] The pattern feature generation module 200 is used to perform initial modeling, static analysis and disturbance prediction based on the cross-sectional geometric definition parameters, pipe jacking construction disturbance parameters and engineering geological parameters, and output structural vulnerability pattern features and construction disturbance pattern features.
[0183] The risk resonance potential field construction module 300 is used to perform feature coupling calculation on the structural vulnerability mode features and construction disturbance mode features, and output the risk resonance potential field.
[0184] The risk resonance potential field update module 400 is used to output the region in the risk resonance potential field where the risk resonance potential value is greater than the preset initial threshold as the initial analysis domain.
[0185] It is also used to improve the accuracy of repeated feature coupling calculations in the initial analysis domain and its neighborhood, update and output the updated risk resonance potential field;
[0186] The analysis domain update module 500 is used to adjust and output the initial threshold based on the updated risk resonance potential field, and output the region where the risk resonance potential value is greater than the adjusted initial threshold as the updated analysis domain.
[0187] The convergence judgment module 600 is used to calculate the spatial overlap between the initial analysis domain and the updated analysis domain and determine whether the first convergence threshold has been reached.
[0188] It is also used to update the output analysis domain to the final focused analysis domain if the desired result is achieved.
[0189] It is also used to update the analysis domain to a new initial analysis domain and iterate until the spatial overlap reaches the first convergence threshold or the number of iterations reaches a preset value if the target is not reached.
[0190] It is also used to output the current updated analysis domain as the final focused analysis domain when the number of iterations reaches the maximum number of iterations and still has not converged, or to output the region where the risk resonance potential value meets the preset screening conditions as the final focused analysis domain, and output the non-converged state signal.
[0191] The design output module 700 is used to perform refined mechanical analysis within the final focused analysis domain and output design results based on the analysis results.
[0192] This embodiment has the same beneficial effects as the embodiments described above.
[0193] The technical scope of this invention is not limited to the content described above. Those skilled in the art can make various modifications and variations to the above embodiments without departing from the technical concept of this invention, and all such modifications and variations should fall within the protection scope of this invention.
Claims
1. A method for parametric design and automatic structural verification of underground pipe jacking shafts, characterized in that, include: Obtain the cross-sectional geometric parameters, pipe jacking construction disturbance parameters, and engineering geological parameters of the target working well; Based on the cross-sectional geometric definition parameters, pipe jacking construction disturbance parameters, and engineering geological parameters, initial modeling, static analysis, and disturbance prediction are performed, and structural fragility mode characteristics and construction disturbance mode characteristics are output. Perform feature coupling calculations on the structural vulnerability mode features and construction disturbance mode features to output the risk resonance potential field; The region in the risk resonance potential field whose risk resonance potential value is greater than the preset initial threshold is output as the initial analysis domain; In the initial analysis domain and its neighborhood, the accuracy of repeated feature coupling calculations is improved, and the updated risk resonance potential field is updated and output. Based on the updated risk resonance potential field, the initial threshold is adjusted and output. The region where the risk resonance potential value is greater than the adjusted initial threshold is output as the updated analysis domain. Calculate the spatial overlap between the initial and updated analysis domains and determine whether the first convergence threshold has been reached: If this is achieved, the output update analysis domain becomes the final focused analysis domain; If not, the output update analysis domain is set to the new initial analysis domain and iteratively updated until the spatial overlap reaches the first convergence threshold or the number of iterations reaches the preset value. When the number of iterations reaches the maximum number of iterations and still has not converged, the update analysis domain with the minimum spatial overlap during the output iteration process is the final focus analysis domain. A refined mechanical analysis is performed within the final focused analysis domain, and the design results are output based on the analysis results.
2. The method for parametric design and automatic structural verification of underground pipe jacking shafts according to claim 1, characterized in that, The process of obtaining structurally fragile pattern features specifically includes: Based on the cross-sectional geometric parameters and engineering geological parameters, a parameterized structural model is constructed and initial soil and water loads are applied. Finite element analysis was performed on the structural model bearing the initial soil and water load to obtain the initial stress field; Based on the stress state at each analysis point in the initial stress field, calculate the structural fragility mode characteristics of that analysis point; Among them, the feature vector of the structural vulnerability mode feature is used to characterize the potential vulnerability direction of the analysis point, and its feature value is used to characterize the vulnerability intensity of the analysis point.
3. The method for parametric design and automatic structural verification of underground pipe jacking shafts according to claim 2, characterized in that, The formula for calculating the structural fragility mode characteristics is as follows: ； in, Indicates structural fragility pattern characteristics, and These are the first and second principal stresses at this analysis point. and These are the corresponding first and second principal direction vectors. and The weighting function is positively correlated with the magnitudes of the first and second principal stresses, satisfying... ,symbol This represents the vector tensor product.
4. The method for parametric design and automatic structural verification of underground pipe jacking shafts according to claim 2, characterized in that, The process of obtaining the characteristics of construction disturbance modes specifically includes: Based on the disturbance parameters and engineering geological parameters of pipe jacking construction, predict the additional shear strain field of the soil caused by pipe jacking construction; The additional shear strain field is mapped onto the surface of the target working well wall to obtain the initial soil constraint reduction coefficient at each mapping point of the well wall. For each mapping point, the dominant direction vector of construction disturbance at that mapping point is determined based on its spatial location and geometric relationship with the pipe jacking axis. Calculate the spatial rate of change of the initial soil constraint reduction coefficient at each mapping point along the dominant direction vector of construction disturbance, and use it as the disturbance intensity gradient. Construction disturbance mode features are constructed based on the initial soil constraint reduction coefficient, the dominant direction vector of construction disturbance, and the disturbance intensity gradient.
5. The method for parametric design and automatic structural verification of underground pipe jacking shafts according to claim 4, characterized in that, The process of performing feature coupling calculations on the structural vulnerability mode features and construction disturbance mode features to output the risk resonance potential field specifically includes: For each analysis point corresponding to the structural vulnerability mode characteristics, obtain its structural vulnerability mode characteristics and the dominant direction vector of construction disturbance and the disturbance intensity gradient corresponding to the mapping point of the analysis point on the surface of the target working well wall; The calculation and analysis point is used to quantify the consistency between the main structural vulnerability direction and the main construction disturbance direction at that point as a mode coupling factor. Based on the maximum eigenvalue of the structural vulnerability mode characteristics of the analysis point, the mode coupling factor, and the disturbance intensity gradient and initial soil constraint reduction coefficient of the mapping point, the risk resonance potential value of the point is calculated. The risk resonance potential field is constituted by the risk resonance potential values of all analysis points.
6. The method for parametric design and automatic structural verification of underground pipe jacking shafts according to claim 5, characterized in that, The formula for calculating the mode coupling factor is: ; in, Represents the mode coupling factor. The first principal direction vector representing the structurally fragile pattern characteristics. This represents the dominant direction vector of construction disturbance. To adjust the preset coefficient of gradient influence intensity, Indicates the gradient of disturbance intensity; The formula for calculating the risk resonance potential is: ; in, Indicates the risk resonance potential value. Characteristic of structural fragility patterns The largest eigenvalue, This is the initial soil constraint reduction factor.
7. The method for parametric design and automatic structural verification of underground pipe jacking shafts according to claim 5, characterized in that, The process of calculating the spatial overlap between the initial and updated analysis domains also includes trend constraints on the iterative process, specifically including: During multiple iterations, the geometric features of multiple updated analysis domains generated in the previous iterations are obtained, including the centroid location and volume. Based on the geometric characteristics of multiple update analysis domains, the spatial evolution trend of the update analysis domains during the iteration process is calculated; Based on the characteristics of construction disturbance patterns, the expected disturbance trends for the corresponding time periods in spatial evolution are determined. Determine whether the spatial evolution trend deviates from the expected perturbation trend; if a deviation occurs, then smooth the boundary of the updated analysis domain obtained in the current iteration based on the expected perturbation trend.
8. The method for parametric design and automatic structural verification of underground pipe jacking shafts according to claim 1, characterized in that, In each iteration, while calculating the spatial overlap between the initial and updated analysis domains and determining the first convergence threshold, a parallel assessment of the stability of risk intensity is also included, specifically: In each iteration, obtain the updated risk resonance potential field generated in the current iteration, as well as the risk resonance potential field generated in the previous iteration; The average rate of change and peak value change of the updated risk resonance potential field and the previous risk resonance potential field within the updated analysis domain are calculated to generate intensity change characteristic data; at the same time, the spatial overlap between the initial analysis domain and the updated analysis domain is calculated. Determine whether the spatial overlap reaches the first convergence threshold and whether the intensity change feature reaches the second convergence threshold; if both reach the threshold, the system is considered converged and a convergence status signal is output; if either fails to reach the threshold, the system is considered non-converged and a non-converged status signal is output. After receiving the convergence status signal, the updated analysis domain is determined as the final focused analysis domain.
9. The method for parametric design and automatic structural verification of underground pipe jacking shafts according to claim 2, characterized in that, After outputting the design results, the process also includes stress transfer updating of the design results, specifically including: After generating the design results, the boundary constraints of the initial structural model are globally updated; Finite element analysis was performed on the updated structural model to obtain the overall stress field after constraint degradation. Calculate the stress variation field between the overall stress field and the initial stress field; The region in the stress variation field where the stress variation is greater than a preset variation threshold and is located outside the focused analysis domain is defined as the stress migration risk zone. The design deliverables should include reinforcement design parameters and / or construction monitoring and control recommendations for stress migration risk areas, and an updated design deliverable should be output.
10. A parametric design and automatic structural calculation system for underground pipe jacking shafts, characterized in that, The method for parametric design and automatic structural verification of underground pipe jacking shafts as described in any one of claims 1-9 includes: The data acquisition module is used to acquire the cross-sectional geometric definition parameters, pipe jacking construction disturbance parameters, and engineering geological parameters of the target working well. The pattern feature generation module is used to perform initial modeling, static analysis and disturbance prediction based on the cross-sectional geometric definition parameters, pipe jacking construction disturbance parameters and engineering geological parameters, and output structural vulnerability pattern features and construction disturbance pattern features. The risk resonance potential field construction module is used to perform feature coupling calculations on the structural vulnerability mode features and construction disturbance mode features, and output the risk resonance potential field. The risk resonance potential field update module is used to output the region in the risk resonance potential field where the risk resonance potential value is greater than the preset initial threshold as the initial analysis domain; It is also used to improve the accuracy of repeated feature coupling calculations in the initial analysis domain and its neighborhood, update and output the updated risk resonance potential field; The analysis domain update module is used to adjust and output the initial threshold based on the updated risk resonance potential field, and output the region where the risk resonance potential value is greater than the adjusted initial threshold as the updated analysis domain. The convergence judgment module is used to calculate the spatial overlap between the initial analysis domain and the updated analysis domain and determine whether the first convergence threshold has been reached. It is also used to update the output analysis domain to the final focused analysis domain if the desired result is achieved. It is also used to update the analysis domain to a new initial analysis domain and iterate until the spatial overlap reaches the first convergence threshold or the number of iterations reaches a preset value if the target is not reached. It is also used to output the update analysis domain with the minimum spatial overlap during the iteration process as the final focus analysis domain when the number of iterations reaches the maximum number of iterations and still has not converged; The design output module is used to perform refined mechanical analysis within the final focused analysis domain and output design results based on the analysis results.