Foundation pit excavation supporting cooperative control method based on finite element dynamic feedback

Abstract

The invention discloses a foundation pit excavation and support cooperative control method based on finite element dynamic feedback, which comprises the following steps: S1, establishing a foundation pit support finite element model, defining constitutive parameters of a soil body small strain hardened soil model HSS and support structure rigidity, and obtaining an initial displacement field and a stress field through step-by-step excavation simulation; and S2, iteratively calibrating parameters based on the simulation result and the field monitoring data, and constructing a construction stage dynamic adjustment threshold library which comprises a displacement warning value (UXmax), a stress safety coefficient (SF is greater than or equal to 1.3) and a support activation time sequence. According to the method, dynamic parameter iteration is achieved, a finite element model is reversely optimized through monitoring data, and the parameter solidification problem of a traditional method is solved; automatic closed-loop control is achieved, manual intervention is reduced, and the response speed is increased to the minute level; and economical efficiency is improved, waste of materials such as steel is reduced through accurate control, and the comprehensive cost is reduced by 15%-25%.

Description

Technical Field

[0001] The present invention relates to the technical field of construction engineering, and in particular to a foundation pit excavation support collaborative control method based on finite element dynamic feedback. Background Art

[0002] Foundation pit support is to ensure the safety of underground structure construction and the surrounding environment of the foundation pit. The support, reinforcement and protection measures adopted for the side walls and surrounding areas must meet the requirements of the ultimate state of bearing capacity (structural stability) and the ultimate state of normal use (deformation control).

[0003] 1. Shallow foundation pit support (depth of 3-5 meters) Inclined column support / anchor support: Suitable for larger foundation pits or mechanical excavation scenarios.

[0004] Steel pile cross baffle support: suitable for clay or sandy soil with low groundwater level and good soil quality.

[0005] Temporary retaining wall / short pile diaphragm support: used in situations where slope is insufficient or rapid construction is required.

[0006] Traditional foundation pit support construction relies on empirical formulas and static design, which has the following defects: The finite element model parameters deviate significantly from the on-site soil characteristics, resulting in excessive predicted displacement errors (generally >20%); delayed activation of the support structure can easily lead to cumulative deformation; and there is a lack of a rapid response mechanism for sudden loads (such as soil piling and rainfall). Summary of the Invention

[0007] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a foundation pit excavation support collaborative control method based on finite element dynamic feedback.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions: A collaborative control method for foundation pit excavation and support based on finite element dynamic feedback includes the following steps: Step S1: Establish a finite element model for foundation pit support, define the HSS constitutive parameters of the soil small strain hardening soil model and the stiffness of the support structure, and obtain the initial displacement field and stress field through step-by-step excavation simulation; Step S2: Iteratively calibrate parameters based on simulation results and field monitoring data to build a dynamic adjustment threshold library for the construction phase, including displacement warning value (UX_max), stress safety factor, and support activation sequence; Step S3: Real-time construction data is collected through IoT sensors and dynamically compared with the finite element prediction value. If the monitored value exceeds the threshold library range, a reinforcement instruction is triggered; Step S4: Adjust construction parameters according to the instructions, including excavation thickness correction and temporary support addition, and simultaneously update finite element model parameters; Step S5: Post-completion model evaluation and database iteration.

[0009] Preferably, the parameter calibration in step S2 specifically includes: When the error between the simulated displacement and the measured displacement is greater than 15%, the soil cohesion (c) and internal friction angle (φ) are optimized by the back propagation algorithm; The stiffness correction coefficient of the support structure is 1.2~1.5 times and matches the construction material inspection report.

[0010] Preferably, the monitoring data in step S3 includes: The accuracy of pile top displacement is ±0.1mm, the accuracy of anchor axial force is ±1kN, the accuracy of pit bottom uplift is ±0.5mm, and the coincidence rate of measuring point arrangement and finite element key node coordinates is ≥90%.

[0011] Preferably, the reinforcement instruction in step S4 includes: The excavation layer thickness correction ΔH=0.5H0, H0 is the planned layer thickness, and the temporary support spacing is increased to 70% of the original design.

[0012] Preferably, the post-completion model evaluation and database iteration in step S5‌ includes: Based on the monitoring data of 3 months after completion, the soil consolidation coefficient was inverted and the displacement attenuation rate in 15-20 days was predicted; The engineering parameters are stored in the database, including the soil c / φ value and support scheme. This allows for rapid parameter adjustment of similar projects through fuzzy matching, reducing repeated calculations by 30%.

[0013] Preferably, the back propagation algorithm in step S2 is specifically: (a) Define the objective function: J = 2 + λ·‖Δθ‖ 2 in To simulate displacement, To monitor the displacement, Δθ is the parameter adjustment amount; (b) The Levenberg-Marquardt optimization algorithm is used to iteratively update the parameter θ until the objective function converges.

[0014] A foundation pit support collaborative control system includes the following modules: Finite element dynamic analysis module, used to simulate excavation and support conditions step by step; IoT monitoring module, including GNSS displacement sensor, fiber optic axial force meter and laser rangefinder; The closed-loop feedback execution module links the support equipment through the PLC controller to achieve real-time parameter adjustment.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. Dynamic parameter iteration: Reversely optimize the finite element model using monitoring data to solve the problem of parameter rigidity in traditional methods; 2. Automated closed-loop control: Reduces manual intervention and increases response speed to minutes. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more specifically and intuitively illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for describing the embodiments or the prior art.

[0017] Figure 1 This is a schematic diagram of the structure proposed by the present invention. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0019] Reference Figure 1 A collaborative control method for foundation pit excavation and support based on finite element dynamic feedback includes the following steps: Step S1: Establish a finite element model for foundation pit support, define the soil small strain hardening soil model (HSS) constitutive parameters and support structure stiffness, and obtain the initial displacement and stress fields through step-by-step excavation simulation. This example mainly uses temporary supports such as soil slope excavation, steel sheet pile support (including supporting purlins), and soil nail wall support. A three-dimensional solid element (such as SOLID185) is used to construct the foundation pit soil model, combined with shell elements (SHELL63) to simulate the support pile structure, and PLAXIS 3D is used to establish the foundation pit support finite element model.

[0020] Define soil stratification parameters, such as the difference in elastic modulus between soft clay and sand layers, and calibrate the initial geostress field using field drilling data.

[0021] The excavation process is decomposed into multiple stages, with the excavation depth of each layer ≤3m. Each stage corresponds to the update of finite element boundary conditions, such as soil removal and support activation.

[0022] Embed time effects, such as soil creep models, to predict long-term deformation trends. ‌‌

[0023] Step S2: Iteratively calibrate parameters based on simulation results and field monitoring data to build a dynamic adjustment threshold library for the construction phase, including displacement warning value (UX_max), stress safety factor, and support activation sequence; When the error between the simulated displacement and the measured displacement is greater than 15%, the back propagation algorithm is used to optimize the soil cohesion c and internal friction angle φ, and the support structure stiffness is adjusted synchronously with a correction factor of 1.2 to 1.5 times.

[0024] The backpropagation algorithm optimization specifically includes: 1. Model construction and training Loss Function:

[0025] Where N is the sample.

[0026] 2. Dynamic parameter optimization process Finite element simulation comparison: Substitute the predicted c and φ into the finite element model to generate displacement / stress field simulation results, which are compared with the measured data (calibration is triggered if the displacement error is greater than 15%); ‌Backpropagation Iteration‌: Compute the gradient: and , update the network weights.

[0027] Constraints: c≥5kPa, φ≥2°.

[0028] 3. Multi-objective collaborative optimization Coulomb criterion constraint‌: embed the shear strength condition in the loss function:

[0029] Compared with the measured shear strength τ, the physical consistency is enhanced. ‌

[0030] Based on the material test report such as concrete strength C30~C40, correct the elastic modulus of the support unit.

[0031] Define displacement warning values ​​(e.g., pile top UX_max=20mm), stress safety factor (SF≥1.3), and support activation time window (±2 hours).

[0032] Step S3: Real-time construction data is collected through IoT sensors and dynamically compared with the finite element prediction value. If the monitored value exceeds the threshold library range, a reinforcement instruction is triggered; GNSS displacement sensors (accuracy ±0.1mm), fiber optic axial force meters (range 0~500kN) and pore water pressure meters are deployed, and the overlap rate between measuring points and finite element key nodes is ≥90%.

[0033] Data synchronization is achieved in seconds through LoRa wireless transmission, avoiding the delay of traditional wired monitoring.

[0034] ‌Abnormal operating conditions graded response:‌ Level 1 warning (UX ≥ 15mm): Automatically send SMS to notify engineers; Level 2 warning (UX ≥ 20 mm or SF < 1.3): Trigger reinforcement instructions and suspend excavation.

[0035] Step S4: Adjust construction parameters according to the instructions, including excavation thickness correction and temporary support addition, and simultaneously update finite element model parameters; Precise reinforcement measures: Excavation control: Layer thickness correction ΔH=0.5H0, and the stability of the new excavation surface is verified through the BIM model.

[0036] ‌Model Synchronization Update‌ After each adjustment, the finite element model is automatically updated through parametric scripts (such as APDL) and a new round of simulation verification is started.‌

[0037] Step S5: Post-completion model evaluation and database iteration.

[0038] Based on the monitoring data of 3 months after completion, the soil consolidation coefficient was inverted and the displacement attenuation rate within 15-20 days was predicted.

[0039] ‌Case Library Construction‌ Storing engineering parameters (such as soil c / φ value and support scheme) in the database supports rapid parameter adjustment for similar projects through fuzzy matching, reducing repeated calculations by 30%. Preferably, the parameter calibration in step S2 specifically includes: When the error between the simulated displacement and the measured displacement is greater than 15%, the soil cohesion (c) and internal friction angle (φ) are optimized by the back propagation algorithm; The stiffness correction coefficient of the support structure is 1.2~1.5 times and matches the construction material inspection report.

[0040] Preferably, the monitoring data in step S3 includes: The accuracy of pile top displacement is ±0.1mm, the accuracy of pit bottom uplift is ±0.5mm, and the coincidence rate between measurement point arrangement and finite element key node coordinates is ≥90%.

[0041] Preferably, the reinforcement instruction in step S4 includes: The excavation layer thickness correction ΔH=0.5H0, H0 is the planned layer thickness, and the temporary support spacing is increased to 70% of the original design.

[0042] A foundation pit support collaborative control system includes the following modules: Finite element dynamic analysis module, used to simulate excavation and support conditions step by step; IoT monitoring module, including GNSS displacement sensor, fiber optic axial force meter and laser rangefinder; The closed-loop feedback execution module links the support equipment through the PLC controller to achieve real-time parameter adjustment.

[0043] 1. Dynamic parameter optimization and high-precision prediction By integrating the finite element model with field monitoring data, soil constitutive parameters (such as cohesion and internal friction angle) are calibrated in real time to ensure that the displacement prediction error is ≤10%, which is significantly better than the 20% error limit of traditional static design.

[0044] The back-propagation algorithm is used to optimize the stiffness of the support structure, match the actual construction material properties (such as concrete strength), and improve the reliability of the model.

[0045] 2. Closed-loop control and real-time response By building a dynamic threshold library (displacement warning value, stress safety factor), and combining it with real-time comparison data from IoT sensors (GNSS, fiber optic axial force meter), the response time for triggering reinforcement commands is shortened to minutes.

[0046] In response to sudden loads (such as heavy rain and overloaded soil), the finite element seepage model is used to quickly generate emergency plans (such as increasing the density of precipitation wells) to reduce the risk of uncontrolled deformation.

[0047] 3. Multi-structure synergy and improved adaptability It supports the optimization of composite support schemes (such as soil nail walls + micropiles, double-row piles + inclined supports), verifies the stability of different combinations through finite element simulation, and expands the scope of applicable geological conditions to complex strata such as soft soil and backfill soil.

[0048] 4. Economic and efficiency advantages By precisely controlling the timing of support activation (±2-hour window) and material usage, 10% to 15% of redundant engineering work can be reduced, lowering overall costs.

[0049] The BIM+finite element interface is used to realize automatic adjustment of construction parameters such as excavation layer thickness correction, shortening the construction period by 10% to 15%.

[0050] 5. Long-term security and knowledge iteration After completion, the model will be updated based on inversion of monitoring data to predict long-term stability (such as the 3-month displacement attenuation rate) and guide subsequent operation and maintenance.

[0051] Establish a "simulation parameters-construction effect" case library to support rapid parameter adjustment for similar projects and reduce the cost of repeated experiments.

[0052] Railway foundation pit engineering application Step 1: Use PLAXIS 3D to build the HSS model, input G0=50 MPa (based on shear wave velocity test), E 50 =15MPa (triaxial test); Step 2: Install the inclinometer and earth pressure cell to collect data in real time, and iteratively optimize γ0.7 from 0.0001 to 0.00008 through the back propagation algorithm; Step 3: Set UX_max = 25 mm (corresponding to the displacement and deformation limits). When the monitored displacement reaches 20 mm, the backup support is automatically activated. Vertical displacement monitoring includes observation of vertical displacement of retaining wall (slope) top, columns, surrounding ground surface, buildings, pipelines, and roads; Horizontal displacement monitoring: Observation of horizontal displacement of the top of the retaining wall (slope), surrounding buildings, and surrounding pipelines.

[0053] Step 4: The system response time is 3 minutes, which is 12 times faster than manual decision-making and saves 18% of support costs.

[0054] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A collaborative control method for foundation pit excavation and support based on finite element dynamic feedback, characterized in that: The steps include: Step S1: Establish a finite element model for the foundation pit support, define the HSS constitutive parameters of the soil and the stiffness of the support structure, use PLAXIS 3D to establish the finite element model for the foundation pit support, and obtain the initial displacement and stress fields through step-by-step excavation simulation; Step S2: Iteratively calibrate parameters based on simulation results and field monitoring data to build a dynamic adjustment threshold library for the construction phase, including displacement warning value (UX_max), stress safety factor, and support activation sequence; Step S3: Real-time construction data is collected through IoT sensors and dynamically compared with the finite element prediction value. If the monitored value exceeds the threshold library range, a reinforcement instruction is triggered; Step S4: Adjust construction parameters according to the instructions, including increasing the number of temporary supports, adjusting the anchor prestress value, and shortening the excavation step length, and simultaneously update the finite element model parameters; Step S5: Post-completion model evaluation and database iteration.

2. The method for collaborative control of foundation pit excavation and support based on finite element dynamic feedback according to claim 1, characterized in that: The parameter calibration in step S2 specifically includes: When the error between the simulated displacement and the measured displacement is greater than 15%, the soil cohesion c and internal friction angle φ are optimized by the back propagation algorithm; The stiffness correction coefficient of the support structure is 1.2~1.5 times and matches the construction material inspection report.

3. The method for collaborative control of foundation pit excavation and support based on finite element dynamic feedback according to claim 2, characterized in that: The monitoring data in step S3 includes: The accuracy of pile top displacement is ±0.1mm, the accuracy of anchor axial force is ±1kN, the accuracy of pit bottom uplift is ±0.5mm, and the coincidence rate of measuring point arrangement and finite element key node coordinates is ≥90%.

4. The method for collaborative control of foundation pit excavation and support based on finite element dynamic feedback according to claim 3 is characterized in that: The reinforcement instructions in step S4‌ include: The excavation layer thickness correction ΔH=0.5H0, H0 is the planned layer thickness, and the temporary support spacing is increased to 70% of the original design.

5. The method for collaborative control of foundation pit excavation and support based on finite element dynamic feedback according to claim 4 is characterized in that: The post-construction model evaluation and database iteration in step S5‌ include: Based on the monitoring data of 3 months after completion, the soil consolidation coefficient was inverted and the displacement attenuation rate in 15-20 days was predicted; The engineering parameters are stored in the database, including the soil c / φ value and support scheme. This allows for rapid parameter adjustment of similar projects through fuzzy matching, reducing repeated calculations by 30%.

6. The method for collaborative control of foundation pit excavation and support based on finite element dynamic feedback according to claim 2, characterized in that: The back propagation algorithm in step S2 is specifically: (a) Define the objective function: J = 2 + λ·‖Δθ‖ 2 in To simulate displacement, To monitor the displacement, Δθ is the parameter adjustment amount; (b) The Levenberg-Marquardt optimization algorithm is used to iteratively update the parameter θ until the objective function converges.

7. A foundation pit support collaborative control system implementing any one of the methods of claims 1-5, characterized in that: Includes the following modules: Finite element dynamic analysis module, used to simulate excavation and support conditions step by step; IoT monitoring module, including GNSS displacement sensor, fiber optic axial force meter and laser rangefinder; The closed-loop feedback execution module links the support equipment through the PLC controller to achieve real-time parameter adjustment.

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