Servo support type deep foundation pit enclosure wall lateral deformation measuring method and device
By acquiring the mechanical pressure compensation data of the servo support device, determining the design stiffness range and extracting linear features, filtering the inelastic deviation dataset, and generating the soil continuous stiffness function, the problem of signal aliasing in the servo system was solved, and accurate monitoring of the deformation of the retaining wall was achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHANGHAI JIAOTONG UNIV
- Filing Date
- 2026-04-13
- Publication Date
- 2026-05-12
AI Technical Summary
In existing deep foundation pit projects, there are assembly gaps in the mechanical connection parts of the servo steel support system, which leads to signal aliasing, making it impossible to accurately monitor the deformation of the retaining wall. There is a lack of feature separation methods based on physical priors, making it impossible to extract real information on soil property changes from mixed noise.
By acquiring the mechanical pressure compensation data of the servo support device, determining the design stiffness range based on the device parameters, performing linear feature extraction, filtering the inelastic deviation dataset, generating the soil continuous stiffness function, constructing and solving the force balance equation of the retaining wall, and obtaining the lateral deformation curve of the retaining wall.
It effectively identifies and eliminates low-stiffness backlash signals caused by mechanical backlash in servo data, extracts real information on soil property changes, and achieves accurate monitoring of retaining wall deformation.
Smart Images
Figure CN122020100A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of wall deformation detection technology, specifically to a method and device for measuring the lateral deformation of a servo-supported deep foundation pit retaining wall. Background Technology
[0002] Existing deep foundation pit projects often employ servo steel support systems to maintain axial force stability. Although the servo system generates high-frequency axial force and elongation data during automatic compensation, directly using this data to invert the deformation of the retaining wall presents significant challenges. The main reason is that the mechanical connection parts of the servo system (such as the hinge) have assembly gaps, and the "low stiffness" characteristic exhibited during its closing process is mathematically highly similar to the "wall retreat" caused by soil rheology behind the wall, resulting in severe signal aliasing. Current technology lacks physical prior feature separation methods, making it impossible to extract the true soil property change information from the mixed noise, thus hindering the accurate monitoring of wall deformation using servo data. Summary of the Invention
[0003] To address the technical problem that the lack of physical prior feature separation methods in related technologies makes it impossible to extract real soil property change information from mixed noise, thus preventing the accurate monitoring of wall deformation using servo data, this application provides a servo-supported deep foundation pit retaining wall lateral deformation measurement method and device.
[0004] The specific technical solution adopted is as follows: The mechanical pressure compensation data of the servo support device during the automatic compensation process is obtained, and the design stiffness range is determined based on the device parameters of the servo support device. The mechanical pressure compensation data includes axial force increment data and mechanical extension data. Using the design stiffness range as a constraint, linear feature extraction is performed on the mechanical compression data to determine the elastic baseline. Based on the elastic baseline, the inelastic deviation dataset that meets the preset conditions is selected from the mechanical pressure compensation data. The inelastic deviation dataset is the data after removing the noise caused by assembly gaps. Based on the inelastic deviation dataset and the elastic baseline, the benchmark value of the soil reaction coefficient is updated to generate the continuous stiffness function of the soil. Based on the soil continuous stiffness function and mechanical compression data, the force balance equation of the retaining wall is constructed and solved, and the lateral deformation curves of the retaining wall at different depths are obtained.
[0005] In one possible implementation of this application, determining the design stiffness range based on the device parameters of the servo support device includes: Based on the device parameters of the servo support device, the elastic compression caused by a unit axial force increment is calculated. The range of elastic compression is expanded according to the first preset multiple to obtain the design stiffness range.
[0006] In one possible implementation of this application, the design stiffness range is used as a constraint condition, and linear feature extraction is performed on the mechanical compression data to determine the elastic baseline, including: Random sampling consensus algorithm is used to extract any two sets of axial force increment data and mechanical extension data from mechanical pressure compensation data, and a linear equation is constructed. The design stiffness range is used as a constraint condition for the linear equation. If the slope of the line corresponding to the linear equation is within the design stiffness range, then multiple interior points in the mechanical compensation data are calculated. The linear equation with the most interior points corresponds to the straight line, which is used as the elastic baseline in the automatic compensation process.
[0007] In one possible implementation of this application, if the slope of the line corresponding to the linear equation is within the design stiffness range, then the calculation of multiple interior points in the mechanical compensation data includes: If the slope of the line corresponding to the linear equation is within the design stiffness range, then traverse all data points in the mechanical pressure compensation data and calculate the Euclidean distance between each data point and the line corresponding to the linear equation. Data points whose Euclidean distance is less than a preset tolerance threshold are used as multiple interior points in the mechanical pressure compensation data.
[0008] In one possible implementation of this application, based on an elastic baseline, the inelastic deviation dataset that meets preset conditions in the mechanical compression data is filtered, including: Calculate the first sum between the elasticity prediction value corresponding to the elasticity baseline and the preset tolerance threshold; For any given moment, if the mechanical extension data is greater than the first sum and the axial force increment data is greater than the maximum value of the axial force increment data that is a second preset multiple, then the mechanical pressure compensation data at the current moment is determined to meet the preset conditions. Based on mechanical pressure data at multiple times that meet preset conditions, an inelastic deviation dataset is constructed.
[0009] In one possible implementation of this application, based on the inelastic deviation dataset and the elastic baseline, the benchmark value of the soil reaction coefficient is updated to generate a continuous soil stiffness function, including: Based on the inelastic deviation dataset and the elastic baseline, the stiffness response deviation at different depths is calculated. The stiffness response deviation is mapped to the soil reaction reduction factor. The soil reaction coefficient benchmark value is updated using the soil reaction reduction factor to generate the continuous stiffness function of the soil.
[0010] In one possible implementation of this application, the soil reaction coefficient benchmark value is updated using a soil reaction reduction factor to generate a continuous soil stiffness function, including: By using the soil reaction reduction factor, the benchmark value of the soil reaction coefficient is updated to obtain the soil reaction coefficient at multiple depths. By using an interpolation algorithm to process the soil reaction reduction coefficient at different depths, a continuous reduction coefficient curve is obtained. Based on the continuous reduction coefficient curve, the soil reaction coefficient is smoothed to generate a continuous soil stiffness function.
[0011] In one possible implementation of this application, based on the soil continuous stiffness function and mechanical compression data, the force equilibrium equation of the retaining wall is constructed and solved to obtain the lateral deformation curves of the retaining wall at different depths, including: Determine the bending resistance data of the retaining wall and the corresponding soil pressure at different depths; Based on bending capacity data, soil continuous stiffness function, mechanical pressure data, and soil pressure, a force equilibrium equation is constructed, which is a fourth-order ordinary differential equation. The force equilibrium equations were transformed into a system of linear equations using the finite difference method for numerical solution, resulting in the lateral deformation curves of the retaining wall at different depths.
[0012] In one possible implementation of this application, the force equilibrium equations are transformed into a system of linear equations using the finite difference method for numerical solution, resulting in lateral deformation curves of the retaining wall at different depths, including: The enclosure wall is divided into multiple wall nodes along the depth direction; For any wall node, the fourth-order ordinary differential equation is transformed into a linear algebraic equation using the difference formula; By integrating the linear algebraic equations of each wall node, a matrix equation is obtained that includes the stiffness matrix, the displacement vector to be solved, and the load vector. The matrix equations were solved using Gaussian elimination to obtain the lateral displacement values of each wall node. The lateral displacement values were smoothed to obtain the lateral deformation curves of the retaining wall at different depths.
[0013] To achieve the above objectives, a servo-supported deep foundation pit retaining wall lateral deformation measurement device is also provided. The device includes: a memory, a processor, and a servo-supported deep foundation pit retaining wall lateral deformation measurement program stored in the memory and executable on the processor. The servo-supported deep foundation pit retaining wall lateral deformation measurement program is configured to implement the steps of the above-mentioned servo-supported deep foundation pit retaining wall lateral deformation measurement method.
[0014] This application has, but is not limited to, the following technical effects: By acquiring mechanical compression data from the servo support device during automatic compensation, and based on the device parameters, the design stiffness range is determined. The mechanical compression data includes axial force increment data and mechanical extension data. Using the design stiffness range as a constraint, linear feature extraction is performed on the mechanical compression data to determine the elastic baseline. The elastic baseline is then used to filter out inelastic deviation datasets that meet preset conditions. Since the inelastic deviation datasets are data after removing noise caused by assembly gaps, false deformation reports due to assembly gaps are prevented in subsequent calculations. The soil reaction coefficient baseline value is updated using the inelastic deviation datasets and the elastic baseline to generate a continuous soil stiffness function. Based on the continuous soil stiffness function and the mechanical compression data, the force balance equation of the retaining wall is constructed and solved, yielding the lateral deformation curves of the retaining wall at different depths. In this application, an inelastic deviation dataset that meets preset conditions in mechanical compaction data is screened using an elastic baseline. This effectively identifies and removes low-stiffness falloff signals caused by mechanical gap closure in the servo data, solving the technical problem of distinguishing between mechanical gaps and soil rheology. It extracts real soil property change information from mixed noise, thereby achieving accurate monitoring of wall deformation results. Attached Figure Description
[0015] Figure 1 This is a flowchart illustrating the first embodiment of the servo-supported deep foundation pit retaining wall lateral deformation measurement method of this application; Figure 2 This is a schematic diagram of the device structure of the hardware operating environment involved in the embodiments of this application. Detailed Implementation
[0016] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.
[0017] This application provides a method for measuring the lateral deformation of a servo-supported deep foundation pit retaining wall. In the first embodiment of this method, referring to... Figure 1 The methods include: Step S10: Obtain the mechanical pressure compensation data of the servo support device during the automatic compensation process, and determine the design stiffness range based on the device parameters of the servo support device. The mechanical pressure compensation data includes axial force increment data and mechanical extension data.
[0018] As an example, the servo-supported deep foundation pit retaining wall lateral deformation measurement method can be applied to the servo-supported deep foundation pit retaining wall lateral deformation measurement device. The servo-supported deep foundation pit retaining wall lateral deformation measurement device belongs to the servo-supported deep foundation pit retaining wall lateral deformation measurement system, which belongs to the servo-supported deep foundation pit retaining wall lateral deformation measurement equipment.
[0019] As an example, the servo support device and the servo-supported deep foundation pit retaining wall lateral deformation measurement device are the same device. For ease of understanding, the servo support device will be used to explain the following in detail. The main purpose of this device is to use the jack extension amount and axial force data to invert the deformation of the wall. Before performing the wall deformation inversion process, it is necessary to obtain a high-quality data source that reflects the pure mechanical stress state, that is, mechanical pressure compensation data. After obtaining the mechanical pressure compensation data, it is also necessary to perform data cleaning and other preprocessing work on these data to eliminate the errors introduced by the change of ambient temperature and provide accurate incremental data for subsequent feature separation.
[0020] As an example, when the servo support device is first started or reset, global variables used for subsequent calculations need to be initialized to ensure a closed loop in the calculation logic. Specifically, the system establishes an array of soil reaction coefficients corresponding to the full depth of the retaining wall in memory. The length of this array corresponds to the number of discrete nodes divided from top to bottom of the retaining wall at preset intervals (1 meter in this embodiment). The system reads the geological survey report and assigns each depth node... The corresponding benchmark values of soil reaction coefficients are filled into the array, i.e., the soil reaction coefficient array. ,in, This represents the baseline value of the soil reaction coefficient at the j-th depth node. Specifically, before any servo support pressure replenishment action is detected, the system assumes that the soil behind the wall is in its initial design state and has not undergone rheological degradation.
[0021] As an example, mechanical pressure compensation data includes axial force increment data and mechanical extension data. The axial force increment data is calculated by reading the real-time axial force from the pressure sensor in the hydraulic circuit. The change in axial force between the current moment and the starting moment is calculated, and the change in axial force is used as the axial force increment data during this period, with the unit being kilonewtons (kN).
[0022] As an example, the mechanical extension data can be obtained by subtracting the theoretical thermal elongation of the steel support due to temperature changes from the total extension of the jack read by the displacement sensor on the jack itself, in mm. The temperature value of the steel pipe surface is read by the temperature sensor attached to the surface of the steel support, in degrees Celsius.
[0023] As an example, the automatic compensation of the servo support device is intermittent. In order to extract effective force characteristics, it is necessary to accurately lock the time window of the transient process of "compensation". Specifically, the mechanical compensation data can be obtained in the following ways: First, by monitoring the first The operating status status of the servo-driven steel-supported pump station controller. When the status status changes from "pressure holding state" to "pressure increasing state", the following is determined: The track support begins automatic compensation; the system records this moment as the start time. At this point, immediately switch to high-frequency sampling mode (in this embodiment, the sampling frequency is set to 10Hz) and begin synchronously recording sensor data. When the status bit is detected to return to the "holding voltage state" and the duration exceeds the preset stabilization delay... (In this embodiment, 5 seconds) When the compensation process ends, this moment is recorded as the end moment. .
[0024] Within a defined time window Within, including the time window For a duration of 10 seconds or more, the system collects and stores the following raw data sequence: Real-time axial force numerical sequence , Total extension of jack numerical sequence and the numerical sequence of steel pipe surface temperature After obtaining these three original sequences, a moving average filter is applied to each of them, with the sliding window size set to 5 sampling points to smooth the data curve.
[0025] For each time t within the sampling window: Calculate the change in temperature at the current time relative to the initial temperature. .
[0026] Based on the linear thermal expansion formula in physics, calculate the theoretical thermal elongation of the steel support due to temperature change at that moment. The calculation method is as follows: in, The coefficient of linear expansion of steel (usually taken as...) ), For the first The total design length of the steel support. This formula shows that the longer the steel support or the greater the temperature rise, the more significant the thermal elongation.
[0027] As an example, subtracting the initial extension and thermal expansion from the total monitored extension of the jack yields the mechanical extension of the jack caused solely by force. The calculation method is as follows: in, This represents the mechanical extension amount of the jack at time t, which is the data on the mechanical extension amount.
[0028] Next, calculate the change in axial force at the current time relative to the initial time. The axial force increment data is obtained, and the calculation method is as follows: Through the above calculations, the first... Data set of axial force-extension increments at the support location during this motion This refers to mechanical pressure compensation data. In the mechanical pressure compensation data, at each time t, there exists a data pair containing mechanical extension data and axial force increment data. This dataset is represented as follows: This dataset, having eliminated environmental temperature interference, purely reflects the deformation response of the steel support under mechanical forces. Before proceeding to the next stage of calculations, it is necessary to examine the axial force-elongation increment dataset. The maximum axial force increment, if the maximum axial force increment If the value is less than the preset micro-perturbation threshold (50kN in this embodiment), the current action is determined to be an invalid perturbation (it may be a false trigger or a small fluctuation). No subsequent rheological characteristic analysis is performed, and the original parameters at this position are maintained unchanged and the current process is terminated.
[0029] As an example, the device parameters of the servo support device are the design parameters of the device, such as the support area. Through these device parameters, the theoretical compression of the steel pipe is determined. To prevent the subsequent fitting algorithm from mistakenly identifying the mechanical clearance closure stage (characterized by a very large slope, i.e., very small stiffness) as the normal elastic deformation stage, the boundary conditions for stiffness search need to be set using the physical parameters determined by the steel support. Then, the design stiffness range is calculated through the theoretical compression, and the design stiffness range is used as the constraint condition for subsequent calculations.
[0030] As an example, the design stiffness range can be the design stiffness range of the steel support, used to constrain the slope range of the subsequent fitting algorithm.
[0031] Step S10 includes: Based on the device parameters of the servo support device, the elastic compression caused by a unit axial force increment is calculated.
[0032] As an example, elastic compression The calculation method can be: in, For the first Total design length of the rail support (unit: mm). The elastic modulus of steel (usually taken as...) ), This refers to the cross-sectional area of the steel support, the effective cross-sectional area of the steel support (excluding the hollow portion), not the outer contour area (unit: ),in, It represents the theoretical elastic compression of steel pipe caused by a unit increase in axial force under ideal clearance-free conditions, which is also the compliance characteristic.
[0033] The range of elastic compression is expanded according to the first preset multiple to obtain the design stiffness range.
[0034] As an example, the first preset multiple can be 0.8 or 1.5, and there is no specific limitation.
[0035] As an example, considering on-site installation deviations and material heterogeneity, the actual elastic compression is allowed to fluctuate to some extent around the theoretical value. This embodiment sets the allowable range of the effective slope (i.e., elastic compression) to be 0.8 to 1.5 times the theoretical value, and designs the stiffness range... Represented as: This interval will serve as a mandatory constraint for the random sampling consensus algorithm in subsequent steps. This means that any fitted straight line whose slope falls outside this interval (such as a gap closure line with an excessively large slope) will be judged by the algorithm as "violating physical laws" and directly eliminated.
[0036] Step S20: Using the design stiffness range as a constraint, perform linear feature extraction on the mechanical compression data to determine the elastic baseline.
[0037] As an example, the design stiffness range is used as a constraint condition, and linear features are extracted from the mechanical compression data to determine the elastic baseline of the steel support in this compensation process.
[0038] As an example, the elastic baseline is a straight line obtained by fitting the axial force increment data and mechanical extension data in the mechanical compression data, representing the theoretical elastic extension.
[0039] Step S20 further includes steps S21 to S23, including: Step S21: Extract any two sets of axial force increment data and mechanical extension data from the mechanical pressure compensation data using the random sampling consensus algorithm, and construct a linear equation.
[0040] As an example, a dataset of mechanical pressure compensation data It includes elastic segment data (interior points) that conform to Hooke's law, as well as nonlinear segment data (outer points) caused by gaps or rheology. In order to extract the true elastic trend from this strong noise background, this step uses a constrained random sample consensus algorithm.
[0041] Specifically, from the dataset of mechanical pressure compensation data Two non-overlapping data points are randomly selected from the data. and . This represents the axial force increment and mechanical extension data at data point a. A linear equation is constructed based on these two points. Calculate the slope of the line. and intercept .
[0042] Step S22: Use the design stiffness range as a constraint condition for the linear equation. If the slope of the line corresponding to the linear equation is within the design stiffness range, calculate multiple interior points in the mechanical pressure compensation data.
[0043] As an example, the design stiffness interval is used as a constraint on the linear equation to examine the calculated slope. Does it fall within the design stiffness range? Inside.
[0044] like (For example If the stiffness is too soft (which may indicate a closed gap segment), the model is deemed invalid, discarded, and the sampling steps of the random sampling consensus algorithm are repeated.
[0045] like If the model is deemed physically reasonable, proceed to the next step of interior point statistics.
[0046] Step S22 includes: If the slope of the line corresponding to the linear equation is within the design stiffness range, then all data points in the mechanical pressure compensation data are traversed, and the Euclidean distance between each data point and the line corresponding to the linear equation is calculated.
[0047] Data points whose Euclidean distance is less than a preset tolerance threshold are used as multiple interior points in the mechanical pressure compensation data.
[0048] As an example, iterating through the dataset corresponding to the mechanical pressure compensation data. All For each data point, calculate the Euclidean distance from each point to the line corresponding to the linear equation. Count the number of data points whose distance is less than a preset tolerance threshold (1.0 mm in this embodiment), and record this as the number of interior points of the model (i.e., the linear equation mentioned above).
[0049] Step S23: The straight line corresponding to the linear equation with the most interior points is used as the elastic baseline in the automatic compensation process.
[0050] As an example, repeat the above steps for a total of This step (50 times in this example to ensure a high probability of finding the optimal solution) involves selecting the model with the most interior points (i.e., the linear equation) from all valid models that have passed the physical constraint test. This is determined as the elastic baseline for the steel support in this automatic compensation process, and is represented as follows: Step S30: Based on the elastic baseline, filter the inelastic deviation dataset that meets the preset conditions in the mechanical pressure compensation data. The inelastic deviation dataset is the data after removing the noise caused by assembly gaps.
[0051] As an example, due to the mechanical assembly gaps in servo systems, directly measured datasets often contain a mixture of two types of data: "gap closure" (low stiffness) and "elastic compression" (high stiffness). The aforementioned RANSAC algorithm has already eliminated pure gap closure data points with abnormally large slopes (extremely low stiffness) through slope constraints. Then, using the material properties determined by the steel support as a physical benchmark, it locks a unique elastic baseline from the mixed data, thereby extracting abnormal deviation features representing soil stiffness attenuation. Furthermore, after determining the elastic baseline, any behavior that significantly deviates from this baseline contains additional physical information in the "axial force-extension" coordinate system: Data points located below the elastic baseline (extension less than the predicted value): usually originate from frictional resistance and are ignored.
[0052] Data points located above the elastic baseline (extension greater than predicted value) mean that under the same axial force, the actual extension is greater than the theoretical elastic elongation. This indicates that the system stiffness is softer than the theoretical value. This "softening" phenomenon is a direct reflection of the decrease in constraint capacity caused by soil rheology.
[0053] Based on this, the data points in the mechanical pressure compensation data are traversed to select two data points that meet the preset conditions, thereby constructing an inelastic deviation dataset and eliminating noise caused by assembly gaps.
[0054] Step S30 includes: Calculate the first sum between the elasticity prediction value corresponding to the elastic baseline and the preset tolerance threshold.
[0055] As an example, the first sum is represented as: ,in, Indicates the elasticity forecast value. This indicates the preset tolerance threshold, which can be 3mm.
[0056] For any given moment, if the mechanical extension data is greater than the first sum and the axial force increment data is greater than the maximum value of the axial force increment data that is a second preset multiple, then the mechanical pressure compensation data at the current moment is determined to meet the preset conditions.
[0057] As an example, the upper deviation condition and the high stress level condition are set as preset conditions, specifically: Upper deviation condition: The measured mechanical extension is significantly greater than the elastic prediction value, i.e. .
[0058] High stress level conditions: The axial force increment is in the high range of this loading process, i.e., the axial force increment data... The second preset multiple is 0.5. This represents the maximum value of the axial force increment data. This condition is based on soil mechanics experience, namely that the rheological effect of soil is more significant under high stress.
[0059] When both of the above conditions are met simultaneously, the mechanical pressure compensation data at the current moment is determined to meet the preset conditions.
[0060] Based on mechanical pressure data at multiple times that meet preset conditions, an inelastic deviation dataset is constructed.
[0061] As an example, multiple data points that meet preset conditions are selected from the mechanical pressure data to construct an inelastic deviation dataset.
[0062] Step S40: Based on the inelastic deviation dataset and the elastic baseline, update the benchmark value of the soil reaction coefficient to generate the soil continuous stiffness function.
[0063] As an example, based on the inelastic deviation dataset and the elastic baseline, the stiffness response deviation is calculated, and the soil reaction coefficient baseline value is updated using the stiffness response deviation to generate the soil continuous stiffness function.
[0064] As an example, the soil continuous stiffness function can be a function obtained by converting multiple updated discrete data points into data points that are continuously distributed along the depth. The soil continuous stiffness function is the curve of the change of the soil reaction coefficient, and the soil reaction coefficient represents the soil stiffness value corresponding to any depth.
[0065] Step S40 includes steps S41 to S42: Step S41: Based on the inelastic deviation dataset and the elastic baseline, calculate the stiffness response deviation at different depths.
[0066] As an example, if the filtered inelastic deviation dataset... An empty value indicates that the process exhibits purely elastic behavior, causing the stiffness response to deviate by a certain degree. Otherwise, calculate the mean of the relative deviations of all data points in the inelastic deviation dataset to obtain the dimensionless stiffness response deviation. : in, The number of samples in the inelastic deviation dataset. To prevent division by zero of extremely small positive numbers (such as 1e-5), the unit is the same as L. This represents the jack extension measured by the sensor and then compensated for by temperature at the corresponding axial force increment ΔF at that discrete point. This represents (measured value - theoretical value) / theoretical value. If the sample size is greater than or equal to the minimum sample threshold (e.g., 10), otherwise let =0. This formula calculates the relative percentage error between the measured elongation and the theoretical elastic elongation. For example, This indicates that under the same stress, the actual deformation is 5% greater than the purely elastic deformation. The larger this value is, the more severe the reduction in the equivalent stiffness of the system, that is, the deeper the rheological degree of the soil behind the wall.
[0067] Step S42: Map the stiffness response deviation to the soil reaction reduction coefficient, update the soil reaction coefficient benchmark value using the soil reaction reduction coefficient, and generate the soil continuous stiffness function.
[0068] As an example, the stiffness response deviation measures the stiffness decay at a single point, but the deformation analysis of the retaining wall depends on the soil parameters at the full depth. Therefore, it is necessary to transform this "single-point monitoring feature" into "full-field mechanical parameters" that can be used for structural calculations.
[0069] Furthermore, by utilizing a nonlinear mapping function, the dimensionless stiffness response deviation is... Converted to soil reaction reduction factor This coefficient physically represents the ratio of the actual stiffness of the soil at the current moment to the design stiffness (range 0~1).
[0070] This embodiment uses an exponential decay model to calculate the soil reaction reduction coefficient. : in, : No. The soil reaction reduction factor at the support location. Stiffness response deviation The rheological sensitivity coefficient is a key empirical parameter relating microscopic deformation and macroscopic stiffness, used to adjust the rate of stiffness decay. In this formula, The larger the numerical value (i.e., the more severe the deviation of the measured deformation from the elastic reference), the larger the absolute value of the negative value of the exponent term, and the more accurate the calculated result. The closer it is to 0, that is, the more severe the soil rheology, the lower its effective restraint stiffness on the wall.
[0071] As an example, determine The specific methods could be: 1. Empirical value method: Based on the geological survey report of the area where the foundation pit is located, for soft clay areas (with significant rheology). The recommended value range is [5.0, 15.0]; for sandy soil areas (where rheology is relatively weak), The recommended value range is [1.0, 5.0]. This embodiment is for a typical soft soil foundation pit, and the value is... .
[0072] 2. Inverse Calibration Method: In the early stages of foundation pit excavation (such as after the first support is erected), the wall displacement curve is measured using a portable inclinometer. The measured displacement is then substituted into the subsequent structural calculation equations to perform a reverse calculation that minimizes the calculation error. The value is determined and used consistently in subsequent monitoring.
[0073] Step S42 includes: By using the soil reaction reduction factor, the benchmark value of the soil reaction coefficient is updated to obtain the soil reaction coefficient at multiple depths.
[0074] As an example, the device stores an array of soil reaction coefficients at all depths, containing reference values for soil reaction coefficients at various depths. When the calculated value of the first... Soil reaction reduction factor at the support location Then, a local update operation is performed to extract the first element from the full-depth soil reaction coefficient array. The depth of the support For each node, update the soil reaction coefficient of that node. : In particular, Indicates depth The benchmark value of soil reaction coefficient, Indicates the first The soil reaction reduction factor at the support location, where the first... The depth corresponding to the support position of the road and the depth Similarly, this operation only updates... The value at this specific location remains unchanged while the values at other locations in the array remain unchanged. This mechanism allows the array to act like a "blackboard," recording the latest soil state information fed back by different supports at different times (asynchronous actions), thus achieving spatiotemporal fusion of multi-support data.
[0075] By using an interpolation algorithm to process the soil reaction reduction coefficient at different depths, a continuous reduction coefficient curve is obtained.
[0076] As an example, since the servo support only provides discrete probe points at specific depths, while the retaining wall is a continuously stressed component, it is necessary to transform the discretely updated array into a function that is continuously distributed along the depth. This is based on the updated soil reaction coefficient array. An interpolation algorithm is executed to generate a continuous reduction coefficient curve. Then, the soil reaction coefficient is smoothed using this curve to obtain the continuous soil stiffness function. : Specifically, for the area between supports: for two adjacent supports (e.g., the first...) Tao and the First In the depth region between the two supports, linear interpolation or cubic spline interpolation is used to generate the reduction coefficient of the intermediate transition region based on the reduction coefficient of the two end support points. In this embodiment, cubic spline interpolation is preferred to ensure the continuity of the first derivative of the stiffness function and avoid calculation inflection points caused by abrupt stiffness changes at the support.
[0077] For the boundary region: For the region above the first support, the reduction factor of the first support is used; for the region below the last support, the reduction factor of the last support is used. After processing the soil reaction reduction factor in the region between supports and the soil reaction reduction factor in the boundary region, a continuous reduction factor curve is generated.
[0078] Based on the continuous reduction coefficient curve, the soil reaction coefficient is smoothed to generate a continuous soil stiffness function.
[0079] As an example, the continuous stiffness function of soil The calculation method is as follows: in, This is a continuous reduction factor curve for the entire depth. This represents the baseline value of the soil reaction coefficient. During the interpolation algorithm, the main process involves interpolating the soil reaction reduction coefficient. The soil reaction reduction coefficient at each depth is represented as a smooth curve. This curve is then multiplied by the baseline value of the soil reaction coefficient in the original array. For any depth, the soil reaction coefficient is represented as... ,and It applies to various depths. The curve obtained after connecting the curves can be used to smooth the soil reaction coefficient and generate a continuous soil stiffness function.
[0080] Step S50: Based on the soil continuous stiffness function and mechanical pressure data, construct and solve the force balance equation of the retaining wall to obtain the lateral deformation curves of the retaining wall at different depths.
[0081] As an example, by combining the soil continuous stiffness function with the real-time support axial force in the mechanical compression data, the force equilibrium differential equation of the retaining wall is constructed and solved, thereby outputting the lateral displacement curve along the depth.
[0082] As an example, the lateral deformation curve represents the distribution curve of lateral displacement of the retaining wall at different depths. The initial inclination data of the wall before excavation can be obtained, or the initial cantilever deformation can be calculated based on the excavation conditions before the first support is erected, as the initial reference value of the deformation curve.
[0083] Step S50 includes steps S51 to S53: Step S51: Determine the bending resistance data of the retaining wall and the soil pressure corresponding to different depths.
[0084] As an example, bending capacity data represents the bending capacity of a wall itself, which is determined by the wall material (concrete) and cross-sectional dimensions.
[0085] As an example, earth pressure represents the active earth pressure exerted by the soil behind the wall, which is usually assumed to be a distributed load that increases with depth according to Rankine or Coulomb earth pressure theory.
[0086] Step S52: Based on the bending capacity data, soil continuous stiffness function, mechanical pressure data, and soil pressure, the force balance equation is constructed. The force balance equation is a fourth-order ordinary differential equation.
[0087] As an example, considering the retaining wall of a deep foundation pit (such as a diaphragm wall) as a beam placed vertically on a non-uniform elastic foundation, the following fourth-order ordinary differential equation is established to describe its force equilibrium state, thus constructing the force equilibrium equation: The meanings and functions of each physical quantity in this equation are as follows: Enclosing walls at depth Lateral displacement at the location (unit: m), which is the final output target of this application. The active earth pressure q is positive when it points inwards from the foundation pit, while the support reaction force F and the soil resistance point outwards from the foundation pit.
[0088] (Bending resistance data): Represents the bending resistance of the wall itself. The bending stiffness of the wall (constant) is determined by the wall material (concrete) and cross-sectional dimensions.
[0089] (Soil resistance term): Represents the passive support effect of the soil within the pit on the wall. This is used here. This represents the stiffness function of continuous soil, where the equilibrium equations are established based on a wall of unit width (1m). Where, at a certain point... If the deformation is smaller (i.e., rheological changes have occurred), then the soil provides less resistance for the same displacement, resulting in the wall needing to undergo greater deformation to reach equilibrium.
[0090] (The data item corresponding to soil pressure, or load item): represents the active earth pressure applied to the soil behind the wall, which is usually preset as a distributed load that increases with depth according to Rankine or Coulomb earth pressure theory.
[0091] (Support reaction force): Represents the concentrated thrust exerted on the wall by each servo steel support. Among them, The Dirac function represents the concentrated force at each point. When constructing the load vector of the linear equation system, the support reaction force is directly superimposed onto the depth of the support. The load components of the corresponding wall nodes are not differentiated from the Dirac function itself. This represents the real-time axial force (absolute value at the current moment) included in the mechanical pressure compensation data, which ensures that the equations accurately reflect the current support load state.
[0092] Step S53: The force equilibrium equation is converted into a linear equation system by the finite difference method and numerically solved to obtain the lateral deformation curves of the retaining wall at different depths.
[0093] As an example, due to the continuous soil stiffness function in the equation and load items As the depth varies arbitrarily, it is difficult to obtain an analytical solution. This step uses the finite difference method to transform the differential equation into a system of linear equations for numerical solution. The principle can be summarized as follows: computers cannot handle "continuous" walls (because there are infinitely many points), so we "cut" the wall into many segments, calculate only the displacement of these cutting points (nodes), and finally connect them into a line.
[0094] Step S53 includes: The enclosure wall is divided into multiple wall nodes along the depth direction; For any wall node, the fourth-order ordinary differential equation is transformed into a linear algebraic equation using the difference formula.
[0095] As an example, the wall is discretized along the depth direction as follows: There are [number] wall nodes, with a node spacing of [distance]. (In this example, 1m is used).
[0096] Difference scheme construction: Using the five-point difference formula to approximate the fourth derivative term in the differential equation: Substituting this approximation into the equilibrium equation, for each wall node... For each of these, a linear algebraic equation can be established to calculate the first... When considering the stress state of a wall node, the derivative is no longer calculated; instead, the value itself is used. ) and the two points above it ( ) and the two points below ( , The displacement weighted sum is used instead.
[0097] As an example, since a fourth-order equation requires four boundary conditions to achieve a definite solution (two at each end), this embodiment sets the following default boundary conditions to ensure the solvability of the system of equations: Because the equation involves two points at the top and two at the bottom, the top of the wall (which has no point at the top) and the bottom of the wall (which has no point at the bottom) require special handling; this is called boundary conditions. Top boundary of the wall ( ): Set as a free end. That is, set boundary condition 1: bending moment is 0 ( Boundary condition 2: Shear force is 0 ( This aligns with the actual working conditions of most enclosure walls where the top is only connected by a capping beam and there are no strong external constraints. For cases where the top of the wall has a capping beam, the capping beam can be simplified to an equivalent horizontal spring constraint; for cases without strong constraints, it can be simplified to a free end.
[0098] Wall base boundary ( ): Set as an elastically fixed end, set boundary condition 3: displacement is 0 ( The corner stiffness depends on the subgrade coefficient of the soil below the wall. If the wall is extremely deep, it can be simplified to fully embedded (boundary condition 4: corner). ).
[0099] By integrating the linear algebraic equations of each wall node, a matrix equation is obtained that includes the stiffness matrix, the displacement vector to be solved, and the load vector.
[0100] The matrix equations were solved using Gaussian elimination to obtain the lateral displacement values of each wall node.
[0101] As an example, after the boundary conditions are set, the linear algebraic equations of all wall nodes are solved simultaneously to form a huge system of equations, which is then used to construct a matrix equation: ,in, This is the stiffness matrix (including the stiffness of the wall and soil). Let be the nodal displacement vector to be determined. This is the load vector (containing active earth pressure and the axial force of the steel supports monitored in real time). The matrix equation is solved using Gaussian elimination or LU decomposition to directly obtain the lateral displacement values of each discrete wall node. .
[0102] The lateral displacement values were smoothed to obtain the lateral deformation curves of the retaining wall at different depths.
[0103] As an example, spline interpolation is used to calculate discrete displacement points. Connecting these curves into a smooth, continuous curve yields the lateral deformation curves of the retaining wall at different depths at the current moment. .
[0104] As an example, after obtaining the lateral deformation curve, the curve is output to the monitoring terminal screen for engineers to assess the safety of the foundation pit in real time. When the maximum displacement value of the curve exceeds the warning threshold, the system can trigger an audible and visual alarm. By periodically executing the above calculation process (e.g., once every 10 minutes), all-weather, automated dynamic monitoring of the deformation of the retaining wall can be achieved.
[0105] This application provides a method for measuring the lateral deformation of a servo-supported deep foundation pit retaining wall. It acquires mechanical compression data from the servo support device during automatic compensation, determines the design stiffness range based on the device parameters, and uses the mechanical compression data (including axial force increment and mechanical extension data) as constraints. Linear feature extraction is performed on the mechanical compression data to determine an elastic baseline. The elastic baseline is then used to filter out inelastic deviation datasets that meet preset conditions. Since the inelastic deviation datasets are data after removing noise caused by assembly gaps, false deformation reports due to assembly gaps are prevented in subsequent calculations. The soil reaction coefficient baseline is updated using the inelastic deviation datasets and the elastic baseline to generate a continuous soil stiffness function. Based on the continuous soil stiffness function and the mechanical compression data, the force balance equation of the retaining wall is constructed and solved, yielding the lateral deformation curves of the retaining wall at different depths. In this application, an inelastic deviation dataset that meets preset conditions in mechanical compaction data is screened using an elastic baseline. This effectively identifies and removes low-stiffness falloff signals caused by mechanical gap closure in the servo data, solving the technical problem of distinguishing between mechanical gaps and soil rheology. It extracts real soil property change information from mixed noise, thereby achieving accurate monitoring of wall deformation results.
[0106] Reference Figure 2, Figure 2 This is a schematic diagram of the device structure of the hardware operating environment involved in the embodiments of this application.
[0107] like Figure 2 As shown, the servo-supported deep foundation pit retaining wall lateral deformation measurement device may include: a processor 1001, a memory 1003, and a communication bus 1002. The communication bus 1002 is used to realize the connection and communication between the processor 1001 and the memory 1003.
[0108] Optionally, the servo-supported deep foundation pit retaining wall lateral deformation measurement device may also include a user interface, a network interface, a camera, RF (Radio Frequency) circuitry, sensors, a WiFi module, etc. The user interface may include a display screen and an input submodule such as a keyboard; optional user interfaces may also include standard wired or wireless interfaces. The network interface may include standard wired or wireless interfaces (such as a Wi-Fi interface).
[0109] Those skilled in the art will understand that Figure 2 The structure of the servo-supported deep foundation pit retaining wall lateral deformation measurement device shown does not constitute a limitation on the servo-supported deep foundation pit retaining wall lateral deformation measurement device. It may include more or fewer components than shown, or combine certain components, or have different component arrangements.
[0110] like Figure 2 As shown, the memory 1003, serving as a storage medium, may include an operating system, a network communication module, and a servo-supported deep foundation pit retaining wall lateral deformation measurement program. The operating system is a program that manages and controls the hardware and software resources of the servo-supported deep foundation pit retaining wall lateral deformation measurement equipment, supporting the operation of the servo-supported deep foundation pit retaining wall lateral deformation measurement program and other software and / or programs. The network communication module is used to enable communication between the various components within the memory 1003, as well as communication with other hardware and software in the servo-supported deep foundation pit retaining wall lateral deformation measurement system.
[0111] exist Figure 2 In the servo-supported deep foundation pit retaining wall lateral deformation measurement device shown, the processor 1001 is used to execute the servo-supported deep foundation pit retaining wall lateral deformation measurement program stored in the memory 1003 to implement the steps of the servo-supported deep foundation pit retaining wall lateral deformation measurement method described above.
[0112] The specific implementation method of the servo-supported deep foundation pit retaining wall lateral deformation measurement device of this application is basically the same as the various embodiments of the servo-supported deep foundation pit retaining wall lateral deformation measurement method described above, and will not be repeated here.
[0113] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.
[0114] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.
[0115] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.
[0116] The above are merely preferred embodiments of this application and do not limit the scope of this application. Any equivalent structural or procedural transformations made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the scope of protection of this application.
[0117] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
[0118] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.
Claims
1. A method for measuring the lateral deformation of a servo-supported deep foundation pit retaining wall, characterized in that, The method includes: The mechanical pressure compensation data of the servo support device during the automatic compensation process is obtained, and the design stiffness range is determined based on the device parameters of the servo support device. The mechanical pressure compensation data includes axial force increment data and mechanical extension data. Using the design stiffness range as a constraint, linear feature extraction is performed on the mechanical compression data to determine the elastic baseline; Based on the elastic baseline, the inelastic deviation dataset that meets the preset conditions is selected from the mechanical pressure compensation data. The inelastic deviation dataset is the data after removing noise caused by assembly gaps. Based on the inelastic deviation dataset and the elastic baseline, the benchmark value of the soil reaction coefficient is updated to generate the continuous stiffness function of the soil. Based on the soil continuous stiffness function and the mechanical compression data, the force balance equation of the retaining wall is constructed and solved to obtain the lateral deformation curves of the retaining wall at different depths.
2. The method for measuring the lateral deformation of a servo-supported deep foundation pit retaining wall as described in claim 1, characterized in that, The determination of the design stiffness range based on the device parameters of the servo support device includes: Based on the device parameters of the servo support device, the elastic compression caused by a unit axial force increment is calculated. The range of elastic compression is expanded according to a first preset multiple to obtain the design stiffness range.
3. The method for measuring the lateral deformation of a servo-supported deep foundation pit retaining wall as described in claim 1, characterized in that, The step of using the design stiffness range as a constraint condition to perform linear feature extraction on the mechanical compression data and determine the elastic baseline includes: Random sampling consensus algorithm is used to extract any two sets of axial force increment data and mechanical extension data from the mechanical pressure compensation data, and a linear equation is constructed. The design stiffness range is used as a constraint condition for the linear equation. If the slope of the straight line corresponding to the linear equation is within the design stiffness range, then multiple interior points in the mechanical pressure compensation data are calculated. The linear equation with the most interior points corresponds to the straight line, which is used as the elastic baseline in the automatic compensation process.
4. The method for measuring the lateral deformation of a servo-supported deep foundation pit retaining wall as described in claim 3, characterized in that, If the slope of the line corresponding to the linear equation is within the design stiffness range, then multiple interior points in the mechanical pressure compensation data are calculated, including: If the slope of the line corresponding to the linear equation is within the design stiffness range, then all data points in the mechanical pressure compensation data are traversed, and the Euclidean distance between each data point and the line corresponding to the linear equation is calculated. Each data point whose Euclidean distance is less than a preset tolerance threshold is used as a plurality of interior points in the mechanical pressure compensation data.
5. The method for measuring the lateral deformation of a servo-supported deep foundation pit retaining wall as described in claim 1, characterized in that, The step of filtering inelastic deviation datasets that meet preset conditions from the mechanical compression data based on the elastic baseline includes: Calculate the first sum between the elasticity prediction value corresponding to the elastic baseline and the preset tolerance threshold; For mechanical pressure compensation data at any given time, if the mechanical extension data is greater than the first sum value and the axial force increment data is greater than the maximum value of the axial force increment data by a second preset multiple, then it is determined that the mechanical pressure compensation data at the current time meets the preset conditions. Based on mechanical pressure data at multiple times that meet preset conditions, an inelastic deviation dataset is constructed.
6. The method for measuring the lateral deformation of a servo-supported deep foundation pit retaining wall as described in claim 1, characterized in that, The process of updating the soil reaction coefficient benchmark value based on the inelastic deviation dataset and the elastic baseline to generate a continuous soil stiffness function includes: Based on the inelastic deviation dataset and the elastic baseline, the stiffness response deviation at different depths is calculated. The stiffness response deviation is mapped to a soil reaction reduction coefficient. The soil reaction coefficient benchmark value is updated using the soil reaction reduction coefficient to generate a continuous soil stiffness function.
7. The method for measuring the lateral deformation of a servo-supported deep foundation pit retaining wall as described in claim 6, characterized in that, The step of updating the soil reaction coefficient benchmark value using the soil reaction reduction coefficient to generate a continuous soil stiffness function includes: By using the soil reaction reduction coefficient, the benchmark value of the soil reaction coefficient is updated to obtain the soil reaction coefficient at multiple depths. By using an interpolation algorithm to process the soil reaction reduction coefficient at different depths, a continuous reduction coefficient curve is obtained. Based on the continuous reduction coefficient curve, the soil reaction coefficient is smoothed to generate a continuous soil stiffness function.
8. The method for measuring the lateral deformation of a servo-supported deep foundation pit retaining wall as described in claim 1, characterized in that, Based on the soil continuous stiffness function and the mechanical compression data, the force balance equation of the retaining wall is constructed and solved to obtain the lateral deformation curves of the retaining wall at different depths, including: Determine the bending resistance data of the retaining wall and the corresponding soil pressure at different depths; Based on the bending capacity data, the soil continuous stiffness function, the mechanical pressure data, and the soil pressure, a force balance equation is constructed, which is a fourth-order ordinary differential equation. The force equilibrium equations were transformed into a system of linear equations using the finite difference method for numerical solution, resulting in the lateral deformation curves of the retaining wall at different depths.
9. The method for measuring the lateral deformation of a servo-supported deep foundation pit retaining wall as described in claim 8, characterized in that, The process involves converting the force equilibrium equations into a linear system of equations using the finite difference method for numerical solution, resulting in lateral deformation curves of the retaining wall at different depths. This includes: The enclosure wall is divided into multiple wall nodes along the depth direction; For any of the aforementioned wall nodes, the fourth-order ordinary differential equation is transformed into a linear algebraic equation using the difference formula; By integrating the linear algebraic equations of each wall node, a matrix equation is obtained that includes the stiffness matrix, the displacement vector to be solved, and the load vector. The matrix equations were solved using Gaussian elimination to obtain the lateral displacement values of each wall node. The lateral displacement values are smoothed to obtain the lateral deformation curves of the retaining wall at different depths.
10. A servo-supported deep foundation pit retaining wall lateral deformation measuring device, characterized in that, The device includes: a memory, a processor, and a servo-supported deep foundation pit retaining wall lateral deformation measurement program stored in the memory and executable on the processor, the servo-supported deep foundation pit retaining wall lateral deformation measurement program being configured to implement the steps of the servo-supported deep foundation pit retaining wall lateral deformation measurement method as described in any one of claims 1 to 9.