Construction monitoring method of movable formwork based on finite element analysis

Abstract

The application discloses a mobile formwork construction monitoring method based on finite element analysis, relates to the technical field of finite element analysis, and can solve the technical problem of how to effectively improve the accuracy of stress monitoring data of the mobile formwork based on finite element analysis under a complex construction environment, and comprises the following steps: acquiring time sequence stress data of a finite element model of the mobile formwork; determining a stress change interference value of each grid according to the time sequence stress data of each grid; determining a stress space discrete value of each grid at each sampling time according to the time sequence stress data of each grid and all grids in the spatial neighborhood of each grid; determining a local interference index of each grid at each sampling time according to the stress change interference value of each grid and the stress space discrete value at each sampling time; and identifying and correcting stress data affected by interference in the finite element model according to the local interference indexes of all grids.

Description

Technical Field

[0001] This invention relates to the field of finite element analysis technology, and specifically to a method for monitoring the construction of a moving formwork based on finite element analysis. Background Technology

[0002] As a core piece of equipment in on-site pouring construction of large-scale bridge projects, the safety and controllability of mobile formwork during construction are crucial to ensuring the overall project quality and schedule. In this technical field, precise monitoring methods are typically required to monitor the stress state of the structure in real time.

[0003] Finite element analysis, as a mature numerical simulation method, can establish a mechanical model of a mobile formwork and, by combining it with sensor-measured data, simulate the construction process and conduct safety assessments, forming an important technological foundation in this field. However, in actual complex outdoor construction scenarios, the mobile formwork structure is large, and its monitoring data is highly susceptible to transient interference from dynamic environmental loads such as short-term strong winds.

[0004] Existing monitoring methods, at the data processing level, typically involve directly comparing measured data with finite element simulation results or making threshold judgments, failing to deeply distinguish between the actual stress response of the structure and abnormal data fluctuations caused by the external environment. This limitation makes the monitoring system prone to misjudgments when disturbances occur, reducing the accuracy and reliability of safety warnings and decision support during construction, and impacting construction efficiency and risk management levels. Summary of the Invention

[0005] To address the technical challenge of effectively improving the accuracy of stress monitoring data for moving formwork based on finite element analysis in complex construction environments, this invention aims to provide a method for monitoring the construction of moving formwork based on finite element analysis. The specific technical solution adopted is as follows: In a first aspect, the present invention provides a method for monitoring the construction of a moving formwork based on finite element analysis, comprising: acquiring time-series stress data of a finite element model of the moving formwork; wherein the time-series stress data includes stress data collected over time by multiple meshes in the finite element model during construction; determining the stress variation disturbance value of each mesh based on the time-series stress data of each mesh; wherein the stress variation disturbance value is used to comprehensively characterize the stability of the stress value fluctuation over time and the intensity of the instantaneous impact; determining the stress spatial discrete value of each mesh at each sampling time based on the time-series stress data of each mesh and all meshes in the spatial neighborhood of each mesh at each sampling time; wherein the stress spatial discrete value is used to characterize the uniformity of stress distribution in the local area where the mesh is located; determining the local disturbance index of each mesh at each sampling time based on the stress variation disturbance value of each mesh and the stress spatial discrete value at each sampling time; and identifying and correcting the stress data affected by disturbance in the finite element model based on the local disturbance index of all meshes.

[0006] Secondly, this invention provides a mobile formwork construction monitoring system based on finite element analysis, comprising: a data acquisition module, a stress interference assessment module, a stress distribution assessment module, a local interference calculation module, and an interference correction module; the data acquisition module is used to acquire time-series stress data of the finite element model of the mobile formwork; wherein, the time-series stress data includes stress data collected over time by multiple meshes in the finite element model during construction; the stress interference assessment module is used to determine the stress change interference value of each mesh based on the time-series stress data of each mesh; wherein, the stress change interference value is used to comprehensively characterize the stability of the stress value fluctuation over time and the stress experienced by the mesh. The system includes: an instantaneous impact intensity assessment module; a stress distribution evaluation module, used to determine the stress spatial discrete value of each grid at each sampling time based on the temporal stress data of each grid and all grids in the spatial neighborhood of each grid at each sampling time; where the stress spatial discrete value is used to characterize the stress distribution uniformity of the local region where the grid is located; a local disturbance calculation module, used to determine the local disturbance index of each grid at each sampling time based on the stress change disturbance value of each grid and the stress spatial discrete value at each sampling time; and a disturbance correction module, used to identify and correct the stress data affected by disturbance in the finite element model based on the local disturbance index of all grids.

[0007] Thirdly, the present invention provides an electronic device, comprising: a processor and a memory; wherein the memory is used to store one or more programs, the one or more programs including computer-executable instructions, and when the electronic device is running, the processor executes the computer-executable instructions stored in the memory to cause the electronic device to perform the mobile formwork construction monitoring method based on finite element analysis as described in the first aspect and any possible implementation thereof.

[0008] The present invention has the following beneficial effects: by constructing feature indicators from both time and space dimensions to quantify the impact of environmental interference on monitoring data, and by accurately identifying and correcting abnormal data based on the fused local interference index, it effectively removes data noise caused by dynamic environmental loads such as short-term strong gusts, and significantly improves the accuracy and reliability of monitoring the construction process of mobile formwork based on finite element analysis. Attached Figure Description

[0009] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a schematic diagram of the architecture of a mobile formwork construction monitoring system based on finite element analysis, provided as an embodiment of the present invention. Figure 2 This is a flowchart illustrating a method for monitoring the construction of a moving formwork based on finite element analysis, provided as an embodiment of the present invention. Detailed Implementation

[0011] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the specific implementation methods, structures, features, and effects of the present invention will be described in detail below with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0012] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0013] The following description, in conjunction with the accompanying drawings, details a specific scheme for a mobile formwork construction monitoring method based on finite element analysis provided by the present invention.

[0014] For example, such as Figure 1 The diagram shown is an architectural schematic of a mobile formwork construction monitoring system (hereinafter referred to as the construction monitoring system) based on finite element analysis, according to an embodiment of the present invention. The construction monitoring system 10 includes: a data acquisition module 11, a stress interference assessment module 12, a stress distribution assessment module 13, a local interference calculation module 14, an interference correction module 15, and a finite element modeling module 16. The modules are described in detail below: (1) Data acquisition module 11.

[0015] The data acquisition module 11 is responsible for collecting and constructing a core dataset from the hardware sensor network and finite element software at the mobile formwork construction site for subsequent interference analysis and correction, providing accurate and continuous stress data input for the entire monitoring process.

[0016] Optionally, the data acquisition module 11 is used to acquire the temporal stress data of the finite element model of the moving formwork. The temporal stress data includes stress data collected over time from multiple meshes in the finite element model during construction.

[0017] Specifically, the data acquisition module 11 collects strain data of the moving formwork in real time using strain gauge sensors deployed at key load-bearing parts and key connection points. These raw electrical signals are amplified, filtered, and converted from analog to digital by the data acquisition unit before being transmitted to the data processing server. The data processing server calculates and converts the strain data into stress data at the corresponding physical locations based on pre-stored elastic modulus parameters of the moving formwork material.

[0018] Following this, the data acquisition module 11 calls the finite element model interface and, through a coordinate mapping algorithm, accurately associates and assigns these measured stress data to the corresponding mesh nodes in the finite element model. Finally, the data acquisition module 11 synchronously collects and records the stress data of all meshes at a preset sampling frequency (e.g., 10Hz), forming a time-series stress data sequence with timestamps.

[0019] The data acquisition module 11 synchronously sends the generated time-series stress data to the stress disturbance assessment module 12 and the stress distribution assessment module 13 as the data basis for their independent analysis.

[0020] (2) Stress interference assessment module 12.

[0021] The stress disturbance assessment module 12 is responsible for receiving time-series stress data from the data acquisition module 11, and conducting in-depth analysis of the stress value variation pattern of each grid from a time perspective, quantifying the degree of dynamic environmental disturbance, and finally outputting the stress change disturbance value of each grid. This module is key to distinguishing between normal stress growth and abnormal environmental impact.

[0022] Optionally, the stress disturbance assessment module 12 is used to determine the stress change disturbance value of each grid based on the time-series stress data of each grid. The stress change disturbance value is used to comprehensively characterize the stability of the grid stress value fluctuation over time and the intensity of the instantaneous impact.

[0023] For example, the stress disturbance assessment module 12 can be divided into a change calculation submodule 121, a stability assessment submodule 122, and an impact assessment submodule 123 to achieve a step-by-step, detailed assessment, which will be described below: (2.1) Change calculation submodule 121.

[0024] Optionally, the variation calculation submodule 121 is used to determine the stress variation value of the mesh between each pair of adjacent sampling times based on the stress values ​​of the mesh at multiple consecutive sampling times.

[0025] Specifically, the variation calculation submodule 121 reads the temporal stress data of the specified grid in chronological order, and calculates the difference between the stress values ​​at two adjacent sampling times in turn, thereby obtaining a series of stress change value sequences that reflect the instantaneous rate of stress change. This sequence is simultaneously output to the stability assessment submodule 122 and the impact assessment submodule 123.

[0026] (2.2) Stationarity assessment submodule 122.

[0027] Optionally, the stability assessment submodule 122 is used to determine a first characteristic index based on the stress variation value of the mesh. The first characteristic index characterizes the stability of the fluctuation of the mesh stress value over time.

[0028] Specifically, the stationarity assessment submodule 122 first performs curve fitting (e.g., using the least squares method) on the received stress change value sequence to obtain a stress change fitting curve, which characterizes the ideal change trend when undisturbed. Then, it calculates the residual of each actual stress change value relative to this fitting curve. Finally, the stationarity assessment submodule 122 combines the mean of all stress change values, the absolute difference between each change value and the mean, and the corresponding residuals to calculate a quantified first characteristic index. The larger the index value, the more unstable the stress change is over time, and the more likely it is to be disturbed.

[0029] (2.3) Impact assessment submodule 123.

[0030] Optionally, the impact assessment submodule 123 is used to determine a second characteristic index based on the stress change value of the mesh. The second characteristic index characterizes the intensity of the instantaneous impact experienced by the mesh.

[0031] Specifically, the impact assessment submodule 123 scans the stress change value sequence to find the maximum and minimum values. Then, it calculates the ratio of the maximum to the minimum value. This ratio directly reflects the relative intensity of the instantaneous positive and negative impacts experienced by the stress during the observation period; a larger ratio indicates the presence of a strong transient disturbance impact.

[0032] The stress disturbance assessment module 12 finally merges the first characteristic index obtained by the stability assessment submodule 122 and the second characteristic index obtained by the impact assessment submodule 123 to generate the stress change disturbance value of each grid and output it to the local disturbance calculation module 14.

[0033] (3) Stress distribution assessment module 13.

[0034] The stress distribution assessment module 13 is responsible for analyzing the stress distribution relationship between a single grid and its surrounding grids at each sampling time from a spatial dimension, assessing whether the uniformity of the local stress field has been disrupted, thereby identifying spatial anomalies caused by non-uniform loads (such as strong gusts of wind).

[0035] Optionally, the stress distribution evaluation module 13 is used to determine the spatial discrete value of stress for each grid at each sampling time based on the temporal stress data of each grid and all grids in the grid's spatial neighborhood at each sampling time. The spatial discrete value of stress is used to characterize the uniformity of stress distribution in the local region where the grid is located.

[0036] Specifically, for each mesh, the stress distribution assessment module 13 first automatically determines its spatial neighborhood based on the topological relationship of the finite element model. For example, it selects a preset number (e.g., 24) of meshes that are at the same horizontal level and closest to it to form its spatial neighborhood. At each sampling time, the stress distribution assessment module 13 obtains the current stress values ​​of the mesh and all its neighboring meshes, forming a discrete set of data points in three-dimensional spatial coordinates. Then, it uses a surface fitting method (such as least squares surface fitting) to fit these data points, obtaining a mathematical surface that can reflect the "ideal" smooth stress distribution of the local area. After that, it calculates the geometric distance from the actual stress value of the mesh and each neighboring mesh to this fitted surface, i.e., the fitting residual. Finally, the stress distribution assessment module 13 calculates the product of the mean and variance of all fitting residuals, and uses this product value as the spatial discrete value of the stress of the mesh at the current sampling time. The larger this value is, the more the stress value at that point deviates spatially from the smooth stress field formed around it, and the higher the possibility of local disturbance.

[0037] The stress distribution assessment module 13 outputs the calculated stress spatial discrete values ​​of each grid at each sampling time to the local disturbance calculation module 14 in real time.

[0038] (4) Local interference calculation module 14.

[0039] The local interference calculation module 14 is responsible for integrating the interference assessment results of the time and space dimensions, and calculating a single index that can comprehensively reflect the degree of interference of the grid at a specific moment, providing a direct basis for accurately locating and correcting abnormal data.

[0040] Optionally, the local disturbance calculation module 14 is used to determine the local disturbance index of each grid at each sampling time based on the stress change disturbance value of each grid and the stress spatial discrete value at each sampling time.

[0041] Specifically, the local disturbance calculation module 14 receives stress change disturbance values ​​(a time-invariant characteristic value) from each grid in the stress disturbance assessment module 12, and stress spatial discrete values ​​(a time-varying characteristic value) from each grid in the stress distribution assessment module 13 at each sampling time. For a specific grid at a specific sampling time, the local disturbance calculation module 14 performs a fusion calculation, for example, by directly multiplying the stress change disturbance value by the current stress spatial discrete value. The resulting product is the local disturbance index of that grid at that sampling time. This index integrates information on both "whether historical changes are stable" and "whether the current spatial distribution is consistent," and its value directly and comprehensively indicates the severity of environmental disturbance to the data at that location.

[0042] The local disturbance calculation module 14 outputs the calculated local disturbance index matrix of all grids at all sampling times to the disturbance correction module 15.

[0043] (5) Interference correction module 15.

[0044] The interference correction module 15 is responsible for identifying, eliminating and repairing abnormal stress data in the finite element model based on the local interference index, and finally outputting a clean and reliable stress cloud map, thereby improving the accuracy of the results of the entire monitoring system.

[0045] Optionally, the disturbance correction module 15 is used to identify and correct the stress data affected by disturbance in the finite element model based on the local disturbance index of all meshes.

[0046] For example, the interference correction module 15 can be divided into a threshold determination submodule 151, an anomaly identification submodule 152, and a data repair submodule 153 to complete the entire process from judgment to repair. These submodules are described below: (5.1) Threshold determination submodule 151.

[0047] Optionally, the threshold determination submodule 151 is used to determine the interference threshold based on the local interference index of all grids at all sampling times.

[0048] Specifically, the threshold determination submodule 151 statistically analyzes the global statistical distribution characteristics of the entire local disturbance index matrix (covering all spatial locations and all time points) provided by the local disturbance calculation module 14, for example, by calculating the mean and standard deviation of all indices. Then, a disturbance threshold is set based on statistical principles (such as the three-standard-deviation criterion). Grid data with indices exceeding this threshold are considered highly suspicious disturbed data.

[0049] (5.2) Anomaly identification submodule 152.

[0050] Optionally, the anomaly identification submodule 152 is used to identify the stress data of the grid with a local interference index greater than the interference threshold at the corresponding sampling time as interfered data.

[0051] Specifically, the anomaly identification submodule 152 compares the local disturbance index of each grid at each time step with the disturbance threshold provided by the threshold determination submodule 151. For all indices exceeding the threshold, the corresponding grid number and sampling time are recorded, and these are marked as "disturbed data". These data points are temporarily isolated from the original dataset.

[0052] (5.3) Data Repair Submodule 153.

[0053] Optionally, the data repair submodule 153 is used to remove the disturbed data and interpolate the stress value of the grid containing the disturbed data at the corresponding sampling time.

[0054] Specifically, the data repair submodule 153 first removes all disturbed data points marked by the anomaly identification submodule 152 from the original time-series stress data, forming a dataset containing gaps. Then, for each gap location (i.e., a grid at a specific missing moment), the data repair submodule 153 uses the stress values ​​of other undisturbed grids in the spatial neighborhood of that grid at the same sampling moment, employing spatial interpolation algorithms (such as inverse distance weighted interpolation or Kriging interpolation) to recalculate the stress estimate at the gap. Finally, the complete and clean stress data after all gaps have been repaired is reconstructed and used to update the stress contour map of the finite element model, thereby achieving more accurate real-time monitoring of the construction process.

[0055] (6) Finite element modeling module 16.

[0056] Optionally, before the data acquisition module 11 begins operation, the construction monitoring system 10 also includes a finite element modeling module 16. The finite element modeling module 16 is used to construct a finite element model of the movable formwork based on its structural parameters. These structural parameters include the geometric shape, cross-sectional parameters, and material parameters of the components.

[0057] Specifically, the finite element modeling module 16 receives the three-dimensional geometric model and material properties of the moving formwork from design drawings or a database. This module can call the kernel or interface of CAE software (such as ABAQUS or ANSYS) to automatically perform steps such as geometry cleanup, mesh generation, material property assignment, and preliminary setting of boundary conditions and load cases, generating a finite element model file for simulation analysis. This model is the geometric and physical basis for data mapping by the data acquisition module 11 and for calculations by all subsequent analysis modules.

[0058] The above describes the mobile formwork construction monitoring system 10 based on finite element analysis and its included modules.

[0059] For example, such as Figure 2 The diagram shown is a flowchart of a mobile formwork construction monitoring method based on finite element analysis according to an embodiment of the present invention, including the following steps: S201. Obtain the time-series stress data of the finite element model of the moving formwork. The time-series stress data includes stress data collected over time from multiple meshes within the finite element model during construction.

[0060] It should be noted that before performing this step, the finite element modeling module 16 in the construction monitoring system 10 described above will construct a finite element model of the moving formwork based on the structural parameters of the moving formwork; wherein, the structural parameters include the geometric shape, cross-sectional parameters and material parameters of the components.

[0061] Specifically, the finite element modeling module 16 receives the geometric model of the moving formwork from the design database or 3D drawings, calls the kernel or interface of CAE software (such as ABAQUS), and automatically performs steps such as mesh generation (e.g., using hexahedral or tetrahedral elements), assignment of material properties (e.g., elastic modulus, Poisson's ratio), and setting of boundary conditions (e.g., support point constraints) to generate a finite element model file for simulation analysis, providing a geometric and physical basis for subsequent data mapping and analysis.

[0062] Following this, this step can be performed by the data acquisition module 11 in the construction monitoring system 10 described above, specifically including the following steps: (1) By arranging sensors at key load-bearing parts and key connection points of the moving formwork, strain data of the moving formwork is collected in real time.

[0063] Optionally, the data acquisition module 11 uses a network of resistance strain gauges or fiber optic grating sensors deployed at key stress locations such as the main beam, legs, and booms of the mobile formwork to collect micro-strain signals reflecting local structural deformation in real time at a sampling frequency of 10Hz.

[0064] (2) Determine the stress data at the corresponding position on the moving mold based on the strain data and the material parameters of the moving mold.

[0065] Furthermore, the data acquisition module 11 calculates the real-time stress value at the sensor installation location by multiplying the collected micro-strain value by the elastic modulus of the corresponding component material (e.g., 210 GPa for steel) according to Hooke's Law.

[0066] (3) Map the stress data to the corresponding mesh of the finite element model and collect it at the preset sampling frequency to obtain time-series stress data.

[0067] Finally, the data acquisition module 11, based on the spatial correspondence between the sensor's physical coordinates and the finite element model's mesh nodes, uses nearest neighbor or shape function interpolation algorithms to distribute the calculated stress values ​​to the corresponding finite element mesh nodes, and continuously records the stress data of all meshes at a frequency of 10Hz, forming a time-stamped stress dataset.

[0068] Therefore, the data acquisition module 11, through the above steps, can provide an accurate, continuous stress data stream that is spatially correlated with the finite element model for subsequent analysis.

[0069] S202. Based on the time-series stress data of each grid, determine the stress variation disturbance value for each grid. The stress variation disturbance value is used to comprehensively characterize the stability of the stress value fluctuation over time and the intensity of the instantaneous impacts it receives.

[0070] For example, this step can be performed by the stress interference assessment module 12 in the construction monitoring system 10 described above. Specifically, the stress interference assessment module 12 first calculates the stress change value sequence of each grid at adjacent sampling times; then, based on the sequence, it analyzes the stability of the stress change fluctuation to obtain a first characteristic index, and evaluates the instantaneous impact intensity to obtain a second characteristic index; finally, it combines the first characteristic index and the second characteristic index (e.g., by weighted summation or multiplication) to calculate the stress change interference value of the grid. It should be noted that the specific process for determining the stress change interference value of each grid is described in S301-S304 below, and will not be repeated here.

[0071] In another possible implementation, the stress disturbance assessment module 12 can also use a direct calculation method based on the statistical distribution characteristics of the variation values ​​(such as skewness and kurtosis) when determining the stress change disturbance value for each grid: First, the skewness and kurtosis of all stress change values ​​for each grid are calculated; skewness is used to measure the asymmetry of the distribution of the variation value sequence to assess non-stationarity; kurtosis is used to measure the sharpness of the sequence distribution to identify abnormal shocks. Finally, the normalized absolute value of skewness and the kurtosis value are combined and directly used as the stress change disturbance value for that grid.

[0072] Alternatively, the stress interference assessment module 12 can also incorporate different preset benchmark variation patterns for different construction stages (such as the initial and middle stages of pouring). By calculating the overall deviation of the actual stress variation sequence of each grid from the current stage's benchmark curve (such as calculating the root mean square error), and combining this deviation with the range of the variation sequence itself (the difference between the maximum and minimum values), a stress variation interference value is formed. This method provides a clear working condition reference for interference assessment, making it more targeted.

[0073] Therefore, the stress disturbance assessment module 12 can quantify the degree of dynamic environmental disturbance to each grid stress data from a time dimension, providing key time feature inputs for subsequent comprehensive judgment.

[0074] S203. Based on the temporal stress data of each grid and all grids in its spatial neighborhood at each sampling time, determine the spatial discrete value of stress for each grid at each sampling time. The spatial discrete value of stress is used to characterize the uniformity of stress distribution in the local region where the grid is located.

[0075] For example, this step can be performed by the stress distribution assessment module 13 in the construction monitoring system 10 described above. Specifically, the stress distribution assessment module 13 first defines the spatial neighborhood (e.g., 24 neighborhood) for each grid; for each sampling time, it extracts the current stress values ​​of the grid and all grids in its neighborhood to form a spatial point set; secondly, it performs surface fitting on the point set to obtain an ideal stress distribution surface; then, it calculates the distance from each actual stress value to the fitted surface (i.e., the fitting residual); finally, it calculates the spatial discrete value of the stress at the current time based on the mean and variance of all fitting residuals. It should be noted that the specific process for determining the spatial discrete value of stress is described in S401-S404 below, and will not be repeated here.

[0076] In another possible implementation, the stress distribution evaluation module 13 can also employ an evaluation method based on local stress gradient analysis when determining the spatial discrete values ​​of stress: first, the stress gradient vector of each grid along different directions in its spatial neighborhood is calculated, and then the variance of the gradient vectors of all neighboring grids is calculated. The larger the gradient variance, the more drastic and inconsistent the changes in the local stress field, and the higher the degree of spatial dispersion. Using this gradient variance value as the spatial discrete value of stress can more directly reflect the abrupt changes in stress in physical space.

[0077] Alternatively, the stress distribution assessment module 13 can dynamically determine the effective analysis neighborhood of each grid based on the real-time pouring location. For example, a dynamic radius can be set with the pouring point as the center, and only grids within this radius can be included in the spatial neighborhood of the current grid for calculation. The resulting spatial discrete stress values ​​can more sensitively capture the stress distribution anomalies caused by the non-uniformity of the concrete flow front, synchronizing spatial analysis with the construction process.

[0078] Therefore, the stress distribution assessment module 13 can identify stress distribution anomalies caused by non-uniform loads (such as gusts) from a spatial dimension, providing key spatial characteristic basis for judging local disturbances.

[0079] S204. Based on the stress variation disturbance value of each grid and the stress spatial discrete value at each sampling time, determine the local disturbance index of each grid at each sampling time.

[0080] For example, this step can be performed by the local interference calculation module 14 in the construction monitoring system 10 described above.

[0081] Specifically, the local disturbance calculation module 14 receives the stress change disturbance value (a feature value that does not change with time) for each grid from the stress disturbance assessment module 12, and the stress spatial discrete value (a feature value that changes with time) for each grid at each sampling time from the stress distribution assessment module 13. The core calculation performed by this module is to fuse spatiotemporal features to quantify the degree of local disturbance.

[0082] For any grid Its local disturbance index at the t-th sampling time By adjusting the stress variation disturbance value of the mesh The spatial discrete value of stress at the same sampling time t as the grid The product is then normalized to obtain the result. For example, the calculation formula is as follows: in, This represents the maximum-minimum normalization function, used to map the output value within the parentheses to a range of 0 to 1. Here, the normalization calculation is based on statistical analysis of a large set of historical data from numerous experiments to determine the maximum and minimum values. Furthermore, if the normalized object is greater than or equal to the maximum value in the aforementioned historical data set, the result is 1; if the normalized object is less than or equal to the minimum value in the aforementioned historical data set, the result is 0.

[0083] It should be noted that the interference features in the time dimension are fused through multiplication ( ) and spatial dimension interference characteristics ( ), their product The magnitude of the exponent comprehensively reflects the severity of disturbance to the grid at that moment. The exponent only increases significantly when the grid is unstable in time and inconsistent in space, thus enabling accurate quantification and location of anomalous data.

[0084] Therefore, the local interference calculation module 14 assigns a quantified local interference index that integrates spatiotemporal characteristics to each grid in the finite element model at each sampling time, thereby transforming the judgment of the degree of interference from a single qualitative threshold into a continuous and comparable numerical matrix, providing a core basis for accurately identifying and locating abnormal data in subsequent steps.

[0085] S205. Based on the local disturbance index of all meshes, identify and correct the stress data affected by disturbance in the finite element model.

[0086] For example, this step can be performed by the interference correction module 15 in the construction monitoring system 10 described above, and specifically includes the following steps: (1) Determine the interference threshold based on the local interference index of all grids at all sampling times.

[0087] Specifically, the threshold determination submodule 151 in the interference correction module 15 calculates the global mean and standard deviation of the entire local interference index matrix. Then, based on statistical principles, it sets the interference threshold as "mean + 3 × standard deviation," serving as the boundary between normal and interfered data. For example, for monitoring data of a certain type of mobile formwork during the standard construction phase, the calculated mean is 0.15 and the standard deviation is 0.05. Based on the three-standard-deviation criterion, the interference threshold is set to: 0.15 + 3 × 0.05 = 0.30. This threshold indicates that data points with a local interference index exceeding 0.3 are considered highly suspicious interfered data. The specific value of the threshold can be adaptively adjusted according to the statistical characteristics of monitoring data from different projects and different construction phases.

[0088] (2) The stress data of the grid with a local disturbance index greater than the disturbance threshold at the corresponding sampling time are identified as disturbed data.

[0089] In this step, the anomaly identification submodule 152 in the interference correction module 15 iterates through and compares each local interference index with the interference threshold determined in the previous step. The grid numbers whose indices exceed the threshold and their corresponding sampling times are recorded and marked as "interference-affected data".

[0090] (3) Remove the disturbed data and interpolate the stress values ​​of the grid containing the disturbed data at the corresponding sampling time.

[0091] Furthermore, the data repair submodule 153 in the interference correction module 15 removes all marked disturbed data points from the original time-series stress data, forming a dataset containing data gaps. Then, for each gap (i.e., a certain grid at a certain time), the stress value at the gap is recalculated using a spatial interpolation algorithm (such as the inverse distance weighting method) based on the stress values ​​of other grids in the spatial neighborhood of that grid that were not marked as disturbed at the same time.

[0092] Therefore, the interference correction module 15 can automatically identify and repair abnormal stress data caused by environmental interference, output a clean and reliable stress dataset, and generate a finite element stress cloud map that more accurately reflects the true stress state of the structure.

[0093] Based on the above technical solution, this invention quantifies the impact of environmental interference on monitoring data by constructing feature indicators from both time and space dimensions, and accurately identifies and corrects abnormal data based on the fused local interference index. This effectively removes data noise caused by dynamic environmental loads such as short-term strong gusts, and significantly improves the accuracy and reliability of monitoring the construction process of mobile formwork based on finite element analysis.

[0094] For example, in another method for monitoring the construction of a moving formwork based on finite element analysis provided in one embodiment of the present invention, the stress change disturbance value of each grid is determined according to the time-series stress data of each grid, specifically including the following steps: S301. Based on the stress values ​​of each grid at multiple consecutive sampling times, determine the stress change value of each grid between each pair of adjacent sampling times.

[0095] Optionally, this step can be performed by the change calculation submodule 121 in the stress disturbance assessment module 12.

[0096] Specifically, the variation calculation submodule 121 reads the temporal stress data of each grid in chronological order. For any given grid... Its stress value at the t-th sampling time is The variation calculation submodule 121 calculates the instantaneous stress change of the mesh between each pair of adjacent sampling times, using the following formula: in, Represents a grid The stress change value at the t-th sampling time; n represents the mesh index, which ranges from 1 to N, where N is the total number of meshes in the finite element model; Represents a grid The stress value at the t-th sampling time; T represents the total number of consecutive sampling times analyzed.

[0097] For example, if the data sampling frequency is 10Hz and the analysis time window is 30 seconds, then the total number of continuous sampling moments T is 301 (including the initial moment). The rules for its value are: the time window should be able to cover a complete construction sub-stage (such as the start-up process of a single pour) to include sufficient dynamic change information; at the same time, the window length should take into account the real-time performance of the calculation, usually several seconds to tens of seconds, which can be determined according to the construction process rhythm and the typical duration of interference.

[0098] Understandably, the above formula calculates the instantaneous change in stress within a unit sampling interval by subtracting the stress value from the stress value at the next moment. This sequence Reflects the grid The rate and direction of stress change over time are fundamental data for subsequent analysis of wave stability and impact strength.

[0099] S302. Determine the first characteristic index based on the stress variation value of each grid. The first characteristic index is used to characterize the stability of the stress value fluctuation of the grid over time.

[0100] Optionally, this step can be performed by the stability assessment submodule 122 in the stress disturbance assessment module 12, specifically including the following steps: (1) Perform curve fitting on all stress change values ​​for each grid to obtain the stress change fitting curve.

[0101] Specifically, the stationarity assessment submodule 122 targets each grid cell. , in time period All stress variation values ​​obtained within the data sequence are used as observation data points. Subsequently, the stationarity assessment submodule 122 uses the least squares method to perform curve fitting on the data sequence, obtaining an optimal fitting curve. This fitting curve represents the regular trend that the stress variation of the grid should follow under ideal conditions that exclude random disturbances.

[0102] (2) The first characteristic index is determined based on the residual of each stress change value of each grid relative to the stress change fitting curve and the mean of all stress change values.

[0103] Furthermore, the stability assessment submodule 122 first calculates each stress variation value. The vertical distance to the fitted curve, i.e., the fitting residual. . The smaller the value, the more closely the stress change at that moment conforms to the ideal trend; conversely, a larger value may indicate disturbance. Next, the module calculates the mean of all stress change values. For example, the first characteristic index is calculated using the following formula. : in, Represents a grid The first characteristic indicator; Represents the stress change value at the t-th sampling time. The fitting residual relative to the fitted curve; Represents a grid The mean of stress changes at all sampling times. It represents a very small positive number to prevent the denominator from being zero; for example, it can be 10 to the power of negative 5.

[0104] It should be noted that the above formula quantifies the stability of stress fluctuations using a weighted average method. It measures the relative magnitude of the instantaneous change's deviation from the average change. This deviation is then compared with the residual, which reflects whether the point deviates from the overall trend of change. Multiply the values ​​and sum them over all time points. When the stress change at a given time point deviates significantly from the average (large numerator) and severely violates the fitting trend (large residual), its effect on... The contribution will increase significantly. Therefore, The larger the value, the more drastic and unstable the stress change of the grid is over time, and the higher the possibility of it being subject to continuous or irregular disturbances.

[0105] S303. Determine the second characteristic index based on the stress variation value of each grid. The second characteristic index is used to characterize the intensity of the instantaneous impact on the grid.

[0106] Optionally, this step can be performed by the impact assessment submodule 123 in the stress disturbance assessment module 12, specifically including the following steps: (1) Obtain the maximum and minimum stress change values ​​for each grid.

[0107] Specifically, the impact assessment submodule 123 traverses the mesh. Find the maximum and minimum values ​​among all stress variations.

[0108] (2) Determine the second characteristic index based on the maximum and minimum values.

[0109] Furthermore, the impact assessment submodule 123 calculates the second characteristic index using the following formula: in, Represents a grid The second characteristic indicator; Represents a grid The maximum value in the stress change sequence is usually the largest positive change; Represents a grid The minimum value in the stress change value sequence is usually the largest negative change (its absolute value is used in the calculation). It represents a very small positive number to prevent the denominator from being zero; for example, it can be 10 to the power of negative 5.

[0110] It should be noted that the above formula quantifies the intensity of an instantaneous impact by calculating the ratio of extreme stress changes. (Molecule) This reflects the maximum positive change (increase in stress) experienced during the observation period; the denominator... This reflects the maximum negative change (stress reduction). The ratio The larger the value, the more significant the difference in magnitude between the positive and negative changes, indicating a strong, unidirectional transient disturbance. Conversely, if the stress change is stable and the amplitudes of positive and negative fluctuations are similar, then... A value close to 1 indicates no significant strong impact.

[0111] S304. Determine the stress variation disturbance value for each grid based on the first and second characteristic indices.

[0112] Furthermore, the stress disturbance assessment module 12 will calculate the first characteristic index. Second characteristic index The values ​​are then fused to generate the final stress change disturbance value. For example, the calculation is as follows: in, , They are respectively , The weights satisfy Its size can be set according to the implementation scenario without special restrictions. In this embodiment, , Set them to 0.5 and 0.5 respectively. This represents the maximum-minimum normalization function, used to map the output value within the parentheses to a range of 0 to 1. In the context of... When performing normalization calculations, the dataset used is the first characteristic index value of all grids in a large amount of historical experimental data; that is, the normalization is performed using a reliable historical dataset. Perform uniform normalization. If a normalized object is greater than or equal to the maximum value in the aforementioned historical data set, the result is set to 1; if a normalized object is less than or equal to the minimum value in the aforementioned historical data set, the result is set to 0. The maximum and minimum values ​​are preset empirical extreme values ​​obtained from the statistics of the historical data set. The calculation method is similar, based on the second characteristic index value of all grids in a large amount of historical experimental data. Normalized objects that exceed the numerical range of the data set are processed in the same way as described above.

[0113] Understandably, the above formula first... and Max-min normalization is performed separately to transform both values ​​into a unified [0, 1] interval, thus eliminating differences in data dimensionality and magnitude caused by different calculation sources. Then, a weighted summation of the normalized indicators is performed to synthesize the two interfering features along the time dimension. Weighting coefficients. and This determines the contribution ratio of fluctuation stability and instantaneous impact intensity to the final disturbance value. Through the analysis of... and linear combination, The value reflects both the sustained fluctuation characteristics of stress changes and the relative strength of transient impact intensity across the entire mesh. When the stress change in the mesh is neither stable nor subjected to strong impacts, It will yield larger values; if only one feature is significant, The value is determined by the normalized value of this feature and its weight. This fusion method can flexibly quantify the overall degree of interference according to the actual scenario and effectively distinguish between continuous interference, single impact, and normal construction changes. Therefore, the stress change interference value As a comprehensive scalar, it effectively quantifies the overall degree of dynamic environmental disturbance experienced by each grid in the time dimension.

[0114] Based on the above technical solution, this embodiment of the invention performs multi-feature analysis on the stress time series data of each grid. First, it extracts a first feature index reflecting the fluctuation of the change process and a second feature index reflecting the intensity of transient impacts. Then, it merges these two into a unified stress change interference value. This method can effectively distinguish between regular stress changes caused by normal construction progress and abnormal fluctuations and impacts caused by environmental disturbances such as short-term strong winds, providing a reliable basis for subsequent precise quantification of interference effects from a time perspective.

[0115] For example, in another method for monitoring the construction of a moving formwork based on finite element analysis provided in one embodiment of the present invention, the spatial discrete value of stress for each grid at each sampling time is determined based on the temporal stress data of each grid and all grids in the spatial neighborhood of each grid at each sampling time. Specifically, this includes the following steps: S401. Obtain a preset number of neighboring grids centered at each grid and at the same horizontal level to form the spatial neighborhood of the grid.

[0116] In this step, the stress distribution assessment module 13, based on the mesh topology of the finite element model, evaluates each mesh... Define its spatial neighborhood. Specifically, using Using the center coordinates as a reference, a predetermined number of grids are selected that are at the same horizontal level (i.e., have the same z-coordinate or a difference within a very small tolerance range) and are closest to it in the xy-plane. This neighborhood covers the grids. In the horizontally nearest region, ensure that the spatial analysis can capture the continuity of the local stress distribution.

[0117] For example, the preset number can be set to 24, forming a 24-neighborhood system. The specific value selection rules are as follows: the neighborhood range should be able to cover the directly affected area of ​​the mesh to reflect the local stress gradient; at the same time, the calculation scale needs to be controlled, and the number is usually set according to the trade-off between mesh density and computational efficiency. For example, 8-neighborhood and 24-neighborhood are common choices.

[0118] S402. Perform surface fitting based on the stress values ​​of each grid and all grids in the spatial neighborhood of each grid at each sampling time to obtain the stress distribution fitting surface at each sampling time.

[0119] Specifically, the stress distribution assessment module 13, for each sampling time t, for the mesh and its spatial neighborhood This module collects the stress values ​​of all grids within the region at the current time. Each grid is represented by a three-dimensional data point (x, y, s) consisting of its x, y coordinates and stress value. Using the least squares method, this module fits a polynomial surface of a preset degree (such as a quadratic surface) to all data points. The fitting objective is to minimize the sum of the squares of the perpendicular distances from all data points to this surface, ultimately obtaining an ideal, smooth stress distribution surface equation describing the stress distribution of the local region at time t. .

[0120] S403. Determine the stress value of each grid and each grid in the spatial neighborhood of each grid, and the fitting residual of the stress distribution fitting surface.

[0121] Furthermore, the stress distribution evaluation module 13 evaluates each mesh involved in the fitting (including...) itself and its neighborhood Calculate the actual stress value of all meshes in the dataset. The fitted surface obtained in step S402 The vertical geometric distance. This distance is the fitting residual of the grid at the current moment. The calculation formula is the same as the formula for the distance from a point to a surface: in, Indicates the number of neighbors within the neighborhood. i Grids (index) i cover and The fitting residual at the t-th sampling time; Represents a grid The actual stress value at the t-th sampling time; The function representing the calculation of the perpendicular distance from a point to a surface is existing technology, and the specific calculation process will not be described in detail.

[0122] Understandably, the distance calculated by the above formula directly quantifies the degree to which the actual stress value of each grid deviates from the "local ideal smooth distribution". The smaller the value, the more the stress at that point conforms to the overall distribution pattern formed by the surrounding area; the larger the value, the more likely that the point is subject to local disturbance.

[0123] S404. Based on the mean and variance of all fitted residuals, determine the stress spatial discrete value of each grid at each sampling time.

[0124] For example, after obtaining the set of fitted residuals for all meshes in a local neighborhood, the stress distribution evaluation module 13 first calculates the mean of these residuals. and variance The mesh is then calculated using the following formula. The discrete values ​​of stress space at the t-th sampling time: in, Represents a grid The discrete values ​​of stress space at the t-th sampling time; Represents a grid The mean of the fitting residuals of all grids in its spatial neighborhood at time t; Represents a grid The variance of the fitting residuals of all grids in its spatial neighborhood at time t. This represents the maximum-minimum normalization function, used to map the output value within the parentheses to a range of 0 to 1. In this normalization calculation, the maximum and minimum values ​​are statistically derived from a large set of historical data obtained from numerous historical experiments. Furthermore, if the normalized object is greater than or equal to the maximum value in the aforementioned historical data set, the normalization result is 1; if the normalized object is less than or equal to the minimum value in the aforementioned historical data set, the normalization result is 0.

[0125] It should be noted that the above formula comprehensively measures the spatial dispersion of the local stress field by multiplying the mean and variance. Mean This reflects the average degree to which the region deviates from an ideal smooth surface; variance This reflects the magnitude of the fluctuation in the degree of deviation at each point, i.e., the degree of dispersion. Multiplying the two means that when most points in the region generally deviate from the ideal surface (large mean) and the deviations of individual points differ significantly (large variance), The value will increase sharply. This accurately corresponds to the state where external disturbances (such as gusts of wind) cause the local stress distribution to be both abnormal and chaotic. Conversely, if the stress distribution is uniform and smooth, the mean and variance of the residuals will be very small. The value is also small.

[0126] Based on the above technical solution, this embodiment of the invention quantifies the deviation between the stress value at each point and the ideal local distribution by constructing a spatial neighborhood for each grid and performing surface fitting. Then, the spatial discrete value of stress is obtained by multiplying the mean and variance of the residuals. This method can effectively identify local stress field distortions and inconsistencies caused by non-uniform loads (such as strong gusts of wind), providing key features from a spatial dimension for determining whether the data is disturbed.

[0127] It should be noted that the order of the above embodiments of the present invention 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.

[0128] 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 monitoring the construction of a moving formwork based on finite element analysis, characterized in that, The method includes: Acquire time-series stress data of the finite element model of the moving formwork; wherein, the time-series stress data includes stress data collected over time by multiple meshes in the finite element model during construction; Based on the time-series stress data of each grid, a stress variation disturbance value is determined for each grid; wherein, the stress variation disturbance value is used to comprehensively characterize the stability of the stress value of the grid over time and the intensity of the instantaneous impact. Based on the temporal stress data at each sampling time for each grid and all grids in the spatial neighborhood of each grid, the spatial discrete value of stress for each grid at each sampling time is determined; wherein, the spatial discrete value of stress is used to characterize the stress distribution uniformity in the local region where the grid is located; Based on the stress variation disturbance value of each grid and the stress spatial discrete value at each sampling time, the local disturbance index of each grid at each sampling time is determined; Based on the local disturbance index of all meshes, identify and correct the stress data affected by disturbance in the finite element model.

2. The method for monitoring the construction of a moving formwork based on finite element analysis according to claim 1, characterized in that, Based on the temporal stress data of each grid, the stress variation disturbance value of each grid is determined, specifically including: Based on the stress values ​​of each grid at multiple consecutive sampling times, determine the stress change value of each grid between each pair of adjacent sampling times; A first characteristic index is determined based on the stress change value of each grid; wherein the first characteristic index is used to characterize the stability of the stress value of the grid over time. A second characteristic index is determined based on the stress change value of each grid; wherein the second characteristic index is used to characterize the intensity of the instantaneous impact on the grid; The stress variation disturbance value of each grid is determined based on the first characteristic index and the second characteristic index.

3. The method for monitoring the construction of a moving formwork based on finite element analysis according to claim 2, characterized in that, Based on the stress variation value of each of the grids, a first characteristic index is determined, specifically including: Curve fitting is performed on all stress variation values ​​for each grid to obtain a stress variation fitting curve; The first characteristic index of each grid is determined based on the residual of each stress change value relative to the stress change fitting curve and the mean of all stress change values.

4. The method for monitoring the construction of a moving formwork based on finite element analysis according to claim 2, characterized in that, Based on the stress variation value of each of the grids, a second characteristic index is determined, specifically including: Obtain the maximum and minimum stress variation values ​​for each of the grids; The second characteristic index of each grid is determined based on the maximum value and the minimum value.

5. The method for monitoring the construction of a moving formwork based on finite element analysis according to claim 1, characterized in that, Based on the temporal stress data at each sampling time for each grid and all grids within its spatial neighborhood, the spatial discrete value of stress for each grid at each sampling time is determined, specifically including: A predetermined number of neighboring grids centered at the same horizontal level are obtained to form the spatial neighborhood of each grid. Based on the stress values ​​of each grid and all grids in the spatial neighborhood of each grid at each sampling time, a surface fitting is performed to obtain the stress distribution fitting surface at each sampling time; Determine the stress value of each grid and each grid in the spatial neighborhood of each grid, and the fitting residual of the stress distribution fitting surface; Based on the mean and variance of all fitted residuals, the stress spatial discrete value of each grid at each sampling time is determined.

6. The method for monitoring the construction of a moving formwork based on finite element analysis according to claim 1, characterized in that, Based on the local disturbance index of the mesh, identify and correct the stress data affected by disturbance in the finite element model, specifically including: The interference threshold is determined based on the local interference index of all grids at all sampling times; The stress data of grids with a local disturbance index greater than the disturbance threshold at the corresponding sampling time are identified as disturbed data. Remove the disturbed data and interpolate the stress value of the grid containing the disturbed data at the corresponding sampling time.

7. The method for monitoring the construction of a moving formwork based on finite element analysis according to claim 6, characterized in that, The interference threshold is determined based on the local interference index of all grids at all sampling times, specifically including: Determine the statistical distribution characteristics of all local disturbance indices; The interference threshold is determined based on the statistical distribution characteristic value.

8. The method for monitoring the construction of a moving formwork based on finite element analysis according to claim 6, characterized in that, The stress value of the grid containing the disturbed data at the corresponding sampling time is interpolated, specifically including: interpolating the stress data based on the undisturbed stress data in the preset spatial neighborhood of the grid.

9. The method for monitoring the construction of a moving formwork based on finite element analysis according to claim 1, characterized in that, Before acquiring the time-series stress data of the finite element model of the moving formwork, the method further includes: Based on the structural parameters of the movable formwork, a finite element model of the movable formwork is constructed; wherein, the structural parameters include the geometric shape, cross-sectional parameters, and material parameters of the components.

10. The method for monitoring construction of a moving formwork based on finite element analysis according to any one of claims 1-9, characterized in that, Obtain the time-series stress data of the finite element model of the moving formwork, specifically including: By arranging sensors at key load-bearing parts and key connection points of the moving mold, strain data of the moving mold is collected in real time. Based on the strain data and the material parameters of the movable mold, the stress data at the corresponding position on the movable mold is determined; The stress data is mapped to the corresponding mesh of the finite element model and collected at a preset sampling frequency to obtain the time-series stress data.

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