Fabricated building node stress monitoring and design feedback system based on BIM

Abstract

The invention discloses an assembly type building node stress monitoring and design feedback system based on BIM, and relates to the technical field of building engineering structure monitoring, a building information modeling model is constructed, component geometric information, a connection mode and a load path associated with nodes are extracted, and a node mechanical attribute initial parameter set is formed; in combination with node real-time stress monitoring data, Fourier transform and wavelet decomposition are carried out, and frequency domain characteristic parameters are extracted; constructing a finite element correction model, and simulating a stress response path under a multi-load combination; the predicted stress peak value is compared with the actually measured stress peak value, structural abnormal nodes are identified, parameter optimization is executed according to node construction information, the component size, the steel bar anchoring length or the concrete grade are automatically adjusted, an optimized parameter set is generated and written back into a building information modeling model, and closed-loop correction is formed; continuous monitoring, abnormity diagnosis and intelligent optimization of the node stress state can be achieved, and the safety and the intelligent level of assembly type building structure design are improved.

Description

Technical Field

[0001] This invention relates to the field of building engineering structural monitoring technology, specifically to a BIM-based prefabricated building node stress monitoring and design feedback system. Background Technology

[0002] Prefabricated buildings have been widely adopted in urban construction and rural housing due to their advantages such as high efficiency, environmental friendliness, energy conservation, and high standardization. The connection nodes between prefabricated components are critical parts of the building structure, determining its overall safety and durability. However, in actual engineering projects, these nodes often bear combined forces from different directions and are susceptible to stress concentration, crack initiation, and even failure due to construction errors, deviations in component prefabrication accuracy, and unpredictable loads.

[0003] Current common structural monitoring methods mostly rely on overall deformation monitoring or static stress monitoring, which makes it difficult to perform real-time, precise, and visualized stress perception and evolution tracking of key nodes in prefabricated buildings. Especially under complex working conditions such as nodes being subjected to periodic micro-load superposition (such as the combined effects of wind and earthquake on the top floor of high-rise buildings) and temperature gradient changes (such as the joints of north-south oriented components), the damage mechanism of nodes is more hidden and traditional methods are difficult to identify in a timely manner.

[0004] Meanwhile, although BIM (Building Information Modeling) technology is currently being used in construction modeling and schedule management, a real-time feedback mechanism between nodal stress information and design models has not yet been established. This makes it impossible to achieve closed-loop adjustment of "structural state - design parameters - nodal construction," resulting in high redundancy in structural design and large fluctuations in nodal safety performance, which has become a key issue restricting the further promotion of prefabricated buildings.

[0005] Therefore, how to accurately monitor and analyze the stress of prefabricated building nodes based on BIM models and feed the monitoring results back to the design stage in real time, so as to optimize the node construction parameters and improve the structural performance, has become a key technical problem that urgently needs to be solved. Summary of the Invention

[0006] The purpose of this invention is to provide a BIM-based prefabricated building node stress monitoring and design feedback system to address the shortcomings of the prior art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a BIM-based prefabricated building node stress monitoring and design feedback system, comprising: The BIM modeling module acquires the BIM model of the target prefabricated building structure, extracts the component information, node connection methods and load path data contained therein, and constructs the initial parameter set P0 of node mechanical properties. The monitoring module performs real-time three-dimensional stress monitoring on preset key nodes based on the spatial location of nodes in the BIM model, and obtains the original stress data sequence E0 of the nodes. The signal processing module performs Fourier transform and wavelet decomposition on the original stress data sequence E0 of the nodes to extract the frequency domain feature parameter set F0 of the nodes under small loads and environmental disturbances. The simulation prediction module combines the initial parameter set P0 of the node mechanical properties with the frequency domain characteristic parameter set F0 to construct a node stress evolution prediction model M based on the finite element modified model, and simulates the stress response path of the node under multiple load combinations. The diagnostic judgment module outputs the predicted stress peak value from the nodal stress evolution prediction model M. Peak stress measured by the sensor Perform dynamic comparison to obtain error value ,like Set threshold If so, the identified node is a structurally abnormal node N; The design feedback module automatically adjusts the size of the connecting components, the anchorage length of the node reinforcement, or the concrete grade of the node for the structurally abnormal node N based on its construction parameters in the BIM model, and generates an optimized parameter set P1. The model update module synchronously updates the optimized parameter set P1 to the BIM model, forming the structurally optimized BIM model B1, thus realizing the closed-loop correction of node design parameters. The closed-loop control module, the repetitive monitoring module, and the model update module re-monitor and provide feedback on the nodes in B1 until all critical nodes are reached. Complete stress optimization of all structural nodes within the safety tolerance range.

[0008] Preferably, the BIM modeling module further includes: Used to extract the geometric information of components associated with target nodes from the BIM model, including the component's cross-sectional width, height, thickness, material type, and spatial arrangement direction; The spatial arrangement direction is calculated by the spatial vector formed by the coordinates of the start and end points of the components, and the material types include ordinary reinforced concrete, prestressed concrete and steel-concrete composite structures.

[0009] Preferably, the extraction of the frequency domain feature parameter set F0 of the node under micro-load and environmental disturbance includes the following steps: A fast Fourier transform is performed on the original nodal stress data sequence E0 to convert the time-domain stress signal into a frequency-domain signal spectrum. The frequency domain signal spectrum is decomposed into multiple scales using continuous wavelet transform, and the energy distribution coefficients at different time scales are extracted to identify high-frequency disturbance components and low-frequency load response characteristics. The dominant frequency, frequency energy concentration, and spectral drift rate of the nodal stress signal are calculated to form a set of nodal frequency domain response indices. The frequency domain response index set is used as the frequency domain feature parameter set F0.

[0010] Preferably, the construction of the nodal stress evolution prediction model M based on the finite element modified model includes the following steps: A nodal stress evolution prediction model M is established using the initial parameter set P0 of the nodal mechanical properties as input. A frequency domain characteristic parameter set F0 is introduced to correct the frequency response of the finite element simulation results; Based on the corrected finite element modified model, a nodal stress evolution prediction model M is constructed to establish a multi-load coupled response path for nodal stress changing with time.

[0011] Preferably, the nodal stress evolution prediction model M based on the corrected finite element modified model constructs a multi-load coupled response path for nodal stress changing over time, including: Based on the corrected nodal stress evolution prediction model M, the time history data of dead load, live load and wind load on the nodes are imported to construct a multi-load time-varying input sequence. The time-step integration method is used to dynamically solve the nodal stress evolution prediction model M, calculate the stress response values ​​of the nodes in three dimensions at each discrete time point, and form a stress time program sequence. By using a load coupling mapping function to superimpose the phase and amplitude of different load components, the combined effect of nodal action under actual load combination conditions is simulated. The stress peak points, response acceleration variation trends, and stress recovery rates between adjacent loads in the program sequence are extracted as characteristic outputs of the multi-load coupled response path.

[0012] Preferably, the predicted stress peak value output by the nodal stress evolution prediction model M is... Peak stress measured by the sensor Dynamic comparison includes the following steps: Extract the predicted stress time sequence from the output of the nodal stress evolution prediction model M, calculate the maximum stress value among them, and take it as the peak value of the nodal predicted stress. ; Extract the measured stress sequence within the same time interval from the real-time 3D stress monitoring data of the corresponding node, calculate the maximum value, and use it as the peak value of the sensor's measured stress. ; Predicting peak stress Compared with the measured peak stress Perform absolute difference calculation to obtain the error value. and with the set error threshold Compare; like Greater than the set threshold If so, the node is identified as a structurally abnormal node N, and its spatial coordinates are marked.

[0013] Preferably, generating the optimized parameter set P1 includes the following steps: Extract the construction parameters of the structurally abnormal node N in the building information modeling model, including the cross-sectional dimensions of the connecting components, the anchorage length of the node reinforcement, and the concrete strength grade; Based on the error value between the predicted peak stress and the measured peak stress Match the corresponding construction parameter adjustment strategy and establish a parameter optimization mapping relationship; By using a finite number of iterations, the cross-sectional dimensions of the component are adjusted first. If the stress recovery does not meet the safety threshold, the anchorage length of the steel bars is increased or the concrete strength grade is increased in sequence. With the goal of minimizing structural modifications, the parameter adjustment range is determined, and the updated construction parameters are packaged to generate an optimization parameter set P1.

[0014] Preferably, the simulation prediction module further includes: constructing a load coupling mapping function Fcouple(t) to perform phase alignment and amplitude superposition of various loads over time.

[0015] The technical effects and advantages provided by the present invention in the above technical solution are as follows: 1. This invention integrates Building Information Modeling (BIM) technology, real-time sensing and monitoring, frequency domain feature extraction, and finite element simulation prediction to achieve dynamic perception and response optimization of the stress state at nodes in prefabricated buildings. Compared to traditional methods that rely solely on static design and post-construction inspection, this invention can quantitatively assess the stress state of key nodes in real time during structural operation, promptly identify structural anomalies, and automatically feed them back to the design model for closed-loop correction. This significantly improves the structural safety and design adaptability of prefabricated buildings during both the construction and operation phases.

[0016] 2. The nodal stress evolution prediction model constructed in this invention combines measured spectral characteristics with finite element correction simulation, improving prediction accuracy and the realism of structural behavior simulation, and avoiding the deviation problem between traditional models and actual structural responses. Simultaneously, through parameter optimization mapping and a multi-round closed-loop iteration mechanism, it can minimize adjustments to key parameters such as component dimensions, rebar anchorage length, and concrete strength grade without disrupting the original building design logic. This ensures structural safety while also considering construction costs and material utilization efficiency, demonstrating significant engineering practical value. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0018] Figure 1 This is a flowchart of the system modules of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] For examples, please refer to Figure 1 As shown in this embodiment, the BIM-based prefabricated building node stress monitoring and design feedback system includes: The BIM modeling module acquires the BIM model of the target prefabricated building structure, extracts the component information, node connection methods, and load path data contained therein, and constructs the initial parameter set P0 of the node mechanical properties.

[0021] Obtain the BIM model of the target prefabricated building structure and extract the geometric information of the components associated with the target nodes from the BIM model. This geometric information includes: the cross-sectional dimensions of the components, the material type used by the components, and the spatial orientation of the components. Specifically, the cross-sectional dimensions include the width, height, and thickness of the cross-section; the material type can be ordinary reinforced concrete, prestressed concrete, or steel-concrete composite materials, etc., and is obtained through the component attribute annotation field in the building information model; the orientation is calculated by the vector direction formed by the spatial coordinate points of the components and is used to determine the stress direction relationship of the nodes in subsequent steps.

[0022] The connection methods between components at target nodes are identified, including three types: hinged connections, semi-rigid connections, and rigid connections. Based on the connection node information fields between components in the Building Information Modeling (BIM) model, combined with the construction atlas and component connection construction annotations, the connection type is determined by looking up a table, and connection stiffness parameters are constructed based on different connection types. These connection stiffness parameters are represented in two-dimensional or three-dimensional stiffness matrix form, where rigid connections correspond to high stiffness values, hinged connections correspond to rotational stiffness values ​​close to zero, and semi-rigid connections correspond to values ​​in the intermediate range. All stiffness parameters are assigned values ​​according to the empirical formulas for typical connection stiffness values ​​provided in the "Code for Design of Building Structures".

[0023] Based on the load path data in the Building Information Modeling (BIM) model, the main load types acting on the nodes are extracted. Load types include dead load, live load, and wind load, specifically derived from the load definition table in the BIM model. By tracing the load paths acting on the surrounding components of the node one by one and projecting the forces to the node center, the magnitude and direction of the resultant force on the node are calculated using vector superposition. The magnitude of the resultant force is the combined value of each load component along the node direction, and the direction of the resultant force is the spatial direction angle defined by the direction cosine of the vector.

[0024] The extracted component geometric information, node connection stiffness parameters, and node stress information are uniformly normalized. The normalization method uses linear normalization, converting each parameter value into a dimensionless value within the interval [0,1] to eliminate the impact of dimensional inconsistencies on subsequent modeling accuracy. The specific processing steps are as follows: Let the original parameter value be X, the maximum value be Xmax, and the minimum value be Xmin; then the normalized value is (X-Xmin) / (Xmax-Xmin). After processing, the parameters are arranged in an ordered manner according to the spatial location of the nodes, forming an initial parameter set P0 for node mechanical properties, which is used for subsequent node stress feature extraction and evolution prediction model construction.

[0025] The monitoring module, based on the spatial location of nodes in the BIM model, performs real-time three-dimensional stress monitoring on preset key nodes and obtains the original stress data sequence E0 of the nodes.

[0026] The location coordinates of key nodes are determined based on the spatial location information of nodes in the Building Information Modeling (BIM) model. This spatial location information is determined by the three-dimensional coordinate parameters of the nodes in the BIM model, including the horizontal, vertical, and lateral coordinates of the nodes in the global building coordinate system. Key nodes are selected based on their importance to the structure under load; specifically, nodes located at the intersection of vertical load-bearing paths, locations of abrupt changes in component stiffness, or interfaces of prefabricated components are marked as preset key nodes, and a unique identifier is established for each node in the BIM model.

[0027] Three-dimensional stress sensors are deployed at each pre-defined key node to collect the stress response of the node in three orthogonal directions. The three-dimensional stress sensors are fixedly installed on the surface of the component near the node or in the core area of ​​the node, with their installation direction consistent with the coordinate axis direction in the building information modeling model to ensure the spatial correspondence of the stress data. The stress collected by the sensors includes normal stress components along the three orthogonal directions, and the sampling frequency is set according to the dynamic response characteristics of the building structure to cover stress fluctuations caused by minute load changes.

[0028] The raw signals acquired by the three-dimensional stress sensor are synchronously acquired and time-stamped. Specifically, within the same sampling time, the stress sampling values ​​in three directions are uniformly timestamped to form the three-dimensional stress state data of the node at that time. Through continuous sampling, a set of stress data arranged in chronological order is formed within a preset monitoring time period.

[0029] The continuously acquired three-dimensional stress state data are categorized according to unique node identifiers and stored in time series format to construct the original node stress data sequence E0. The original node stress data sequence E0 consists of multiple three-dimensional stress vectors arranged in chronological order, each corresponding to the stress values ​​of that node in three spatial directions at a given sampling time. This original node stress data sequence E0 serves as the foundational input data for subsequent frequency domain feature extraction and node stress evolution analysis.

[0030] The signal processing module performs Fourier transform and wavelet decomposition on the original stress data sequence E0 of the nodes to extract the frequency domain feature parameter set F0 of the nodes under small loads and environmental disturbances.

[0031] The original stress data sequence E0 at each node is processed by a Fast Fourier Transform (FFT) to convert the three-dimensional time-domain stress signal into a frequency-domain signal spectrum. The FFT is implemented using the Discrete Fourier Transform (DFT) algorithm, and the calculation steps include: inputting the time-domain stress sequence in each direction into the transform function to obtain the frequency-amplitude correspondence, forming a spectrum function. The frequency range is determined by the sampling frequency, and the amplitude represents the stress intensity corresponding to that frequency component, forming a stress amplitude distribution map of the node at each frequency.

[0032] A continuous wavelet transform is performed on the frequency domain signal spectrum to achieve energy distribution analysis at different time scales. The continuous wavelet transform uses the Morlet wavelet, which has good localization properties, as the mother wavelet function, and sets the scale range from... arrive It covers the high-frequency to low-frequency range. The wavelet transform process obtains wavelet coefficient matrices at different scales through convolution operations, and then extracts the high-frequency disturbance components and low-frequency load response change characteristics within the corresponding time period, ensuring the ability to perform joint time-frequency analysis on non-stationary stress signals.

[0033] Subsequently, based on the obtained wavelet transform results, the frequency domain response characteristic index of the nodal stress signal is calculated. The index includes: The dominant frequency value, which is the frequency component corresponding to the maximum energy, represents the dominant response frequency of the node; Frequency energy concentration, which is the proportion of the main frequency energy to the total frequency domain energy, is used to reflect the concentration of the signal spectrum. The spectral drift rate, or the rate at which the dominant frequency position changes within different monitoring periods, is defined as the difference between the dominant frequency values ​​of two adjacent periods divided by the time interval, and is measured in Hertz per second.

[0034] Finally, the above-mentioned dominant frequency value, frequency energy concentration and spectral drift rate are combined to form a frequency domain response index set, which is then used as the frequency domain characteristic parameter set F0 and input into the subsequent nodal stress evolution prediction model to identify abnormal change trends and load response characteristics of the nodal stress state.

[0035] The simulation prediction module, combining the initial parameter set P0 of the nodal mechanical properties and the frequency domain characteristic parameter set F0, constructs a nodal stress evolution prediction model M based on a finite element modified model to simulate the stress response path of the node under multiple load combinations. Specifically, the nodal stress evolution prediction model M is a numerical analysis model that uses the initial parameter set P0 of the nodal mechanical properties as input to establish a finite element simulation model of the node, introduces the frequency domain characteristic parameter set F0 to correct the frequency response of the finite element simulation model, and uses the time-step integration method to calculate the stress sequence of the node under multiple load combinations based on the corrected finite element simulation model.

[0036] First, a three-dimensional finite element simulation model of the node is established based on the component cross-sectional dimensions, material parameters, and connection stiffness information in the initial parameter set P0 of the node's mechanical properties. Solid elements are used for meshing; the element type is an eight-node hexahedral element, and the edge length of the mesh element is set to one-tenth of the minimum cross-sectional dimension of the component to ensure simulation accuracy. The constitutive relation of the material model is selected according to the type of material used in the node component. For reinforced concrete members, a multilinear elastoplastic constitutive model is adopted, and the yield strength, elastic modulus, and Poisson's ratio are assigned values ​​according to the value table provided in the national standard "Code for Design of Concrete Structures" GB 50010-2010; for steel members, an ideal elastoplastic model is adopted, with the material yield point taken as 345 MPa and the elastic modulus as... Pascal, Poisson's ratio is 0.3.

[0037] Regarding boundary condition settings, fixed constraints or free boundaries are set for each end component based on the spatial topological relationship of the components connected to the nodes in the building information modeling model. For example, when a node connects to the bottom of a column, its vertical direction is set as a fixed support; when it connects to the end of a beam, the horizontal end is set as a sliding support or semi-rigid constraint, and the stiffness value is taken from the corresponding component in the connection stiffness matrix parameter.

[0038] In the preliminary simulation calculation, standard dead loads (such as self-weight and constant loads on components) are applied to obtain the initial stress distribution results of the nodes, which serve as the basic response data of the model.

[0039] After completing the preliminary finite element simulation, the dominant frequency, frequency energy concentration, and spectral drift rate contained in the frequency domain characteristic parameter set F0 are introduced as the benchmark for measured spectrum correction. Let the simulated dominant frequency of the nodes be fsim, and the measured dominant frequency be fexp; then the difference between the simulated and measured dominant frequencies is defined as... If Δf exceeds the threshold of 0.5 Hz, the frequency response correction process is initiated.

[0040] The correction method involves adjusting the model parameters using a least-squares iterative algorithm, including the following two approaches: Adjust the load input frequency and waveform function: Change the original constant load input to a time-varying periodic excitation function, for example, replace the original constant load with a sinusoidal load. Where A is the amplitude, f is the adjustment frequency, and t represents the time.

[0041] Adjusting the node connection stiffness matrix: While maintaining the overall structural mechanical consistency, the rotational stiffness value Kr in the connection stiffness matrix is ​​adjusted. The optimization goal is to match the simulated spectrum with the measured spectrum in terms of dominant frequency and energy distribution.

[0042] The iteration stops when the difference between the simulated and measured dominant frequencies, Δf, is less than 0.5 Hz, and the spectral energy concentration error is less than 5%. Time history data of various loads acting on nodes are extracted from the building structure design model, including: dead loads (such as floor loads and self-weight), modeled as constant functions or slowly changing linear functions over time; live loads (such as crowd loads and construction equipment loads), modeled as step functions with intermittent periodicity; and wind loads, modeled as sinusoidal superposition functions with random disturbances, the expression of which is: Where Ai is the amplitude of the wind component and fi is the frequency component. This represents the phase difference.

[0043] The above load time function is projected according to the nodal direction component and combined into a time-varying vector input sequence of nodal load action. The sampling time interval is set to 0.1 seconds and the total duration is set to 60 seconds to ensure coverage of typical wind vibration and live load cycles.

[0044] It should be noted that the least squares iterative algorithm uses the difference Δf between the predicted simulated main frequency and the measured main frequency as the objective function, and employs gradient descent for iterative updates. In each iteration, the connection stiffness Kr and the excitation frequency f are adjusted to make the loss function... Minimize. The algorithm convergence criterion is: the change in the loss function between the current round and the previous round is less than the square of 0.01 Hz, or the number of iterations reaches 100.

[0045] The finite element model under the aforementioned time-varying load input was dynamically solved using the Newmark-β integration method. Newmark parameters were selected as β=0.25 and γ=0.5 to ensure numerical stability and solution accuracy. The calculation steps included: Calculate the total structural stiffness matrix and mass matrix at each time step; Apply the load input at the current time step and solve for the stress components of the nodes in the x, y, and z directions; The stress values ​​at each time step are categorized by node number and time sequence, and the output is a stress-time program sequence S(t), where each item is in three-dimensional vector form: .

[0046] It should be noted that the solution of the finite element model can be implemented in commercial structural analysis platforms that support multiphysics dynamics (such as ANSYS or ABAQUS), or it can be implemented through secondary development based on open source finite element solvers (such as OpenSees) using embedded Python control scripts.

[0047] A load coupling mapping function Fcouple(t) is constructed to perform phase alignment and amplitude superposition of various loads over time. The function is defined as follows: ;in: These are time-varying functions for dead load, live load, and wind load, respectively. This is the weighting factor for the load direction; This is the phase offset, set according to the time delay relationship between loads. The direction weighting coefficient... The calculation is based on the projection ratio of each type of load in three dimensions. Specifically, the standard distribution of each type of load in the x, y, and z directions is calculated, and its modulus is normalized and used as a weighting coefficient. For example, the angle of action of wind load is... ,but .

[0048] The input load obtained by superimposing the above functions better reflects the coupling effect of various loads in actual building operation, thus improving the physical consistency of simulation predictions.

[0049] After completing the stress-time program sequence calculation, the following key evolution indicators are extracted from it: Stress peak point Find the stress vector with the largest modulus in the complete time series, which represents the maximum stress condition at the node; The trend of acceleration change a(t): The rate of change per unit time is calculated by taking the first derivative of the stress value, and then the second derivative is taken to represent the acceleration, reflecting the impact of sudden load change on the node; Stress recovery rate r(t): The rate of stress decrease between two consecutive load peaks. To determine the elastic recovery capability of the node structure. Peak stress refers to the maximum stress value that appears in the stress time sequence of a node within a certain load cycle or during a certain load event. It usually represents the maximum stress state of a node under combined loads and reflects its critical stress capacity. (Stress valley value) refers to the value at which the stress valley is located. The minimum stress value that occurs during the natural attenuation of nodal stress. This reflects the structure's ability to release or alleviate stress after experiencing a peak load. Δt (time interval) represents the stress level from the peak stress. From the moment of occurrence to the stress valley value The time difference between the times of occurrence.

[0050] The above three indicators are used as the core output features of the stress evolution path to provide quantitative basis for subsequent anomaly identification and design feedback.

[0051] The diagnostic judgment module outputs the predicted stress peak value from the nodal stress evolution prediction model M. Peak stress measured by the sensor Perform dynamic comparison, if Set threshold If so, the identified node is a structurally abnormal node N.

[0052] First, the stress time sequence of the target node is extracted from the output of the nodal stress evolution prediction model M. This time sequence is the sequence of predicted data output by the model within the simulation period [0, T] at a fixed time step Δt. Assuming a total simulation time T = 60 seconds and a time step Δt = 0.1 seconds, a total of 600 sets of three-dimensional stress data are output. Each set of data is denoted as: Where i = 1, 2, ..., 600; calculate the modulus (Euclidean norm) of each group of three-dimensional stress components: The maximum modulus value across all time points is taken as the peak predicted stress at the node. : The unit is megapascal (MPa).

[0053] Then, the measured stress time sequence within the same time interval [0,T] as the simulation model is obtained from the data collected by the three-dimensional stress sensor deployed at the node location. The sensor sampling frequency is consistent with the simulation model, which is 10 Hz (i.e., sampling once every 0.1 seconds).

[0054] The measured three-dimensional stress components at each time point are denoted as: Where i = 1, 2, ..., 600; similarly, calculate the modulus of each group of measured stresses: ; Traverse all time series data to obtain the measured peak stress. : The unit is megapascal (MPa).

[0055] Predicting peak stress Compared with the measured peak stress Perform absolute difference calculation and define error. The difference in modulus between the two: This error measures the degree of deviation between the prediction model and actual monitoring data at the peak stress level. The error unit is also megapascals (MPa), and it is a positive number. A judgment threshold is set. This is used to determine whether a node exhibits abnormal structural behavior. Threshold The settings are based on the following:

[0056] The present invention preferably uses The standard is set at 6 MPa, which can be adjusted according to the node category in subsequent implementation.

[0057] Error value With threshold Comparison: If If the node prediction is basically consistent with the actual measurement, the node is considered to be under normal stress. This indicates that the simulation model cannot accurately capture the actual node response, and the actual node stress deviates significantly, suggesting that the structure may have one of the following conditions: The node connection stiffness has failed or varied; the load input is incomplete (e.g., the actual load exceeds the design value); the node has initial cracks or latent damage; the model parameters fail to fully reflect the actual structural behavior. Therefore, this node is marked as structurally anomalous node N.

[0058] The spatial location information of the abnormal node N is extracted from the building information modeling model and includes the following three-dimensional coordinate information: XN=(xN,yN,zN); where: xN is the east-west coordinate of the node in the building model; yN is the north-south coordinate of the node; and zN is the vertical coordinate of the node (i.e., the floor height).

[0059] This coordinate information is stored in the BIM model as node numbers and attribute labels, and can be used as a positioning basis for subsequent optimization design adjustments.

[0060] The design feedback module automatically adjusts the dimensions of connecting components, the anchorage length of the node reinforcement, or the concrete grade of the node for structurally abnormal nodes N based on their construction parameters in the BIM model, generating an optimized parameter set P1.

[0061] First, locate the position of structural anomaly node N in the building information modeling model, and obtain the construction information associated with this node through its unique identifier, including: Connecting component cross-sectional dimensions: Extract the cross-sectional geometric parameters of all components connected to node N, including the cross-sectional width, height, and thickness of components such as beams, columns, and slabs, in millimeters; Node reinforcement anchorage length: Read the reinforcement construction information within the node to obtain the main reinforcement anchorage length La, in millimeters. This value is extracted based on the reinforcement family definition field in the design model. Node concrete strength grade: Read the concrete material properties of the pouring section to which the node belongs, obtain the strength grade identifier (such as C30, C40), and convert it into a numerical strength value (for example, C30 is 30 MPa).

[0062] All parameters are saved in a unified data format for use in subsequent optimization algorithms.

[0063] The structural parameters obtained in the previous step are compared with the stress peak error obtained in the anomaly identification stage. Perform correlation to construct a stress error-parameter adjustment mapping function. This function is used to match the corresponding optimization and adjustment strategy. Its structure is as follows: when Megapascal: It is recommended to adjust the component cross-sectional dimensions and set the optimization priority weight to 1; When 5 MPa < ≤10 MPa: It is recommended to increase the anchorage length of the reinforcing bars based on the adjustment of the cross-sectional dimensions, with a weight of 0.8; when >10 MPa: Prioritize increasing the concrete strength grade. If insufficient, adjust the other two values, with weights set to 0.6 (anchoring) and 0.4 (section dimensions), respectively.

[0064] The above mapping relationship is established based on the empirical regression model and is used for parameter priority ranking and range constraints during the optimization calculation process.

[0065] It should be noted that the stress error-parameter adjustment mapping function A regression analysis was conducted on historical monitoring data from 30 typical engineering cases to construct a framework. The parameter is the independent variable, and the parameter adjustment is the dependent variable. A piecewise optimization curve is established using polynomial fitting. This curve is stored as a two-dimensional table and integrated into the model control script for easy automatic invocation by the optimization algorithm.

[0066] The node construction parameters are optimized and simulated using a fixed-step iterative method. After each adjustment, the nodal stress evolution prediction model M of the finite element correction model is executed to recalculate the predicted peak stress. .

[0067] The adjustment strategy is as follows: Component cross-sectional dimensions adjustment: Based on the original dimensions, the width and height are increased by 20 mm in each step, with a maximum increase of 1.5 times the original dimensions; Reinforcement anchorage length adjustment: Increased by 50 mm in each iteration, with a maximum increase of 1.3 times the design anchorage length; Concrete strength grade adjustment: Adjusted progressively according to the commonly used national standard grade sequence (e.g., C30→C35→C40→C45), up to a maximum of C50. In each iteration, the peak stress is re-simulated and predicted. And calculate the corrected error. Determine whether the following termination conditions are met: ,in The preferred safety tolerance threshold is 4 MPa.

[0068] If the termination condition is met, the current parameter combination is the optimization result; if it is not met and the upper limit of the set number of iterations (e.g., 10 times) is reached, the optimization is terminated and marked as design overrun.

[0069] The final construction parameters obtained from the above iterations are output as the structure optimization parameter set P1. This parameter set contains the following fields: Component cross-sectional dimensions: [width, height, thickness], in millimeters; Reinforcement anchorage length: Units are in millimeters; Concrete strength grade: The unit is megapascal. The optimization parameter set P1, as the design update data for the structural anomaly node N, is transmitted to the construction parameter table of the corresponding node in the building information modeling model, completing the input preparation for model update and closed-loop design feedback.

[0070] The model update module synchronously updates the optimized parameter set P1 to the BIM model, forming the structurally optimized BIM model B1, thus realizing the closed-loop correction of node design parameters.

[0071] First, the structural anomaly node N is precisely located in the building information modeling model using its spatial coordinate information. This coordinate information includes the node's three directional components in the building coordinate system, denoted as (xn, yn, zn), which correspond to the east-west direction, the north-south direction, and the vertical height, respectively.

[0072] In the Building Information Modeling (BIM) model, a spatial query method is used to invoke the coordinate index mechanism. This method iterates through and compares the geometric center coordinates of all components in the component instance set, selecting component objects whose coordinate matching error with the node does not exceed 10 millimeters. It then determines whether these component objects have a geometric connection to the node. Upon successful matching, the system retrieves the component numbers, types, component families, and parameter fields of all components connected to that node, preparing for the parameter replacement operation.

[0073] The optimized parameter set P1 is the set of structural parameters generated after iterative design optimization of structural anomaly nodes, mainly including: Optimized cross-sectional dimensions of the connecting component: [Width B1, unit: mm; Height H1, unit: mm]; Optimized rebar anchorage length La1, unit: mm; The optimized concrete strength grade is C1, in megapascals (MPa).

[0074] In this step, first open the parameter editing interface of the building information modeling model and enter the attribute table structure of the target component.

[0075] For component section dimension fields, such as “Section Width” and “Section Height”, write B1 and H1 respectively; Write La1 to the "Anchor Length" field in the rebar family parameter table; Write C1 to the "Concrete Grade" field in the material property table of the structural segment to which the node belongs, with the data type being a numeric strength value (e.g., write 40 for C40).

[0076] Parameter updates are achieved through a component family instantiation overload mechanism, ensuring that new parameters take effect immediately and are applied to all constructed representations associated with node N in the model.

[0077] After parameter replacement is completed, to avoid model conflicts and geometric inconsistencies caused by changes in component dimensions, a geometric consistency verification process is executed. This process mainly includes the following two checks: Component geometric boundary overlap detection: Check whether the spatial distance between the updated component boundary box and the adjacent component is less than the set minimum clear distance threshold, which is set to 20 mm; Component node alignment check: Ensure that component offset caused by changes in cross-sectional dimensions does not affect the original alignment relationship of nodes, and make fine adjustments to the position of connecting components with a deviation angle greater than 2 degrees.

[0078] If a collision conflict or spatial mismatch is found, the specific component number, conflict type, and location will be recorded in the log for engineers to manually confirm whether to accept the optimized model.

[0079] After verification, the updated Building Information Modeling (BIM) model is saved and named "Structural Optimization Model B1". Simultaneously, in the node attribute table of the BIM model, an "Optimization Status" flag field is added for each optimized node, containing the following information: Optimized label: Optimized; Optimization source: Stress deviation feedback; Optimized timestamp: YYYY-MM-DD HH:MM; Summary of optimized parameters: Lists the updated cross-sectional dimensions, anchorage lengths, and concrete grades.

[0080] The final optimized BIM model B1 not only reflects the latest structural design status, but also provides a data foundation for subsequent closed-loop monitoring and model version management, realizing closed-loop correction and digital tracking of node design parameters.

[0081] Using the above methods, the building information modeling model enables real-time synchronous updates of structural performance optimization results, effectively opening up the feedback channel between structural safety monitoring results and building design data, and providing support for the design and management of prefabricated buildings throughout their entire life cycle.

[0082] The closed-loop control module, the repetitive monitoring module, and the model update module re-monitor and provide feedback on the nodes in B1 until all critical nodes are reached. Complete stress optimization of all structural nodes within the safety tolerance range.

[0083] First, based on model B1, all critical nodes defined in the model are re-identified. These critical nodes are the set of nodes marked as safety control points during the structural design phase, defined based on factors including: intersections of structural force paths, areas of abrupt stiffness changes, vertical connection interfaces, and important functional areas. The set of critical nodes is denoted as {N1, N2, ..., Ni, ..., Nn}, where n is the total number of critical nodes.

[0084] For each node Ni, extract its three-dimensional coordinates and construction information in the B1 model to prepare for the re-monitoring process.

[0085] Based on the key node locations, three-dimensional stress sensors are redeployed or activated on-site at the building site to continuously monitor each node Ni for extended periods. The monitoring cycle is the same as the initial monitoring (preferably 60 seconds, sampling frequency 10 Hz). The procedure for obtaining the measured stress at node Ni is then executed. .

[0086] Simultaneously, the updated nodal local finite element simulation model is invoked to perform stress evolution prediction on the same node Ni, and the corresponding predicted stress time sequence is obtained. The measured peak stress was extracted separately using the modulus calculation method. Predicting peak stress Calculate the nodal stress error: .

[0087] Set a uniform safety tolerance threshold This threshold is used to measure whether the predicted-measured error at a node is acceptable. The threshold is set based on structural grade, design specifications, and application scenario, with a preferred value of 4 MPa. Error assessment is performed for each critical node Ni. like Marked as "stress meets tolerance"; like It is marked as "stress does not meet tolerance" and enters the optimization process again.

[0088] The judgment results are summarized into a full-structure key node judgment matrix M, where: If all of the matrix The values ​​are all less than or equal to If this happens, the closed-loop termination decision will be triggered.

[0089] For the set of nodes in M ​​that are determined to have "stress not meeting tolerance" Following the aforementioned optimization logic, the following steps are repeated: parameter extraction; parameter mapping and strategy matching; iterative optimization of component cross-section, anchorage length, or concrete grade; and optimization of the parameter set. Generate; replace corresponding parameters in the Building Information Modeling (BIM) model and update the B1 model. After the update, the model version is iterated to B2, and an update log is recorded, including node number, parameters before and after optimization, optimization rounds, and error changes.

[0090] After completing the updated Building Information Modeling (BIM) model B2, a new round of stress monitoring and error assessment will be conducted. The termination condition will be determined as follows: All satisfy .

[0091] When the above conditions are met, the structural stress state is considered to have met the design safety requirements, and the closed-loop process terminates. Model version B2 is the final structural stress optimization building information modeling model.

[0092] If there are still nodes that do not meet the tolerance requirements, continue iterating until the termination condition is met or the maximum allowed number of optimization rounds is reached (preferably up to 5 rounds).

[0093] Finally, the optimized parameter sets, stress response data, and error change trends of all key nodes are written into the structural performance log, forming a complete closed-loop optimization record of the structure. Model B2, as the structural design result, is simultaneously imported into the building information modeling database during the construction phase and supports its use during the structural operation and maintenance phase, achieving the goal of closed-loop control throughout the entire lifecycle from design to monitoring to optimization to redesign.

[0094] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A BIM-based prefabricated building node stress monitoring and design feedback system, characterized in that: include: The BIM modeling module acquires the BIM model of the target prefabricated building structure, extracts the component information, node connection methods and load path data contained therein, and constructs the initial parameter set P0 of node mechanical properties. The monitoring module performs real-time three-dimensional stress monitoring on preset key nodes based on the spatial location of nodes in the BIM model, and obtains the original stress data sequence E0 of the nodes. The signal processing module performs Fourier transform and wavelet decomposition on the original stress data sequence E0 of the nodes to extract the frequency domain feature parameter set F0 of the nodes under small loads and environmental disturbances. The simulation prediction module combines the initial parameter set P0 of the node mechanical properties with the frequency domain characteristic parameter set F0 to construct a node stress evolution prediction model M based on the finite element modified model, and simulates the stress response path of the node under multiple load combinations. The diagnostic judgment module outputs the predicted stress peak value from the nodal stress evolution prediction model M. Peak stress measured by the sensor Perform dynamic comparison to obtain error value ,like Set threshold If so, the identified node is a structurally abnormal node N; The design feedback module automatically adjusts the size of the connecting components, the anchorage length of the node reinforcement, or the concrete grade of the node for the structurally abnormal node N based on its construction parameters in the BIM model, and generates an optimized parameter set P1. The model update module synchronously updates the optimized parameter set P1 to the BIM model, forming the structurally optimized BIM model B1, thus realizing the closed-loop correction of node design parameters. The closed-loop control module, the repetitive monitoring module, and the model update module re-monitor and provide feedback on the nodes in B1 until all critical nodes are reached. Complete stress optimization of all structural nodes within the safety tolerance range.

2. The BIM-based prefabricated building node stress monitoring and design feedback system according to claim 1, characterized in that: The BIM modeling module further includes: Used to extract the geometric information of components associated with target nodes from the BIM model, including the component's cross-sectional width, height, thickness, material type, and spatial arrangement direction; The spatial arrangement direction is calculated by the spatial vector formed by the coordinates of the start and end points of the components, and the material types include ordinary reinforced concrete, prestressed concrete and steel-concrete composite structures.

3. The BIM-based prefabricated building node stress monitoring and design feedback system according to claim 1, characterized in that: The extraction of the frequency domain feature parameter set F0 of the node under small loads and environmental disturbances includes the following steps: A fast Fourier transform is performed on the original nodal stress data sequence E0 to convert the time-domain stress signal into a frequency-domain signal spectrum. The frequency domain signal spectrum is decomposed into multiple scales using continuous wavelet transform, and the energy distribution coefficients at different time scales are extracted to identify high-frequency disturbance components and low-frequency load response characteristics. The dominant frequency, frequency energy concentration, and spectral drift rate of the nodal stress signal are calculated to form a set of nodal frequency domain response indices. The frequency domain response index set is used as the frequency domain feature parameter set F0.

4. The BIM-based prefabricated building node stress monitoring and design feedback system according to claim 1, characterized in that: The construction of the nodal stress evolution prediction model M based on the finite element modified model includes the following steps: A nodal stress evolution prediction model M is established using the initial parameter set P0 of the nodal mechanical properties as input. A frequency domain characteristic parameter set F0 is introduced to correct the frequency response of the finite element simulation results; Based on the corrected finite element modified model, a nodal stress evolution prediction model M is constructed to establish a multi-load coupled response path for nodal stress changing with time.

5. The BIM-based prefabricated building node stress monitoring and design feedback system according to claim 4, characterized in that: The nodal stress evolution prediction model M based on the corrected finite element modified model constructs a multi-load coupled response path for nodal stress changing with time, including: Based on the corrected nodal stress evolution prediction model M, the time history data of dead load, live load and wind load on the nodes are imported to construct a multi-load time-varying input sequence. The time-step integration method is used to dynamically solve the nodal stress evolution prediction model M, calculate the stress response values ​​of the nodes in three dimensions at each discrete time point, and form a stress time program sequence. By using a load coupling mapping function to superimpose the phase and amplitude of different load components, the combined effect of nodal action under actual load combination conditions is simulated. The stress peak points, response acceleration variation trends, and stress recovery rates between adjacent loads in the program sequence are extracted as characteristic outputs of the multi-load coupled response path.

6. The BIM-based prefabricated building node stress monitoring and design feedback system according to claim 1, characterized in that: The predicted stress peak value output by the nodal stress evolution prediction model M Peak stress measured by the sensor Dynamic comparison includes the following steps: Extract the predicted stress time sequence from the output of the nodal stress evolution prediction model M, calculate the maximum stress value among them, and take it as the peak value of the nodal predicted stress. ; Extract the measured stress sequence within the same time interval from the real-time 3D stress monitoring data of the corresponding node, calculate the maximum value, and use it as the peak value of the sensor's measured stress. ; Predicting peak stress Compared with the measured peak stress Perform absolute difference calculation to obtain the error value. and with the set error threshold Compare; like Greater than the set threshold If so, the node is identified as a structurally abnormal node N, and its spatial coordinates are marked.

7. The BIM-based prefabricated building node stress monitoring and design feedback system according to claim 1, characterized in that: The process of generating the optimization parameter set P1 includes the following steps: Extract the construction parameters of the structurally abnormal node N in the building information modeling model, including the cross-sectional dimensions of the connecting components, the anchorage length of the node reinforcement, and the concrete strength grade; Based on the error value between the predicted peak stress and the measured peak stress Match the corresponding construction parameter adjustment strategy and establish a parameter optimization mapping relationship; By using a finite number of iterations, the cross-sectional dimensions of the component are adjusted first. If the stress recovery does not meet the safety threshold, the anchorage length of the steel bars is increased or the concrete strength grade is increased in sequence. With the goal of minimizing structural modifications, the parameter adjustment range is determined, and the updated construction parameters are packaged to generate an optimization parameter set P1.

8. The BIM-based prefabricated building node stress monitoring and design feedback system according to claim 1, characterized in that: The simulation prediction module further includes: constructing a load coupling mapping function Fcouple(t) to perform phase alignment and amplitude superposition of various loads over time.

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