BIM-based monitoring and design feedback system for stress of fabricated building nodes

Abstract

The application discloses a BIM-based prefabricated building node stress monitoring and design feedback system, relates to the technical field of building engineering structure monitoring, and comprises the following steps: constructing a building information modeling model, extracting component geometric information, connection modes and load paths associated with the node, and forming an initial parameter set of node mechanical properties; combining real-time stress monitoring data of the node, performing Fourier transform and wavelet decomposition, and extracting frequency domain characteristic parameters; constructing a finite element correction model, simulating stress response paths under multiple load combinations; comparing predicted and measured stress peaks, identifying abnormal nodes, and performing parameter optimization according to node construction information, automatically adjusting component size, steel reinforcement anchoring length or concrete grade, generating an optimized parameter set, and writing back to the building information modeling model to form a closed-loop correction; the application can realize continuous monitoring, abnormal diagnosis and intelligent optimization of the stress state of the node, and improve the safety and intelligent level of prefabricated building structure design.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of building engineering structure monitoring, in particular to a fabricated building node stress monitoring and design feedback system based on BIM. BACKGROUND

[0002] Fabricated buildings have been widely promoted in urban buildings and rural residences due to their efficient construction, environmental protection and energy saving, high standardization and other advantages. The connection nodes between fabricated components are the key parts of building structure stress and deformation, which determine the safety and durability of the overall structure. However, in actual engineering, the node parts often bear complex forces from different directions, are affected by construction errors, component prefabrication precision deviation and load unpredictability, and are prone to stress concentration, crack initiation and even failure and damage at the node.

[0003] The current common structure monitoring method relies on overall deformation monitoring or static stress monitoring, which is difficult to realize real-time, fine and visual stress perception and evolution tracking of the key nodes of fabricated buildings, especially under complex working conditions such as periodic micro-amplitude load superposition (such as wind vibration superposition of high-rise building top) and temperature gradient change (such as north-south oriented component joint) of the node. The node damage mechanism is more concealed, and the traditional method is difficult to identify in time.

[0004] At the same time, although the current BIM (Building Information Modeling) technology has been applied in construction modeling and construction period management, it has not established a real-time feedback mechanism of node stress information and design model, and cannot realize the closed-loop adjustment of "structure state-design parameter-node structure", resulting in high redundancy of structure design, large fluctuation of node safety performance, which becomes the key problem restricting the further promotion of fabricated buildings.

[0005] Therefore, how to realize accurate stress monitoring and analysis of fabricated building nodes based on BIM model, and real-time feedback of the monitoring results to the design link, so as to optimize the node structure parameters and improve the structure performance, has become a key technical problem that needs to be solved at present. SUMMARY

[0006] The purpose of the present application is to provide a fabricated building node stress monitoring and design feedback system based on BIM to solve the problems in the background art.

[0007] In order to achieve the above purpose, the present application provides the following technical scheme: a fabricated building node stress monitoring and design feedback system based on BIM, comprising:

[0008] a BIM modeling module, obtaining a BIM model of a target fabricated building structure, extracting component information, node connection mode and load path data contained therein, and constructing an initial parameter set P0 of node mechanical properties;

[0009] a monitoring module for monitoring a preset key node in real time based on a spatial position of the node in a BIM model to obtain a raw data sequence E0 of a stress of the node;

[0010] a signal processing module for performing Fourier transform and wavelet decomposition on the raw data sequence E0 of the stress of the node to extract a frequency domain characteristic parameter set F0 of the node under micro-amplitude load and environmental disturbance;

[0011] a simulation and prediction module for combining the initial parameter set P0 of the mechanical properties of the node and the frequency domain characteristic parameter set F0 to construct a stress evolution prediction model M of the node based on a finite element correction model to simulate a stress response path of the node under a plurality of load combinations;

[0012] a diagnosis and determination module for comparing a predicted stress peak value output by the stress evolution prediction model M of the node with a measured stress peak value of the sensor to obtain an error value > a set threshold value , and identifying the node as a structural abnormal node N.

[0013] a design feedback module for automatically adjusting a size of a connecting component, an anchoring length of a reinforcing steel bar of the node, or a grade of concrete of the node according to construction parameters of the structural abnormal node N in the BIM model to generate an optimized parameter set P1.

[0014] a model updating module for synchronously updating the optimized parameter set P1 to the BIM model to form a BIM model B1 after optimization of the structure to realize closed-loop correction of the design parameters of the node.

[0015] a closed-loop control module for repeating the monitoring module to the model updating module to re-monitor and feedback the node in B1 until the stress of all key nodes satisfies a safety tolerance range, and completes stress optimization of all nodes of the structure.

[0016] Preferably, the BIM modeling module further comprises:

[0017] a component geometry information extraction module for extracting component geometry information associated with a target node from the BIM model, including a cross-sectional width, a height, a thickness, a material type, and a spatial arrangement direction of the component;

[0018] the spatial arrangement direction is obtained by calculation of a spatial vector composed of coordinates of start and end points of the component, and the material type includes ordinary reinforced concrete, prestressed concrete, and steel-concrete composite structure.

[0019] Preferably, the extraction of the frequency domain characteristic parameter set F0 of the node under micro-amplitude load and environmental disturbance comprises the following steps:

[0020] ​​​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.

[0021] 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.

[0022] 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.

[0023] The frequency domain response index set is used as the frequency domain feature parameter set F0.

[0024] Preferably, the construction of the nodal stress evolution prediction model M based on the finite element modified model includes the following steps:

[0025] A nodal stress evolution prediction model M is established using the initial parameter set P0 of the nodal mechanical properties as input.

[0026] A frequency domain characteristic parameter set F0 is introduced to correct the frequency response of the finite element simulation results;

[0027] 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.

[0028] 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:

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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:

[0034] The maximum stress value in the stress time sequence is calculated as the predicted stress peak value of the node ;

[0035] The maximum value in the stress time sequence extracted from the real-time monitoring data of the corresponding node in the same time interval is calculated as the measured stress peak value of the sensor ;

[0036] The predicted stress peak value and the measured stress peak value are subjected to absolute difference calculation to obtain an error value , which is compared with a set error threshold value ;

[0037] If is greater than the set threshold value , the node is determined as a structural abnormal node N, and its spatial coordinate position is marked.

[0038] Preferably, the generation of the optimized parameter set P1 comprises the following steps:

[0039] The construction parameters of the structural abnormal node N in the building information modeling model are extracted, including the connected member cross-sectional size, the node steel reinforcement anchoring length, and the concrete strength grade;

[0040] According to the error value between the predicted stress peak value and the measured stress peak value, a corresponding construction parameter adjustment strategy is matched to establish a parameter optimization mapping relationship;

[0041] Through a limited number of iterations, the member cross-sectional size is preferentially adjusted, and if the stress recovery does not satisfy the safety threshold value, the steel reinforcement anchoring length or the concrete strength grade is sequentially increased;

[0042] The parameter adjustment amplitude is determined with the minimum structural modification amount as the optimization target, and the updated construction parameters are packaged to generate the optimized parameter set P1.

[0043] Preferably, the simulation prediction module further comprises a load coupling mapping function Fcouple(t) for phase alignment and amplitude superposition of various loads according to time.

[0044] In the above technical solution, the present application provides the following technical effects and advantages:

[0045] 1. The application realizes dynamic perception and response optimization of the stress state of the fabricated building node by fusing building information modeling technology, real-time sensing monitoring, frequency domain feature extraction and finite element simulation prediction. Compared with the traditional method which only relies on static design and post-detection, the application can quantitatively evaluate the stress state of the key node in the structure operation process, identify the abnormal parts of the structure in time, and automatically feed back to the design model for closed-loop correction, which significantly improves the structural safety and design adaptability of the fabricated building in the construction stage and operation stage.

[0046] 2. The node stress evolution prediction model constructed in the application combines measured frequency spectrum features and finite element correction simulation, improves the prediction accuracy and the authenticity of structure behavior simulation, and avoids the deviation problem between traditional models and actual structure responses. At the same time, through parameter optimization mapping and multi-round closed-loop iteration mechanism, the minimum adjustment of key parameters such as component size, steel anchoring length and concrete strength grade can be realized without destroying the original design logic of the building, which guarantees the safety of the structure while taking into account the construction cost and material utilization efficiency, and has good engineering practical value. BRIEF DESCRIPTION OF DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art based on these drawings.

[0048] Figure 1 The system module flowchart of the present application. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical scheme and advantages of the embodiments of the present application more clear, the technical scheme in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0050] Embodiment, please refer to Figure 1 The BIM-based fabricated building node stress monitoring and design feedback system described in the present embodiment comprises:

[0051] A BIM modeling module acquires a BIM model of a target fabricated building structure, extracts component information, node connection mode and load path data contained therein, and constructs an initial parameter set P0 of node mechanical properties.

[0052] A BIM model of a target fabricated building structure is acquired, and component geometric information associated with a target node is extracted from the BIM model. The geometric information includes: a cross-sectional dimension of the component, a material type used by the component, and an arrangement direction of the component in space. Specifically, the cross-sectional dimension includes a cross-sectional width, a height, and a thickness, the material type can be ordinary reinforced concrete, prestressed concrete, or steel-concrete composite material, etc., and is obtained through a component attribute annotation field in the building information modeling model; and the arrangement direction is calculated through a vector direction formed by spatial coordinate points of the component, and is used for subsequent judgment of a stress direction relationship of the node.

[0053] A connection mode between components at the target node is identified, including three types of hinged connection, semi-rigid connection, and rigid connection. Based on connection node information fields between components in the building information modeling model, and in combination with construction drawings and component connection construction annotations, the connection type is determined through a table lookup method, and connection stiffness parameters are constructed based on different connection forms. The connection stiffness parameters are represented in the form of two-dimensional or three-dimensional stiffness matrices, wherein the rigid connection corresponds to a high stiffness value, the hinged connection corresponds to a rotation stiffness value close to zero, and the semi-rigid connection corresponds to a value in an intermediate interval, and all stiffness parameters are assigned according to a typical connection stiffness value empirical formula provided in the Load Code for Building Structures.

[0054] Based on load path data in the building information modeling model, main load types borne by the node are extracted. The load types include dead load, live load, and wind load, and are specifically obtained by analyzing a load definition table in the building information modeling model. By tracking the load paths acting on the components around the node one by one, and projecting the forces to the center position of the node, the size and direction of the resultant force borne by the node are calculated using the vector superposition method. The size of the resultant force is a combined value of each load component along the direction of the node, and the direction of the resultant force is a spatial direction angle defined by the direction cosine of the vector.

[0055] The extracted component geometric information, node connection stiffness parameters, and node stress information are uniformly normalized. The normalization method adopts a linear normalization method, that is, each parameter value is converted into a dimensionless value in the interval [0, 1] to eliminate the influence of inconsistent dimensions on the subsequent modeling accuracy. The specific processing steps are: assuming that the original value of the parameter is X, the maximum value is Xmax, and the minimum value is Xmin, then the normalized value is (X-Xmin) / (Xmax-Xmin). After processing, the parameters are sequentially arranged according to the spatial position of the node to form an initial parameter set P0 of the mechanical properties of the node, which is used for subsequent node stress feature extraction and evolution prediction model construction.

[0056] A monitoring module is configured to monitor the three-dimensional stress of the preset key node in real time based on the spatial position of the node in the BIM model, and obtain a node stress original data sequence E0.

[0057] The position coordinates of the key nodes are determined based on spatial position information of the nodes in the building information modeling model. The spatial position information is determined by three-dimensional coordinate parameters of the nodes in the building information modeling model, including lateral coordinates, longitudinal coordinates and vertical coordinates of the nodes in a global coordinate system of the building. The key nodes are selected according to the stress importance of the nodes in the structure, specifically: the nodes located at the intersection of vertical load-bearing paths, the positions of sudden changes in member stiffness or the connection interfaces of fabricated members are marked as preset key nodes, and a unique identification of the nodes is established in the building information modeling model.

[0058] A three-dimensional stress sensor is arranged at each preset key node for collecting stress responses of the node in three orthogonal directions. The three-dimensional stress sensor is fixedly installed on a surface of a member near the node or a core area of the node, and an installation direction of the three-dimensional stress sensor is consistent with a coordinate axis direction in the building information modeling model, so as to ensure a corresponding relationship of stress data in a spatial direction. The stress collected by the sensor includes normal stress components in three orthogonal directions, and a sampling frequency is set according to a dynamic response characteristic of the building structure, so that the stress fluctuation caused by a micro-amplitude load change can be covered.

[0059] The original signals collected by the three-dimensional stress sensor are subjected to synchronous collection and time marking processing. Specifically, the stress sampling values in three directions are uniformly time-stamped at the same sampling time, so as to form three-dimensional stress state data of the node at the time. Through continuous sampling, a stress data set arranged in time sequence is formed in a preset monitoring time period.

[0060] The continuously collected three-dimensional stress state data are classified according to the unique identification of the node, and are stored in a time sequence form to construct a node stress original data sequence E0. The node stress original data sequence E0 is composed of a plurality of three-dimensional stress vectors arranged in time sequence, and each three-dimensional stress vector corresponds to stress values of the node in three spatial directions at a sampling time. The node stress original data sequence E0 serves as basic input data for subsequent frequency domain feature extraction and node stress evolution analysis.

[0061] The signal processing module performs Fourier transform and wavelet decomposition on the node stress original data sequence E0 to extract a frequency domain feature parameter set F0 of the node under micro-amplitude load and environmental disturbance.

[0062] The original data sequence E0 of the node stress is subjected to fast Fourier transform processing to convert the three-dimensional time-domain stress signal into a frequency-domain signal spectrum. The fast Fourier transform is implemented by using a discrete Fourier transform algorithm, and the calculation steps include: inputting the time-domain stress sequence in each direction into a transform function to obtain a frequency-amplitude mapping relationship and form a frequency spectrum function. The frequency range is determined by the sampling frequency, and the amplitude represents the stress intensity corresponding to the frequency component, forming a stress amplitude distribution spectrum of the node at each frequency.

[0063] The frequency-domain signal spectrum is subjected to continuous wavelet transform to realize energy distribution analysis at different time scales. The continuous wavelet transform selects a Morlet wavelet with good localization characteristics as a mother wavelet function, and sets the scale range from to , covering the high-frequency to low-frequency interval. The wavelet transform process obtains a wavelet coefficient matrix at different scales through convolution operation, and then extracts the high-frequency disturbance component and low-frequency load response change characteristics in the corresponding time period, ensuring the time-frequency joint analysis capability of the non-stationary stress signal.

[0064] Subsequently, on the basis of the obtained wavelet transform result, the frequency-domain response characteristic indicators of the node stress signal are calculated. The indicators include:

[0065] The main frequency value, i.e. the frequency component corresponding to the maximum energy, represents the main response frequency of the node;

[0066] The frequency energy concentration degree, i.e. the proportion of the main frequency energy to the total frequency energy, is used to reflect the concentration of the signal spectrum;

[0067] The spectrum drift rate, i.e. the change speed of the main frequency position in different monitoring periods, is defined as the difference between the main frequency values of adjacent two periods divided by the time interval, with the unit of hertz per second.

[0068] Finally, the main frequency value, the frequency energy concentration degree and the spectrum drift rate are combined to form a frequency-domain response indicator set, which is input into the subsequent node stress evolution prediction model as a frequency-domain characteristic parameter set F0, for identifying abnormal change trends and load response characteristics of the node stress state.

[0069] The simulation prediction module combines the initial parameter set P0 of the node mechanical properties and the frequency-domain characteristic parameter set F0 to construct a node stress evolution prediction model M based on a modified finite element model, to simulate the stress response path of the node under multiple load combinations. The node stress evolution prediction model M is: a finite element simulation model of the node is established by taking the initial parameter set P0 of the node mechanical properties as input, the frequency response of the finite element simulation model is corrected by introducing the frequency-domain characteristic parameter set F0, and a numerical analysis model of the stress time sequence of the node under the action of multiple load combinations is calculated based on the corrected finite element simulation model by using a time step integration method.

[0070] Firstly, according to the component cross-section size, material parameters and connection stiffness information in the initial parameter set P0 of the node mechanical properties, a three-dimensional finite element simulation model of the node is established. The model selects solid elements for meshing, the element type is eight-node hexahedral element, and the element edge length is set to one-tenth of the smallest cross-sectional size of the component to ensure simulation accuracy. The material model selects the constitutive relationship according to the type of the material used by the node component:

[0071] For reinforced concrete components, a multi-linear elastic-plastic constitutive model is used, and the yield strength, elastic modulus and Poisson's ratio are assigned according to the value table provided in the national standard "Code for Design of Concrete Structures" GB 50010-2010; for steel components, an ideal elastic-plastic model is used, and the material yield point is valued at 345 megapascals, the elastic modulus is pascal, and the Poisson's ratio is 0.3.

[0072] In terms of boundary condition setting, according to the spatial topological relationship of the components connected by the node in the building information modeling model, the fixed constraints or free boundaries of each end component are set. For example, when the node is connected to the bottom of the column, its vertical direction is set as a fixed support; when connected to the end of the beam, the horizontal end is set as a sliding support or a semi-rigid constraint, and the stiffness value is taken from the corresponding component in the connection stiffness matrix parameters.

[0073] In the preliminary simulation calculation, the standard constant load (such as self-weight and component constant load) is applied, and the initial stress distribution result of the node is obtained as the basic response data of the model.

[0074] After completing the preliminary finite element simulation, the main frequency value, frequency energy concentration degree and frequency spectrum drift rate contained in the frequency domain feature parameter set F0 are introduced as the correction benchmark of the measured frequency spectrum. Let the simulation main frequency of the node be fsim and the measured main frequency be fexp, then define the difference between the simulation main frequency and the measured main frequency , if Δf exceeds the threshold value of 0.5 hertz, the frequency response correction process is started.

[0075] The correction method is to adjust the model parameters through the least squares iterative algorithm, including the following two directions:

[0076] Adjust the load input frequency and waveform function: adjust the original constant load input to a periodic excitation function that changes with time, for example, replace the original constant load with a sinusoidal load , where A is the amplitude, f is the adjusted frequency, and t represents time.

[0077] Adjust the node connection stiffness matrix: under the premise of maintaining the mechanical consistency of the overall structure, adjust the rotational stiffness value Kr in the connection stiffness matrix, and the optimization goal is to match the simulation frequency spectrum and the measured frequency spectrum in the main frequency and energy distribution.

[0078] The iteration stopping condition is that the difference between the simulation frequency and the measured frequency Δf is less than 0.5 Hz, and the error of the spectral energy concentration degree is less than 5%. The time history data of various loads borne by the nodes are extracted from the building structure design model, including: dead load (such as floor load, dead weight), which is modeled as a constant function or a linear function that changes slowly over time; live load (such as crowd load, construction equipment), which is modeled as a step function with intermittent periodicity; wind load, which is modeled as a sine superposition function with random disturbance, and its expression is where Ai is the amplitude of the wind component, fi is the frequency component, is the phase difference.

[0079] The above load time function is projected according to the directional component of the node to combine into a time-varying vector input sequence of the node 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.

[0080] It should be noted that the least squares iteration algorithm takes the difference between the predicted simulation frequency and the measured frequency Δf as the objective function, and uses the gradient descent method for iteration update. In each iteration, by adjusting the connection stiffness Kr and the excitation frequency f, the loss function is minimized. The algorithm convergence criterion is that the change of the loss function between the current round and the previous round is less than 0.01 Hz squared, or the iteration number reaches 100 times.

[0081] The Newmark-β integration method is used to dynamically solve the finite element model under the above time-varying load input. The Newmark parameters are selected as β = 0.25 and γ = 0.5 to ensure numerical stability and solution accuracy. The calculation steps include:

[0082] Calculate the total stiffness matrix and mass matrix of the structure at each time step;

[0083] Apply the load input at the current time step and solve the stress components of the node in x, y, and z directions;

[0084] Classify the stress values at each time step by node number and time sequence, and output as a stress time sequence S(t), where each item is a three-dimensional vector: .

[0085] It should be noted that the solution of the finite element model can be realized in a commercial structural analysis platform that supports multi-physical field dynamic solution (such as ANSYS or ABAQUS), or can be realized through secondary development based on an open-source finite element solver (such as OpenSees) by embedding a Python control script.

[0086] A load coupling mapping function Fcouple(t) is constructed for phase alignment and amplitude superposition of various loads by time. The function is defined as: ; wherein: are time-varying functions of dead load, live load and wind load, respectively; is a load direction weighting coefficient; is a phase offset, which is set according to the time delay relationship between loads. The direction weighting coefficient is calculated according to the projection ratio of each type of load in three-dimensional direction. The specific method is: the standard distribution of each type of load in x, y, z three directions is calculated respectively, and the module length is normalized to be the weighting coefficient. For example, the action angle of wind load is , then .

[0087] The input load superimposed by the above function is more consistent with the coupling effect of various loads in the actual building operation process, and improves the physical consistency of simulation prediction.

[0088] After completing the stress time series calculation, the following key evolution indicators are extracted:

[0089] Stress peak point : find the stress vector with the largest modulus in the complete time series, which represents the maximum stress condition of the node;

[0090] Response acceleration change trend a(t): the change rate in unit time is calculated by the first-order derivative of the stress value, and the second-order derivative is taken to represent the change acceleration, reflecting the influence of load mutation on the node;

[0091] Stress recovery rate r(t): between two consecutive load peaks, the stress decline rate is calculated to judge the elastic recovery ability of the node structure. The (stress peak value) refers to the maximum stress value in the stress time series of the node in a load cycle or in a load event. It usually represents the maximum stress state of the node under combined load, reflecting its critical stress capacity. The (stress valley value) refers to the minimum stress value that appears in the natural decay process of the node stress after . It reflects the ability of the structure to release or relieve stress after the node experiences a load peak. Δt (time interval) represents the time difference between the moment when the stress peak appears and the moment when the stress valley appears.

[0092] The above three indicators are taken as the core output characteristics of the stress evolution path, providing quantitative basis for subsequent anomaly identification and design feedback.

[0093] A diagnostic decision module is used to compare the predicted stress peak value with the measured stress peak value If > threshold value , the node is identified as a structural abnormal node N.

[0094] First, the stress time series of the target node is extracted from the output of the node stress evolution prediction model M. This time series is a sequence of predicted data output by the model at fixed time steps Δt within the simulation period [0, T]. Assuming that the total simulation time T = 60 seconds and the time step Δt = 0.1 seconds, there are 600 sets of three-dimensional stress data output. Each set of data is denoted as: where i = 1, 2,..., 600; the modulus (Euclidean norm) of each set of three-dimensional stress components is calculated as: ; among all time points, the maximum modulus is taken as the predicted stress peak value of the node: ; the unit is megapascal (MPa).

[0095] Then, the measured stress time series within the same time interval [0, T] 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).

[0096] The measured three-dimensional stress components at each time point are denoted as: where i = 1, 2,..., 600; similarly, the modulus of each set of measured stress is calculated as: ; traversing all time series data, the measured stress peak value is obtained: ; the unit is megapascal (MPa).

[0097] The predicted stress peak value is compared with the measured stress peak value , and the absolute difference is calculated, defining the error as the modulus difference between the two: ; this error is used to measure the deviation between the prediction model and the actual monitoring data in terms of stress peak value. The error unit is also megapascal (MPa) and is a positive number. A judgment threshold value is set to determine whether the node has abnormal structural behavior. The threshold value is set according to the following criteria:

[0098]

[0099] The present application preferably sets Set to 6 megapascal (MPa) as a criterion, in the subsequent implementation can be adjusted according to the node category.

[0100] The error value Is compared with the threshold value If , it is determined that the node prediction and the measured value are basically consistent, and it is considered that the node is in a normal stress state; if , it indicates that the simulation model cannot accurately capture the actual response of the node, and there is a significant deviation in the actual stress of the node, indicating that the structure may have one of the following situations:

[0101] The connection stiffness of the node fails or varies; the load input is incomplete (such as the actual load exceeding the design value); the node has initial cracks or hidden damage; the model parameters do not fully reflect the true structure behavior. Therefore, the node is marked as an abnormal node N of the structure.

[0102] The spatial position information of the abnormal node N is extracted from the building information modeling model, including 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; zN is the vertical coordinate of the node (i.e. floor height).

[0103] The coordinate information is stored in the BIM model in the form of node number and attribute label, and can be used as the positioning basis for subsequent optimization design adjustment.

[0104] The design feedback module automatically adjusts the connection component size, node reinforcement anchoring length, or node concrete grade of the abnormal node N of the structure according to its construction parameters in the BIM model to generate an optimized parameter set P1.

[0105] First, locate the position of the abnormal node N of the structure in the building information modeling model, and obtain the construction information associated with the node through the unique identifier of the node, including:

[0106] Connection component cross-sectional size: extract the cross-sectional geometric parameters of all components connected to the node N, including the cross-sectional width, height, and thickness of beam, column, plate, and other components, in millimeters;

[0107] Node reinforcement anchoring length: read the node reinforcement construction information to obtain the main reinforcement anchoring length La, in millimeters, which is extracted from the reinforcement family definition field in the design model;

[0108] Node concrete strength grade: read the concrete material properties of the node's pouring section to obtain the strength grade identifier (such as C30, C40), and convert it to a numerical strength value (e.g. C30 is 30 megapascal).

[0109] All parameters are saved in a unified data format for subsequent optimization algorithm calls.

[0110] The structural parameters obtained in the previous step are associated with the stress peak error obtained in the anomaly identification stage to construct a stress error-parameter adjustment mapping function for matching the corresponding optimization adjustment strategy. The function structure is as follows:

[0111] When the stress error is less than 5 MPa, it is recommended to adjust the cross-sectional size of the member, and the optimization priority weight is set to 1; When the stress error is 5 MPa to 10 MPa, it is recommended to increase the anchoring length of the steel bar on the basis of adjusting the cross-sectional size, and the weight is set to 0.8;

[0112] When the stress error is greater than 10 MPa, it is recommended to increase the concrete strength grade first, and if it is not enough, the other two adjustments are added, and the weights are set to 0.6 (anchoring) and 0.4 (cross-sectional size), respectively.

[0113]

[0114] The above mapping relationship is established based on an empirical regression model, which is used for parameter priority sorting and range constraint in the optimization calculation process.

[0115] It should be noted that the stress error-parameter adjustment mapping function is constructed by regression analysis of the historical monitoring data of 30 typical engineering cases. Taking as the independent variable and the parameter adjustment amount as the dependent variable, a segmented optimization curve is established by polynomial fitting. The curve is stored as a two-dimensional table and integrated into the model control script for easy automatic calling by the optimization algorithm.

[0116] A fixed step iterative method is used to optimize the node construction parameters, and after each adjustment, the node stress evolution prediction model M of the finite element correction model is executed to recalculate the predicted stress peak .

[0117] The adjustment strategy is: member cross-sectional size adjustment: based on the original size, the width and height are enlarged by 20 mm each step, and the maximum is not more than 1.5 times the original size; steel bar anchoring length adjustment: increase by 50 mm each iteration, and the maximum is not more than 1.3 times the design anchoring length; concrete strength grade adjustment: sequentially increase according to the national standard commonly used grade sequence (such as C30→C35→C40→C45), and the highest is C50. In each iteration, the predicted stress peak is re-simulated, and the corrected error is calculated to determine whether the following termination conditions are met:

[0118] , where ​​​​The safety tolerance threshold is preferably 4 MPa.

[0119] 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 iteration number (such as 10 times) is reached, the optimization is terminated and marked as design overrun.

[0120] The final construction parameters obtained by the above iteration are output as the structure optimization parameter set P1. The parameter set includes the following fields:

[0121] Member section size: [width, height, thickness] in millimeters; reinforcement anchorage length: in millimeters; concrete strength grade: in MPa. The optimization parameter set P1 is used as the design update data for the structure anomaly node N and is transmitted to the construction parameter table of the corresponding node in the building information modeling model to complete model updating and closed-loop design feedback input preparation.

[0122] The model updating module synchronously updates the optimization parameter set P1 to the BIM model to form the structure-optimized BIM model B1, realizing closed-loop correction of node design parameters.

[0123] First, the spatial coordinate information of the structure anomaly node N is accurately positioned in the building information modeling model. The coordinate information includes three directional components of the node in the building coordinate system, denoted as (xn, yn, zn), corresponding to the east-west direction, the north-south direction, and the vertical height, respectively.

[0124] In the building information modeling model, the spatial query method is used to call the coordinate indexing mechanism to traverse and compare the geometric center coordinates of all members in the member instance set, select the member objects with a matching error of no more than 10 millimeters with the node coordinates, and judge whether there is a geometric connection relationship between the node and the member objects. After successful matching, all member numbers, member types, member families, and parameter field tables connected to the node are obtained to prepare for parameter replacement operations.

[0125] The optimization parameter set P1 is the structure parameter set generated after the iterative design optimization of the structure anomaly node, mainly including:

[0126] Optimized connection member section size: [width B1, unit: millimeters; height H1, unit: millimeters];

[0127] Optimized reinforcement anchorage length La1, unit: millimeters;

[0128] Optimized concrete strength grade C1, unit: MPa.

[0129] In this step, first open the parameter editing interface of the building information modeling model and enter the attribute table structure of the target member.

[0130] For the component section size fields, such as "Section Width" and "Section Height", write B1 and H1 respectively;

[0131] For the "Anchor Length" field in the reinforcement family parameter table, write La1;

[0132] For the "Concrete Grade" field in the material attribute table of the structure section to which the node belongs, write C1, and the data type is a numerical strength value (such as C40 written as 40).

[0133] Parameter updating is achieved through component family instantiation overload mechanism, ensuring that new parameters take effect immediately and are applied to all construction expressions associated with node N in the model.

[0134] After parameter replacement is completed, to avoid model conflicts and geometric inconsistency problems caused by component size changes, perform geometric consistency checking process. This process mainly includes the following two detections:

[0135] Component geometric boundary overlap detection: check whether the spatial distance between the updated component bounding box and the adjacent component is less than the set minimum clearance threshold, which is set to 20mm;

[0136] Component node alignment check: ensure that the component offset caused by section size change does not affect the original alignment relationship of the node, and adjust the position of the connecting component with a deviation angle greater than 2 degrees.

[0137] If collision conflicts or spatial mismatches are found, record the specific component number, conflict type and location in the log for engineers to manually confirm whether to accept the optimized model.

[0138] After verification, save the updated building information modeling model, and name the model version as "structure optimized model B1". At the same time, in the node attribute table of the building information modeling model, add a "optimization status" flag field for each optimized node, and write the following contents:

[0139] Optimization identification: optimized;

[0140] Optimization source: stress deviation feedback;

[0141] Optimization timestamp: YYYY-MM-DD HH:MM;

[0142] Optimization parameter summary: list the updated section size, anchor length and concrete grade.

[0143] The final structure-optimized BIM model B1 not only reflects the latest structure design state, but also provides a data basis for subsequent monitoring closed-loop and model version management, realizing the closed-loop correction and digital tracking of node design parameters.

[0144] Through the above method, the building information modeling model realizes real-time synchronous updating of structure performance optimization results, effectively opens up the feedback channel between structure safety monitoring results and building design data, and provides support for the whole life cycle design and management of fabricated buildings.

[0145] The closed-loop control module, the repeated monitoring module to the model updating module, re-monitors and feeds back the nodes in B1 until the stress of all key nodes in the structure meets the safety tolerance range. Satisfy the safety tolerance range, complete the stress optimization of all structure nodes.

[0146] First, take B1 as the basic model, and re-identify all the key nodes set in the model. The key nodes are the node set marked as safety control points in the structure design stage, and the definition basis includes: structure stress path intersection, stiffness mutation area, vertical connection interface, important function area, etc. The key node set is denoted as {N1, N2,..., Ni,..., Nn}, where n is the total number of key nodes.

[0147] For each node Ni, extract its three-dimensional coordinates and construction information in the B1 model, and prepare to enter the re-monitoring process.

[0148] Based on the position of the key node, redeploy or activate the three-dimensional stress sensor on the building site, continuously monitor each node Ni for a certain period, and the monitoring period is the same as the initial monitoring (preferably 60 seconds, sampling frequency 10 Hz), to obtain the measured stress time series of the node Ni .

[0149] At the same time, call the updated node local finite element simulation model to execute stress evolution prediction on the same node Ni, obtain the corresponding predicted stress time series , and extract the measured stress peak value ; the predicted stress peak value by modulus calculation method. Calculate the node stress error: .

[0150] Set a unified safety tolerance threshold to measure whether the node prediction-measured error is acceptable. The threshold is set according to the structure level, design specification and application scenario, and the preferred value is 4 megapascals (MPa). Error judgment is performed on each key node Ni:

[0151] If , it is marked as "stress meets tolerance";

[0152] If , mark as "stress not meet tolerance" and enter the re-optimization process.

[0153] The results are summarized as the full-structure key node decision matrix M, where: If all values in the matrix are less than or equal to , trigger the closed-loop termination condition.

[0154] For the node set in M that is determined as "stress not meet tolerance", re-execute the following steps according to the optimization logic described above: parameter extraction; parameter mapping and strategy matching; iterative optimization of member section, anchorage length or concrete grade; optimization parameter set generation; corresponding parameter replacement in the building information modeling model and B1 model update. After the update, the model version is iterated to B2, and the update log is recorded, including node number, optimization parameters before and after optimization, optimization round, error change.

[0155] After completing the updated building information modeling model B2, a new round of stress monitoring and error evaluation is performed. The termination condition is: , both meet .

[0156] When the above conditions are met, it is considered that the structure stress state has met the design safety requirements, and the closed-loop process is terminated. Model version B2 is the final structure stress optimization building information modeling model.

[0157] If there are still nodes that do not meet the tolerance, continue to iterate until the termination condition is met or the maximum allowed optimization round (preferably up to 5 rounds) is reached.

[0158] Finally, the optimization parameter set, stress response data and error change trend of all key nodes are written into the structure performance log to form a complete structure closed-loop optimization record. Model B2 is imported into the construction stage building information modeling database as the structure design result and supports the structure operation and maintenance stage call, achieving the whole-cycle closed-loop control goal from design-monitoring-optimization-redesign.

[0159] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any skilled person in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present 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 nodal stress data sequence E0 to extract the frequency domain feature parameter set F0 of the node under micro-load and environmental disturbance. Specifically, it includes: forming a frequency domain response index set by combining the main frequency value, frequency energy concentration and spectral drift rate of the nodal stress signal, and using it as the frequency domain feature parameter set F0. 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 the finite element modified model. This model simulates the stress response path of the nodes under multiple load combinations, specifically including: Using the initial parameter set P0 of the nodal mechanical properties as input, a nodal stress evolution prediction model M is established; 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 model M, a multi-load coupled response path of nodal stress changing with time is constructed, which further includes: based on the corrected nodal stress evolution prediction model M, importing the time history data of dead load, live load and wind load on the node to construct a multi-load time-varying input sequence; using the time-step integration method to dynamically solve the nodal stress evolution prediction model M, calculating the stress response values ​​of the node in three dimensions at each discrete time point, forming a stress time sequence; using the load coupling mapping function to superimpose the phase and amplitude of different load components to simulate the nodal composite effect under actual load combination conditions; extracting the stress peak point, response acceleration change trend and stress recovery rate between adjacent loads in the stress time sequence as the feature output of the multi-load coupled response path; The diagnostic judgment module dynamically compares the predicted stress peak value σm output by the nodal stress evolution prediction model M with the measured stress peak value σe from the sensor to obtain the error value. ,in ,like If an error threshold δ is set, then 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 repeated monitoring module, and the model update module re-monitor and provide feedback on the nodes in B1 until the Δσ of all key nodes meets the safety tolerance range, thus completing the stress optimization of all structural nodes.

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 dynamic comparison of the predicted stress peak value σm output by the nodal stress evolution prediction model M with the measured stress peak value σe by the sensor includes the following steps: Extract the predicted stress time sequence output by the nodal stress evolution prediction model M, calculate the maximum stress value in it, and take it as the peak value of the nodal predicted stress σm; 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 measured stress σe. The error value is obtained by calculating the absolute difference between the predicted peak stress σm and the measured peak stress σe. And compare it with the set error threshold δ; If Δσ is greater than the set error threshold δ, the node is identified as a structurally abnormal node N, and its spatial coordinates are marked.

5. 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 Δσ between the predicted peak stress and the measured peak stress, a corresponding structural parameter adjustment strategy is matched, and a parameter optimization mapping relationship is established. 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.

6. 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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