Full-cycle management method for steel reinforced concrete components based on industrial big data

Abstract

The present invention discloses a full-cycle management method for steel-concrete composite members based on industrial big data, including: establishing a member model through BIM modeling software, screening the buckling-prone areas by combining finite element buckling analysis, setting buckling monitoring points on the surface of the analysis plate and setting plate monitoring points at the splicing edges; extracting the model coordinate data to generate processing parameters to guide the cutting of the plate and the marking of the monitoring points; obtaining the actual coordinates of each monitoring point in the steel member by laser scanning and comparing them with the model coordinates, and triggering rework if the error exceeds the limit; constructing a neural network model, inputting various parameters related to the buckling of the analysis plate to obtain the predicted out-of-plane displacement value of the buckling monitoring point and determining compliance; collecting the final deformation data after on-site construction for model iteration and optimization. The present invention solves the problems of low efficiency, error accumulation and lack of full-cycle closed-loop management in the traditional method, significantly improves the processing accuracy and structural safety of steel-concrete composite members, and reduces the hardware cost and detection time.

Description

Technical Field

[0001] The present invention relates to the field of intelligent construction of buildings. More specifically, the present invention relates to a full-cycle management method for steel reinforced concrete components based on industrial big data. Background Art

[0002] A steel reinforced concrete component (SRC component) is a composite structure formed by using a steel section as the skeleton and outer layer, with concrete wrapped or embedded. In structures such as steel plate shear walls and precast steel columns, the structural form of steel plates wrapped with concrete has the advantages of both the flexural capacity of steel and the compressive performance of concrete, and the excellent mechanical property that the outer steel plate can work together with the concrete, and is widely used in key parts such as the core tube of high-rise buildings and long-span bridges.

[0003] The outer steel plate of such components needs to be processed and formed through processes such as cutting and welding, and be closely combined with the concrete to achieve collaborative stress. However, during the processing stage, due to factors such as cutting errors and welding thermal deformation, the steel plate is prone to local initial displacement; during the on-site welding stage, uneven welding heat input or residual stress release will cause the steel plate to deviate from the preset position. The above initial displacement causes gaps or uneven stress distribution on the contact surface between the steel plate and the concrete. During the subsequent stress process, due to the initial deformation of the steel plate, buckling occurs prematurely, resulting in the steel plate being unable to work together with the concrete. Since the interface performance between the steel section and the concrete directly affects the overall structural bearing capacity, the accumulation of initial displacement may lead to the bearing capacity of the steel reinforced concrete structure being much lower than the design target, reducing the structural safety and durability. The difficult part in controlling and solving this problem is that it is difficult to quantify and check the deformation in real time during the processing and welding of steel section components. The traditional manual measurement and quality inspection method relies on experience adjustment and lacks an accurate prediction and instant feedback mechanism based on a theoretical model.

[0004] In existing building management, industrial big data and building information model technology have been widely used in construction progress management, resource allocation and other links. For the existing technology of steel reinforced concrete structures, most focus on the flow management or model visualization display in the construction stage, and fail to deeply intervene in the whole life cycle of steel section components, especially quality management. Specifically, although the component model established by using BIM modeling software in the design stage can export geometric data and can form a dynamic association with processing parameters; however, the processing error data in actual processing is not fed back to the design model to optimize subsequent production; the correlation between on-site welding deformation, concrete pouring parameters and steel section deformation in the construction stage has not been systematically analyzed.

[0005] In the quality monitoring of steel reinforced concrete structures, point cloud scanning technology is often used to obtain the three-dimensional coordinates of the component surface, and identify processing errors by comparing with the component model established by BIM modeling software. However, the point cloud data volume is huge, data processing depends on high-performance computing devices, and the coordinate matching algorithm is complex, resulting in a calculation time of up to several hours, which cannot meet the real-time monitoring requirements during processing or construction. The lag of the existing point cloud technology makes it difficult to effectively integrate into the actual production process.

[0006] Therefore, there is an urgent need to propose a new full-cycle management method for steel reinforced concrete components, which can effectively manage the entire process of steel reinforced concrete design, processing, and on-site construction. Summary of the Invention

[0007] An object of the present invention is to provide a full-cycle management method for steel reinforced concrete components based on industrial big data, which realizes the quality control of the entire process of steel reinforced concrete components by connecting data streams through various links.

[0008] To achieve these objects and other advantages of the present invention, according to one aspect of the present invention, there is provided a full-cycle management method for steel reinforced concrete components based on industrial big data, including the following steps:

[0009] S1. Establish a component model in BIM modeling software, select each plate in contact with concrete on each side of the component model as the analysis plate, import it into finite element calculation software for buckling analysis, and select several monitoring points on the non-concrete contact surface of the analysis plate. The monitoring points include buckling monitoring points and plate monitoring points;

[0010] S2. Extract the component contour data and monitoring point coordinates from the component model to form processing parameters, and perform cutting of steel plate profiles and marking of monitoring points based on the processing parameters to complete the processing of steel components;

[0011] S3. After the steel component is processed, perform scanning to obtain the actual coordinates of each monitoring point on the steel component and compare them with the model coordinates of each monitoring point in the component model. If the error between the actual coordinates and the model coordinates exceeds the processing threshold, reprocess the steel component;

[0012] S4. Establish a prediction model for the out-of-plane displacement of buckling monitoring points, input the initial out-of-plane displacement values of buckling monitoring points, plate geometric parameters, plate welding parameters, and in-filled concrete parameters of each analysis plate, and obtain the predicted out-of-plane displacement values of buckling monitoring points after the concrete pouring is completed. If the predicted out-of-plane displacement values of buckling monitoring points exceed the allowable buckling deformation amount, reprocess the steel component, and repeat steps S3 to S4 until each analysis plate meets the requirements;

[0013] S5. The steel section members are installed, welded and filled with internal concrete on site. After the concrete begins to set, the final out-of-plane displacement values of the buckling monitoring points on the steel section members are collected by a laser scanner. The final out-of-plane displacement values of the buckling monitoring points, the corresponding plate welding parameters and the parameters of the filled concrete are fed back to the out-of-plane displacement prediction model of the buckling monitoring points for training.

[0014] Preferably, step S1 includes the following steps:

[0015] S11. Establish a three-dimensional component model in the BIM modeling software. Each plate in the component model needs to be modeled independently. After model checking, select the plate that is in contact with concrete on one side and has a non-concrete contact surface on the other side as the analysis plate and transfer it to the finite element calculation software as a finite element model. Assign material properties to each calculation object in the finite element calculation software;

[0016] S12. Set constraint conditions for the analysis plate in the finite element calculation software, apply design loads and perform non-linear buckling analysis to obtain the easily buckling areas in the analysis plate. Select at least one point on the non-concrete contact surface of the plate in the easily buckling area as the buckling monitoring point;

[0017] S13. Mark the buckling monitoring points on each analysis plate in the component model and set a number of plate monitoring points at the splicing edges of each analysis plate and other plates.

[0018] Preferably, step S12 includes the following steps:

[0019] S121. Take the state of the steel section members after all on-site connection measures are completed according to the design requirements and before concrete is poured on site as the basis for the constraint conditions at the edges of each analysis plate. Apply constraint conditions to the edges of the analysis plate in the finite element calculation software. The constraint conditions include unconstrained and six-degree-of-freedom constraints;

[0020] S122. Apply design loads to the analysis plate in the finite element calculation software and extract the Mises stress data of each point in the first-order buckling mode of the analysis plate , select the area where , is the Mises stress, is the ultimate buckling stress, K is the buckling coefficient, E is the material elastic modulus, v is the Poisson's ratio, t is the plate thickness, s is the length of the analysis plate in the force direction;

[0021] S123. Select the point with the largest displacement in the easily buckling area as the buckling monitoring point.

[0022] Preferably, step S3 includes the following steps:

[0023] S31. Establish a component model coordinate system. In the component model coordinate system, define the model coordinate set of all monitoring points , and calculate the model distances between any two monitoring points to form a model distance set . Perform laser scanning on the processed profiled steel component to obtain the actual coordinate set of all monitoring points in the profiled steel component , and calculate the actual distances between any two monitoring points to form an actual distance set , where N is the number of monitoring points, is the model coordinate set, is the l model coordinate of the -th monitoring point, is the actual coordinate set, l is the actual coordinate of the -th

[0024] S32. Traverse all multi-point combinations of the monitoring points , extract the corresponding model distances from the model distance set , extract the corresponding actual distances from the actual distance set , calculate the global distance error sum of this multi-point combination, and select the multi-point combination that makes the smallest as the optimal reference point group. Among them, is the number of monitoring points within the multi-point combination, M is the monitoring point index, a1, a2, aM and ai are the monitoring point indices within the multi-point combination, aj is the model distance between and ai , aj and is the actual distance between ai and aj ;

[0025] S33. Align the actual optimal reference point group with the center point of the optimal reference point group in the component model coordinate system, and translate the actual coordinates of the monitoring points to the component model coordinate system to obtain the translated corrected actual coordinates , calculate their three-dimensional deviation from the corresponding model coordinates . If is greater than the processing threshold, reprocess the profiled steel component. Among them, The i corrected actual coordinates after translating the monitoring points to the component model coordinate system .

[0026] Preferably, step S4 includes the following steps:

[0027] S41. Establish an independent coordinate system for each analysis plate body based on the positions of the plate body monitoring points on each analysis plate body of the profiled steel member, and obtain the initial out-of-plane displacement values of the buckling monitoring points on each analysis plate body;

[0028] S42. Use the plate body geometric parameters, initial out-of-plane displacement values of the buckling monitoring points, plate body welding parameters, in-filled concrete parameters and final out-of-plane displacement values of the buckling monitoring points in the historical processing data of each profiled steel member as sample set data to construct a prediction model for the out-of-plane displacement of the buckling monitoring points;

[0029] S43. Input the plate body geometric parameters, plate body welding parameters, and in-filled concrete parameters of the current analysis plate body into the trained prediction model for the out-of-plane displacement of the buckling monitoring points, output the predicted out-of-plane displacement value of the buckling monitoring points, allow the buckling deformation amount to be calculated based on the plate shell buckling theory, consider the critical displacement and the safety factor, and if the predicted out-of-plane displacement value of the buckling monitoring points exceeds the allowable buckling deformation amount, determine that the analysis plate body does not meet the requirements and readjust the profiled steel member.

[0030] Preferably, in step S42, the plate body geometric parameters include the plate thickness, width-thickness ratio, height-thickness ratio, and root mean square error of each monitoring point on the plate in the actual processing of the analysis plate body, the plate body welding parameters include the on-site welding line energy, total on-site weld length, designed distance between the buckling monitoring point and the nearest weld, and the in-filled concrete parameters include the concrete compressive strength, concrete water-binder ratio, concrete elastic modulus, and concrete slump. Among them, a neural network model including an input layer, a hidden layer and an output layer is used. The input layer of the neural network model includes multiple input neuron nodes, multiple hidden neuron nodes are arranged on the hidden layer, each of the input neuron nodes is respectively connected to each of the hidden neuron nodes, and the hidden neuron nodes are all connected to the output layer. The plate body geometric parameters, initial out-of-plane displacement values of the buckling monitoring points, plate body welding parameters, and in-filled concrete parameters are used as the input values of the input neuron nodes, and the output result is compared with the corresponding final out-of-plane displacement value of the buckling monitoring point, and the neural network model is optimized by the algorithm to obtain the prediction model for the out-of-plane displacement of the buckling monitoring points.

[0031] Preferably, the allowable buckling deformation amount , where f is the safety factor, with a value range of 2.0 to 4.0, D is the bending stiffness of the plate body, Ua For the allowable buckling deformation quantity, s It is for analyzing the length of the plate body in the force application direction.

[0032] In a second aspect, the present invention provides a full-cycle management system for steel-concrete composite members based on industrial big data, applying the above-mentioned full-cycle management method for steel-concrete composite members based on industrial big data, including: an industrial big data processing module, a point position monitoring module, and a neural network prediction module that are connected by signals to each other;

[0033] The industrial big data processing module includes a component processing sub-module and a data storage sub-module. The component processing sub-module includes BIM modeling software and finite element calculation software. The BIM modeling software is used to model each steel member, set buckling monitoring points and plate body monitoring points, and export sheet metal processing information. The data storage sub-module is used to collect and store the initial out-of-plane displacement values of the buckling monitoring points of each steel member after processing, the corresponding plate body welding parameters, the in-filled concrete parameters, and the final out-of-plane displacement values of the buckling monitoring points during on-site construction;

[0034] The point position monitoring module includes two groups of laser scanners. One group of the two groups of laser scanners is arranged at the construction site, and the other group is arranged in the steel member processing factory. The laser scanner can collect the position information of the buckling monitoring points and the plate body monitoring points on the steel member;

[0035] The neural network prediction module uses the data in the data storage sub-module to train and optimize the neural network model, and obtains the predicted out-of-plane displacement value of the buckling monitoring point after inputting the plate body geometric parameters, the initial out-of-plane displacement value of the buckling monitoring point, the plate body welding parameters, and the in-filled concrete parameters.

[0036] In a third aspect, the present invention provides a computer device, including a memory and a processor. Computer instructions are stored in the memory, and the processor executes the above-mentioned full-cycle management method for steel-concrete composite members based on industrial big data by executing the computer instructions.

[0037] The present invention has at least the following beneficial effects:

[0038] First, the present invention exports processing parameters through the component model made by the BIM modeling software, and obtains the actual coordinates by scanning after processing, and compares them with the component model in real time. If the error exceeds the limit, the rework process is triggered. The final deformation data collected during the construction stage is further used to update the neural network model, forming a data closed-loop of "design - processing - construction - optimization". Compared with the existing steel structure method that relies on manual inspection and empirical adjustment, this method penetrates each link through the data flow, significantly reduces the information lag, and realizes the full-process quality control of the steel-concrete structure.

[0039] Second, the present invention constructs an out-of-plane displacement prediction model for buckling monitoring points based on a neural network model. The input parameters include multivariate data such as plate geometry parameters, plate welding parameters, and infilled concrete parameters. Through training with historical data, the neural network model can dynamically adapt to different process conditions, and the output results are closer to the actual working conditions. Through multi-parameter coupling analysis, this solution greatly reduces the error in predicting the out-of-plane displacement value of buckling monitoring points, providing a reliable basis for process adjustment.

[0040] Third, the present invention proposes a coordinate alignment algorithm based on an optimal reference point group. It extracts the set of model distances between monitoring points from the component model. By traversing multiple combinations of actual scanned points, it calculates the sum of global distance errors and selects the combination with the smallest error as the optimal reference point group for coordinate alignment. This method only needs to process the data of a small number of key monitoring points. Compared with the traditional matching technology after point cloud scanning, this technical solution greatly improves the detection efficiency while ensuring accuracy, significantly reduces the scanning cost, and is applicable to real-time quality monitoring in processing workshops and construction sites.

[0041] Fourth, in the finite element analysis stage, the present invention identifies the prone-to-buckle areas of the plate through nonlinear buckling calculation, selects the point with the largest displacement in this area as the buckling monitoring point, and simultaneously adds plate monitoring points at the splicing edges of the plate to track the cumulative errors in the processing and welding stages. Compared with the existing method of randomly or uniformly distributing points, this solution guides the positioning of monitoring points through mechanical analysis, making the monitoring data more directly reflect the weak parts of the component and avoiding potential risks caused by monitoring blind spots.

[0042] Fifth, the present invention can complete the coordinate acquisition of monitoring points using a conventional laser scanner, and reduces the dependence on hardware accuracy through an optimized algorithm. The algorithm for coordinate alignment with the optimal reference point group can automatically compensate for environmental vibrations or equipment pose deviations, ensuring data reliability under complex working conditions such as dust in the welding workshop and vibration during concrete pouring. Compared with the traditional quality inspection method that requires strict environmental control for point cloud scanning, this solution greatly reduces the hardware cost and is more adaptable to the flexibility and timeliness requirements of the construction site.

[0043] Other advantages, objectives, and features of the present invention will be partially reflected by the following description and partially understood by those skilled in the art through the research and practice of the present invention. Brief Description of the Drawings

[0044] Figure 1 It is the overall flowchart in a technical solution of the present invention;

[0045] Figure 2 It is the schematic diagram of the monitoring points of a profiled steel member in a technical solution of the present invention. Detailed Description of the Invention

[0046] The following further describes the present invention in detail in conjunction with the accompanying drawings and specific embodiments, so that those skilled in the art can implement it with reference to the text of the specification.

[0047] It should be understood that terms such as "having", "comprising", and "including" used herein do not exclude the presence or addition of one or more other elements or their combinations.

[0048] It should be noted that the experimental methods described in the following embodiments are all conventional methods unless otherwise specified, and the reagents and materials can be obtained from commercial channels unless otherwise specified; in the description of the present invention, it should be noted that unless otherwise clearly defined and limited, the terms "installed", "connected", and "set" should be understood in a broad sense. For example, they can be fixedly connected and set, or detachably connected and set, or integrally connected and set. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations. The orientation or positional relationship indicated by terms such as "lateral", "longitudinal", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention.

[0049] As Figure 1-2 shown, the present invention provides a full-cycle management method for steel reinforced concrete members based on industrial big data, including the following steps:

[0050] S1. Establish a component model in BIM modeling software. Select each plate in the component model that is in contact with concrete on one side as the analysis plate, import it into finite element calculation software for buckling analysis. Select several monitoring points on the non-concrete contact surface of the analysis plate. The monitoring points include buckling monitoring points and plate monitoring points. Specifically, establish a three-dimensional component model of steel components in BIM modeling software such as Revit and Tekla according to the design drawings. Since the buckling of the steel-concrete structure has the greatest impact on the overall force, and for the convenience of subsequent monitoring operations, the plate in contact with concrete on one side is selected as the analysis plate. Import the model of the analysis plate into finite element calculation software, which can be ANAYS, ABAQUS, etc. Apply the design load to the model of the analysis plate for nonlinear buckling analysis, extract the Mises stress distribution under the first-order buckling mode and screen out the prone-to-buckle areas. Select the point with the largest displacement in the prone-to-buckle area as the buckling monitoring point, and add plate monitoring points at the splicing edges of the analysis plate and other plates. The plate monitoring points can be 10-20 mm away from the edge. Accurately locate the monitoring points through mechanical analysis to provide a data basis for subsequent processing and deformation monitoring.

[0051] S2. Extract the component contour data and the coordinates of the monitoring points from the component model to form processing parameters, and based on the processing parameters, perform cutting of steel plate profiles and marking of monitoring points to complete the processing of steel components. Specifically, extract the contour data of the analysis plate and the coordinates of the monitoring points from the component model to obtain a file such as DXF format that records the coordinate positions, and generate CNC cutting codes based on this. Use a CNC plasma cutting machine to cut the steel plate according to the contour, and simultaneously mark the monitoring points on the surface of the plate with a laser marking machine. The cut plates are welded and assembled into a complete steel component. During the processing, ensure that the edge splicing points of each plate are aligned and the welds are smooth.

[0052] S3. After the steel section members are processed, they are scanned to obtain the actual coordinates of each monitoring point on the steel section members and compare them with the model coordinates of each monitoring point in the member model. If the error between the actual coordinates and the model coordinates exceeds the processing threshold, the steel section member is reprocessed. Specifically, after the steel section members are processed, the actual coordinate data of all monitoring points on the surface of the steel section members are quickly collected by laser scanning technology and compared with the preset model coordinates in the member model. Optionally, a lightweight algorithm is used to screen the combination of reference points with the highest matching degree with the member model, and the global distance error sum between the monitoring points is calculated to determine the optimal coordinate system alignment scheme, so as to accurately calculate the three-dimensional position deviation of each monitoring point. The processing threshold can be set to ±2mm with reference to the existing steel structure processing specifications. If the deviation of any monitoring point exceeds the processing threshold, a rework instruction is generated and fed back to the processing link to readjust or reprocess the steel section member. Compared with the point cloud scanning technology, the scanning and calculation time of this solution is greatly reduced, and it does not require high-performance hardware support.

[0053] S4. Establish a prediction model for the out-of-plane displacement of the buckling monitoring points. Input the initial out-of-plane displacement values of the buckling monitoring points, the geometric parameters of the plate body, the welding parameters of the plate body, and the parameters of the infilled concrete of each analysis plate body to obtain the predicted out-of-plane displacement values of the buckling monitoring points after the concrete pouring is completed. If the predicted out-of-plane displacement value of the buckling monitoring point exceeds the allowable buckling deformation amount, the steel section member is reprocessed. Repeat steps S3 - S4 until each analysis plate body meets the requirements. Specifically, a neural network model is constructed. The neural network model can adopt a BP neural network model. The input parameters include parameters related to plate body buckling such as the geometric dimensions of the plate body, the welding line energy in subsequent on-site welding operations, and the elastic modulus of the concrete. The neural network model adopts a double hidden layer structure, and the output value is the predicted out-of-plane displacement value of the buckling monitoring point. The neural network model can optimize the network weights through the particle swarm algorithm and use historical data to train the model.

[0054] S5. The steel section members are installed, welded, and internally filled with concrete on site. After the concrete begins to set, the final out-of-plane displacement values of the buckling monitoring points on the steel section members are collected by a laser scanner, and the final out-of-plane displacement values of the buckling monitoring points, the corresponding welding parameters of the plate body, and the parameters of the infilled concrete are fed back to the prediction model for the out-of-plane displacement of the buckling monitoring points for training. Specifically, after the concrete begins to set, the final position data of each monitoring point are collected by a laser scanner, the construction parameters and deformation data are normalized and then stored, and input into the neural network model for incremental training. Optionally, the small batch gradient descent method is used to update the weights. Through dynamic feedback, the model prediction error decreases as the data accumulates.

[0055] In this technical solution, the systematic optimization of the full-cycle management of steel-concrete composite members is realized. First, based on BIM modeling and finite element buckling analysis, the monitoring points are accurately positioned to ensure that the monitoring data focuses on the weak parts of the structure. Then, through digital processing parameter transfer and automated cutting technology, each plate in the steel member is processed and welded to form a steel member. In the monitoring of the finished steel member, a lightweight scanning device is used to replace the traditional point cloud scanning device for surface monitoring of the steel member. The position information of each monitoring point is compared with the component model established in the BIM modeling software. If the overall processing of the steel member does not meet the requirements, rework is carried out. Then, further use the existing data, historical data, and design data to predict the buckling deformation of the component after construction is completed and decide whether to reprocess. This solution effectively monitors and manages the quality of steel members throughout the cycle, and the comprehensive cost and implementation difficulty are greatly reduced compared with the existing method of using point cloud scanning data for quality control, and the efficiency is greatly improved.

[0056] In another technical solution, step S1 includes the following steps:

[0057] S11. Establish a three-dimensional component model in the BIM modeling software. Each plate in the component model needs to be modeled independently. After model checking, select the plate that is in contact with concrete on one side and has a non-concrete contact surface on the other side as the analysis plate and transfer it to the finite element calculation software as a finite element model. In the finite element calculation software, assign material properties to each calculation object. Specifically, establish a three-dimensional component model of the steel member in the BIM modeling software to ensure that each plate is modeled independently to avoid overlapping or gap problems between models. Then, screen out the plates that are in contact with concrete on one side and have a non-concrete contact surface on the other side, and export them as independent geometric files. The format can be SAT, STEP, etc., which can be imported into the finite element calculation software. In the finite element calculation software, assign material properties to the analysis plate according to the design requirements.

[0058] S12. Set constraint conditions for the analysis plate in the finite element calculation software, apply the design load and perform nonlinear buckling analysis to obtain the easily buckling area in the analysis plate. Select at least one point on the non-concrete contact surface of the plate in the easily buckling area as the buckling monitoring point. Specifically, apply constraint conditions to the edge of the analysis plate in the finite element calculation software, load the concrete side pressure according to the design code, and apply a line load on the stressed side. Perform nonlinear buckling analysis, extract the Mises stress distribution of each point in the first-order buckling mode, select the area where it exceeds the limit buckling stress as the easily buckling area, and select the point with the maximum displacement in the easily buckling area.

[0059] S13. Mark buckling monitoring points on each analysis plate in the component model and set a number of plate monitoring points at the splicing edges of each analysis plate and other plates. At least one plate monitoring point is set on each edge of the analysis plate, and it is set at a distance of 10 mm to 20 mm from the edge line.

[0060] In another technical solution, step S12 includes the following steps:

[0061] S121. Take the state of the steel member after all on-site connection measures are completed according to the design requirements and before the concrete is poured on-site as the basis for the constraint conditions at the edges of each analysis plate. Apply constraint conditions to the edges of the analysis plate in the finite element calculation software. The constraint conditions include unconstrained and six-degree-of-freedom constraints. Specifically, before the finite element analysis, it is necessary to clarify the actual constraint state of the steel member before the concrete is poured. Based on this state, apply constraint conditions to the edges of the analysis plate in the finite element calculation software. Among them, there are two types of constraint types: unconstrained is used to simulate the state when the edge of the analysis plate is not welded, and six-degree-of-freedom constraint is used to simulate the rigid connection after the edge of the analysis plate is welded, so as to accurately simulate the boundary conditions in the actual working conditions, ensure that the buckling analysis results conform to the construction reality, and avoid prediction distortion caused by deviation of constraint assumptions.

[0062] S122. Apply the design load to the analysis plate in the finite element calculation software, and extract the Mises stress data of each point of the analysis plate in the first-order buckling mode. , select area as the prone-to-buckle area, where , is the Mises stress, is the ultimate buckling stress, K is the buckling coefficient, E is the material elastic modulus, v is the Poisson's ratio, t is the plate thickness, s is the length of the analysis plate in the force direction. Specifically, first apply the design load to the analysis plate in the finite element calculation software to simulate the actual stress state; then perform nonlinear buckling analysis and extract the Mises stress distribution data in the first-order buckling mode to reflect the most likely deformation form when the plate is unstable, where K is the buckling coefficient, and the reference value according to the relevant design code is 0.425 to 4.0, and it is taken according to the edge constraint conditions of the analysis plate. Among them, the buckling coefficient K is used to quantify the buckling resistance of the plate under different boundary conditions. KThe value of is based on the classical plate and shell buckling theory and the Chinese national standard GB50017-2017 "Steel Structure Design Standard". In engineering practice, generally, the analyzed plate in a steel section member is in a state of simply supported on four sides or simply supported on three sides and free on one side before concrete pouring. If the four sides of the analyzed plate are welded to other structures, it is regarded as a simply supported plate on four sides. K = 4. If three sides of the analyzed plate are welded to other plates and the other side is not welded and fixed, it is regarded as a plate simply supported on three sides and free on one side. K = 0.425. For the mixed constraint forms in between, the buckling coefficient table in various specification guides can be referred to and matched according to the boundary conditions. K Value.

[0063] S123. Select the point with the largest displacement in the easily buckling area as the buckling monitoring point. In the easily buckling area screened in step S122, extract the displacement data of each point, and select the point with the maximum displacement as the buckling monitoring point to ensure that the buckling monitoring point is set at the position with the most significant deformation.

[0064] In another technical solution, step S3 includes the following steps:

[0065] S31. Establish a component model coordinate system. In the component model coordinate system, define the model coordinate set of all monitoring points , and calculate the model distance between any two monitoring points to form a model distance set . Perform laser scanning on the processed steel section member to obtain the actual coordinate set of all monitoring points in the steel section member , and calculate the actual distance between any two monitoring points to form an actual distance set , where is the model coordinate set, is the l model coordinate of the monitoring point, is the actual coordinate set, is the l actual coordinate of the monitoring point; specifically, first, in the BIM modeling software, take the geometric center or key splicing point of the component model as the origin to establish a three-dimensional rectangular coordinate system of the component model coordinate system, and export the model coordinate data of all monitoring points from the BIM modeling software. The number of monitoring points is N, and the monitoring point label is l , and the model coordinate of each monitoring point . Next, it is necessary to calculate the model distance between any two monitoring points to form a model distance set , where ai and aj are indexes of different monitoring points;

[0066] The actual coordinate data of the processed section steel members are obtained through laser scanning technology, and the actual coordinates of each monitoring point are After the scanning is completed, the set of actual coordinates of all monitoring points is extracted , and the set of actual distances is calculated , where the distance between any two monitoring points is calculated in the same way.

[0067] S32. Traverse all multi-point combinations of the monitoring points , and extract the corresponding model distances from the set of model distances , and extract the corresponding actual distances from the set of actual distances , calculate the sum of the global distance errors of this multi-point combination , and select the multi-point combination that makes the smallest as the optimal reference point group. Among them, , is the number of monitoring points in the multi-point combination, M is the monitoring point index, a1, a2, aM is the monitoring point index, ai and aj are the monitoring point indices within the multi-point combination, is ai and aj the model distance between, is ai and aj the actual distance between. Specifically, it is necessary to traverse all possible multi-point combinations, select one monitoring point from each analysis plate body as the multi-point combination, where the multi-point combination contains at least three monitoring points, calculate the sum of the global distance errors between the actual distances of each group of multi-point combinations and the corresponding model distances, where , and screen out the smallest combination as the optimal reference point group.

[0068] S33. Align the actual optimal reference point group with the center point of the optimal reference point group in the component model coordinate system, translate the actual coordinates of the monitoring points to the component model coordinate system to obtain the translated corrected actual coordinates , calculate its three-dimensional deviation from the corresponding model coordinates , if is greater than the processing threshold, then reprocess the section steel member. Among them, is the corrected actual coordinate after the th i monitoring point is translated to the component model coordinate system , specifically, the center point alignment is used to eliminate the overall position deviation between the actual scanned data and the theoretical model. By translating the coordinates of the actual monitoring points into the component model coordinate system, it can ensure that both are under the same reference, so as to accurately identify the real local deviation. The processing threshold can refer to the steel structure processing specification.

[0069] In another technical solution, step S4 includes the following steps:

[0070] S41. Establish an independent coordinate system for each analysis plate body based on the positions of the plate body monitoring points on each steel section component, and obtain the initial out-of-plane displacement values of the buckling monitoring points on each analysis plate body. Specifically, an independent local coordinate system is established for each analysis plate body to eliminate the interference of the overall structure deformation on the local buckling analysis. The geometric center of the plate body monitoring points of each analysis plate body is used as the origin of the coordinate system of this analysis plate body. The X-axis and Y-axis are defined according to the plane direction of the analysis plate body, and the Z-axis is perpendicular to the surface of the analysis plate body. The deviation value of the buckling monitoring point in the Z-axis direction in the actual data obtained by laser scanning is used as the initial out-of-plane displacement value of the buckling monitoring point.

[0071] S42. Use the plate body geometric parameters, initial out-of-plane displacement values of the buckling monitoring points, plate body welding parameters, internal concrete filling parameters, and final out-of-plane displacement values of the buckling monitoring points in the historical processing data of each steel section component as sample set data to construct a prediction model for the out-of-plane displacement of the buckling monitoring points. Specifically, the neural network model uses a BP neural network model, and the input layer contains neurons corresponding to the parameters. Specifically, first, the data is normalized to eliminate the dimension difference; then, the neural network structure is designed, and the particle swarm optimization algorithm is used to optimize the network weights, and the prediction error is minimized through iterative training.

[0072] S43. Input the plate body geometric parameters, plate body welding parameters, and internal concrete filling parameters of the current analysis plate body into the trained prediction model for the out-of-plane displacement of the buckling monitoring points, and output the predicted out-of-plane displacement value of the buckling monitoring points. The allowable buckling deformation is calculated based on the plate shell buckling theory, considering the critical displacement and the safety factor. If the predicted out-of-plane displacement value of the buckling monitoring points exceeds the allowable buckling deformation, it is determined that this analysis plate body does not meet the requirements, and the steel section component is readjusted. Specifically, the allowable buckling deformation can be calculated according to the plate shell buckling theory to accurately quantify the maximum allowable deformation of the structure before instability, and the output result of the neural network model is compared with the allowable buckling deformation to determine whether to trigger the process adjustment.

[0073] In another technical solution, in step S42, the geometric parameters of the plate body include plate thickness, width-thickness ratio, height-thickness ratio, and the root mean square error of each monitoring point on the plate during the actual processing of the analyzed plate body. The welding parameters of the plate body include the on-site welding line energy of the analyzed plate body, the total length of the on-site welds, the designed distance between the buckling monitoring point and the nearest weld. The parameters of the infilled concrete include concrete compressive strength, concrete water-binder ratio, concrete elastic modulus, and concrete slump. Among them, a neural network model including an input layer, a hidden layer, and an output layer is adopted. The input layer of the neural network model includes multiple input neuron nodes. Multiple hidden neuron nodes are arranged on the hidden layer. Each input neuron node is respectively connected to each hidden neuron node. The hidden neuron nodes are all connected to the output layer. The geometric parameters of the plate body, the initial out-of-plane displacement value of the buckling monitoring point, the welding parameters of the plate body, and the parameters of the infilled concrete are used as the input values of the input neuron nodes. The output result is compared with the final out-of-plane displacement value of the corresponding buckling monitoring point, and the algorithm optimizes the neural network model to obtain the out-of-plane displacement prediction model of the buckling monitoring point.

[0074] In this technical solution, the neural network model is a BP neural network model. The method for training and optimizing the neural network model includes the following steps;

[0075] A1. Several factors such as the geometric parameters of the plate body, the initial out-of-plane displacement value of the buckling monitoring point, the welding parameters of the plate body, and the parameters of the infilled concrete are influencing factors. The total number of parameter types is used as the number of input neuron nodes m, and the predicted out-of-plane displacement value of the buckling monitoring point is used as the only output value, that is, the number of nodes c in the output layer = 1, and the number of hidden neuron nodes c1 in the hidden layer is , where a is a random constant between 1 and 10;

[0076] A2. Normalize the sample set data, and its mathematical expression is , where x i represents the sample data of the influencing factor, x min . x max are respectively the minimum and maximum values in the sample data, is the dimensionless processed influencing factor data;

[0077] A3. Initialize the mapping relationship between the population particles and the weights and thresholds of the BP neural network, including particle dimension, initial velocity, population size, learning factor, and inertia weight;

[0078] A4. Input the normalized input variables and output variables into the neural network model, calculate the fitness function value of the particle, and obtain the historical optimal fitness and global fitness of the particle. The fitness function value of the particle is the mean square error of the calculation result, and its function expression is , where represents the predicted value of the i-th sample, and y i is the true value of the i-th sample, and n is the total number of calculation results of the neural network model;

[0079] A5. Perform iterative calculation on the particle fitness, update the historical optimal fitness and the global fitness according to the preset update conditions until the preset iteration end condition is met;

[0080] A6. Update the weights and thresholds of the neural network model to obtain the out-of-plane displacement prediction model for the buckling monitoring point.

[0081] In another technical solution, the allowable buckling deformation , where f is the safety factor, with a value range of 2.0 to 4.0, D is the bending stiffness of the plate body, U a is the allowable buckling deformation, s is the length of the plate body in the force direction.

[0082] An industrial big data-based full-cycle management system for steel-concrete composite members applies the above-mentioned industrial big data-based full-cycle management method for steel-concrete composite members, and includes: an industrial big data processing module, a point monitoring module, and a neural network prediction module that are interconnected by signals; the industrial big data processing module includes a component processing sub-module and a data storage sub-module. The component processing sub-module includes BIM modeling software and finite element calculation software. The BIM modeling software is used to model each steel member, set buckling monitoring points and plate body monitoring points, and export sheet metal processing information. The data storage sub-module is used to collect and store the initial out-of-plane displacement values of the buckling monitoring points of each steel member after processing, the corresponding plate welding parameters, the internal concrete filling parameters, and the final out-of-plane displacement values of the buckling monitoring points during on-site construction; the point monitoring module includes two groups of laser scanners. One group of the two groups of laser scanners is set at the construction site, and the other group is set in the steel member processing factory. The laser scanner can collect the position information of the buckling monitoring points and the plate body monitoring points on the steel member; the neural network prediction module uses the data in the data storage sub-module to train and optimize the neural network model, and inputs the plate geometric parameters, the initial out-of-plane displacement values of the buckling monitoring points, the plate welding parameters, and the internal concrete filling parameters to obtain the predicted out-of-plane displacement values of the buckling monitoring points as the output.

[0083] A computer device includes a memory and a processor. Computer instructions are stored in the memory, and the processor executes the computer instructions to perform the above-mentioned full-cycle management method for steel-concrete composite members based on industrial big data. This computer device can be any terminal device including mobile phones, laptop computers, desktop computers, tablet computers, PDAs (Personal Digital Assistants), POS (Point of Sales) terminals, in-vehicle computers, etc.

[0084] The computer instructions are stored in a readable storage medium, such as a computer floppy disk, USB flash drive, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disc, etc. The instructions include several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention.

[0085] It should be noted that although the above steps are described in a specific order, it does not mean that the steps must be executed in the above specific order. In fact, some of these steps can be executed concurrently or even in a different order, as long as the required functions can be achieved. The number of devices and the processing scale described here are used to simplify the description of the present invention, and it is obvious to those skilled in the art for the application, modification, and variation of the present invention.

[0086] Although the embodiments of the present invention have been disclosed as above, it is not limited to only the applications listed in the specification and embodiments. It can be fully applied to various fields suitable for the present invention. For those familiar with the field, additional modifications can be easily made. Therefore, without departing from the general concept defined by the claims and the equivalent scope, the present invention is not limited to the specific details and the illustrated and described examples here.

Claims

1. A full-cycle management method for steel reinforced concrete components based on industrial big data, characterized in that It includes the following steps: S1. Establish a component model in BIM modeling software. Select the plate bodies in the component model that are in contact with concrete on each single surface as the analysis plate bodies, import them into finite element calculation software for buckling analysis, and select several monitoring points on the non-concrete contact surfaces of the analysis plate bodies. The monitoring points include buckling monitoring points and plate body monitoring points; S2. Extract the component contour data and the coordinates of the monitoring points from the component model to form processing parameters. Based on the processing parameters, cut the steel plate profiles and mark the monitoring points to complete the processing of the steel component; S3. After the processing of the steel component is completed, scan it to obtain the actual coordinates of each monitoring point on the steel component and compare them with the model coordinates of each monitoring point in the component model. If the error between the actual coordinates and the model coordinates exceeds the processing threshold, reprocess the steel component; S4. Establish a prediction model for the out-of-plane displacement of the buckling monitoring points. Input the initial out-of-plane displacement values of the buckling monitoring points, the geometric parameters of the plate bodies, the welding parameters of the plate bodies, and the parameters of the infilled concrete for each analysis plate body to obtain the predicted out-of-plane displacement values of the buckling monitoring points after the concrete pouring is completed. If the predicted out-of-plane displacement values of the buckling monitoring points exceed the allowable buckling deformation amount, reprocess the steel component, and repeat steps S3 - S4 until each analysis plate body meets the requirements; S5. Install, weld, and pour the internal concrete for the steel component on-site. After the concrete begins to set, collect the final out-of-plane displacement values of the buckling monitoring points on the steel component through a laser scanner, and feedback the final out-of-plane displacement values of the buckling monitoring points, the corresponding plate body welding parameters, and the infilled concrete parameters to the prediction model for the out-of-plane displacement of the buckling monitoring points for training.

2. The full-cycle management method for steel reinforced concrete members based on industrial big data according to claim 1, characterized in that Step S1 includes the following steps: S11. Establish a three-dimensional component model in the BIM modeling software. Each plate body in the component model needs to be modeled independently. After model checking, select the plate bodies that are in contact with concrete on one side and have a non-concrete contact surface on the other side as the analysis plate bodies and transfer them to the finite element calculation software as finite element models, and assign material properties to each calculation object in the finite element calculation software; S12. Set constraint conditions for the analysis plate bodies in the finite element calculation software, apply design loads, and perform non-linear buckling analysis to obtain the easily buckling areas in the analysis plate bodies. Select at least one point on the non-concrete contact surface of the plate bodies in the easily buckling areas as the buckling monitoring points; S13. Mark the buckling monitoring points on each analysis plate body in the component model and set several plate body monitoring points at the splicing edges of each analysis plate body and other plate bodies.

3. The full-cycle management method for steel reinforced concrete components based on industrial big data according to claim 2, characterized in that, Step S12 includes the following steps: S121. Based on the state of the steel component when all on-site connection measures are completed according to the design requirements and before the concrete is poured on-site as the basis for the constraint conditions at the edges of each analysis plate body, apply constraint conditions to the edges of the analysis plate bodies in the finite element calculation software. The constraint conditions include unconstrained and six-degree-of-freedom constraints; S122. Apply the design load to the analysis plate body in the finite element calculation software, and extract the Mises stress data of each point of the analysis plate body in the first-order buckling mode. , select the area as the prone-to-buckle area, where , is the Mises stress, is the ultimate buckling stress, K is the buckling coefficient, E is the material elastic modulus, v is the Poisson's ratio, t is the plate thickness, s is the length of the analysis plate body in the force direction; S123. Select the point with the largest displacement in the easily buckling area as the buckling monitoring point.

4. The full-cycle management method for steel reinforced concrete components based on industrial big data according to claim 1, characterized in that, Step S3 includes the following steps: S31. Establish a component model coordinate system, and define the model coordinate set of all monitoring points in the component model coordinate system. , and calculate the model distance between any two monitoring points to form a model distance set , perform laser scanning on the processed steel structure to obtain the actual coordinate set of all monitoring points in the steel structure , and calculate the actual distance between any two monitoring points to form an actual distance set ,in, N The number of monitoring points, is the model coordinate set, For the l Model coordinates of monitoring points , is the actual coordinate set, For the l The actual coordinates of the monitoring points ; S32. Traverse the multi-point combinations of all monitoring points , extract the corresponding model distances from the model distance set , extract the corresponding actual distances from the actual distance set , calculate the sum of the global distance errors of this multi-point combination , select the multi-point combination that makes the smallest as the optimal reference point group, where is the number of monitoring points in the multi-point combination, is the monitoring point index, M is the monitoring point index, a1, a2, aM and ai are the monitoring point indices within the multi-point combination, aj is the model distance between and ai , aj and is the actual distance between ai and aj ; S33. Align the actual optimal reference point group with the center point of the optimal reference point group in the component model coordinate system, and translate the actual coordinates of the monitoring points to the component model coordinate system to obtain the translated corrected actual coordinates , and calculate its three-dimensional deviation from the corresponding model coordinates . If is greater than the processing threshold, reprocess the steel component. Among them, is the corrected actual coordinate of the i monitoring point after being translated to the component model coordinate system .

5. The full-cycle management method for steel reinforced concrete components based on industrial big data according to claim 1, characterized in that Step S4 includes the following steps: S41. Establish an independent coordinate system for each analysis plate body based on the positions of the plate body monitoring points on the profiled steel member, and obtain the initial out-of-plane displacement values of the buckling monitoring points on each analysis plate body. S42. Use the plate body geometric parameters, initial out-of-plane displacement values of the buckling monitoring points, plate body welding parameters, internal filled concrete parameters, and final out-of-plane displacement values of the buckling monitoring points in each profiled steel member in the historical processing data as sample set data to construct a prediction model for the out-of-plane displacement of the buckling monitoring points. S43. Input the plate body geometric parameters, plate body welding parameters, and internal filled concrete parameters of the current analysis plate body into the trained prediction model for the out-of-plane displacement of the buckling monitoring points, and output the predicted out-of-plane displacement value of the buckling monitoring point. Allow the buckling deformation quantity to be calculated based on the plate and shell buckling theory, considering the critical displacement and safety factor. If the predicted out-of-plane displacement value of the buckling monitoring point exceeds the allowable buckling deformation quantity, determine that the analysis plate body does not meet the requirements, and readjust the profiled steel member.

6. The full-cycle management method for steel reinforced concrete components based on industrial big data according to claim 5, characterized in that In step S42, the plate body geometric parameters include the plate thickness, width-thickness ratio, height-thickness ratio, and root mean square error of each monitoring point position on the plate during the actual processing of the analysis plate body. The plate body welding parameters include the on-site welding line energy, total on-site weld length, designed distance between the buckling monitoring point and the nearest weld. The internal filled concrete parameters include the concrete compressive strength, concrete water-binder ratio, concrete elastic modulus, and concrete slump. Among them, a neural network model including an input layer, a hidden layer, and an output layer is adopted. The input layer of the neural network model includes multiple input neuron nodes. Multiple hidden neuron nodes are arranged on the hidden layer. Each input neuron node is respectively connected to each hidden neuron node. The hidden neuron nodes are all connected to the output layer. Use the plate body geometric parameters, initial out-of-plane displacement values of the buckling monitoring points, plate body welding parameters, and internal filled concrete parameters as the input values of the input neuron nodes, compare the output result with the corresponding final out-of-plane displacement value of the buckling monitoring point, and optimize the neural network model by the algorithm to obtain the prediction model for the out-of-plane displacement of the buckling monitoring points.

7. The full-cycle management method for steel reinforced concrete components based on industrial big data according to claim 5, wherein, The allowable buckling deformation , where f is the safety factor, with a value range of 2.0 to 4.0, D is the flexural stiffness of the plate body, U a is the allowable buckling deformation, s is the length of the plate body in the force direction.

8. A full-cycle management system for steel reinforced concrete components based on industrial big data, which is applied to the full-cycle management method for steel reinforced concrete components based on industrial big data according to any one of claims 1 to 7, characterized in that, Including: An industrial big data processing module, a point position monitoring module, and a neural network prediction module that are interconnected by signals; The industrial big data processing module includes a component processing sub-module and a data storage sub-module. The component processing sub-module includes BIM modeling software and finite element calculation software. The BIM modeling software is used to model each profiled steel member, set the buckling monitoring points and plate body monitoring points, and export the sheet metal processing information. The data storage sub-module is used to collect and store the initial out-of-plane displacement values of the buckling monitoring points of each profiled steel member after processing, the corresponding plate body welding parameters, internal filled concrete parameters, and the final out-of-plane displacement values of the buckling monitoring points during on-site construction; The point position monitoring module includes two groups of laser scanners. One group of the two groups of laser scanners is set at the construction site, and the other group is set in the profiled steel member processing factory. The laser scanner can collect the position information of the buckling monitoring points and plate body monitoring points on the profiled steel member. The neural network prediction module utilizes the data in the data storage sub-module to train and optimize the neural network model. After inputting the geometric parameters of the plate body, the initial out-of-plane displacement value of the buckling monitoring point, the welding parameters of the plate body, and the parameters of the infilled concrete, the predicted out-of-plane displacement value of the buckling monitoring point is obtained as the output.

9. A computer device, characterized in that, It includes a memory and a processor. Computer instructions are stored in the memory, and the processor executes the computer instructions to execute the full-cycle management method for steel-concrete composite members based on industrial big data according to any one of claims 1 to 7.

Images