Intelligent optimization arrangement method and equipment for steel bars in beam-column joints of rigid structures

Abstract

This invention provides a method and device for intelligent optimization of rebar arrangement in stiffened structural beam-column joints. To address the problems of cumbersome, time-consuming, inefficient, and overly reliant on manual experience in rebar arrangement for stiffened structural joints, this invention proposes a novel method for intelligent optimization of rebar arrangement in stiffened structural beam-column joints. The main technical components are as follows: Rebar arrangement parameters for the joint to be optimized are extracted through a rebar parameter extraction model and input into a rebar arrangement optimization parameter selection module for parameter selection. Based on all eligible rebar arrangement parameters output by the intelligent optimization model for stiffened structural joints, an optimization function calculates and obtains the optimal rebar arrangement parameters. This application enables the rapid acquisition of optimal rebar arrangement parameters for stiffened structural beam-column joints through simple drawing input.

Description

Technical Field

[0001] This invention relates to a method and equipment for intelligent optimization of the arrangement of reinforcing bars in beam-column joints of rigid structures. Background Technology

[0002] As a high-performance building form, rigid structures are widely used in modern engineering construction. With their superior load-bearing performance, excellent seismic resistance, and high space utilization, they are commonly adopted in high-rise buildings, large public facilities, industrial plants, bridges, and special-purpose buildings.

[0003] During on-site construction, there are often a series of problems such as difficulties in inserting steel bars, excessively dense steel bars, and inconvenient anchoring welding. Experienced engineering technicians are required to optimize the steel bar layout in advance and optimize different types of nodes one by one. The process is tedious, time-consuming, and relies too much on manual experience. Summary of the Invention

[0004] The purpose of this invention is to provide a method and equipment for intelligent optimization of the arrangement of reinforcing bars in beam-column joints of rigid structures.

[0005] To address the above problems, this invention provides a method for intelligent optimization of the reinforcement arrangement of beam-column joints in stiffened structures, comprising:

[0006] The first information acquisition and processing module acquires sample steel reinforcement layout parameters from the steel reinforcement layout drawings of the stiffened structural nodes.

[0007] The first model training module, based on the obtained steel reinforcement layout drawings and sample steel reinforcement layout parameters of the stiffened structural nodes, and using artificial intelligence methods, establishes a node steel reinforcement parameter extraction model.

[0008] The second information acquisition and processing module collects sample steel bar layout parameters and optimizes layout rules;

[0009] The second model training module trains an intelligent optimization layout model for the reinforcement of stiffened structural nodes based on the sample reinforcement layout parameters and optimization layout rules.

[0010] The rebar layout optimization parameter selection module allows users to set the rebar layout parameters to be optimized based on requirements. The intelligent optimization layout model for rebar at stiffened structural nodes optimizes the rebar layout parameters according to the optimization rules, selecting rebar layout information parameters that meet the requirements of each stiffened structural node. All qualified rebar layout information parameter groups for each stiffened structural node are input into the optimization function to obtain the optimal layout scheme.

[0011] Furthermore, in the above method, the sample reinforcement arrangement parameters include: reinforcement type x, reinforcement diameter d, reinforcement spacing s, number of reinforcement rows m, and total number of reinforcements n.

[0012] Furthermore, in the above method, the first information acquisition and processing module acquires sample reinforcement layout parameters from the reinforcement layout drawings of the stiffening structure nodes, including:

[0013] The rebar arrangement parameters are labeled to facilitate identification by the first model training module; and the data units of the rebar arrangement parameters are unified, with the rebar diameter d and rebar spacing s being uniformly set to "mm"; the rebar type x, rebar number m, and total number of rebars n are set to "dimensionless".

[0014] Furthermore, in the above method, the input to the node reinforcement parameter extraction model is the reinforcement layout drawing of the stiffened structural node;

[0015] The output of the node reinforcement parameter extraction model is reinforcement type x, reinforcement diameter d, reinforcement spacing s, number of reinforcement rows m, and total number of reinforcements n.

[0016] Furthermore, in the above method, the first model training module, based on the obtained reinforcement layout drawings and sample reinforcement layout parameters of the stiffened structural nodes, and using artificial intelligence methods, establishes a node reinforcement parameter extraction model, including:

[0017] The model for extracting node reinforcement parameters can take the form of a DNN, RNN, or Transformer model.

[0018] The training process of the node reinforcement parameter extraction model adopts optimization algorithms such as SGD and Adam until the target loss function reaches the preset expected value, at which point the training can be stopped; after the training is completed, the parameter group model of the template support system component is obtained, namely the node reinforcement parameter extraction model.

[0019] Furthermore, in the above method, the rebar layout optimization parameter selection module allows setting the rebar layout parameters to be optimized according to requirements.

[0020] The rebar layout parameters to be optimized include one or more of the following: rebar type x, rebar diameter d, rebar spacing s, number of rebar rows m, and total number of rebars n.

[0021] Furthermore, in the above method, the optimized layout rules include:

[0022] The number of rebar rows m is less than or equal to 3; the rebar spacing s is between 25 and 300 mm; the optimized range for the total number of rebars n is 75% to 125%n0, where n0 is the initial number of rebars; the optimized range for the rebar diameter is 75% to 125%d0, and between 12 and 40 mm, where d0 is the initial rebar diameter; the rebar anchorage methods include: direct anchorage, welding of connecting plates, and rebar sleeve connection; avoid pouring spaces smaller than the preset threshold or complex geometric shapes.

[0023] Furthermore, in the above method, the reinforcement arrangement information parameters of each stiffened structural node output by the intelligent optimization layout model of the stiffened structural node appear in groups. Suppose that there are j groups of reinforcement arrangement information parameters for each stiffened structural node that meet the requirements, that is, the output matrix is ​​as follows:

[0024]

[0025] The optimized function is:

[0026] p=p1(x)+p2(n,d)+p3(s)+p4(m);

[0027]

[0028] Where, d p The target rebar diameter is set by the designer; n0 is the initial rebar quantity; w1, w2, w3, and w4 are weighting coefficients, with weight values ​​ranging from 0 to 1. The weight values ​​for each item should be determined by the project team based on the characteristics and requirements of different projects; the rebar type is x; the rebar diameter is d; the rebar spacing is s; the number of rebar rows is m; and the total number of rebars is n.

[0029] p represents the score of a set of rebar arrangement information parameters for a certain reinforced steel structure node. The set of rebar arrangement information parameters with the highest score from the output of the optimization function is selected as the result output.

[0030] According to another aspect of the present invention, a computer-readable storage medium is also provided, having stored thereon computer-executable instructions, wherein when executed by a processor, the computer-executable instructions cause the processor to employ the method described in any of the preceding claims.

[0031] According to another aspect of the present invention, a calculator device is also provided, comprising:

[0032] Processor; and

[0033] A memory configured to store computer-executable instructions, which, when executed, cause the processor to: employ the method described in any of the preceding descriptions.

[0034] Compared with existing technologies, this invention addresses the problems of cumbersome, time-consuming, inefficient, and overly reliant on manual experience in the reinforcement arrangement process of stiffened structural joints. It proposes a novel intelligent optimization method for reinforcement arrangement in beam-column joints of stiffened structures. The main technical components are as follows:

[0035] The rebar arrangement parameters of the nodes to be optimized are extracted from the rebar parameters of the model and input into the rebar arrangement optimization parameter selection module for parameter selection. Based on all the rebar arrangement parameters that meet the conditions output by the intelligent optimization rebar arrangement model of stiffened structural nodes, the optimization function calculates and obtains the optimal rebar arrangement parameters. This application enables the rapid acquisition of the optimal rebar arrangement parameters for stiffened structural beam-column nodes through simple drawing input.

[0036] Compared with the current methods for arranging reinforcing bars in stiff structural joints, the main advantages of this invention are as follows:

[0037] (1) High efficiency. The present invention provides an intelligent optimization method for the arrangement of reinforcing bars in stiff structural nodes, which automatically recognizes drawings and intelligently optimizes the arrangement of reinforcing bars, with a speed far exceeding that of traditional manual arrangement methods;

[0038] (2) The overall optimization parameters are highly integrated. The existing method of reinforcement arrangement at the nodes of rigid structures continuously adjusts individual parameters based on human experience, and then checks whether other parameters meet the requirements in turn. If one of them does not meet the requirements, the next set of adjustments must be made. This method comprehensively considers various parameters, performs overall optimization scoring according to preset rules, and selects the best solution. Attached Figure Description

[0039] Figure 1 This is a flowchart of a method for intelligent optimization of the arrangement of reinforcing bars in a rigid structural beam-column joint according to an embodiment of the present invention. Detailed Implementation

[0040] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0041] In this specification, references to "an embodiment" or "this embodiment" mean that a particular feature, structure, or characteristic described in connection with that embodiment is included in at least one embodiment of the invention. The phrase "in one embodiment" appearing throughout this specification does not necessarily refer to the same embodiment in all instances.

[0042] It should be noted that the embodiments of the present invention describe the process steps in a specific order; however, this is only for illustrating the specific embodiment and not for limiting the order of the steps. On the contrary, in different embodiments of the present invention, the order of the steps can be adjusted according to the process.

[0043] like Figure 1 As shown, this invention provides a method for intelligent optimization of the reinforcement arrangement of beam-column joints in stiffened structures, comprising:

[0044] Step S1: The first information acquisition and processing module acquires the sample steel reinforcement layout parameters from the steel reinforcement layout drawings of the stiffened structural nodes.

[0045] Preferably, the sample reinforcement arrangement parameters include: reinforcement type x, reinforcement diameter d, reinforcement spacing s, number of reinforcement rows m, and total number of reinforcements n;

[0046] Here, the first information acquisition and processing module's acquisition work includes collecting relevant drawings of the reinforcement layout of the stiffened structure nodes, and processing the reinforcement layout information such as reinforcement type x, reinforcement diameter d, reinforcement spacing s, number of reinforcement rows m, and total number of reinforcements n.

[0047] Preferably, the processing work of the first information acquisition and processing module also includes labeling the rebar arrangement parameters to facilitate identification by the first model training module; and unifying the data units of the rebar arrangement parameters, with the rebar diameter d and rebar spacing s being uniformly set to "mm", and the rebar type x, rebar number m, and total number of rebars n being "dimensionless".

[0048] Step S2, the first model training module, based on the obtained steel reinforcement layout drawings and sample steel reinforcement layout parameters of the stiffened structure nodes, and using artificial intelligence methods, establishes a node steel reinforcement parameter extraction model;

[0049] Here, the input to the nodal reinforcement parameter extraction model is the reinforcement layout drawing of the stiffened structural node;

[0050] The output of the node reinforcement parameter extraction model is reinforcement type x, reinforcement diameter d, reinforcement spacing s, number of reinforcement rows m, and total number of reinforcements n;

[0051] Optionally, the form of the node reinforcement parameter extraction model is not limited, and may include: DNN, RNN and Transformer models, all of which can be used to establish the parameter group model of the formwork support system components;

[0052] Preferably, to ensure training effectiveness, the training set for the node reinforcement parameter extraction model needs to exceed 10,000 sets of data.

[0053] Preferably, the training process of the node reinforcement parameter extraction model can use optimization algorithms such as SGD and Adam until the target loss function reaches the preset expected value, at which point the training can be stopped; after the training is completed, the parameter group model of the template support system component is obtained, that is, the node reinforcement parameter extraction model.

[0054] Step S3, the first information acquisition and processing module, collects sample steel bar layout parameters and optimizes layout rules;

[0055] Step S4, the second model training module trains an intelligent optimization layout model for the steel reinforcement of the stiffened structure nodes based on the sample steel reinforcement layout parameters and optimization layout rules.

[0056] Step S5, Rebar Layout Optimization Parameter Selection Module: Set the rebar layout parameters to be optimized according to requirements;

[0057] Preferred, the rebar layout parameters to be optimized include one or more of the following: rebar type x, rebar diameter d, rebar spacing s, number of rebar rows m, and total number of rebars n;

[0058] Step S6: Intelligent optimization layout model for reinforcing bars in stiffened structural nodes. Based on the optimization layout rules, the reinforcement layout parameters to be optimized are optimized to select reinforcement layout information parameters that conform to each stiffened structural node. All reinforcement layout information parameters of each stiffened structural node are input into the optimization function to obtain the layout scheme.

[0059] Preferably, the optimized layout rules include: the number of rebar rows m is less than or equal to 3; the rebar spacing s is between 25 and 300 mm; the optimized range for the total number of rebars n is 75% to 125%n0, where n0 is the initial number of rebars; the optimized range for the rebar diameter is 75% to 125%d0, and between 12 and 40 mm, where d0 is the initial rebar diameter; the rebar anchorage methods include: direct anchorage, welding of connecting plates, and rebar sleeve connection; and avoiding pouring spaces smaller than a preset threshold or complex geometric shapes.

[0060] The reinforcement arrangement information parameters of each stiffened structural node output by the intelligent optimization model for reinforcement arrangement of stiffened structural nodes appear in groups. Suppose that there are j groups of reinforcement arrangement information parameters for a certain stiffened structural node that meet the requirements, that is, the output matrix is ​​as follows:

[0061]

[0062] The optimized function is:

[0063] p=p1(x)+p2(n,d)+p3(s)+p4(m);

[0064]

[0065] Where, d p The target rebar diameter is set by the designer; n0 is the initial rebar quantity; w1, w2, w3, and w4 are weighting coefficients, with weight values ​​ranging from 0 to 1. The weight values ​​for each item should be determined by the project team based on the characteristics and requirements of different projects; the rebar type is x; the rebar diameter is d; the rebar spacing is s; the number of rebar rows is m; and the total number of rebars is n.

[0066] p represents the score of a set of rebar arrangement information parameters for a certain reinforced steel structure node. The set of rebar arrangement information parameters with the highest score from the output of the optimization function is selected as the result output.

[0067] According to another aspect of the present invention, a computer-readable storage medium is also provided, having stored thereon computer-executable instructions, wherein when executed by a processor, the computer-executable instructions cause the processor to employ the method described in any of the preceding claims.

[0068] According to another aspect of the present invention, a calculator device is also provided, comprising:

[0069] Processor; and

[0070] A memory configured to store computer-executable instructions, which, when executed, cause the processor to: employ the method described in any of the preceding descriptions.

[0071] In summary, to address the problems of cumbersome, time-consuming, inefficient, and overly reliant on manual experience in the reinforcement arrangement process of stiffened structural nodes, this invention proposes a novel intelligent optimization method for the reinforcement arrangement of beam-column nodes in stiffened structures. The main technical components are as follows: a node reinforcement parameter extraction model extracts the reinforcement arrangement parameters to be optimized for the node; a reinforcement arrangement optimization parameter selection module selects the parameters to be optimized; and based on the output of the intelligent optimization arrangement model for stiffened structural node reinforcement, all eligible reinforcement arrangement parameters are used, and an optimization function calculates the optimal reinforcement arrangement parameters. This application enables the rapid acquisition of optimal formwork support system component parameters through simple parameter input.

[0072] Compared with the current methods for arranging reinforcing bars in stiff structural joints, the main advantages of this invention are as follows:

[0073] (1) High efficiency. The present invention provides an intelligent optimization method for the arrangement of reinforcing bars in stiff structural nodes, which automatically recognizes drawings and intelligently optimizes the arrangement of reinforcing bars, with a speed far exceeding that of traditional manual arrangement methods;

[0074] (2) The overall optimization parameters are highly integrated. The existing method of reinforcement arrangement at the nodes of rigid structures continuously adjusts individual parameters based on human experience, and then checks whether other parameters meet the requirements in turn. If one of them does not meet the requirements, the next set of adjustments must be made. This method comprehensively considers various parameters, performs overall optimization scoring according to preset rules, and selects the best solution.

[0075] For detailed descriptions of the various device embodiments of the present invention, please refer to the corresponding sections of the various method embodiments; they will not be repeated here.

[0076] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

[0077] It should be noted that the present invention can be implemented in software and / or a combination of software and hardware, for example, using an application-specific integrated circuit (ASIC), a general-purpose computer, or any other similar hardware device. In one embodiment, the software program of the present invention can be executed by a processor to implement the steps or functions described above. Similarly, the software program of the present invention (including associated data structures) can be stored in a computer-readable recording medium, such as RAM memory, a magnetic or optical drive, a floppy disk, or similar devices. Furthermore, some steps or functions of the present invention can be implemented in hardware, for example, as circuitry that works with a processor to perform the various steps or functions.

[0078] Furthermore, a portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. The program instructions invoking the methods of the invention may be stored in a fixed or removable recording medium, and / or transmitted via a data stream in a broadcast or other signal-carrying medium, and / or stored in the working memory of a computer device operating according to the program instructions. Here, an embodiment of the invention includes an apparatus comprising a memory for storing computer program instructions and a processor for executing the program instructions, wherein, when the computer program instructions are executed by the processor, the apparatus is triggered to operate the methods and / or technical solutions based on the foregoing embodiments of the invention.

[0079] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims. Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in the apparatus claims may also be implemented by a single unit or device in software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any particular order.

Claims

1. A method for intelligent optimization of reinforcement arrangement in beam-column joints of stiffened structures, characterized in that, include: Step S1: The first information acquisition and processing module acquires the sample steel reinforcement layout parameters from the steel reinforcement layout drawings of the stiffened structural nodes. Step S2, the first model training module, based on the reinforcement layout drawings and sample reinforcement layout parameters of the stiffened structure nodes, and using artificial intelligence methods, establishes a node reinforcement parameter extraction model; Step S4, the second information acquisition and processing module, acquires sample steel bar layout parameters and optimizes layout rules; Step S5, the second model training module trains an intelligent optimization layout model for the steel reinforcement of the stiffened structure nodes based on the sample steel reinforcement layout parameters and optimization layout rules. Step S6, Rebar Layout Optimization Parameter Selection Module: Set the rebar layout parameters to be optimized according to requirements; Step S7: Intelligent optimization layout model for steel reinforcement in stiffened structural nodes. According to the optimization layout rules, the steel reinforcement layout parameters to be optimized are optimized to select steel reinforcement layout information parameters that meet the requirements of each stiffened structural node. All steel reinforcement layout information parameter groups that meet the requirements of each stiffened structural node are input into the optimization function to obtain the optimal layout scheme. The sample steel reinforcement layout parameters include: steel reinforcement type x, steel reinforcement diameter d, steel reinforcement spacing s, number of steel reinforcement rows m, and total number of steel reinforcements n; The reinforcement arrangement information parameters of each stiffened structural node output by the intelligent optimization model for reinforcement arrangement of stiffened structural nodes appear in groups. Suppose that there are j groups of reinforcement arrangement information parameters for a certain stiffened structural node that meet the requirements, that is, the output matrix is ​​as follows: The optimized function is: p=p1(x)+p2(n,d)+p3(s)+p4(m); Where, d p Let n be the target rebar diameter, n0 be the initial rebar quantity, w1, w2, w3, and w4 be weighting coefficients with a value range of 0 to 1; and let x be the rebar type, d be the rebar diameter, s be the rebar spacing, m be the number of rebar rows, and n be the total number of rebars. p represents the score of a set of rebar arrangement information parameters for a certain reinforced steel structure node. The set of rebar arrangement information parameters with the highest score from the output of the optimization function is selected as the result output.

2. The intelligent optimization arrangement method for reinforcement bars in the beam-column joint of a stiffened structure as described in claim 1, characterized in that, The first information acquisition and processing module acquires sample reinforcement layout parameters from the reinforcement layout drawings of the stiffened structural nodes, including: The rebar arrangement parameters are labeled to facilitate identification by the first model training module; and the data units of the rebar arrangement parameters are unified, with the rebar diameter d and rebar spacing s being uniformly set to "mm"; the rebar type x, rebar number m, and total number of rebars n are set to "dimensionless".

3. The intelligent optimization arrangement method for reinforcement bars in beam-column joints of stiffened structures as described in claim 1, characterized in that, The input to the node reinforcement parameter extraction model is the reinforcement layout drawing of the stiffened structural node; The output of the node reinforcement parameter extraction model is reinforcement type x, reinforcement diameter d, reinforcement spacing s, number of reinforcement rows m, and total number of reinforcements n.

4. The intelligent optimization arrangement method for reinforcement bars in beam-column joints of stiffened structures as described in claim 1, characterized in that, The first model training module, based on the obtained reinforcement layout drawings and sample reinforcement layout parameters of the stiffened structural nodes, and using artificial intelligence methods, establishes a node reinforcement parameter extraction model, including: The form of the node reinforcement parameter extraction model includes: DNN, RNN or Transformer model; The training process of the node reinforcement parameter extraction model adopts the SGD and Adam optimization algorithms until the target loss function reaches the preset expected value, at which point the training can be stopped; after the training is completed, the parameter group model of the template support system component is obtained, namely the node reinforcement parameter extraction model.

5. The intelligent optimization arrangement method for reinforcement bars in beam-column joints of stiffened structures as described in claim 1 or 2, characterized in that, The rebar layout optimization parameter selection module allows you to set the rebar layout parameters to be optimized according to your needs. The rebar layout parameters to be optimized include one or more of the following: rebar type x, rebar diameter d, rebar spacing s, number of rebar rows m, and total number of rebars n.

6. The intelligent optimization arrangement method for reinforcement bars in beam-column joints of stiffened structures as described in claim 1, characterized in that, The optimized layout rules include: The number of rebar rows m is less than or equal to 3; the rebar spacing s is between 25 and 300 mm; the optimized range for the total number of rebars n is 75% to 125%n0, where n0 is the initial number of rebars; the optimized range for the rebar diameter is 75% to 125%d0, and between 12 and 40 mm, where d0 is the initial rebar diameter; the rebar anchorage methods include: direct anchorage, welding of connecting plates, and rebar sleeve connection; avoid pouring spaces smaller than the preset threshold or complex geometric shapes.

7. A computer-readable storage medium having stored thereon computer-executable instructions, wherein, When the computer-executable instructions are executed by the processor, the processor causes the processor to: employ the method as described in any one of claims 1 to 6.

8. A calculator device, wherein, include: processor; as well as A memory configured to store computer-executable instructions, which, when executed, cause the processor to: employ the method as described in any one of claims 1 to 6.

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