BIM software user reinforcement tendency parameter optimization method

Abstract

The invention discloses a BIM software user reinforcement tendency parameter optimization method. The method comprises the steps that a user tendency parameter weight calculation unit reads past reinforcement data; steel bar types are grouped according to stress, the types of elements in each reinforcing bar stress group serve as the mean value of Gaussian distribution, a Gaussian distribution function is generated in combination with super parameters, and a density function is obtained; each reinforcement stress group corresponds to a probability density function, and a contrast table is formed according to the mapping relation of the stress corresponding probability density functions; probability density of all elements in a pre-configured steel bar type tendency table is calculated and normalized, and the tendency weight of each element in the steel bar type tendency table is obtained; inputting the tendency weight into a reinforcement calculation unit, and calculating an optimal reinforcement scheme; and the user modifies the reinforcement scheme to obtain reinforcement data and stores the reinforcement data in the reinforcement type tendency table as input during next reinforcement. The invention relates to the technical field of constructional engineering, and can solve the technical problems in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of construction engineering, and in particular to a method for optimizing reinforcement tendency parameters of BIM software users. Background Art

[0002] Building Information Modeling (BIM) has become an indispensable key technology in the architecture, engineering, and construction (AEC) industry. BIM models not only integrate a building's geometry, spatial relationships, and geographic information, but also include attribute data, construction plans, and maintenance information. This provides digital support for the entire lifecycle of engineering projects, significantly improving design efficiency, construction quality, and operations and maintenance management. Currently, BIM technology is rapidly developing towards intelligence, collaboration, and visualization.

[0003] There is a structural reinforcement module in the BIM software, whose function is to calculate the optimal reinforcement deployment plan based on the force of each structural component and the structural engineer's reinforcement type usage preference and other information. The basic scheme of the existing technology for the tendency information of the reinforcement type usage is: let the structural engineer extract a part of the reinforcement type tendency table with equal weight as the input of the automatic reinforcement algorithm in the reinforcement calculation unit to calculate the reinforcement plan. The existing reinforcement type tendency table cannot be used to configure too small a range of reinforcement types, otherwise when calculating the reinforcement plan, there may be no solution, that is, among the elements of the reinforcement type tendency table, even if the most reinforcement is deployed, it is impossible to find a reinforcement type that can bear its force.

[0004] Among them, the steel bar type preference table is used to store the steel bar types that structural engineers prefer to use when arranging reinforcement. It is a subset of the steel bar type table and is recorded in the BIM software system through autonomous configuration.

[0005] The existing reinforcement scheme has the following technical problems:

[0006] 1. The equal weight method cannot reflect the more detailed reinforcement tendencies of structural engineers, and can only obtain a relatively rough reinforcement usage tendency.

[0007] 2. During the reinforcement process, structural engineers are unable to carefully count the individual steel bar types, resulting in the inability to assign detailed weights to each type of steel bar.

[0008] Therefore, it is necessary to provide a method for optimizing reinforcement tendency parameters of BIM software users to solve the above technical problems. Summary of the Invention

[0009] The purpose of the present invention is to provide a method for optimizing reinforcement tendency parameters of BIM software users, which can solve the above technical problems.

[0010] The present invention is achieved in that:

[0011] A method for optimizing reinforcement tendency parameters of BIM software users, comprising the following steps:

[0012] Step 1: Provide a user preference parameter weight calculation unit, read all the user's reinforcement data within a certain time window in the past through the user preference parameter weight calculation unit, group the reinforcement type selected by the user according to the force, and obtain a number of reinforcement force groups;

[0013] Step 2: For each reinforcement force group, the type of each element in the reinforcement force group is used as the mean μ of the Gaussian distribution, combined with the hyperparameter σ, to generate a Gaussian distribution function N(μ,σ 2 ), calculate the Riemann sum of all elements in the reinforcement force group for the Gaussian distribution function, and obtain the following density function:

[0014]

[0015] Step 3: Generate a probability density function F for each reinforcement force group. According to the mapping relationship between force and probability density function, form a comparison table between force and probability density function F. This comparison table is regarded as an overall probability density function M.

[0016] Step 4: Calculate the probability density of all elements in the pre-configured steel bar type tendency table and normalize them to obtain the tendency weight of each element in the steel bar type tendency table;

[0017] Step 5: Input the inclination weight obtained in step 4 into the reinforcement calculation unit and calculate the optimal reinforcement scheme;

[0018] Step 6: Based on the reinforcement scheme obtained in step 5, the user further modifies the reinforcement data and stores it in the reinforcement type tendency table as input for the reinforcement calculation unit during the next reinforcement.

[0019] In step 3, M(x,i) is used as the overall probability density function, where x represents the force and i represents the steel bar type; M(x,i) represents the probability density of using steel bar type i for structural reinforcement when calculating the force value x.

[0020] Described step 4 comprises the following sub-steps:

[0021] Step 4.1: Read the force value x of the structural member;

[0022] Step 4.2: Read the currently configured reinforcement information in the reinforcement type preference table from the configuration information of the BIM software system;

[0023] Step 4.3: For each element in the steel bar type tendency table, calculate its probability density using the probability density function M(x,i).

[0024] In step 4.3, the probability density function M(x,i) is calculated as follows:

[0025] Step 4.3.1: In the comparison table generated in step 3, find whether there is a density function F corresponding to the force value x. If so, proceed to step 4.3.2. If not, proceed to step 4.3.3.

[0026] Step 4.3.2: Directly use the density function F found to calculate the probability density;

[0027] Step 4.3.3: Find the first force value and the second force value that wrap the force value x, set as x1 and x2 respectively. The density function corresponding to the first force value x1 is F1, and the density function corresponding to the second force value x2 is F2. Use linear combination Calculate the probability density.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] The present invention utilizes the user's past reinforcement usage habits stored in the steel bar type tendency table. By extracting the reinforcement usage habit information within a certain time window in the past and combining it with the current steel bar type comparison table, the steel bar type weight coefficient used in this reinforcement is calculated. Compared with the traditional equal weight method, the weight coefficient can more finely reflect the user's steel bar selection tendency, thereby generating better reinforcement data; at the same time, detailed weight assignment can be performed on each type of steel bar, which is convenient for detailed statistics of individual steel bar types and serves as input for the automatic reinforcement algorithm during the next reinforcement. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a principle block diagram of the method for optimizing reinforcement tendency parameters of BIM software users of the present invention;

[0031] Figure 2 is a curve diagram of the density function in the method for optimizing reinforcement tendency parameters of BIM software users of the present invention;

[0032] Figure 3 It is a flow chart of the method for optimizing reinforcement tendency parameters of BIM software users of the present invention;

[0033] Figure 4 It is a calculation flow chart of the probability density function in the BIM software user reinforcement tendency parameter optimization method of the present invention. DETAILED DESCRIPTION

[0034] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0035] Please see the attached Figure 3 , a method for optimizing reinforcement tendency parameters of BIM software users, comprising the following steps:

[0036] Please see the attached Figure 1 ,Step 1: Provide a user preference parameter weight calculation unit, read all the reinforcement data of the user in a certain time window in the past through the user preference parameter weight calculation unit, group the steel bar type selected by the user according to the force, and obtain several reinforcement force groups.

[0037] All user reinforcement data is stored in the reinforcement type preference table. When users reinforce structural components, they can select reinforcement data within a certain time window in the past according to actual needs. The time and length of this time window can be determined by the similarity between the current reinforcement requirements and the reinforcement requirements of the time window to improve calculation accuracy.

[0038] Structural reinforcement: This refers to the allocation of reinforcement based on the load conditions of structural components such as columns, beams, and slabs, ensuring that the structural components meet the designed load requirements. Generally, when reinforcing structural components, it is best to ensure that the minimum amount of steel is used.

[0039] Step 2: For each reinforcement force group, the type of each element in the reinforcement force group is used as the mean μ of the Gaussian distribution, combined with the hyperparameter σ, to generate a Gaussian distribution function N(μ,σ 2 ), calculate the Riemann sum of all elements in the reinforcement force group for the Gaussian distribution function, and obtain the following density function: As attached Figure 2 shown.

[0040] The density function represents the probability density of the corresponding steel bar type selected when a certain force value is fixed. The Gaussian distribution function is used to calculate the user's previous reinforcement schemes through the density function.

[0041] Normal distribution: Also known as normal distribution or Gaussian distribution, usually denoted as X~N(μ,σ 2 ). Where μ is the mathematical expectation (mean) of the normal distribution, σ 2 is the variance of the normal distribution.

[0042] Step 3: Generate a probability density function F for each reinforcement force group. According to the mapping relationship between force and probability density function, form a comparison table between force and probability density function F; regard this comparison table as an overall probability density function M.

[0043] In step 3, M(x,i) is used as the overall probability density function, where x represents the force and i represents the steel bar type; M(x,i) represents the probability density of using steel bar type i for structural reinforcement when calculating the force value x.

[0044] Please see the attached Figure 3 ,Step 4: Calculate the probability density of all elements in the preconfigured steel bar type tendency table and normalize them to obtain the tendency weight of each element in the steel bar type tendency table.

[0045] Described step 4 comprises the following sub-steps:

[0046] Step 4.1: Read the force value x of the structural member.

[0047] Step 4.2: Read the currently configured reinforcement information in the reinforcement type preference table from the configuration information of the BIM software system.

[0048] Step 4.3: For each element in the steel bar type tendency table, calculate its probability density using the probability density function M(x,i).

[0049] Due to the discreteness of the previous steel bar type usage tendency data, the probability density function M(x,i) is not continuous in the dimension of stress value x, so all probability density functions M(x,i) are not simple function calculations.

[0050] Please see the attached Figure 4 In step 4.3, the probability density function M(x,i) is calculated as follows:

[0051] Step 4.3.1: In the comparison table generated in step 3, find out whether there is a density function F corresponding to the force value x. If so, proceed to step 4.3.2. If not, proceed to step 4.3.3.

[0052] Step 4.3.2: Directly use the density function F found to calculate the probability density.

[0053] Step 4.3.3: Find the first force value and the second force value that wrap the force value x, set as x1 and x2 respectively. The density function corresponding to the first force value x1 is F1, and the density function corresponding to the second force value x2 is F2. Use linear combination Calculate the probability density.

[0054] Step 5: Input the inclination weight obtained in step 4 into the reinforcement calculation unit and automatically calculate the optimal reinforcement scheme.

[0055] By using the statistical users' previous reinforcement schemes, the tendency weight of each element in the reinforcement type tendency table is normalized to obtain the tendency weight of each element in the reinforcement type tendency table, and more specific and detailed reinforcement type usage tendency weights are calculated to replace the mean weight of the existing technology, so that a better reinforcement scheme can be calculated.

[0056] Step 6: Based on the reinforcement scheme obtained in step 5, the user further modifies the reinforcement data and stores it in the reinforcement type tendency table as input for the reinforcement calculation unit during the next reinforcement.

[0057] The above are only preferred embodiments of the present invention and are not intended to limit the scope of protection of the invention. Therefore, any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for optimizing reinforcement tendency parameters of BIM software users, characterized by: The following steps are involved: Step 1: Provide a user preference parameter weight calculation unit, read all the user's reinforcement data within a certain time window in the past through the user preference parameter weight calculation unit, group the reinforcement type selected by the user according to the force, and obtain a number of reinforcement force groups; Step 2: For each reinforcement force group, the type of each element in the reinforcement force group is used as the mean μ of the Gaussian distribution, combined with the hyperparameter σ, to generate a Gaussian distribution function N(μ,σ 2 ), calculate the Riemann sum of all elements in the reinforcement force group for the Gaussian distribution function, and obtain the following density function: Step 3: Generate a probability density function F for each reinforcement force group. According to the mapping relationship between force and probability density function, form a comparison table between force and probability density function F; regard this table as an overall probability density function M. Step 4: Calculate the probability density of all elements in the pre-configured steel bar type tendency table and normalize them to obtain the tendency weight of each element in the steel bar type tendency table; Step 5: Input the inclination weight obtained in step 4 into the reinforcement calculation unit and calculate the optimal reinforcement scheme; Step 6: Based on the reinforcement scheme obtained in step 5, the user further modifies the reinforcement data and stores it in the reinforcement type tendency table as input for the reinforcement calculation unit during the next reinforcement.

2. The method for optimizing reinforcement tendency parameters of BIM software users according to claim 1, characterized in that: In step 3, M(x,i) is used as the overall probability density function, where x represents the force and i represents the steel bar type; M(x,i) represents the probability density of using steel bar type i for structural reinforcement when calculating the force value x.

3. The method for optimizing reinforcement tendency parameters of BIM software users according to claim 1, characterized in that: Described step 4 comprises the following sub-steps: Step 4.1: Read the force value x of the structural member; Step 4.2: Read the currently configured reinforcement information in the reinforcement type preference table from the configuration information of the BIM software system; Step 4.3: For each element in the steel bar type tendency table, calculate its probability density using the probability density function M(x,i).

4. The method for optimizing reinforcement tendency parameters of BIM software users according to claim 3 is characterized by: In step 4.3, the probability density function M(x,i) is calculated as follows: Step 4.3.1: In the comparison table generated in step 3, find whether there is a density function F corresponding to the force value x. If so, proceed to step 4.3.

2. If not, proceed to step 4.3.

3. Step 4.3.2: Directly use the density function F found to calculate the probability density; Step 4.3.3: Find the first force value and the second force value that wrap the force value x, set as x1 and x2 respectively. The density function corresponding to the first force value x1 is F1, and the density function corresponding to the second force value x2 is F2. Use linear combination Calculate the probability density.

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