Design method and system of concrete formwork support system based on machine learning

Abstract

The present invention discloses a design method and system for a concrete formwork support system based on machine learning, the method comprising: inputting the structural parameters of the concrete to be supported into a parameter group model of the formwork support system to obtain multiple groups of support system parameter information; calculating the score values ​​of the multiple groups of support system parameter information according to the safety margin score, the construction convenience score, the construction period score and the construction cost score; and outputting the parameter group of the formwork support system according to the score value. The above-mentioned design method and system for a concrete formwork support system based on machine learning can quickly output the preferred parameter group of the formwork support system after inputting the structural parameters of the concrete through the concrete formwork support system optimization design method based on machine learning, which is much faster than traditional methods such as manual trial calculation, improves the design efficiency, and the preferred parameters have strong integrity.

Description

Technical Field

[0001] The present invention relates to a design method and system of a concrete formwork support system based on machine learning, and belongs to the field of intelligent construction of civil engineering. Background Art

[0002] Formwork is widely used in cast-in-place concrete structure engineering, and its support system is an important structure to ensure the safety of the concrete pouring process. The basic principle of concrete formwork support is to build a formwork support system so that the formwork can withstand the deadweight and force during the concrete pouring process, ensuring the structural stability and construction quality during the construction process.

[0003] Generally, the design of formwork support system mainly relies on manual calculations, which often requires engineering technicians to continuously adjust and calculate the parameters of small beams, main beams, support rods and other component parameters based on experience. The process is cumbersome and time-consuming. Summary of the invention

[0004] In view of the problem that the existing formwork support system design process is cumbersome and time-consuming, the present invention provides a design method and system for a concrete formwork support system based on machine learning.

[0005] In order to solve the above technical problems, the present invention includes the following technical solutions:

[0006] A method for designing a concrete formwork support system based on machine learning, comprising the following steps:

[0007] Step 1: Input the structural parameters of the concrete to be supported into the template support system to construct a parameter group model, and obtain multiple groups of support system parameter information, wherein the support system parameter information includes template model, beam model and spacing, main beam model, pole model, pole longitudinal distance, pole horizontal distance, construction period, construction cost, and component maximum stress ratio;

[0008] Step 2: Calculate the score values ​​of multiple groups of the support system parameter information according to the safety margin score, the construction convenience score, the construction period score and the construction cost score; wherein the safety margin score is determined by the maximum stress ratio of the component, the construction convenience score is determined by the vertical distance of the pole and the horizontal distance of the pole, the construction period score is determined by the construction period of the support system, and the construction cost score is determined by the construction cost;

[0009] Step three: outputting a parameter group of the template support system according to the scoring value.

[0010] Furthermore, in step three, the step of outputting a parameter group of the template support system according to the score value includes:

[0011] The parameter group with the largest score value is determined as the parameter group of the formwork support system.

[0012] Furthermore, the score value of the support system parameter information=safety margin score*safety margin weight+construction convenience score*construction convenience weight+construction duration score*construction duration weight+construction cost score*construction cost weight.

[0013] Furthermore, the safety margin score x1,i ranges from 0 to 100, which is determined by the maximum stress ratio y of the components of the i-th group of formwork support system. i OK, the calculation formula is: x 1,i =(1-yi)×100.

[0014] Furthermore, the construction convenience score x 2,i The value ranges from 0 to 100, and is determined by the vertical distance l of the vertical poles of the i-th group of formwork support system. a,i And the vertical distance of the pole l b,i Decision, x 2,i The calculation formula is:

[0015] .

[0016] Furthermore, the construction period score x 3,i The construction period t of the i-th group of formwork support system i Decision, x 3,i The calculation formula is as follows:

[0017] ;

[0018] T is the target construction period.

[0019] Further, the construction cost score is composed of the construction cost m of the i-th group of formwork support system i Decision, x 4,i It can be calculated by the following formula:

[0020] ;

[0021] M is the project cost budget.

[0022] Furthermore, the template support system parameter group model construction also includes the step of establishing a database before training:

[0023] Collect calculations, construction plans, and construction budget data including calculations of different concrete formwork support systems;

[0024] Extract concrete structure information, construction cost information and support system information;

[0025] The database is established according to the concrete structure information, the construction cost information and the support system information.

[0026] Further, the concrete structure information includes the thickness of the concrete slab, the height of the concrete beam section, the width of the concrete beam section, and the construction load of pouring concrete;

[0027] The construction cost information: construction period, construction cost;

[0028] The support system information includes: template model, small beam model and spacing, main beam model, vertical pole model, vertical pole distance, and horizontal pole distance.

[0029] The system of the above-mentioned method for designing a concrete formwork support system based on machine learning comprises:

[0030] An input module is used to input the structural parameters of the concrete to be supported into a template support system construction parameter group model to obtain multiple groups of support system parameter information, wherein the support system parameter information includes template model, beam model and spacing, main beam model, pole model, pole longitudinal distance, pole horizontal distance, construction period, construction cost, and component maximum stress ratio;

[0031] A calculation module, used to calculate the score values ​​of multiple groups of the support system parameter information according to the safety margin score, the construction convenience score, the construction period score and the construction cost score; wherein the safety margin score is determined by the maximum stress ratio of the component, the construction convenience score is determined by the vertical distance of the pole and the horizontal distance of the pole, the construction period score is determined by the construction period of the support system, and the construction cost score is determined by the construction cost;

[0032] An output module is used to output a parameter group of the template support system according to the score value.

[0033] Due to the adoption of the above technical solution, the present invention has the following advantages and positive effects compared with the prior art:

[0034] (1) High efficiency. The concrete formwork support system optimization design method based on machine learning can quickly output the optimal formwork support system parameter group after inputting the structural parameters of the concrete. The speed is much higher than traditional methods such as manual calculation, thus improving the design efficiency.

[0035] (2) The optimal parameters have strong integrity. In determining the formwork support system parameter group, the safety margin, construction period, construction cost, construction convenience, etc. are comprehensively considered. If one of the items does not meet the requirements, it is necessary to re-calculate the next group of parameters and comprehensively consider the weights of all parties to make an overall score and select the best formwork support system parameter group. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A flowchart of a method for designing a concrete formwork support system based on machine learning according to an embodiment;

[0037] Figure 2 A structural schematic diagram of a design system for a concrete formwork support system based on machine learning according to an embodiment. DETAILED DESCRIPTION

[0038] The following is a further detailed description of the design method and system of the concrete formwork support system based on machine learning provided by the present invention in conjunction with the accompanying drawings and specific embodiments. The advantages and features of the present invention will become clearer in conjunction with the following description. It should be noted that the accompanying drawings are all in a very simplified form and are not in precise proportions, and are only used to conveniently and clearly assist in explaining the purpose of the embodiments of the present invention.

[0039] The present invention is based on the problem that the current concrete formwork support system design is time-consuming and inefficient, and provides a design method for a concrete formwork support system based on machine learning. This method can quickly obtain the optimal formwork support system component parameters through simple parameter input. Embodiment 1

[0040] like Figure 1 As shown, a design method of a concrete formwork support system based on machine learning in one embodiment includes the following steps:

[0041] Step 1 S10, input the structural parameters of the concrete to be supported into the template support system construction parameter group model, and obtain multiple groups of support system parameter information, wherein the support system parameter information includes template model MB, small beam model Xl and spacing S, main beam model ZL, vertical pole model LG, vertical pole longitudinal distance la, vertical pole horizontal distance lb, construction period t, construction cost m, and component maximum stress ratio y.

[0042] The template support system component parameter group model can be trained in advance, where the input information is the structural parameters of the concrete to be supported, and the output information is the support system parameter information. There will be two or more groups of support system parameter information, and the optimal support system parameter information will be screened out later. At the same time, the template support system component parameter group model can also be trained by the following method.

[0043] In this embodiment, the template support system construction parameter group model also includes the step of establishing a database before training, specifically:

[0044] Collect calculation books, construction plans, and construction budget data for different concrete formwork support systems. The calculation books for concrete formwork support systems are mainly calculated based on relevant national or industry standards to ensure the stability and safety of the formwork support system. Its specific contents usually include: calculation basis, engineering properties, load design, formwork system design, panel verification, beam verification, etc. The construction plan is a specific plan to guide the construction of the concrete formwork support system, and its specific contents usually include: construction preparation, construction process, construction points, safety measures and environmental protection measures, etc. The construction budget data is the basis for construction cost control, and its specific contents usually include: material costs, labor costs, equipment costs, total budget, etc.

[0045] Extract concrete structure information, construction cost information and support system information.

[0046] Among them, the concrete structure information includes: the thickness of the concrete slab h b , Concrete beam section height H l , Concrete beam section width B l , construction load Q for pouring concrete;

[0047] Construction cost information includes: construction period t, construction cost M;

[0048] Support system information includes: formwork model MB, small beam model Xl and spacing S, main beam model ZL, vertical pole model LG, vertical pole distance l a , Horizontal distance of poles l b .

[0049] The database is established based on concrete structure information, construction cost information and support system information. It should be noted that the concrete structure information, construction cost information and support system information can be extracted from the calculation book, construction plan and construction budget data of the concrete formwork support system by using the extraction methods commonly used in the art, such as classification algorithms, convolutional neural networks, recurrent neural networks, etc., which are not limited here.

[0050] The above database is used to establish and train the template support system construction parameter group model.

[0051] The model input is the concrete slab thickness h b , Concrete beam section height H l , Concrete beam section width B l , construction load Q for pouring concrete;

[0052] The model output is template model MB, small beam model Xl and spacing S, main beam model ZL, vertical pole model LG, vertical pole distance l a , Horizontal distance of poles l b, construction period t, construction cost m, and component maximum stress ratio y.

[0053] The model form is not limited. Models such as DNN (fully connected neural network), RNN (recurrent neural network) and Transformer can all be used to establish the parameter group model of the template support system component. To ensure the training effect, the training set needs to have more than 10,000 sets of data.

[0054] The model training process can use optimization algorithms such as SGD (stochastic gradient descent) and Adam until the target loss function reaches the preset expected value, and then the training can be stopped. After the training is completed, the parameter group model of the template support system component is obtained.

[0055] In actual application, the concrete structure information is input into the formwork support system component parameter group model, that is, the concrete slab thickness h b , Concrete beam section height H l , Concrete beam section width B l , the construction load Q for pouring concrete. The template support system component parameter group model will output all support system parameter information that meets the force requirements, that is, multiple groups of support system parameter information. Among them, the support system parameter information includes template model, beam model and spacing, main beam model, pole model, pole longitudinal distance, pole horizontal distance, construction period, construction cost, and component maximum stress ratio. In addition, the unit of the calculated input value should be consistent with the training data unit when training the model to ensure the accuracy of the output result.

[0056] Step 2 S20, calculates the score values ​​of multiple groups of support system parameter information according to the safety margin score, construction convenience score, construction period score and construction cost score; wherein the safety margin score is determined by the maximum stress ratio of the component, the construction convenience score is determined by the vertical distance of the pole and the horizontal distance of the pole, the construction period score is determined by the construction period of the support system, and the construction cost score is determined by the construction cost.

[0057] In step 1, the template support system construction parameter group model will output all support system parameters that meet the force requirements, so the output result is multiple groups of support system parameters. In step 2, the score value of each group of support system parameters will be calculated to determine the optimal support system parameters.

[0058] In this embodiment, the score value of the support system parameter information is calculated by the safety margin score, the construction convenience score, the construction period score and the construction cost score. The specific calculation formula is: the score value of the support system parameter information = safety margin score * safety margin weight + construction convenience score * construction convenience weight + construction period score * construction period weight + construction cost score * construction cost weight.

[0059] Among them, the safety margin score x 1,i The value ranges from 0 to 100, and the maximum stress ratio y of the components of the i-th group of formwork support system i OK, the calculation formula is: x 1,i =(1-y i )×100.

[0060] Construction convenience score x 2,i The value ranges from 0 to 100, and is determined by the vertical distance l of the vertical poles of the i-th group of formwork support system. a,i And the vertical distance of the pole l b,i Decision, x 2,i The calculation formula is:

[0061] .

[0062] Construction duration score x 3,i The construction period t of the i-th group of formwork support system i Decision, x 3,i The calculation formula is as follows:

[0063] ;

[0064] T is the target construction period, which can be selected according to the actual construction plan.

[0065] The construction cost score is composed of the construction cost m of the i-th group of formwork support system i Decision, x 4,i It can be calculated by the following formula:

[0066] ;

[0067] M is the project cost budget, which can be determined based on the actual construction situation.

[0068] The determination of the formwork support system parameters also needs to consider the safety margin weight W 1 , construction convenience weight W 2 、Construction work period weight W 3 , construction cost weight W 4 The weight range is 0~1. The weight values ​​are determined by the project staff according to the characteristics and requirements of different projects and are not limited here.

[0069] Therefore, the score value p of each group of support system parameter information i =x 1,i W 1 +x 2,i W 2 +x 3,i W 3 +x 4,i W 4. p i is the weighted score of the i-th group of formwork support systems.

[0070] Step 3 S30, outputting the template support system parameter group according to the score value. After the score values ​​of each group of support system parameter information are determined, the required template support system parameter values ​​can be determined according to the score values. In this embodiment, the parameter group with the largest score value is determined as the template support system parameter group.

[0071] The above-mentioned design method of the concrete formwork support system based on machine learning obtains the formwork support system component parameter library through information collection and processing and model training modules to establish the formwork support system construction parameter group model. At the construction site, the technicians only need to input the corresponding parameters of the supported concrete structure according to the on-site working conditions, and the formwork support system component parameter group model will enter all component parameter groups that meet the force requirements into the library and calculate and select the optimal component parameter group of the formwork support system. Embodiment 2

[0072] like Figure 2 As shown, a system of a method for designing a concrete formwork support system based on machine learning includes an input module 100, a calculation module 110 and an output module 120.

[0073] The input module 100 is used to input the structural parameters of the concrete to be supported into the template support system construction parameter group model to obtain multiple groups of support system parameter information, wherein the support system parameter information includes template model, small beam model and spacing, main beam model, vertical pole model, vertical pole distance, horizontal pole distance, construction period, construction cost, and maximum stress ratio of components.

[0074] The calculation module 110 is used to calculate the score values ​​of multiple groups of the support system parameter information according to the safety margin score, the construction convenience score, the construction period score and the construction cost score; wherein the safety margin score is determined by the maximum stress ratio of the component, the construction convenience score is determined by the vertical distance of the poles and the horizontal distance of the poles, the construction period score is determined by the construction period of the support system, and the construction cost score is determined by the construction cost.

[0075] The output module 120 is used to output a parameter group of the template support system according to the scoring value.

[0076] Compared with the current formwork support system optimization design method, the present invention has the following advantages:

[0077] (1) High efficiency. The concrete formwork support system optimization design method based on machine learning can quickly output the optimal formwork support system parameter group after inputting the structural parameters of the concrete. The speed is much higher than traditional methods such as manual calculation, thus improving the design efficiency.

[0078] (2) The optimal parameters have strong integrity. In determining the formwork support system parameter group, the safety margin, construction period, construction cost, construction convenience, etc. are comprehensively considered. If one of the items does not meet the requirements, it is necessary to re-calculate the next group of parameters and comprehensively consider the weights of all parties to make an overall score and select the best formwork support system parameter group.

[0079] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0080] The above-mentioned embodiments only express several implementation modes of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the attached claims.

Claims

1. A design method for a concrete formwork support system based on machine learning, characterized in that: The following steps are involved: Step 1: Input the structural parameters of the concrete to be supported into the template support system to construct a parameter group model, and obtain multiple groups of support system parameter information, wherein the support system parameter information includes template model, beam model and spacing, main beam model, pole model, pole longitudinal distance, pole horizontal distance, construction period, construction cost, and component maximum stress ratio; Step 2: Calculate the score values ​​of multiple groups of the support system parameter information according to the safety margin score, the construction convenience score, the construction period score and the construction cost score; wherein the safety margin score is determined by the maximum stress ratio of the component, the construction convenience score is determined by the vertical distance of the pole and the horizontal distance of the pole, the construction period score is determined by the construction period of the support system, and the construction cost score is determined by the construction cost; Step 3, outputting a parameter group of the template support system according to the scoring value; The template support system construction parameter group model is pre-trained using a database, and establishing the database includes the steps of: collecting calculation books, construction plans, and construction budget data containing calculations of different concrete template support systems; extracting concrete structure information, construction cost information, and support system information; and establishing the database based on the concrete structure information, the construction cost information, and the support system information.

2. The design method of a concrete formwork support system based on machine learning as claimed in claim 1, characterized in that: In the step three, the step of outputting a parameter group of the template support system according to the score value includes: The parameter group with the largest score value is determined as the parameter group of the formwork support system.

3. The design method of a concrete formwork support system based on machine learning as claimed in claim 1, characterized in that: The score value of the support system parameter information = safety margin score * safety margin weight + construction convenience score * construction convenience weight + construction period score * construction period weight + construction cost score * construction cost weight.

4. The method for designing a concrete formwork support system based on machine learning as claimed in claim 3, characterized in that: The safety margin score x 1,i The value ranges from 0 to 100, and the maximum stress ratio y of the components of the i-th group of formwork support system i OK, the calculation formula is: x 1,i =(1-y i )×100.

5. The method for designing a concrete formwork support system based on machine learning as claimed in claim 3, characterized in that: The construction convenience score x 2,i The value ranges from 0 to 100, and is determined by the vertical distance l of the vertical poles of the i-th group of formwork support system. a,i And the vertical distance of the pole l b,i Decision, x 2,i The calculation formula is: 。 6. The method for designing a concrete formwork support system based on machine learning as claimed in claim 3, characterized in that: The construction duration score x 3,i The construction period t of the i-th group of formwork support system i Decision, x 3,i The calculation formula is as follows: ； T is the target construction period.

7. The method for designing a concrete formwork support system based on machine learning as claimed in claim 3, characterized in that: The construction cost score is composed of the construction cost m of the i-th group of formwork support system i Decision, x 4,i It can be calculated by the following formula: ； M is the project cost budget.

8. The method for designing a concrete formwork support system based on machine learning as claimed in claim 1, characterized in that: The concrete structure information includes the thickness of the concrete slab, the height of the concrete beam section, the width of the concrete beam section, and the construction load of pouring concrete; The construction cost information: construction period, construction cost; The support system information includes: template model, small beam model and spacing, main beam model, vertical pole model, vertical pole distance, and horizontal pole distance.

9. A system using the method for designing a concrete formwork support system based on machine learning as described in any one of claims 1 to 8, characterized in that: include: An input module is used to input the structural parameters of the concrete to be supported into a template support system construction parameter group model to obtain multiple groups of support system parameter information, wherein the support system parameter information includes template model, beam model and spacing, main beam model, pole model, pole longitudinal distance, pole horizontal distance, construction period, construction cost, and component maximum stress ratio; A calculation module, used to calculate the score values ​​of multiple groups of the support system parameter information according to the safety margin score, the construction convenience score, the construction period score and the construction cost score; wherein the safety margin score is determined by the maximum stress ratio of the component, the construction convenience score is determined by the vertical distance of the pole and the horizontal distance of the pole, the construction period score is determined by the construction period of the support system, and the construction cost score is determined by the construction cost; An output module is used to output a parameter group of the template support system according to the score value.

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