Intelligent determination method of support combination for large-span structures based on deformation coordination

Abstract

The present invention discloses a method for intelligently determining the combination of large-span structural supports based on deformation coordination, which relates to the field of intelligent optimization technology. The method includes: retrieving historical support information, identifying various types of supports, and establishing a support database; establishing a support twin model, and obtaining a support twin model library; performing load force analysis on multiple types of supports, and obtaining a support-deformation database; retrieving the support requirements of the large-span structure, and determining the support combination information; based on the support position and the number of supports, randomly retrieving from the support database to form an initial support combination scheme; based on the support twin model library and the support-deformation database, the initial support combination scheme is optimized for support deformation coordination, and the final support combination scheme is determined. The present invention solves the technical problems of low safety and poor stability in the prior art, and achieves the technical effect of improving the safety and stability of the large-span structure support combination through an intelligent determination method.

Description

Technical Field

[0001] The present invention relates to the field of intelligent optimization technology, and in particular to a method for intelligently determining a combination of support members of a large-span structure based on deformation coordination. Background Art

[0002] With the acceleration of urbanization and the continuous advancement of engineering technology, the design and construction of large-span structural support assemblies are becoming more and more common in various engineering projects. These support assemblies are widely used in the construction of infrastructure such as bridges, highways, tunnels, and large venues to meet the needs of structural support and protection. However, with the expansion of project scale and the increase in the complexity of the construction environment, the design and construction of large-span structural support assemblies are facing more and more challenges. Traditional support assembly design methods are often based on experience and personal judgment, lack of systematicity and scientificity, and it is difficult to ensure the stability and safety of the support assembly. Summary of the invention

[0003] The present application provides an intelligent determination method for the support combination of a large-span structure based on deformation coordination, which is used to solve the technical problem that the support combination design in the prior art is often based on experience and personal judgment, and it is difficult to ensure the stability and safety of the support combination.

[0004] In view of the above problems, the present application provides an intelligent determination method for large-span structure support combinations based on deformation coordination.

[0005] In a first aspect of the present application, a method for intelligently determining a combination of support members for a large-span structure based on deformation coordination is provided, the method comprising:

[0006] Retrieve historical support information, identify various types of supports based on the historical support information, and establish a support database, wherein the support database includes multiple types of supports; according to the support database, establish support twin models for the multiple types of supports respectively, and obtain a support twin model library; perform load stress analysis on the multiple types of supports based on the support twin model library, respectively obtain deformation responses of multiple types of supports under different loads, and obtain a support-deformation database; retrieve the support requirements of the large-span structure, and determine the support combination information based on the support requirements of the large-span structure, wherein the support combination information includes support positions and the number of supports; based on the support positions and the number of supports, perform random retrieval in the support database to form an initial support combination scheme; perform support deformation coordination optimization on the initial support combination scheme based on the support twin model library and the support-deformation database to determine a final support combination scheme.

[0007] The second aspect of the present application provides a large-span structure support combination intelligent determination system based on deformation coordination, the system comprising:

[0008] A bearing database establishment module retrieves historical bearing information, identifies various types of bearings based on the historical bearing information, and establishes a bearing database, wherein the bearing database includes multiple types of bearings; a bearing twin model library establishment module establishes bearing twin models for the multiple types of bearings based on the bearing database, and obtains a bearing twin model library; a bearing-deformation database acquisition module performs load force analysis on the multiple types of bearings based on the bearing twin model library, and obtains deformation responses of multiple types of bearings under different loads, and obtains a bearing-deformation database ; A support combination information determination module is used to retrieve the support requirements of the large-span structure, and determine the support combination information based on the support requirements of the large-span structure, wherein the support combination information includes the support position and the number of supports; an initial support combination scheme generation module is used to randomly retrieve the support database based on the support position and the number of supports to form an initial support combination scheme; a final support combination scheme determination module is used to coordinate and optimize the support deformation of the initial support combination scheme based on the support twin model library and the support-deformation database to determine the final support combination scheme.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] This application retrieves historical support information, identifies various types of supports, and establishes a support database; establishes a support twin model and obtains a support twin model library; performs load force analysis on multiple types of supports and obtains a support-deformation database; retrieves the support requirements of large-span structures and determines support combination information; based on the support position and the number of supports, randomly retrieves from the support database to form an initial support combination plan; based on the support twin model library and the support-deformation database, the initial support combination plan is optimized for support deformation coordination to determine the final support combination plan. This solves the technical problems of poor stability and low safety in the prior art, and achieves the technical effect of improving the stability and safety of the support combination by intelligently determining the support combination of large-span structures based on deformation coordination. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Figure 1 A schematic flow chart of a method for intelligently determining a combination of support members for a large-span structure based on deformation coordination provided in an embodiment of the present application;

[0013] Figure 2A schematic diagram of a process for determining a final support combination scheme in a method for intelligently determining a support combination of a large-span structure based on deformation coordination provided in an embodiment of the present application;

[0014] Figure 3 A schematic diagram of the intelligent structural determination of a large-span structural support combination based on deformation coordination provided in an embodiment of the present application.

[0015] Explanation of the reference numerals: support database establishing module 11, support twin model library establishing module 12, support-deformation database acquiring module 13, support combination information determining module 14, initial support combination scheme generating module 15, final support combination scheme determining module 16. DETAILED DESCRIPTION

[0016] This application provides a method for intelligently determining the support combination of a large-span structure based on deformation coordination, which is used to solve the problem that the support combination determination methods in the prior art are often based on experience and personal judgment, lack of systematicity and scientificity, and are difficult to ensure the stability and safety of the support combination. The technical effect of improving the safety and stability of the support combination is achieved.

[0017] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0018] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices.

[0019] Embodiment 1

[0020] like Figure 1 As shown, the present application provides a method for intelligently determining a combination of large-span structural supports based on deformation coordination, the method comprising:

[0021] Step S100: Retrieving historical support information, identifying various types of supports based on the historical support information, and establishing a support database, wherein the support database includes multiple types of supports;

[0022] In an embodiment of the present application, historical data of various types of bearings are first collected and organized. These data may come from completed engineering projects, experimental data, research reports or other reliable sources. The data should include characteristic information of various bearings, such as material type, cross-sectional dimensions, design loads, stiffness parameters, etc., as well as their performance under different environmental conditions and loads, such as displacement, strain, bearing capacity, etc. The collected historical bearing information may contain noise and errors, so data cleaning and preprocessing are required. This may include removing duplicate information, correcting errors, or standardizing the data.

[0023] The historical bearing information is analyzed to identify different types of bearings by statistical methods. This involves classifying the characteristics of the bearings or identifying different types of bearings. Once the different types of bearings are identified, a database containing these bearings can be established. The database should contain detailed information about each bearing, such as type, size, material, performance parameters, etc. In addition, the database should also contain historical data on the performance of each bearing, such as displacement, stress, etc. under different loading conditions.

[0024] Step S200: establishing bearing twin models for the multiple types of bearings respectively according to the bearing database, and obtaining a bearing twin model library;

[0025] In an embodiment of the present application, the required data fields, such as bearing type, material, size, historical load data, performance, etc., are extracted from the bearing database. These data will be used to train and verify the bearing twin model. Feature engineering is performed on the extracted data to extract features that have predictive capabilities for bearing performance. This may include processing, conversion, and selection of raw data to obtain more representative features. For example, the shape, size, material properties, historical load data, etc. of the bearing can be extracted as features. The data set is divided into multiple local data sets, each of which corresponds to a specific bearing type. A decision tree model is trained for each bearing type. A decision tree model is trained for each local data set. The decision tree model will learn rules for predicting bearing performance from historical data based on the input features. After the training is completed, the corresponding verification data is used, that is, the performance of each local decision tree model is evaluated by comparing the predicted bearing performance with the corresponding verification data. When the model performance is poor, the model parameters need to be readjusted.

[0026] Finally, all trained local decision tree models are stored in a library to obtain the support twin model library.

[0027] Step S300: performing load force analysis on the multiple types of supports based on the support twin model library, respectively obtaining deformation responses of the multiple types of supports under different loads, and obtaining a support-deformation database;

[0028] In an embodiment of the present application, a corresponding support twin model is selected from a support twin model library. The corresponding load conditions are input for each support type. These load conditions can be static loads, dynamic loads or loads under other actual working conditions. A simulation analysis is performed using the selected support twin model and the input load conditions. This process may include inputting the load conditions into the corresponding part of the model, and then running the model to obtain the deformation response of the support. Through simulation analysis, the deformation response of each category of support under different loads can be obtained. These deformation responses can be indicators such as displacement, strain, and stress. Finally, the obtained deformation responses are organized into a database, which can be called a support-deformation database. The database should include deformation response data for each support type, as well as the corresponding load conditions.

[0029] Step S400: Retrieving the support requirements of the large-span structure, and determining support combination information based on the support requirements of the large-span structure, wherein the support combination information includes support positions and support quantities;

[0030] In the embodiments of the present application, the requirements for the supports of the large-span structure are first clarified. These requirements may include the bearing capacity, displacement requirements, durability requirements, etc. of the supports. In addition, the overall performance requirements of the large-span structure, such as stability, seismic resistance, etc., need to be considered. According to the type and design requirements of the large-span structure, the layout principles of the supports are determined. For example, for a beam structure, it may be necessary to arrange the supports at the end or middle of the beam; for an arch structure, it may be necessary to arrange the supports at the arch foot or arch top. Based on the requirements and layout principles of the large-span structure supports, the required support combination information can be determined. The support combination information includes the support position and the number of supports.

[0031] When determining the number of supports, it is necessary to determine the required number of supports based on the scale, span, load, etc. of the long-span structure. Too few supports may lead to structural instability or insufficient bearing capacity, while too many supports may lead to waste and increase structural complexity. Therefore, it is necessary to comprehensively consider various factors to determine the appropriate number of supports.

[0032] Step S500: Based on the support positions and the support quantity, randomly retrieve from the support database to form an initial support combination scheme;

[0033] In an embodiment of the present application, a strategy for randomly retrieving information from a support database is determined. This may include random selection based on specific conditions such as support type, location, quantity, etc., or more complex strategies such as random selection based on genetic algorithms or other optimization algorithms. Then, based on the determined retrieval strategy, eligible support information is randomly selected from the support database. This information should include the type, location, and quantity of the support. Finally, the randomly selected support information is combined to form an initial support combination scheme. This scheme should take into account the overall performance requirements and design objectives of the large-span structure, as well as the layout principles and quantity requirements of the supports.

[0034] Step S600: Based on the support twin model library and the support-deformation database, the initial support combination scheme is optimized in terms of support deformation coordination to determine a final support combination scheme.

[0035] In an embodiment of the present application, a support twin model library is introduced into the optimization process. This library contains decision tree models for various types of supports, which can predict the deformation response of supports under different load conditions. The initial support combination scheme determined before loading includes the type, position and quantity of supports. According to the type and design requirements of the large-span structure, the corresponding load conditions are simulated. These conditions can be static or dynamic, depending on the design requirements. Using the decision tree model in the support twin model library, the simulated load conditions are input into the corresponding model of each support to predict the deformation response of each support.

[0036] By comparing the predicted deformation response with the data in the support-deformation database, the deformation coordination of the initial support combination scheme is evaluated. If the predicted deformation response is significantly different from the data in the database or exceeds the expected range, it means that the scheme may be inconsistent or unreasonable. According to the evaluation results, the initial support combination scheme is adjusted. This may include changing the support type, adjusting the support position, or increasing or decreasing the number of supports. The goal of the adjustment is to make the predicted deformation response more coordinated and consistent with the data in the database.

[0037] Finally, repeat the above steps until a final support combination scheme that meets the design requirements and performance goals is found. After each iteration, the adjusted scheme must be verified and evaluated to ensure its rationality and feasibility. After optimization iterations, the final support combination scheme should meet deformation coordination and other design goals. This scheme can serve as an important basis for the design and construction of large-span structures.

[0038] Furthermore, step S100 in the method provided in the embodiment of the present application further includes:

[0039] Collect design data of historical projects and extract support information from them as historical support information;

[0040] Setting an identification dimension, traversing the historical support information, obtaining a first historical support, parsing the first historical support based on the identification dimension, and determining a first category support, wherein the first category support has a unique number identification;

[0041] The first category of supports is added to the support database.

[0042] In an embodiment of the present application, relevant design data are collected from existing historical projects, including engineering drawings, technical documents, construction records, etc. These data should contain information about the supports, such as type, location, quantity, size, etc. Information about the supports is extracted from the collected design data, such as the type, location, quantity, size, etc. of the supports. This information will be used as historical support information for subsequent identification and analysis. The extracted historical support information is sorted and classified, and organized into a data structure suitable for subsequent processing. This can include establishing a database or data set for subsequent query and analysis.

[0043] When setting the identification dimension, determine the dimension or standard used to identify and parse the bearing. This can include physical properties such as the type, size, and material of the bearing, as well as non-physical properties such as the design parameters and construction process of the bearing. Select the appropriate identification dimension based on actual needs and specific circumstances. Traverse the sorted historical bearing information and find the first historical bearing that meets the identification dimension. This can be achieved by filtering the data set, querying the database, etc. Based on the set identification dimension, parse the first historical bearing to determine the category to which it belongs. This can be achieved by comparing similarities, applying classification algorithms, etc. Based on the analysis results, classify the first historical bearing into the corresponding category bearing. Each category bearing should have a unique number identification for subsequent management and tracking.

[0044] Finally, the determined first category of bearings is added to the bearing database for subsequent query and use. The database should have corresponding data structure and index mechanism to efficiently store and retrieve data.

[0045] Furthermore, the method further comprises:

[0046] Traverse multiple categories of supports and obtain the first category of supports;

[0047] Retrieving a support database, and extracting a first characteristic parameter set of a first category of supports from the support database;

[0048] Importing the first characteristic parameter set into three-dimensional model software to generate a first support twin model;

[0049] The first support twin model is added to the support twin model library.

[0050] In the embodiment of the present application, all types of support information are extracted from the support database. This can be done by querying the database, calling the data set, or other suitable methods. Among all types of supports traversed, the first support that meets a specific condition is selected as the first type of support. This condition can be determined according to actual needs, such as selecting the most common support type, the support type that best meets the design requirements, etc.

[0051] When retrieving the bearing database, information related to the first category of bearings is retrieved from the bearing database based on the previously determined bearing information. This may include the physical properties, design parameters, construction technology, etc. of the bearing. A characteristic parameter set of the first category of bearings is extracted from the retrieved bearing information. This characteristic parameter set should be able to describe the unique properties and characteristics of this category of bearings.

[0052] Select a 3D modeling software that is suitable for importing feature parameter sets and generating twin models. This can be a general CAD software, such as AutoCAD, SolidWorks, etc., or a dedicated twin model generation software. Import the extracted first feature parameter set into the selected 3D modeling software. This can be achieved by importing data files, directly inputting parameters, etc. Based on the imported feature parameter set, use the 3D modeling software to generate a twin model of the first support. This model should be able to simulate and display the physical and performance characteristics of the first category of supports.

[0053] Finally, according to the actual situation and software functions, choose the appropriate way to add the first bearing twin model to the bearing twin model library, including directly importing it into the library, associating it to the library through a link, etc. The execution method is to add the first bearing twin model to the bearing twin model library. This may require entering relevant metadata, parameter information, etc. to establish the correct association and reference. After the addition is completed, the added bearing twin model is verified and confirmed to ensure that it matches the other models in the library and can correctly simulate and predict the performance of the bearing.

[0054] Further, such as Figure 2 As shown, based on the support twin model library and the support-deformation database, the initial support combination scheme is optimized for support deformation coordination to determine a final support combination scheme, and the method further includes:

[0055] Set the support combination evaluation function;

[0056] Evaluating the initial support combination scheme based on the support combination evaluation function to obtain initial combination deformation information of each support in the initial support combination scheme;

[0057] When the initial combined deformation information does not meet the preset deformation requirement, a bearing is selected from the bearing twin model library based on the bearing-deformation database, the initial bearing combination scheme is updated, and an updated bearing combination scheme is obtained;

[0058] The updated bearing combination scheme is evaluated and iteratively optimized based on the bearing combination evaluation function, and when the preset requirements are met, the final bearing combination scheme is determined.

[0059] In the embodiments of the present application, the objectives and key points of the bearing combination evaluation are first clarified. This may include multiple aspects such as the bearing's bearing capacity, stability, durability, and economy. Determine specific evaluation objectives and weights based on project needs and design requirements. Then, for each evaluation objective, formulate corresponding evaluation indicators and parameters. For example, for bearing capacity, the ultimate bearing capacity, working stress range, etc. of each bearing can be evaluated; for stability, the stability coefficient, buckling critical stress, etc. of the bearing can be evaluated; for durability, the service life, fatigue strength, etc. of the bearing can be evaluated. Finally, based on the determined evaluation objectives and evaluation indicators, a function or model that can quantitatively evaluate the bearing combination scheme is established. This function should be able to accept the bearing combination scheme as input and output a comprehensive evaluation score or evaluation result.

[0060] The determined initial support combination scheme is input into the support combination evaluation function. The initial support combination scheme is evaluated using the support combination evaluation function to obtain the initial combination deformation information of each support. This may include support displacement, rotation, stress distribution and other information.

[0061] Check whether the initial combined deformation information meets the preset deformation requirements. If not, select bearings that meet the deformation requirements based on the bearing-deformation database and the bearing twin model library. This can be done by comparing the deformation data in the database, analyzing the prediction results of the twin model, etc. Based on the selected bearings, update the initial bearing combination scheme. This may involve operations such as changing the bearing type, adjusting the bearing position or quantity, etc. The updated scheme should be able to better meet the preset deformation requirements.

[0062] Input the updated support combination scheme into the support combination evaluation function. Use the support combination evaluation function to evaluate the updated scheme and obtain the combined deformation information of each support. Analyze the evaluation results to understand the response and performance of each support in the updated scheme. According to the evaluation results, the shortcomings of the scheme can be further optimized. If it is found that the updated scheme still has shortcomings or needs further improvement, the steps of selecting suitable supports and updating the scheme can be repeated until the requirements are met. After optimization iterations, the final support combination scheme is determined until the evaluation of the support combination scheme meets the preset requirements. In the iterative optimization process, for example, if a certain support deforms more than the surrounding supports under load, then replace the support with greater stiffness. At the same time, look at the average deformation level of the supports in the current combination, and then compare it in the support-deformation database based on this deformation to see which supports correspond to this deformation under the current load, extract them, and then iterate and replace them to obtain the best solution.

[0063] Furthermore, the method further comprises:

[0064]

[0065] Among them, δ(Δh k ) is the estimated value of the vertical deformation of the support in the kth support combination under load, Δh ki is the vertical deformation value of the ith support in the kth support combination scheme under load, is the mean vertical deformation of all supports in the kth support combination scheme under load, n is the number of supports in the support combination information, k is the large-span structure support combination category, i is the large-span structure support number, E ki is the vertical elastic modulus of the i-th support under the k-th support combination.

[0066] In the embodiment of the present application, the vertical load and horizontal load at each support are first calculated. After the calculation is completed, multiple support combination schemes are determined according to the load conditions at the supports, where the load that the support can withstand should be greater than the calculated load.

[0067] For the multiple support combinations determined, calculate the deformation Δh of each support due to the influence of the vertical load in each support combination. ki Then, the average deformation of all supports in each support combination is calculated. Then, based on the calculated deformation and average deformation of all supports in the support combination, the evaluation value of the vertical deformation of all supports is calculated, that is, the variance δ(Δh k ).

[0068] Specifically, firstly, the number and position of the long-span structure supports are determined according to the design, and the supports are defined as SM i, where i = 1, ..., i, ..., n. Next, calculate the vertical load F at each support i , horizontal load V i , where i = 1, ..., i, ..., n. Then, according to the load conditions at the support, a preliminary selection is made, and all possible support combinations are listed to determine multiple support combination schemes, which are numbered as: 1, ..., k, ...m. The i support of the kth support combination scheme is SM ki (F kimax , V kimax , E ki ,h ki , A ki ), where F kimax is the SM of the i-th support combination in the k-th support combination scheme ki Maximum rated vertical load bearing capacity; V kimax is the SM of the i-th support combination in the k-th support combination scheme ki Maximum rated horizontal load bearing capacity; E ki is the SM of the i-th support combination in the k-th support combination scheme ki The elastic modulus of ki is the SM of the i-th support combination in the k-th support combination scheme ki The vertical height of A ki is the SM of the i-th support combination in the k-th support combination scheme ki horizontal cross-sectional area.

[0069] Next, the deformation of each support under the vertical load is calculated for the kth support combination (k = 1, ..., k, ...m), and F is obtained by the formula i Vertical deformation of support i under the action: At the same time, according to this method, the deformation of all supports (i = 1, ..., i, ..., n) under all support combinations (k = 1, ..., k, ...m) is calculated. After that, the average deformation of all supports under the vertical load under the kth support combination is calculated by the formula: At the same time, according to this method, the average deformation of the bearings under all bearing combinations (k = 1, ..., k, ...m) is calculated, and the obtained Then, the variance of all support deformations under vertical load of the kth support combination scheme is calculated: At the same time, according to this method, the variance of the support deformation under all support combinations (k = 1, ..., k, ...m) is calculated to obtain δ(Δh1), ..., δ(Δh k ),…,δ(Δ hm). Finally, the variances of the bearing deformations of all bearing combination schemes (k=1, ..., k, ...m) are compared, and the minimum variance is selected, and the corresponding bearing combination scheme is determined as the bearing combination arrangement mode as the final bearing combination scheme.

[0070] Furthermore, the method further comprises:

[0071] The preset deformation requirements include support combination requirements and support individual requirements;

[0072] Setting a load-deformation constraint table as individual requirements for the bearing, wherein the load-deformation constraint table includes maximum deformation constraints for the bearing under different load conditions;

[0073] When there are individual supports in the combined deformation information that do not satisfy the load-deformation constraint table, the individual supports are updated and optimized;

[0074] Set the expected threshold of the evaluation value as the support combination requirement;

[0075] When there is a support combination in the combined deformation information that does not meet the expected threshold of the evaluation value, the support combination is updated and optimized.

[0076] In the embodiments of the present application, the support combination requirements involve the interaction between the supports and the performance of the overall structure. For example, it may include overall evaluation indicators of the stiffness, stability, and bearing capacity of the support combination. These requirements can be set through overall model analysis, empirical formulas, or comparison of historical cases. The individual requirements of the supports mainly focus on the performance of each support itself. For example, under different load conditions, the deformation of the support should be within its allowable range. These requirements are usually clarified by setting a load-deformation constraint table, and the contents of the table should include the maximum deformation constraints on the supports under different load conditions.

[0077] The load-deformation constraint table is set to clarify the maximum deformation constraint of each bearing under different load conditions. This constraint table can provide designers with an understanding of the working range and performance of the bearing, and can also serve as a basis for evaluating whether the individual bearing meets the requirements.

[0078] When there are individual supports in the combined deformation information that do not meet the load-deformation constraint table, the individual supports need to be updated and optimized, which involves operations such as changing the support type, adjusting the support position or quantity, so that the performance of the support meets the requirements.

[0079] When setting the expected threshold of the evaluation value, first determine the indicators used to evaluate the bearing combination based on the project requirements and design goals. These indicators can be evaluation indicators of the overall stiffness, stability, bearing capacity and other performance of the bearing combination. Then, based on the determined evaluation indicators, set an expected threshold or standard. This expected threshold is the basis for evaluating whether the bearing combination meets the requirements. The setting of the threshold can be based on the empirical data of similar projects, design specifications or comparative analysis.

[0080] When there is a support combination in the combined deformation information that does not meet the expected threshold of the evaluation value, the support combination needs to be updated and optimized. This involves operations such as reselecting and arranging the type and position of the support so that the performance of the overall structure meets the requirements.

[0081] Furthermore, step S600 in the method provided in the embodiment of the present application further includes:

[0082] Set support economic constraints;

[0083] Based on the bearing economic constraint, the bearing deformation coordination optimization process is restricted to determine the final bearing combination scheme.

[0084] In the embodiment of the present application, it is first necessary to clarify the economic objectives of the bearing design. This may include the total cost of the bearing, service life, maintenance requirements, etc. When clarifying the economic objectives, long-term economic benefits should be considered, not just the initial investment cost. According to the clear economic objectives, a corresponding economic constraint model can be established. For example, the economic performance of the bearing can be described by establishing a cost function or a utility function. This function includes factors such as the manufacturing cost, transportation cost, installation cost, expected service life, and maintenance cost of the bearing. Based on the economic constraint model, the corresponding constraints are determined. For example, an upper limit on the total cost, a lower limit on the service life, etc. can be set. These constraints should match the economic objectives of the project to ensure that the designed bearing is economically reasonable.

[0085] Economic constraints should be considered when performing deformation coordination optimization. This can be achieved by incorporating economic constraints into the optimization algorithm or model. For example, an optimization algorithm with economic constraints can be used to find the optimal solution that meets all constraints. During the deformation coordination optimization process, multiple iterations and adjustments may be required. This involves changing the type, size, material, etc. of the support to achieve the best economic benefits while meeting all constraints.

[0086] After optimization and adjustment, the final support combination scheme should meet all constraints, including the set economic constraints. This scheme can serve as an important reference for the design and construction of large-span structures.

[0087] In summary, the embodiments of the present application have at least the following technical effects:

[0088] This application retrieves historical support information, identifies various types of supports, and establishes a support database; establishes a support twin model and obtains a support twin model library; performs load force analysis on multiple types of supports and obtains a support-deformation database; retrieves the support requirements of large-span structures and determines support combination information; based on the support position and the number of supports, randomly retrieves from the support database to form an initial support combination scheme; based on the support twin model library and the support-deformation database, coordinates and optimizes the initial support combination scheme with support deformation to determine the final support combination scheme. The present invention solves the technical problems of low safety and poor stability in the prior art, and achieves the technical effect of improving the safety and stability of the support combination of large-span structures through an intelligent determination method.

[0089] Embodiment 2

[0090] Based on the same inventive concept as the method for intelligently determining the combination of large-span structural supports based on deformation coordination in the aforementioned embodiment, Figure 3 As shown, the present application provides a large-span structural support combination intelligent determination system based on deformation coordination, and the system and method embodiments in the present application embodiments are based on the same inventive concept. The system includes:

[0091] A support database establishment module 11, the support database establishment module 11 is used to retrieve historical support information, identify various types of supports based on the historical support information, and establish a support database, wherein the support database includes multiple types of supports;

[0092] A bearing twin model library establishment module 12, wherein the bearing twin model library establishment module 12 is used to establish bearing twin models for the multiple types of bearings respectively according to the bearing database, and obtain a bearing twin model library;

[0093] A support-deformation database acquisition module 13, wherein the support-deformation database acquisition module 13 performs load force analysis on the multiple types of supports based on the support twin model library, respectively acquires deformation responses of the multiple types of supports under different loads, and acquires a support-deformation database;

[0094] A support combination information determination module 14, the support combination information determination module 14 is used to retrieve the support requirements of the large-span structure, and determine the support combination information based on the support requirements of the large-span structure, wherein the support combination information includes the support position and the support quantity;

[0095] An initial support combination scheme generating module 15, wherein the initial support combination scheme generating module 15 randomly retrieves from the support database based on the support positions and the support quantities to form an initial support combination scheme;

[0096] The final support combination scheme determining module 16 performs support deformation coordination optimization on the initial support combination scheme based on the support twin model library and the support-deformation database to determine the final support combination scheme.

[0097] Furthermore, the system also includes:

[0098] Collect design data of historical projects and extract support information from them as historical support information;

[0099] Setting an identification dimension, traversing the historical support information, obtaining a first historical support, parsing the first historical support based on the identification dimension, and determining a first category support, wherein the first category support has a unique number identification;

[0100] The first category of supports is added to the support database.

[0101] Furthermore, the system also includes:

[0102] Traverse multiple categories of supports and obtain the first category of supports;

[0103] Retrieving a support database, and extracting a first characteristic parameter set of a first category of supports from the support database;

[0104] Importing the first characteristic parameter set into three-dimensional model software to generate a first support twin model;

[0105] The first support twin model is added to the support twin model library.

[0106] Furthermore, the system also includes:

[0107] Set the support combination evaluation function;

[0108] Evaluating the initial support combination scheme based on the support combination evaluation function to obtain initial combination deformation information of each support in the initial support combination scheme;

[0109] When the initial combined deformation information does not meet the preset deformation requirement, a bearing is selected from the bearing twin model library based on the bearing-deformation database, the initial bearing combination scheme is updated, and an updated bearing combination scheme is obtained;

[0110] The updated bearing combination scheme is evaluated and iteratively optimized based on the bearing combination evaluation function to determine a final bearing combination scheme.

[0111] Furthermore, the system also includes:

[0112]

[0113] Among them, δ(Δh k ) is the estimated value of the vertical deformation of the support in the kth support combination under load, Δh ki is the vertical deformation value of the ith support in the kth support combination scheme under load, It is the mean vertical deformation of all supports in the kth support combination scheme under load, and n is the number of supports in the support combination information.

[0114] Furthermore, the system also includes:

[0115] The preset deformation requirements include support combination requirements and support individual requirements;

[0116] Setting a load-deformation constraint table as individual requirements for the bearing, wherein the load-deformation constraint table includes maximum deformation constraints for the bearing under different load conditions;

[0117] When there are individual supports in the combined deformation information that do not satisfy the load-deformation constraint table, the individual supports are updated and optimized;

[0118] Set the expected threshold of the evaluation value as the support combination requirement;

[0119] When there is a support combination in the combined deformation information that does not meet the expected threshold of the evaluation value, the support combination is updated and optimized.

[0120] Furthermore, the system also includes:

[0121] Set support economic constraints;

[0122] Based on the bearing economic constraint, the bearing deformation coordination optimization process is restricted to determine the final bearing combination scheme.

[0123] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. Other embodiments are within the scope of the attached claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0124] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0125] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. An intelligent determination method for large-span structural support combinations based on deformation coordination is characterized by: The method comprises: Retrieving historical bearing information, identifying various types of bearings based on the historical bearing information, and establishing a bearing database, wherein the bearing database includes multiple types of bearings; According to the support database, respectively establish support twin models for the multiple types of supports to obtain a support twin model library; Based on the bearing twin model library, load stress analysis is performed on the multiple types of bearings, deformation responses of the multiple types of bearings under different loads are obtained respectively, and a bearing-deformation database is obtained; Retrieving the support requirements of the large-span structure, and determining support combination information based on the support requirements of the large-span structure, wherein the support combination information includes support positions and support quantities; Based on the support positions and the support quantity, randomly retrieve from the support database to form an initial support combination scheme; Based on the support twin model library and the support-deformation database, the initial support combination scheme is optimized in terms of support deformation coordination to determine a final support combination scheme; Wherein, the initial support combination scheme is optimized for support deformation coordination based on the support twin model library and the support-deformation database to determine the final support combination scheme, including: Set the support combination evaluation function; Evaluating the initial support combination scheme based on the support combination evaluation function to obtain initial combination deformation information of each support in the initial support combination scheme; When the initial combined deformation information does not meet the preset deformation requirement, a bearing is selected from the bearing twin model library based on the bearing-deformation database, the initial bearing combination scheme is updated, and an updated bearing combination scheme is obtained; Evaluate and iteratively optimize the updated support combination scheme based on the support combination evaluation function, and determine the final support combination scheme when the preset requirements are met; The support combination evaluation function is: Among them, δ(Δh k ) is the estimated value of the vertical deformation of the support in the kth support combination under load, Δh ki is the vertical deformation value of the ith support in the kth support combination scheme under load, is the mean vertical deformation of all supports in the kth support combination scheme under load, n is the number of supports in the support combination information, k is the large-span structure support combination category, i is the large-span structure support number, E ki is the vertical elastic modulus of the ith support under the kth support combination, F i is the vertical load at each support, h ki is the vertical height of the i-th support in the k-th support combination scheme, A ki is the horizontal cross-sectional area of ​​the i-th support in the k-th support combination scheme.

2. The method according to claim 1, characterized in that Retrieve historical bearing information, identify various types of bearings based on the historical bearing information, and establish a bearing database, including: Collect design data of historical projects and extract support information from them as historical support information; Setting an identification dimension, traversing the historical support information, obtaining a first historical support, parsing the first historical support based on the identification dimension, and determining a first category support, wherein the first category support has a unique number identification; The first category of supports is added to the support database.

3. The method according to claim 2, characterized in that According to the support database, support twin models are respectively established for the multiple types of supports to obtain a support twin model library, including: Traverse multiple categories of supports and obtain the first category of supports; Retrieving a support database, and extracting a first characteristic parameter set of a first category of supports from the support database; Importing the first characteristic parameter set into three-dimensional model software to generate a first support twin model; The first support twin model is added to the support twin model library.

4. The method according to claim 1, characterized in that: The method further comprises: The preset deformation requirements include support combination requirements and support individual requirements; Setting a load-deformation constraint table as individual requirements for the bearing, wherein the load-deformation constraint table includes maximum deformation constraints for the bearing under different load conditions; When there are individual supports in the combined deformation information that do not satisfy the load-deformation constraint table, the individual supports are updated and optimized; Set the expected threshold of the evaluation value as the support combination requirement; When there is a support combination in the combined deformation information that does not meet the expected threshold of the evaluation value, the support combination is updated and optimized.

5. The method according to claim 1, characterized in that The method further comprises: Set support economic constraints; Based on the bearing economic constraint, the bearing deformation coordination optimization process is restricted to determine the final bearing combination scheme.

6. The large-span structural support combination intelligent determination system based on deformation coordination is characterized by: The system is used to implement the method for intelligently determining the combination of large-span structural supports based on deformation coordination as described in any one of claims 1 to 5, and comprises: A support database establishment module, the support database establishment module is used to retrieve historical support information, identify various types of supports based on the historical support information, and establish a support database, the support database includes multiple types of supports; A bearing twin model library establishment module, wherein the bearing twin model library establishment module is used to establish bearing twin models for the multiple types of bearings respectively according to the bearing database, and obtain a bearing twin model library; A support-deformation database acquisition module, wherein the support-deformation database acquisition module performs load force analysis on the multiple types of supports based on the support twin model library, respectively acquires deformation responses of the multiple types of supports under different loads, and acquires a support-deformation database; A support combination information determination module, the support combination information determination module is used to retrieve the support requirements of the large-span structure, and determine the support combination information based on the support requirements of the large-span structure, wherein the support combination information includes the support position and the support quantity; An initial support combination scheme generating module, wherein the initial support combination scheme generating module randomly retrieves from the support database based on the support positions and the support quantities to form an initial support combination scheme; The final support combination scheme determination module performs support deformation coordination optimization on the initial support combination scheme based on the support twin model library and the support-deformation database to determine the final support combination scheme.

Images