Interactive generation type bridge parameter design method and system

Abstract

The invention relates to the technical field of bridge parameterization design, in particular to an interactive generation type bridge parameter design method and system.The method comprises the following steps that based on span, beam height and support counter-force parameters, the effect of the span on the beam height and the influence of the beam height on support counter-force are analyzed, a node and edge weight set is constructed through the parameter relation, a network structure is formed, and the network structure is constructed; and generating a parameter association network weight matrix. According to the method, by constructing the parameter association network, the visualization and analysis efficiency of the parameter relationship among the span, the beam height and the support reaction force is effectively improved, the design flexibility is improved through optimization and integration of the parameter constraint interval, and generation of the dynamic path has higher logicality and real-time response capability; by means of load distribution optimization, accurate adjustment of design parameters under the complex load working condition is ensured, the design robustness and global consistency are enhanced, the adaptability and reliability of parameter design are improved, and the efficiency and precision of bridge design are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge parametric design, and in particular, to an interactive generative bridge parameter design method and system. Background Art

[0002] The technical field of bridge parametric design includes parametric modeling and design methods of bridge structures. In this technical field, the core content is to model and optimize the structural form, construction method, and engineering constraints of bridges through parametric technology. The overall technical field covers the definition of bridge geometric parameters, structural design based on parametric models, and parameter generation methods combined with computer-aided design technology. On this basis, related research also includes the integrated processing of bridge structure design, model optimization, and construction plans based on preset rules to achieve high efficiency and accuracy in the bridge design process.

[0003] Among them, the interactive generative bridge parameter design method refers to using interactive design technology and parametric modeling methods to complete the design of bridge models by automatically generating and dynamically adjusting key parameters in the bridge design process. The technical matters covered by this method include: parameter acquisition based on user input and interaction, dynamic generation of bridge structures through parametric modeling tools, visual adjustment of the geometric form of bridge structures, and generation of parameter combinations based on optimization algorithms. Specifically, the entire design process is completed through the combination of parameter input rules, geometric modeling methods, and optimization algorithms to ensure the real-time and accuracy of parameter generation and adjustment.

[0004] In the prior art, it is difficult to dynamically express the complex coupling relationships between parameters in parametric modeling. Usually, static rules are relied on for parameter adjustment, lacking flexibility and dynamic adaptability, resulting in difficulty in identifying the global impact of associated parameters during the design adjustment process. In terms of multi-parameter constraint processing, the prior art mostly adopts single constraint conditions, ignoring the multiple restrictive effects of interactions between complex parameters on the design, affecting the global consistency of the design scheme. The path generation method is mostly fixed rules or single optimization, which is difficult to meet the requirements of dynamic adjustment in complex bridge design scenarios, restricting the real-time and accuracy of path generation. In the analysis of load effects, a single load model is likely to lead to insufficient response to comprehensive load conditions and cannot effectively support the parameter adjustment and optimization of bridges under complex load conditions. These deficiencies result in obvious defects in design accuracy and adaptability and are difficult to meet the diverse requirements of parametric design for complex design scenarios. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies existing in the prior art and propose an interactive generative bridge parameter design method and system.

[0006] To achieve the above object, the present invention adopts the following technical solutions: An interactive generative bridge parameter design method, comprising the following steps:

[0007] S1: Based on the span, beam height and support reaction force parameters, analyze the effect of the span on the beam height and the influence of the beam height on the support reaction force, construct a set of node and edge weights through parameter relationships, form a network structure, and generate a parameter correlation network weight matrix;

[0008] S2: Based on the parameter correlation network weight matrix, extract the influence range of the span and beam height on the support reaction force, determine the constraint boundary values between parameters, integrate the constraint results into the network matrix, and generate a set of parameter constraint intervals;

[0009] S3: Based on the set of parameter constraint intervals, combine the span and beam height parameters, calculate the weights of each parameter path, select the path combinations that meet the constraint conditions, form a path scheme, and generate a set of parameter dynamic path values;

[0010] S4: Based on the set of parameter dynamic path values, distribute the live load, dead load and wind load to each path, calculate the response of the load to the span and beam height, analyze the adjustment range of each parameter under the action of the load, and generate a load response parameter matrix;

[0011] S5: Based on the load response parameter matrix, globally check the parameter adjustment values of the combined span and beam height, screen the combinations that meet the dynamic path and load response, check all combination results, and generate an optimized integration value of the bridge parameters.

[0012] As a further solution of the present invention, the parameter correlation network weight matrix includes a span node set, a beam height node set, a support reaction force node set, and a weight distribution between nodes. The set of parameter constraint intervals includes a span range constraint, a beam height range constraint, a support reaction force range constraint, and a coupling boundary between the span and the beam height. The set of parameter dynamic path values includes a path weight value, a dynamic range value of the support reaction force, and a combined path of the span and the beam height. The load response parameter matrix includes a live load response coefficient, a dead load response coefficient, a wind load response coefficient, and a path response adjustment value. The optimized integration value of the bridge parameters includes an optimized span value, an optimized beam height value, and an optimized support reaction force value.

[0013] As a further solution of the present invention, the specific steps of analyzing the effect of the span on the beam height and the influence of the beam height on the support reaction force based on the span, beam height and support reaction force parameters, constructing a set of node and edge weights through parameter relationships, and forming a network structure to generate a parameter correlation network weight matrix are as follows:

[0014] S101: Based on the span, beam height, and support reaction force parameters, extract the numerical range and distribution characteristics of the span and beam height. Group the spans according to the parameter distribution law, calculate the change range and fluctuation interval value of the beam height corresponding to the span, set the span change threshold, screen the data with a large span change range, and establish the influence characteristics of the span change on the beam height;

[0015] S102: Based on the influence characteristics of the span change on the beam height, analyze the change trends of the beam height and support reaction force. Fit their change relationship according to the support reaction force data and beam height parameters, determine the key node parameters of the beam height change on the support reaction force, adjust the parameter range, and calculate the correlation degree value between the beam height and the support reaction force to generate a correlation data set of the beam height and the support reaction force;

[0016] S103: Based on the correlation data set of the beam height and the support reaction force, construct a set of node and edge weights for the parameter correlation relationship network. Group the network according to the weight size between nodes, screen the areas with large weight fluctuations and adjust the edge weights, calculate the numerical value of the overall network weight matrix, and generate a parameter correlation network weight matrix.

[0017] As a further solution of the present invention, based on the parameter correlation network weight matrix, the specific steps for extracting the influence range of the span and beam height on the support reaction force, determining the constraint boundary values between parameters, and integrating the constraint results into the network matrix to generate a set of parameter constraint intervals are as follows:

[0018] S201: Based on the parameter correlation network weight matrix, extract the interaction range of the span, beam height, and support reaction force parameters, analyze the interaction weight data between the parameters, screen the main range of the span and beam height affecting the support reaction force according to the weight change trend, calculate the weight distribution of the span and beam height on the support reaction force and determine its range, and generate a set of influence ranges of the span and beam height on the support reaction force;

[0019] S202: Based on the set of influence ranges of the span and beam height on the support reaction force, analyze the distribution law of the weights within the influence range, combine the numerical boundary characteristics between the span, beam height, and support reaction force, calculate the boundary numerical intervals of the span and beam height on the support reaction force parameters, screen the constraint conditions according to the numerical changes and judge their effectiveness, and adjust the upper and lower boundary values of the parameters to generate a set of constraint boundaries between parameters;

[0020] S203: Based on the set of constraint boundaries between parameters, integrate the adjusted constraint boundary data into the parameter correlation network weight matrix, re - distribute the weight values of each in the network matrix according to the constraint boundaries, calculate the weight data distribution value after matrix adjustment, and verify the consistency of the adjusted matrix to generate a set of parameter constraint intervals.

[0021] As a further solution of the present invention, based on the set of parameter constraint intervals, the span and beam height parameters are combined, the weights of each group of parameter paths are calculated, and the path combinations that meet the constraint conditions are selected to form a path scheme. The specific steps for generating the set of dynamic path values of the parameters are as follows:

[0022] S301: Based on the set of parameter constraint intervals, combine the numerical pairs of the span and beam height parameters. For each group of parameter paths, gradually assign weight values according to their numerical characteristics, calculate the path weights and analyze their distribution trend values, screen out the range of parameter combinations with stable weight distribution, and generate the set of path weights of the span and beam height parameters;

[0023] S302: Based on the set of path weights of the span and beam height parameters, analyze the distribution characteristics of the path weights, screen out the combinations whose path weight values meet the parameter constraint conditions, calculate the cumulative weight values of the screened paths, and judge whether they meet the dynamic change conditions, adjust the priority order of the path combinations, and generate the set of path combinations that meet the constraints;

[0024] S303: Based on the set of path combinations that meet the constraints, optimize the part with uneven weights in the path combinations, reassign the parameter weights within the paths according to the optimization results, integrate the optimized path combinations to form the final path scheme, calculate the cumulative dynamic weight value of the path scheme, and generate the set of dynamic path values of the parameters.

[0025] As a further solution of the present invention, the specific formula for calculating the cumulative weight value is as follows:

[0026]

[0027] Among them, Q tot represents the cumulative weight value, q p,j represents the weight of the p-th parameter point in the j-th group of the path combination, z p,j represents the path parameter value of the p-th parameter point in the j-th group of the path combination, represents the average value of the path parameter values in the j-th group of the path combination, and t represents the total number of parameter points in the j-th group of the path combination.

[0028] As a further solution of the present invention, based on the set of dynamic path values of the parameters, the live load, dead load and wind load are allocated to each path, the responses of the loads to the span and beam height are calculated, and the adjustment ranges of each parameter under the action of the loads are analyzed. The specific steps for generating the load response parameter matrix are as follows:

[0029] S401: Based on the set of dynamic path values of the parameters, allocate the live load, dead load and wind load to each parameter point in the path, analyze the numerical distribution characteristics and action ranges of the loads, gradually adjust the load values to meet the bearing capacity of the path, calculate the force data of the loads on the path, and screen out the loads with stable distribution, and generate the set of path load allocation results;

[0030] S402: Based on the set of path load distribution results, calculate the response values of live load, dead load, and wind load on the span and beam height point by point. Analyze the response trend under the action of the load according to the dynamic change range of the parameters, extract the numerical intervals where the load has a significant impact on the span and beam height, and generate a load response numerical data set;

[0031] S403: Based on the load response numerical data set, analyze the adjustment range of each load on the span and beam height parameters. Integrate the parameter response ranges according to the numerical changes between path nodes, construct an adjustment interval matrix of the span and beam height under the action of the load, and generate a load response parameter matrix.

[0032] As a further solution of the present invention, the specific formula for correcting the acting force data is:

[0033]

[0034] where, F k,i represents the corrected acting force value of the k-th parameter point in the path under the load distribution, l k represents the actual span of the k-th point in the path, w j,k represents the weight of the j-th type of load at the k-th parameter point in the path, P j,k represents the distribution value of the j-th type of load at the k-th point in the path, A j,k represents the acting area of the j-th type of load at the k-th point in the path, R k represents the bearing capacity threshold of the k-th point in the path, and m represents the total number of load types.

[0035] As a further solution of the present invention, based on the load response parameter matrix, perform a global check on the parameter adjustment values of the span and beam height combination, screen the combinations that meet the dynamic path and load response, check all combination results, and the specific steps for generating the optimized integration value of the bridge parameters are:

[0036] S501: Based on the load response parameter matrix, analyze the adjustment range of the span and beam height parameter combinations group by group. Check the validity of the combination values point by point according to the constraint conditions of the dynamic path, eliminate the numerical values of the parameters that do not meet the requirements, and screen the parameter combinations that meet the conditions to generate a set of parameter combinations that meet the path constraints;

[0037] S502: Based on the set of parameter combinations that meet the path constraints, calculate the numerical fluctuations of each parameter combination in the load response, analyze the numerical distribution differences generated by the load on the parameter combinations, screen the parameter combinations with balanced load action and the smallest fluctuation range, integrate the selected parameter combinations, and generate a set of balanced load parameter combinations;

[0038] S503: Based on the set of balanced load parameter combinations, globally check the adjusted values of the span and beam height parameter combinations, correct the parameter intervals of the span and beam height according to the load influence values of each combination, integrate the corrected parameter combinations, and calculate the overall optimized values to generate the optimized integration value of the bridge parameters.

[0039] An interactive generative bridge parameter design system, comprising:

[0040] The parameter correlation module analyzes the effect of the span on the beam height and the influence of the beam height on the support reaction force based on the span, beam height and support reaction force parameters, constructs a set of node and edge weights through parameter relationships, and generates a parameter correlation network weight matrix;

[0041] The constraint interval module extracts the influence ranges of the span and beam height on the support reaction force based on the parameter correlation network weight matrix, determines the constraint boundary values between the parameters, and generates a set of parameter constraint intervals;

[0042] The path weight module combines the span and beam height parameters based on the set of parameter constraint intervals, calculates the weights of each parameter path, selects the path combinations that meet the constraint conditions, and generates a set of parameter dynamic path values;

[0043] The load response module distributes the live load, dead load and wind load to each path based on the set of parameter dynamic path values, calculates the response of the load to the span and beam height, and generates a load response parameter matrix;

[0044] The parameter optimization module globally checks the parameter adjustment values of the span and beam height combinations based on the load response parameter matrix, screens the combinations that conform to the dynamic path and load response, and generates the optimized integration value of the bridge parameters.

[0045] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0046] In the present invention, by constructing a parameter correlation network, the visualization and analysis efficiency of the parameter relationships among the span, beam height and support reaction force are effectively improved. The optimized integration of the parameter constraint intervals improves the design flexibility, making the generation of the dynamic path more logical and real-time responsive. The optimization of the load distribution ensures the precise adjustment of the design parameters under complex load conditions, enhances the robustness and global consistency of the design, improves the adaptability and reliability of the parameter design, and significantly improves the efficiency and accuracy of the bridge design. Description of the Drawings

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0048] Figure 1 It is a schematic diagram of the step flow of the present invention;

[0049] Figure 2 It is a flowchart of step S1 of the present invention;

[0050] Figure 3 It is a flowchart of step S2 of the present invention;

[0051] Figure 4 It is a flowchart of step S3 of the present invention;

[0052] Figure 5 It is a flowchart of step S4 of the present invention;

[0053] Figure 6 It is a flowchart of step S5 of the present invention;

[0054] Figure 7 It is a system module diagram of the present invention. Specific Embodiments

[0055] The following will describe the technical solutions in the present invention with reference to the accompanying drawings.

[0056] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as an "example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly, the use of the word "example" is intended to present concepts in a specific manner. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two.

[0057] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same. "(of)", "corresponding" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same.

[0058] In the embodiments of the present invention, sometimes subscripts such as W 1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meanings they express are the same.

[0059] To make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0060] Please refer to Figure 1 , an interactive generative bridge parameter design method, comprising the following steps:

[0061] S1: Based on the span, beam height and support reaction force parameters, analyze the effect of the span on the beam height and the influence of the beam height on the support reaction force, construct a set of node and edge weights through the parameter relationship, form a network structure, and generate a parameter correlation network weight matrix;

[0062] S2: Based on the parameter correlation network weight matrix, extract the influence range of the span and beam height on the support reaction force, determine the constraint boundary values between the parameters, integrate the constraint results into the network matrix, and generate a set of parameter constraint intervals;

[0063] S3: Based on the set of parameter constraint intervals, combine the span and beam height parameters, calculate the weights of each parameter path, select the path combinations that meet the constraint conditions, form a path plan, and generate a set of parameter dynamic path values;

[0064] S4: Based on the set of parameter dynamic path values, distribute the live load, dead load and wind load to each path, calculate the response of the load to the span and beam height, analyze the adjustment range of each parameter under the action of the load, and generate a load response parameter matrix;

[0065] S5: Based on the load response parameter matrix, globally check the parameter adjustment values of the combined span and beam height, screen the combinations that meet the dynamic path and load response, check all the combined results, and generate an optimized integration value of the bridge parameters.

[0066] The parameter correlation network weight matrix includes a span node set, a beam height node set, a support reaction force node set, and the weight distribution between nodes. The set of parameter constraint intervals includes span range constraints, beam height range constraints, support reaction force range constraints, and the coupling boundary between the span and beam height. The set of parameter dynamic path values includes path weight values, dynamic range values of support reaction forces, and combined paths of span and beam height. The load response parameter matrix includes live load response coefficients, dead load response coefficients, wind load response coefficients, and path response adjustment values. The optimized integration value of the bridge parameters includes the optimized span value, the optimized beam height value, and the optimized support reaction force value.

[0067] Please refer to Figure 2 , the specific steps of S1 are:

[0068] S101: Based on the span, beam height, and support reaction force parameters, extract the numerical range and distribution characteristics of the span and beam height. Group the spans according to the parameter distribution law, calculate the change range and fluctuation interval value of the beam height corresponding to the span, set the span change threshold, screen the data with a large span change range, and establish the influence characteristics of the span change on the beam height.

[0069] First, the span and beam height values of each measuring point were extracted from the engineering database. These data sets reflect the size distribution of different structures. Subsequently, statistical analysis was performed on the data to determine its distribution characteristics, such as the mean value, standard deviation, etc. Based on these statistical indicators, the spans were divided into several groups, and each group has similar size characteristics. To accurately control the design and construction standards of the beam, a change threshold was introduced to identify which span changes are significant, that is, by calculating whether the change range of the span exceeds the set threshold, screening out the data with larger changes that may affect the structural safety. This process was strictly quantified through a mathematical model. For example, the threshold was set based on the percentile of the span change in historical data to ensure that only the most representative change data was selected for further analysis. Finally, based on the screened data, a detailed characteristic model describing the influence of the span change on the beam height was established.

[0070] S102: Based on the influence characteristics of the span change on the beam height, analyze the change trends of the beam height and the support reaction force. Fit the change relationship between them according to the support reaction force data and the beam height parameters, determine the key node parameters of the beam height change on the support reaction force, adjust the parameter range, and calculate the correlation degree value between the beam height and the support reaction force to generate a correlation data set of the beam height and the support reaction force.

[0071] By performing a regression analysis on the relationship between the support reaction force data and the beam height, identify the key beam height parameters that affect the change of the support reaction force. This analysis process involves complex mathematical modeling and statistical verification. First, use the collected structural monitoring data to fit the change relationship between the beam height and the support reaction force, and use linear or non-linear regression models to describe the dependence between the two. Then, adjust the model parameters to improve the prediction accuracy and reliability, evaluate the fitness of the model, cover more beam height change situations by adjusting the parameter range, and calculate the correlation degree values of these parameters to provide a scientific basis for the support design. Thus, a comprehensive correlation data set of the beam height and the support reaction force was generated, which provides important input parameters for subsequent structural analysis and optimization.

[0072] S103: Based on the correlation data set of the beam height and the support reaction force, construct the node and edge weight sets of the parameter correlation relationship network. Group the network according to the weight size between nodes, screen the areas with large weight fluctuations and adjust the edge weights, calculate the value of the overall network weight matrix, and generate the parameter correlation network weight matrix.

[0073] When constructing the parameter correlation network, the correlation data set of beam height and support reaction force is processed. The data points are used as nodes through the network analysis tool, and the interaction strength between nodes is used as the weight of the edge. The weight of the edge is determined by the correlation degree value between nodes. The nodes in the network are grouped according to the weight size, and the areas with large weight fluctuations are analyzed and adjusted emphatically. In order to calculate the value of the overall network weight matrix, the formula W = A·D -1 is used for calculation, where A represents the adjacency matrix and D represents the inverse of the degree matrix. After such processing, it is possible to more clearly identify which nodes play a core role in the structure, thereby optimizing the configuration of the entire network. Finally, the weight matrix of the parameter correlation network is generated, providing an optimized structural connection scheme for bridge design.

[0074] Consider a simple network. Suppose there are three nodes, and the connection situation between nodes is as follows: Node 1 is connected to Node 2 and Node 3, and Node 2 is connected to Node 3. The adjacency matrix A is:

[0075]

[0076] The degree matrix D is the number of connections of each node, that is:

[0077]

[0078] The inverse D of the degree matrix -1 is:

[0079]

[0080] Therefore, the weight matrix W is calculated as follows:

[0081]

[0082] This result shows that the connection weight of each node with other nodes is 0.5, which reflects the equal importance of each node in the undirected graph.

[0083] Please refer to Figure 3 , and the specific steps of S2 are:

[0084] S201: Based on the weight matrix of the parameter correlation network, extract the interaction range of span, beam height and support reaction force parameters, analyze the interaction weight data between parameters, screen the main range of span and beam height affecting the support reaction force according to the weight change trend, calculate the weight distribution of span and beam height on the support reaction force and determine its range, and generate a set of influence ranges of span and beam height on the support reaction force;

[0085] The network weight matrix is disassembled into sub - matrices to analyze the weight distribution characteristics of each pair of parameters. Region with high correlation between span and beam height in the influence of support reaction is screened item by item. The influence range is determined by combining the weight change trend. By constructing a weight distribution model, the weight gradients of span and beam height are analyzed through matrix operations, and the upper and lower boundary value ranges of parameters are calculated. Further, data verification and consistency evaluation are carried out on the screening results, and finally a set of influence ranges of span and beam height on support reaction is generated.

[0086] S202: Based on the set of influence ranges of span and beam height on support reaction, analyze the distribution law of weights within the influence range. Combining the numerical boundary characteristics among span, beam height and support reaction, calculate the boundary numerical intervals of span and beam height for the support reaction parameters. Screen the constraint conditions according to the numerical changes and judge their effectiveness. Adjust the upper and lower boundary values of the parameters to generate a set of constraint boundaries between parameters;

[0087] Based on the set of influence ranges of span and beam height on support reaction, according to the formula

[0088]

[0089] Calculate the boundary numerical intervals of span and beam height for the support reaction parameters.

[0090] In the formula, M(x, y) represents the integration result, f(x, y) represents the distribution of influence factors of the span parameter, g(x, y) represents the distribution of influence factors of the beam height parameter, x 1 , x 2 and y 1 , y 2 are the boundary ranges of span and beam height respectively.

[0091] The span range is obtained by monitoring the physical size of the span and taking its range in the actual bridge design, such as from 10 meters to 50 meters. Define x 1 = 10 and x 2 = 50. The beam height range is calculated through the load - bearing capacity of the bridge. Assume the beam height is from 1 meter to 5 meters. Define y 1 = 1 and y 2 = 5. The influence factor distribution functions f(x, y) and g(x, y) are fitted through experimental data. Assume f(x, y)=2x and g(x, y)=3y 2 . Substitute them into the formula:

[0092]

[0093] First, integrate with respect to x:

[0094]

[0095] Then, integrate with respect to y:

[0096]

[0097] Multiply the two:

[0098] M(x,y) = 2400·124 = 297600;

[0099] This result indicates that the integral value of the span and beam height with respect to the support reaction force is 297600, which provides a basis for determining the parameter boundary interval. Combine this value to adjust the upper and lower limits of the parameters to generate a set of constraint boundaries.

[0100] S203: Based on the set of constraint boundaries between parameters, integrate the adjusted constraint boundary data into the weight matrix of the parameter correlation network. Re - distribute the weight values in the network matrix according to the constraint boundaries, calculate the distribution value of the weight data after matrix adjustment, verify the consistency of the adjusted matrix, and generate a set of parameter constraint intervals;

[0101] Re - distribute the adjusted constraint boundary data to the weight matrix of the parameter correlation network. Map the boundary set data to each weight node in the matrix, establish a piece - wise linear relationship model to calculate the distribution value of the weight data after matrix adjustment. Through the consistency verification of the weight node data, analyze the error range after weight adjustment and perform error compensation, and finally output a set of parameter constraint intervals.

[0102] Please refer to Figure 4 , the specific steps of S3 are as follows:

[0103] S301: Based on the set of parameter constraint intervals, combine the numerical pairs of the span and beam height parameters. For each set of parameter paths, gradually assign weight values according to their numerical characteristics, calculate the path weights and analyze their distribution trend values, screen out the range of parameter combinations with stable weight distribution, and generate a set of path weights for the span and beam height parameters;

[0104] Analyze the parameter paths by gradually assigning weight values. Generate a set of parameter paths for the span and beam height according to the numerical characteristics. When calculating the path weights, extract the parameter interaction characteristics through the correlation matrix, construct a path weight distribution diagram, analyze the local and overall trends of the path weights, exclude the path combinations with large fluctuations according to the trend changes, and finally sort out the parameter combinations with stable weight distribution and output a set of path weights for the span and beam height parameters.

[0105] S302: Based on the set of path weights for the span and beam height parameters, analyze the distribution characteristics of the path weights, screen out the combinations whose path weight values meet the parameter constraint conditions, calculate the cumulative weight values of the screened paths, and determine whether they meet the dynamic change conditions, adjust the priority order of the path combinations, and generate a set of path combinations that meet the constraints;

[0106] The specific formula for the cumulative weight value is:

[0107]

[0108] Among them, Q tot represents the cumulative weight value, q p,j represents the weight of the p-th parameter point in the j-th group of path combinations, z p,j represents the path parameter value of the p-th parameter point in the j-th group of path combinations, represents the average value of the path parameter values in the j-th group of path combinations, and t represents the total number of parameter points in the j-th group of path combinations.

[0109] In the above formula, each parameter is parsed one by one and specific numerical values are assigned. The parameters in the j-th group of path combinations include 5 points, and the parameter values of each point are 38, 42, 36, 40, 44, and the weight values are 0.2, 0.25, 0.15, 0.3, 0.1 in sequence. The weight setting is based on the importance of the parameter points, and this value is determined by the proportion of the load capacity of the path points in the historical data. The numerical range is selected as a uniform distribution from 0.1 to 0.3 through research. The average value is calculated based on the path parameter values and is calculated by the arithmetic mean formula after obtaining the parameter values through monitoring.

[0110] Parameter description:

[0111] Total number of parameter points: t = 5

[0112] Path parameter value: z 1,j = 38, z 2,j = 42, z 3,j = 36, z 4,j = 40, z 5,j = 44

[0113] Weight value: q 1,j = 0.2, q 2,j = 0.25, q 3,j = 0.15, q 4,j = 0.3, q 5,j = 0.1

[0114] Average path parameter value:

[0115] Calculation process:

[0116] Calculate the numerator part:

[0117]

[0118] Calculate the denominator part:

[0120]

[0121] Calculation result:

[0122]

[0123] Analysis of numerical results:

[0124] The result shows that the cumulative weight value of the j-th group of screening paths of the path is 0.3. This value reflects the comprehensive influence degree of the deviation of the path parameter values and the weight distribution. A lower cumulative weight value indicates that the distribution between path parameter points is relatively uniform and the weight allocation is reasonable, which is associated with the possibility that the path combination meets the constraint conditions and provides a basis for adjusting the path priority order.

[0125] S303: Based on the set of path combinations that meet the constraints, optimize the part with uneven weights in the path combination, re-allocate the parameter weights within the path according to the optimization results, integrate the optimized path combinations to form the final path plan, calculate the dynamic weight cumulative value of the path plan, and generate a set of parameter dynamic path values;

[0126] By analyzing the balance of the path weight distribution, adjust the path parameter weights for the areas with uneven weights. During the adjustment process, optimize the weight distribution by combining the dynamic characteristics of the path and the constraint conditions. Use the piecewise adjustment method to re-allocate the weights within the path, determine the adjusted path weight distribution plan through repeated verification, further analyze the change trend of the optimized path weights, and finally integrate the optimized paths to form the dynamic weight cumulative value and generate a set of parameter dynamic path values.

[0127] Please refer to Figure 5 , and the specific steps of S4 are as follows:

[0128] S401: Based on the set of parameter dynamic path values, allocate live load, dead load, and wind load to each parameter point in the path, analyze the numerical distribution characteristics and action range of the load, adjust the load values one by one to meet the bearing capacity of the path, calculate the force data of the load on the path, and screen the loads with stable distribution to generate a set of path load allocation results;

[0129] The specific formula for correcting the force data calculation is:

[0130]

[0131] Among them, F k,i represents the corrected force value of the k-th parameter point in the path under the load distribution, l k represents the actual span of the k-th point in the path, w j,k represents the weight of the j-th type of load at the k-th parameter point in the path, P j,k represents the distribution value of the j-th type of load at the k-th point in the path, A j,k represents the action area of the j-th type of load at the k-th point in the path, R k represents the bearing capacity threshold of the k-th point in the path, and m represents the total number of load types.

[0132] Parameter assignment:

[0133] There are 5 parameter points in the path. The span of the k = 3rd parameter point is 10 meters. The total number of load types is 3, including live load, dead load, and wind load. Their distribution weights are 0.4, 0.35, and 0.25. The weight values are set according to the stress contribution ratio of each load to the path points in the monitoring records. The distribution values (unit: kN / m 2 ) are 20, 15, and 10 respectively, and the acting areas (unit: m 2 ) are 25, 30, and 40 respectively. The bearing capacity threshold is 800 kN.

[0134] Specific parameter values: l k = 10w 1,3 = 0.4, w 2,3 = 0.35, w 3,3 = 0.25P 1,3 = 20, P 2,3 = 15, P 3,3 = 10A 1,3 = 25, A 2,3 = 30, A 3,3 = 40R k = 800

[0135] Calculation process:

[0136] Calculate the correction value of each load item by item:

[0137] w 1,3 ·|P 1,3 ·A 1,3 -R k | = 0.4·|20·25 - 800| = 0.4·|500 - 800| = 0.4·

[0138] 300 = 120;

[0139] w 2,3 ·|P 2,3 ·A 2,3 -R k | = 0.35·|15·30 - 800| = 0.35·|450 - 800| =

[0140] 0.35·350 = 122.5;

[0141] w 3,3 ·|P 3,3 ·A 3,3 -R k | = 0.25·|10·40 - 800| = 0.25·|400 - 800| =

[0142] 0.25·400 = 100;

[0143] Sum:

[0144]

[0145] Calculate the final force value:

[0146]

[0147] Numerical result analysis:

[0148] The result shows that the corrected force value at the 3rd parameter point in the path is 34.25, which is used to reflect the comprehensive influence of different load types and parameter points. This value is directly related to the result of the path bearing capacity check, provides a basis for screening loads with stable distribution, and further generates a set of path load distribution results.

[0149] S402: Based on the set of path load distribution results, calculate the response values of live load, dead load, and wind load on the span and beam height point by point, analyze the response trend under the action of the load according to the dynamic change range of the parameters, extract the numerical interval where the load has a significant impact on the span and beam height, and generate a load response numerical data set;

[0150] Based on the set of path load distribution results, according to the formula

[0151]

[0152] Calculate the response values of live load, dead load, and wind load on the span and beam height.

[0153] In the formula, F ij is the load response value, L i and H j are the distribution values of live load and dead load respectively, V(t) is the time function of wind load, t 1 and t 2 are the action time ranges of wind load.

[0154] Assume that the span of the path is 40 meters, the beam height is 2 meters, the distribution value of live load is 2000, the distribution value of dead load is 3000, the time function of wind load is V(t) = 50·sin(t), and the wind load action time range is t 1 = 0 seconds to t 2 = π seconds. Substitute into the formula:

[0155]

[0156] F ij = 300000000·2 = 600000000;

[0157] The results show that the response values of live load, dead load and wind load to the span and beam height are 6,000,000,000, which are used to analyze the trend of parameter response under load and extract the numerical range that has a significant impact on the span and beam height.

[0158] S403: Based on the load response numerical data set, analyze the adjustment range of each load on the span and beam height parameters, integrate the parameter response range according to the numerical changes between path nodes, construct an adjustment interval matrix of the span and beam height under load, and generate a load response parameter matrix;

[0159] Refine the adjustment range of the load on the span and beam height to each node of the path, integrate the parameter response range according to the numerical change law between nodes, determine the adjustment range of each path node under load by constructing an adjustment interval matrix, compare the adjusted matrix data with the dynamic change range of the parameters item by item, and finally form an adjustment interval matrix of the span and beam height under load and generate a load response parameter matrix.

[0160] Please refer to Figure 6 , and the specific steps of S5 are as follows:

[0161] S501: Based on the load response parameter matrix, analyze the adjustment range of the span and beam height parameter combinations group by group, check the validity of the combined values point by point according to the constraint conditions of the dynamic path, eliminate the values of the parameters that do not meet the requirements, and screen the parameter combinations that meet the conditions to generate a set of parameter combinations that meet the path constraints;

[0162] Analyze the adjustment range of the span and beam height parameter combinations group by group, extract the constraint conditions of each parameter combination in the dynamic path, check the parameter combination values with the constraint conditions point by point, eliminate the parameter combinations that do not meet the requirements found in the check, and further verify the screening results item by item in combination with the constraint conditions and the change trend of the parameter combinations after adjustment, and finally output a set of parameter combinations that meet the path constraints.

[0163] S502: Based on the set of parameter combinations that meet the path constraints, calculate the numerical fluctuation of each parameter combination in the load response, analyze the numerical distribution difference generated by the load on the parameter combination, screen the parameter combinations with balanced load action and the smallest fluctuation range, integrate the selected parameter combinations, and generate a set of balanced load parameter combinations;

[0164] Based on the set of parameter combinations that meet the path constraints, calculate the numerical fluctuation of each parameter combination in the load response according to the formula

[0165]

[0166] Calculate the numerical fluctuation of each parameter combination in the load response.

[0167] In the formula, S iis the fluctuation value of the parameter combination, R ij is the j-th response value, is the average response value of the i-th group of parameter combinations, and n is the number of responses.

[0168] Suppose the response values of a certain parameter combination are 50, 55, 52, 48, 53, and calculate the fluctuation value S i . The number of responses n = 5, and the average response value is:

[0169]

[0170] Substitute into the formula:

[0171]

[0172] Calculate the square of each difference:

[0173] (50 - 51.6) 2 = 2.56, (55 - 51.6) 2 = 11.56, (52 - 51.6) 2 = 0.16, (48 - 51.6) 2 = 12.96, (53 - 51.6) 2 = 1.96;

[0174] Sum and substitute:

[0175]

[0176] This result shows that the numerical fluctuation of the parameter combination in the load response is 2.42, which is used to screen the parameter combination with the smallest fluctuation range and balanced load effect, and integrate the selected parameter combinations.

[0177] S503: Based on the set of balanced load parameter combinations, globally check the adjusted values of the span and beam height parameter combinations, modify the parameter intervals of the span and beam height according to the load influence values of each combination, integrate the modified parameter combinations, and calculate the overall optimized value to generate the optimized integration value of the bridge parameters;

[0178] Correlate the load influence values of each combination with the parameter interval data, gradually modify the parameter intervals of the span and beam height for the load influence value of each parameter combination, optimize the adjustment process using the method of piecewise linear interpolation, verify the dynamic load action on the modified parameter combinations, further integrate the response results of the modified parameter combinations and calculate the overall optimized value, and finally generate the optimized integration value of the bridge parameters.

[0179] Please refer to Figure 7 , an interactive generative bridge parameter design system, including:

[0180] The parameter correlation module analyzes the effect of the span on the beam height and the influence of the beam height on the support reaction force based on the span, beam height, and support reaction force parameters, constructs a set of node and edge weights through parameter relationships, and generates a parameter correlation network weight matrix;

[0181] The constraint interval module extracts the influence range of the span and beam height on the support reaction force based on the parameter correlation network weight matrix, determines the constraint boundary values between parameters, and generates a set of parameter constraint intervals;

[0182] The path weight module combines the span and beam height parameters based on the set of parameter constraint intervals, calculates the weights of each parameter path, selects the path combinations that meet the constraint conditions, and generates a set of parameter dynamic path values;

[0183] The load response module distributes the live load, dead load, and wind load to each path based on the set of parameter dynamic path values, calculates the response of the load to the span and beam height, and generates a load response parameter matrix;

[0184] The parameter optimization module globally checks the parameter adjustment values of the span and beam height combinations based on the load response parameter matrix, screens the combinations that meet the dynamic path and load response, and generates an optimized integration value of the bridge parameters.

[0185] As described above, it is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claimed rights.

Claims

1. An interactive generative bridge parameter design method, characterized in that: The following steps are involved: S1: Based on the span, beam height and support reaction parameters, the effect of span on beam height and the influence of beam height on support reaction are analyzed, and the node and edge weight sets are constructed through parameter relationships to form a network structure and generate a parameter-related network weight matrix; S2: Based on the parameter-associated network weight matrix, extract the influence range of span and beam height on support reaction force, determine the constraint boundary value between parameters, integrate the constraint results into the network matrix, and generate a parameter constraint interval set; S3: Based on the parameter constraint interval set, the span and beam height parameters are combined, and the weight of each group of parameter paths is calculated, and a path combination that meets the constraint conditions is selected to form a path plan, and a parameter dynamic path value set is generated; S4: Based on the parameter dynamic path value set, the live load, the dead load and the wind load are allocated to each path, the response of the load to the span and the beam height is calculated, the adjustment range of each parameter under the load is analyzed, and a load response parameter matrix is ​​generated; S5: Based on the load response parameter matrix, globally check the parameter adjustment values ​​of the span and beam height combination, select the combination that meets the dynamic path and load response, check all combination results, and generate the optimized integrated value of the bridge parameters.

2. The interactive generative bridge parameter design method according to claim 1 is characterized in that: The parameter association network weight matrix includes a span node set, a beam height node set, a support reaction node set, and a weight distribution between nodes. The parameter constraint interval set includes a span range constraint, a beam height range constraint, a support reaction range constraint, and a coupling boundary between span and beam height. The parameter dynamic path value set includes a path weight value, a support reaction dynamic range value, and a span and beam height combined path. The load response parameter matrix includes a live load response coefficient, a dead load response coefficient, a wind load response coefficient, and a path response adjustment value. The bridge parameter optimization integration value includes an optimized span value, an optimized beam height value, and an optimized support reaction value.

3. The interactive generative bridge parameter design method according to claim 1, characterized in that: Based on the span, beam height and support reaction parameters, the effect of span on beam height and the influence of beam height on support reaction are analyzed. The node and edge weight sets are constructed through parameter relationships to form a network structure. The specific steps for generating the parameter association network weight matrix are as follows: S101: Based on the span, beam height and support reaction parameters, the numerical range and distribution characteristics of the span and beam height are extracted, the spans are grouped according to the parameter distribution law, the variation range and fluctuation range of the span corresponding to the beam height are calculated, and the span variation threshold is set to filter the data with a large span variation, and the influence characteristics of the span variation on the beam height are established; S102: Based on the influence characteristics of the span change on the beam height, the change trends of the beam height and the support reaction force are analyzed, and the change relationship between the support reaction force data and the beam height parameters is fitted to determine the key node parameters of the beam height change on the support reaction force, adjust the parameter range, and calculate the correlation value of the beam height and the support reaction force to generate a beam height and support reaction force correlation data set; S103: Based on the beam height and support reaction force correlation data set, a node and edge weight set of the parameter correlation relationship network is constructed, the network is grouped according to the weight size between the nodes, the areas with large weight fluctuations are screened and the edge weights are adjusted, the value of the overall network weight matrix is ​​calculated, and the parameter correlation network weight matrix is ​​generated.

4. The interactive generative bridge parameter design method according to claim 1, characterized in that: Based on the parameter association network weight matrix, the influence range of span and beam height on support reaction force is extracted, the constraint boundary value between parameters is determined, and the constraint results are integrated into the network matrix. The specific steps of generating the parameter constraint interval set are as follows: S201: Based on the parameter association network weight matrix, extract the interaction range of span, beam height and support reaction parameters, analyze the interaction weight data between the parameters, screen the main range of span and beam height affecting support reaction according to the weight change trend, calculate the weight distribution of span and beam height on support reaction and determine its range, and generate a set of span and beam height influence ranges on support reaction; S202: Based on the influence range set of the span and beam height on the support reaction, the distribution law of the weights in the influence range is analyzed, and the boundary value interval of the span and beam height on the support reaction parameters is calculated in combination with the numerical boundary characteristics between the span, beam height and the support reaction, and the constraint conditions are selected and their validity is judged according to the numerical changes, and the upper and lower bounds of the parameters are adjusted to generate the constraint boundary set between the parameters; S203: Based on the constraint boundary set between parameters, the adjusted constraint boundary data is integrated into the parameter-associated network weight matrix, each weight value in the network matrix is ​​reallocated according to the constraint boundary, the distribution value of the weight data after the matrix adjustment is calculated, the consistency of the adjusted matrix is ​​verified, and a parameter constraint interval set is generated.

5. The interactive generative bridge parameter design method according to claim 1, characterized in that: Based on the parameter constraint interval set, the span and beam height parameters are combined, and the weight of each group of parameter paths is calculated. The path combination that meets the constraint conditions is selected to form a path plan. The specific steps of generating the parameter dynamic path value set are as follows: S301: Based on the parameter constraint interval set, the value pairs of span and beam height parameters are combined, and weight values ​​are gradually assigned to each group of parameter paths according to their numerical characteristics, and the path weights are calculated and their distribution trend values ​​are analyzed, and the parameter combination range with stable weight distribution is screened out to generate a span and beam height parameter path weight set; S302: Based on the span and beam height parameter path weight set, the distribution characteristics of the path weight are analyzed, the combination of path weight values ​​that meets the parameter constraint conditions is screened, the cumulative weight value of the screened path is calculated, and it is determined whether it meets the dynamic change condition, the priority order of the path combination is adjusted, and a path combination set that meets the constraint is generated; S303: Based on the set of path combinations that meet the constraints, optimize the unevenly weighted parts in the path combinations, reallocate the parameter weight values ​​in the paths according to the optimization results, integrate the optimized path combinations and form a final path plan, calculate the dynamic weight cumulative value of the path plan, and generate a parameter dynamic path value set.

6. The interactive generative bridge parameter design method according to claim 5, characterized in that: The cumulative weight value calculation formula is specifically: Among them, Q tot Represents the cumulative weight value, q p,j represents the weight of the pth parameter point in the jth group of path combination, z p,j Represents the path parameter value of the pth parameter point in the jth group of path combination, represents the average value of the path parameter in the jth group of path combination, and t represents the total number of parameter points in the jth group of path combination.

7. The interactive generative bridge parameter design method according to claim 1, characterized in that: Based on the parameter dynamic path value set, the live load, dead load and wind load are allocated to each path, the response of the load to the span and beam height is calculated, and the adjustment range of each parameter under the load is analyzed. The specific steps of generating the load response parameter matrix are as follows: S401: Based on the parameter dynamic path value set, live load, dead load and wind load are allocated to each parameter point in the path, the numerical distribution characteristics and action range of the load are analyzed, the load values ​​are adjusted one by one to meet the bearing capacity of the path, the force data of the load on the path is calculated, and the load with stable distribution is screened to generate a path load allocation result set; S402: Based on the path load distribution result set, the response values ​​of live load, dead load and wind load to span and beam height are calculated point by point, the response trend under the load is analyzed according to the dynamic change range of the parameters, the value interval where the load has a significant impact on the span and beam height is extracted, and a load response numerical data set is generated; S403: Based on the load response numerical data set, analyze the adjustment range of span and beam height parameters for each load, integrate the parameter response range according to the numerical changes between path nodes, construct an adjustment interval matrix of span and beam height under load, and generate a load response parameter matrix.

8. The interactive generative bridge parameter design method according to claim 7, characterized in that: The calculation formula for the modified force data is specifically: Among them, F k,i represents the modified force value of the kth parameter point in the path under the load distribution, l k represents the actual span of the kth point in the path, w j,k represents the weight of the j-th type of load at the k-th parameter point in the path, P j,k represents the distribution value of the j-th type of load at point k in the path, A j,k Represents the area of ​​action of the j-th type of load at point k in the path, R k represents the load capacity threshold of the kth point in the path, and m represents the total number of load types.

9. The interactive generative bridge parameter design method according to claim 1, characterized in that: Based on the load response parameter matrix, the parameter adjustment values ​​of the span and beam height combination are globally checked, the combination that meets the dynamic path and load response is selected, all combination results are checked, and the specific steps of generating the optimized integrated value of the bridge parameters are as follows: S501: Based on the load response parameter matrix, the adjustment range of the span and beam height parameter combinations is analyzed group by group, the validity of the combination values ​​is checked point by point according to the constraint conditions of the dynamic path, the parameters that do not meet the requirements are numerically eliminated, and the parameter combinations that meet the conditions are screened to generate a parameter combination set that meets the path constraints; S502: Based on the parameter combination set that meets the path constraint, calculate the numerical fluctuation of each parameter combination in the load response, analyze the numerical distribution difference caused by the load on the parameter combination, select the parameter combination with balanced load effect and minimum fluctuation range, integrate the selected parameter combinations, and generate a balanced load parameter combination set; S503: Based on the balanced load parameter combination set, globally check the adjustment values ​​of the span and beam height parameter combinations, correct the parameter ranges of the span and beam height according to the load influence value of each combination, integrate the corrected parameter combinations, and calculate the overall optimized values ​​to generate the bridge parameter optimization integrated value.

10. An interactive generative bridge parameter design system, characterized in that: According to an interactive generative bridge parameter design method according to any one of claims 1 to 9, the system comprises: The parameter association module analyzes the effect of span on beam height and the effect of beam height on support reaction based on span, beam height and support reaction parameters, constructs node and edge weight sets through parameter relationships, and generates a parameter association network weight matrix; The constraint interval module extracts the influence range of span and beam height on support reaction force based on the parameter association network weight matrix, determines the constraint boundary value between parameters, and generates a parameter constraint interval set; The path weight module combines the span and beam height parameters based on the parameter constraint interval set, calculates the weight of each set of parameter paths, selects the path combination that meets the constraint conditions, and generates a parameter dynamic path value set; The load response module distributes live load, dead load and wind load to each path based on the parameter dynamic path value set, calculates the response of the load to the span and beam height, and generates a load response parameter matrix; The parameter optimization module performs a global check on the parameter adjustment values ​​of the span and beam height combination based on the load response parameter matrix, selects the combination that meets the dynamic path and load response, and generates the optimized integrated value of the bridge parameter.

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