An integrated method for intelligent modeling and optimization of steel-concrete composite beam bridges based on human-machine collaboration

Abstract

The present invention discloses an integrated method for intelligent modeling and optimization of a steel-concrete composite beam bridge based on human-machine collaboration, comprising the following steps: 1) determining the structural system, span and hole distribution scheme of the steel-concrete composite beam bridge, and drawing an initial condition diagram; the initial condition diagram includes a bridge initial condition plan diagram and a bridge initial condition elevation diagram; 2) constructing an initial intelligent model of the steel-concrete composite beam bridge based on the initial condition diagram; 3) optimizing the initial intelligent model of the steel-concrete composite beam bridge to obtain an intelligent model of the steel-concrete composite beam bridge. The present invention proposes an integrated framework for intelligent modeling and optimization of a steel-concrete composite beam bridge based on human-machine collaboration, which can realize the intelligent modeling and optimization of a steel-concrete composite beam bridge in an integrated manner, has strong flexibility and adaptability, and can be extended to different types of bridge designs.

Description

Technical Field

[0001] The present invention relates to the field of intelligent modeling and optimization of bridge structures, and specifically to an integrated method for intelligent modeling and optimization of steel-concrete composite beam bridges based on human-machine collaboration. Background Art

[0002] The steel-concrete composite structure combines the advantages of reinforced concrete structure and steel structure, has good economic and social benefits, and can better adapt to the requirements of green, assembly and intelligence. The finite element model is the basis of bridge design. At present, bridge design is mainly based on the manual establishment of finite element calculation models. Finite element model analysis includes three processes: pre-processing, calculation analysis and post-processing. The pre-processing takes up about half of the time of the entire process. The modeling efficiency is low, and the model is prone to errors. Repeated debugging is required to ensure the accuracy of the model. In addition, the manual optimization process is time-consuming and often depends on the experience of the designer, and the optimization results are often not close to the theoretical optimal solution. Therefore, it is urgent to propose an integrated method of intelligent modeling and optimization of steel-concrete composite beam bridges based on human-computer collaboration. Summary of the invention

[0003] The purpose of the present invention is to provide an integrated method for intelligent modeling and optimization of a steel-concrete composite beam bridge based on human-machine collaboration, comprising the following steps:

[0004] 1) Determine the structural system, span and hole distribution scheme of the steel-concrete composite beam bridge, and draw the initial condition diagram; the initial condition diagram includes the initial condition plan diagram and the initial condition elevation diagram of the bridge;

[0005] 2) Based on the initial condition diagram, construct the initial intelligent model of the steel-concrete composite beam bridge;

[0006] 3) The initial intelligent model of the steel-concrete composite beam bridge is optimized to obtain the optimization scheme of the steel-concrete composite beam bridge.

[0007] Furthermore, the initial condition diagram records the plane positioning and elevation of the bridge piers, the plane positioning of the bridge centerline and sideline, and the bridge deck elevation information.

[0008] Furthermore, the steps of constructing the initial intelligent model of the steel-concrete composite beam bridge include:

[0009] 2.1) Using the layer analysis algorithm to read the initial design information in the initial condition diagram;

[0010] 2.2) Generate component elements of the steel-concrete composite beam bridge using the read initial design information;

[0011] 2.3) Generate constraint information of steel-concrete composite beam bridge;

[0012] 2.4) Input various loads, including but not limited to the deadweight of various components, deadweight of asphalt pavement, deadweight of railings, and lane load;

[0013] 2.5) Output the initial intelligent model of steel-concrete composite beam bridge.

[0014] Further, the steps of reading the initial design information in the initial condition diagram using the layer analysis algorithm include:

[0015] Mark the piers, bridge centerline and sideline of the initial condition plan of the bridge, and read the initial condition plan of the bridge using a layer analysis algorithm;

[0016] Mark the piers and top lines of the bridge deck in the initial condition elevation drawing of the bridge, and use the layer analysis algorithm to read the line type information of the initial condition elevation drawing of the bridge, and determine the plane coordinates and elevation of the bottom center of the pier, the plane coordinates of the bridge centerline, the elevation of the bottom center of the bridge support, and the elevation information of the top of the bridge deck.

[0017] Further, the step of generating the component unit of the steel-concrete composite beam bridge includes:

[0018] 2.2.1) Determine the cross-sectional form, preliminary cross-sectional dimensions, material information and support information of the pier system and composite beam; the pier system includes pier columns, tie beams and cap beams;

[0019] 2.2.2) Based on the initial design information, section form, preliminary section size, material information and support information, determine the size and start and end coordinates of each component unit;

[0020] 2.2.3) Formulate unit division criteria, determine the number of each component unit, and number the nodes in the order of abutment support bottom, pier, tie beam, cap beam, steel main beam, cross beam, and bridge deck;

[0021] 2.2.4) Define the fiber cross-section of each component unit;

[0022] 2.2.5) Combine the information from steps 2.2.2)-2.2.4) to generate the component elements of the steel-concrete composite beam bridge.

[0023] Further, the step of defining the fiber cross section of each component unit includes:

[0024] 2.2.4.1) Establish fiber cross-section meshing criteria;

[0025] 2.2.4.2) Define the constitutive relationship of each material and establish a constitutive relationship library.

[0026] 2.2.4.3) Automatically generate cross-sectional dimensions, divide fiber cross sections, and read constitutive relations based on material information.

[0027] Further, the step of generating the constraint information of the steel-concrete composite beam bridge includes:

[0028] 2.3.1) Extract the nodes at the bottom of the pier and the bottom of the abutment support, and define the boundary conditions of the bottom of the pier and the bottom of the abutment support;

[0029] 2.3.2) Extract the nodes at the bottom of the support at the top of the cap beam and the abutment and the nodes of the steel main beam at the corresponding positions, and automatically generate the support units at the top of the cap beam and the abutment.

[0030] 2.3.3) Automatically generate the rigid arm unit between the steel main beam and the bridge deck.

[0031] 2.3.4) Write the boundary conditions, support units and rigid arm unit information into the file to achieve automatic definition of various constraints.

[0032] Furthermore, the deadweight of various components, asphalt pavement, and railings are applied in the form of uniformly distributed line loads;

[0033] Lane loads are applied in the form of uniformly distributed loads and concentrated loads.

[0034] Furthermore, the steps of optimizing the initial intelligent model of the steel-concrete composite beam bridge include:

[0035] 3.1) Select the variables to be optimized for each component and determine the value range of the decision variables; the variables to be optimized include but are not limited to the thickness and reinforcement ratio of the bridge deck, the concrete strength of the bridge deck, the height of the steel main beam, the width and thickness of the steel main beam flange, and the thickness of the steel main beam web;

[0036] 3.2) Establishment of constraint mathematical model, including but not limited to the constraint that the width-to-thickness ratio of the steel main beam flange does not exceed the limit, the height-to-thickness ratio of the steel main beam web does not exceed the limit, the bending bearing capacity of the section does not exceed the bending bearing capacity limit, the shear bearing capacity of the section does not exceed the shear bearing capacity limit, the member deflection does not exceed the deflection limit, and the crack width does not exceed the crack width limit;

[0037] The steel main beam flange width-to-thickness ratio does not exceed the limit constraint as follows:

[0038] B f / t f ≤rf lim (1)

[0039] In the formula, B f is the flange width of the steel main beam, t f is the thickness of the steel main beam flange, rf lim It is the limit value of the width-to-thickness ratio of the steel main beam flange.

[0040] The constraint that the height-to-thickness ratio of the web of the steel main beam does not exceed the limit is as follows:

[0041] h w / t w ≤=rw lim (2)

[0042] In the formula, h w is the web width of the steel main beam, t w is the web thickness of the steel main beam, rw lim It is the limit value of the height-to-thickness ratio of the web of the steel main beam.

[0043] The section bending capacity shall not exceed the section bending capacity limit constraint as follows:

[0044] M≤M lim (3)

[0045] Where M is the bending bearing capacity of the section, M lim is the limit of the section's bending bearing capacity.

[0046] The section shear bearing ratio does not exceed the section shear bearing limit constraint as follows:

[0047] V≤V lim (4)

[0048] Where V is the shear bearing capacity of the section, V lim is the shear bearing capacity limit of the section.

[0049] The member deflection does not exceed the deflection limit constraint as follows:

[0050] δ≤δ lim (5)

[0051] In the formula, δ is the member deflection, δ lim is the member deflection limit.

[0052] The crack width does not exceed the crack width limit constraint as follows:

[0053] w cr ≤w crlim (6)

[0054] In the formula, w cr is the crack width, w crlim is the crack width limit.

[0055] 3.3) Establish the optimization model f of material cost, that is:

[0056]

[0057] Where i is the component material number, i = 1, 2, 3…, n, n is the total number of component materials, A i is the cross-sectional area of ​​the component material, Li is the component material length, P i The unit price of component materials.

[0058] 3.4) The optimization model f is solved by using the optimization algorithm to obtain the optimization parameters of each component, and then the optimization scheme of the steel-concrete composite beam bridge is constructed.

[0059] Furthermore, the optimization algorithm includes but is not limited to: genetic algorithm, particle swarm algorithm, differential evolution algorithm;

[0060] In the process of solving the optimization model f, the parameter adaptive adjustment strategy is used to optimize the population quality and avoid generating invalid individuals, including the following steps:

[0061] a) Fix the width of the upper flange of the steel main beam and make a judgment based on the limit constraint of the width-to-thickness ratio of the steel main beam flange. If the requirement is met, the upper flange thickness will not be changed. If it is not met, it will be automatically updated to the minimum flange thickness that meets the limit constraint of the width-to-thickness ratio of the steel main beam flange.

[0062] b) Fix the width of the lower flange of the steel main beam and make a judgment based on the limit constraint of the width-to-thickness ratio of the steel main beam flange. If the requirement is met, the thickness of the lower flange will not be changed. If it is not met, it will be automatically updated to the minimum flange thickness that meets the limit constraint of the width-to-thickness ratio of the steel main beam flange.

[0063] c) The height of the web of the steel main beam is fixed, and the judgment is made based on the constraint that the height-to-thickness ratio of the web of the steel main beam does not exceed the limit value. If the requirement is met, the web thickness will not be changed. If it is not met, it will be automatically updated to the minimum web thickness that meets the limit value constraint of the height-to-thickness ratio of the web of the steel main beam.

[0064] The technical effect of the present invention is unquestionable. The present invention proposes an intelligent modeling framework for steel-concrete composite beam bridges based on human-machine collaboration. The method uses a layer analysis algorithm to extract initial condition graph information, further realizing intelligent modeling of steel-concrete composite beam bridges. Only a small amount of human-machine interaction is required to realize rapid modeling of similar bridges, greatly reducing the workload of manual modeling, avoiding manual modeling errors, and improving modeling efficiency and quality.

[0065] The present invention proposes an intelligent optimization framework for steel-concrete composite beam bridges. The method uses an optimization algorithm based on an established structural calculation model to optimize structural design parameters, avoiding the defects of long manual optimization cycles and strong subjectivity, and has the advantages of high efficiency and good optimization effect.

[0066] The present invention proposes an integrated framework for intelligent modeling and optimization of steel-concrete composite beam bridges based on human-machine collaboration, which can realize the intelligent modeling and optimization of steel-concrete composite beam bridges in an integrated manner. It has strong flexibility and adaptability and can be extended to different types of bridge designs. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a flow chart of intelligent modeling and optimization integration of an implementation example of the present invention;

[0068] Figure 2 It is a flow chart of intelligent modeling of an implementation example of the present invention;

[0069] Figure 3 An example of drawing an initial condition diagram of a steel-concrete composite beam bridge according to an embodiment of the present invention;

[0070] Figure 4 This is an example of reading the initial condition diagram of a steel-concrete composite beam bridge according to an embodiment of the present invention;

[0071] Figure 5 This is an example of intelligent modeling results of a steel-concrete composite beam bridge according to an embodiment of the present invention;

[0072] Figure 6 The figure is a schematic diagram of the intelligent optimization flow chart of an embodiment of the present invention;

[0073] Figure 7 The figure is a schematic diagram of the intelligent optimization process of an implementation example of the present invention. DETAILED DESCRIPTION

[0074] The present invention is further described below in conjunction with the embodiments, but it should not be understood that the above subject matter of the present invention is limited to the following embodiments. Without departing from the above technical ideas of the present invention, various substitutions and changes are made according to the common technical knowledge and customary means in the art, which should all be included in the protection scope of the present invention.

[0075] Embodiment 1:

[0076] See also Figures 1 to 7 , an integrated method for intelligent modeling and optimization of steel-concrete composite beam bridges based on human-machine collaboration, comprising the following steps:

[0077] 1) Manually determine the structural system, span and hole distribution scheme of the steel-concrete composite beam bridge, and draw the initial condition diagram; the initial condition diagram includes the initial condition plan diagram and the initial condition elevation diagram of the bridge; in this method, except that the initial condition diagram needs to be manually generated in advance and the key information in the initial condition diagram needs to be manually marked in advance, the subsequent modeling and optimization processes are completed automatically.

[0078] 2) Based on the initial condition diagram, construct the initial intelligent model of the steel-concrete composite beam bridge;

[0079] 3) The initial intelligent model of the steel-concrete composite beam bridge is optimized to obtain the optimization scheme of the steel-concrete composite beam bridge.

[0080] The initial condition diagram records the plane positioning and elevation of the bridge piers, the plane positioning of the bridge centerline and sideline, and the bridge deck elevation information.

[0081] The steps to construct the initial intelligent model of the steel-concrete composite beam bridge include:

[0082] 2.1) Using the layer analysis algorithm to read the initial design information in the initial condition diagram;

[0083] 2.2) Generate component elements of the steel-concrete composite beam bridge using the read initial design information;

[0084] 2.3) Generate constraint information of steel-concrete composite beam bridge;

[0085] 2.4) Input various loads, including but not limited to the deadweight of various components, deadweight of asphalt pavement, deadweight of railings, and lane loads; the input loads also include wind loads, temperature, shrinkage creep, earthquake action, foundation displacement, wave flow pressure, etc.

[0086] 2.5) Output the initial intelligent model of steel-concrete composite beam bridge.

[0087] The steps of using the layer analysis algorithm to read the initial design information in the initial condition diagram include:

[0088] Mark the piers, bridge centerline and sideline of the initial condition plan of the bridge, and read the initial condition plan of the bridge using a layer analysis algorithm;

[0089] Mark the piers and top lines of the bridge deck in the initial condition elevation drawing of the bridge, and use the layer analysis algorithm to read the line type information of the initial condition elevation drawing of the bridge, and determine the plane coordinates and elevation of the bottom center of the pier, the plane coordinates of the bridge centerline, the elevation of the bottom center of the bridge support, and the elevation information of the top of the bridge deck.

[0090] The steps to generate member elements for steel-concrete composite beam bridges include:

[0091] 2.2.1) Determine the cross-sectional form, preliminary cross-sectional dimensions, material information and support information of the pier system and composite beam; the pier system includes pier columns, tie beams and cap beams;

[0092] 2.2.2) Based on the initial design information, section form, preliminary section size, material information and support information, determine the size and start and end coordinates of each component unit;

[0093] 2.2.3) Formulate unit division criteria, determine the number of each component unit, and number the nodes in the order of abutment support bottom, pier, tie beam, cap beam, steel main beam, cross beam, and bridge deck;

[0094] 2.2.4) Define the fiber cross-section of each component unit;

[0095] 2.2.5) Combine the information from steps 2.2.2)-2.2.4) to generate the component elements of the steel-concrete composite beam bridge.

[0096] The steps to define the fiber cross-section of each component element include:

[0097] 2.2.4.1) Establish fiber cross-section meshing criteria;

[0098] 2.2.4.2) Define the constitutive relationship of each material and establish a constitutive relationship library.

[0099] 2.2.4.3) Automatically generate cross-sectional dimensions, divide fiber cross sections, and read constitutive relations based on material information.

[0100] The steps to generate restraint information for a steel-concrete composite beam bridge include:

[0101] 2.3.1) Extract the nodes at the bottom of the pier and the bottom of the abutment support, and define the boundary conditions of the bottom of the pier and the bottom of the abutment support;

[0102] 2.3.2) Extract the nodes at the bottom of the support at the top of the cap beam and the abutment and the nodes of the steel main beam at the corresponding positions, and automatically generate the support units at the top of the cap beam and the abutment.

[0103] 2.3.3) Automatically generate the rigid arm unit between the steel main beam and the bridge deck.

[0104] 2.3.4) Write the boundary conditions, support units and rigid arm unit information into the file to achieve automatic definition of various constraints.

[0105] The deadweight of various components, asphalt pavement and railings are applied in the form of uniformly distributed line loads;

[0106] Lane loads are applied in the form of uniformly distributed loads and concentrated loads.

[0107] The steps for optimizing the initial intelligent model of the steel-concrete composite beam bridge include:

[0108] 3.1) Select the variables to be optimized for each component and determine the value range of the decision variables; the variables to be optimized include but are not limited to the thickness and reinforcement ratio of the bridge deck, the concrete strength of the bridge deck, the height of the steel main beam, the width and thickness of the steel main beam flange, and the thickness of the steel main beam web;

[0109] 3.2) Establishment of constraint mathematical model, including but not limited to the constraint that the width-to-thickness ratio of the steel main beam flange does not exceed the limit, the height-to-thickness ratio of the steel main beam web does not exceed the limit, the bending bearing capacity of the section does not exceed the bending bearing capacity limit, the shear bearing capacity of the section does not exceed the shear bearing capacity limit, the member deflection does not exceed the deflection limit, and the crack width does not exceed the crack width limit;

[0110] The steel main beam flange width-to-thickness ratio does not exceed the limit constraint as follows:

[0111] B f / t f ≤rf lim (1)

[0112] In the formula, B f is the flange width of the steel main beam, t f is the thickness of the steel main beam flange, rf lim It is the limit value of the width-to-thickness ratio of the steel main beam flange.

[0113] The constraint that the height-to-thickness ratio of the web of the steel main beam does not exceed the limit is as follows:

[0114] h w / t w ≤rw lim (2)

[0115] In the formula, h w is the web width of the steel main beam, t w is the web thickness of the steel main beam, rw lim It is the limit value of the height-to-thickness ratio of the web of the steel main beam.

[0116] The section bending capacity shall not exceed the section bending capacity limit constraint as follows:

[0117] M≤M lim (3)

[0118] Where M is the bending bearing capacity of the section, M lim is the limit of the section's bending bearing capacity.

[0119] The section shear bearing ratio does not exceed the section shear bearing limit constraint as follows:

[0120] V≤V lim (4)

[0121] Where V is the shear bearing capacity of the section, V lim is the shear bearing capacity limit of the section.

[0122] The member deflection does not exceed the deflection limit constraint as follows:

[0123] δ≤δ lim (5)

[0124] In the formula, δ is the member deflection, δ lim is the member deflection limit.

[0125] The crack width does not exceed the crack width limit constraint as follows:

[0126] w cr ≤w crlim (6)

[0127] In the formula, w cr is the crack width, w crlim is the crack width limit.

[0128] 3.3) Establish the optimization model f of material cost, that is:

[0129]

[0130] Where i is the component material number, i = 1, 2, 3…, n, n is the total number of component materials, A i is the cross-sectional area of ​​the component material, L i is the component material length, P i The unit price of component materials.

[0131] 3.4) Use the optimization algorithm to solve the optimization model f, obtain the optimization parameters of each component, and then construct the intelligent model of the steel-concrete composite beam bridge.

[0132] The optimization algorithms include but are not limited to: genetic algorithm, particle swarm algorithm, differential evolution algorithm;

[0133] In the process of solving the optimization model f, the parameter adaptive adjustment strategy is used to optimize the population quality and avoid generating invalid individuals, including the following steps:

[0134] a) Fix the width of the upper flange of the steel main beam and make a judgment based on the limit constraint of the width-to-thickness ratio of the steel main beam flange. If the requirement is met, the upper flange thickness will not be changed. If it is not met, it will be automatically updated to the minimum flange thickness that meets the limit constraint of the width-to-thickness ratio of the steel main beam flange.

[0135] b) Fix the width of the lower flange of the steel main beam and make a judgment based on the limit constraint of the width-to-thickness ratio of the steel main beam flange. If the requirement is met, the thickness of the lower flange will not be changed. If it is not met, it will be automatically updated to the minimum flange thickness that meets the limit constraint of the width-to-thickness ratio of the steel main beam flange.

[0136] c) The height of the web of the steel main beam is fixed, and the judgment is made based on the constraint that the height-to-thickness ratio of the web of the steel main beam does not exceed the limit value. If the requirement is met, the web thickness will not be changed. If it is not met, it will be automatically updated to the minimum web thickness that meets the limit value constraint of the height-to-thickness ratio of the web of the steel main beam.

[0137] Embodiment 2:

[0138] An integrated method for intelligent modeling and optimization of a steel-concrete composite beam bridge based on human-machine collaboration includes the following steps:

[0139] 1) Determine the structural system, span and hole distribution scheme of the steel-concrete composite beam bridge, and draw the initial condition diagram; the initial condition diagram includes the initial condition plan diagram and the initial condition elevation diagram of the bridge;

[0140] 2) Based on the initial condition diagram, construct the initial intelligent model of the steel-concrete composite beam bridge;

[0141] 3) The initial intelligent model of the steel-concrete composite beam bridge is optimized to obtain the optimization scheme of the steel-concrete composite beam bridge.

[0142] Embodiment 3:

[0143] A method for integrated intelligent modeling and optimization of a steel-concrete composite beam bridge based on human-machine collaboration, the technical content of which is the same as that of Example 2. Furthermore, the initial condition diagram records the plane positioning and elevation of the bridge piers, the plane positioning of the bridge centerline and sideline, and the bridge deck elevation information.

[0144] Embodiment 4:

[0145] A method for intelligent modeling and optimization integration of a steel-concrete composite beam bridge based on human-machine collaboration, the technical content of which is the same as any one of Embodiments 2-3, and further, the step of constructing an initial intelligent model of the steel-concrete composite beam bridge includes:

[0146] 2.1) Using the layer analysis algorithm to read the initial design information in the initial condition diagram;

[0147] 2.2) Generate component elements of the steel-concrete composite beam bridge using the read initial design information;

[0148] 2.3) Generate constraint information of steel-concrete composite beam bridge;

[0149] 2.4) Input various loads, including but not limited to the deadweight of various components, deadweight of asphalt pavement, deadweight of railings, and lane loads; the input loads also include wind loads, temperature, shrinkage creep, earthquake action, foundation displacement, wave flow pressure, etc.

[0150] 2.5) Output the initial intelligent model of steel-concrete composite beam bridge.

[0151] Embodiment 5:

[0152] A method for intelligent modeling and optimization integration of a steel-concrete composite beam bridge based on human-machine collaboration, the technical content of which is the same as any one of Embodiments 2-4, and further, the step of using a layer analysis algorithm to read initial design information in an initial condition diagram comprises:

[0153] Mark the piers, bridge centerline and sideline of the initial condition plan of the bridge, and read the initial condition plan of the bridge using a layer analysis algorithm;

[0154] Mark the piers and top lines of the bridge deck in the initial condition elevation drawing of the bridge, and use the layer analysis algorithm to read the initial condition elevation drawing of the bridge

[0155] The line type information is read using the layer analysis algorithm to determine the plane coordinates and elevation of the center of the pier bottom, the plane coordinates of the bridge centerline, the elevation of the center of the bridge support bottom, and the elevation information of the bridge deck top.

[0156] Embodiment 6:

[0157] A method for intelligent modeling and optimization integration of a steel-concrete composite beam bridge based on human-machine collaboration, the technical content of which is the same as any one of Embodiments 2-5, and further, the step of generating a component unit of the steel-concrete composite beam bridge includes:

[0158] 2.2.1) Determine the cross-sectional form, preliminary cross-sectional dimensions, material information and support information of the pier system and composite beam; the pier system includes pier columns, tie beams and cap beams;

[0159] 2.2.2) Based on the initial design information, section form, preliminary section size, material information and support information, determine the size and start and end coordinates of each component unit;

[0160] 2.2.3) Formulate unit division criteria, determine the number of each component unit, and number the nodes in the order of abutment support bottom, pier, tie beam, cap beam, steel main beam, cross beam, and bridge deck;

[0161] 2.2.4) Define the fiber cross-section of each component unit;

[0162] 2.2.5) Combine the information from steps 2.2.2)-2.2.4) to generate the component elements of the steel-concrete composite beam bridge.

[0163] Embodiment 7:

[0164] A method for intelligent modeling and optimization integration of a steel-concrete composite beam bridge based on human-machine collaboration, the technical content of which is the same as any one of Embodiments 2-6, and further, the step of defining the fiber cross section of each component unit includes:

[0165] 2.2.4.1) Establish fiber cross-section meshing criteria;

[0166] 2.2.4.2) Define the constitutive relationship of each material and establish a constitutive relationship library.

[0167] 2.2.4.3) Automatically generate cross-sectional dimensions, divide fiber cross sections, and read constitutive relations based on material information.

[0168] Embodiment 8:

[0169] A method for intelligent modeling and optimization integration of a steel-concrete composite beam bridge based on human-machine collaboration, the technical content of which is the same as any one of Embodiments 2-7, and further, the step of generating constraint information of the steel-concrete composite beam bridge includes:

[0170] 2.3.1) Extract the nodes at the bottom of the pier and the bottom of the abutment support, and define the boundary conditions of the bottom of the pier and the bottom of the abutment support;

[0171] 2.3.2) Extract the nodes at the bottom of the support at the top of the cap beam and the abutment and the nodes of the steel main beam at the corresponding positions, and automatically generate the support units at the top of the cap beam and the abutment.

[0172] 2.3.3) Automatically generate the rigid arm unit between the steel main beam and the bridge deck.

[0173] 2.3.4) Write the boundary conditions, support units and rigid arm unit information into the file to achieve automatic definition of various constraints.

[0174] Embodiment 9:

[0175] An integrated method for intelligent modeling and optimization of a steel-concrete composite beam bridge based on human-machine collaboration, the technical content of which is the same as any one of Embodiments 2-10, and further, the deadweight of various components, the deadweight of asphalt pavement, and the deadweight of railings are applied in the form of uniformly distributed line loads;

[0176] Lane loads are applied in the form of uniformly distributed loads and concentrated loads.

[0177] Embodiment 10:

[0178] A method for integrating intelligent modeling and optimization of a steel-concrete composite beam bridge based on human-machine collaboration, the technical content of which is the same as any one of Embodiments 2-9, and further, the step of optimizing the initial intelligent model of the steel-concrete composite beam bridge includes:

[0179] 3.1) Select the variables to be optimized for each component and determine the value range of the decision variables; the variables to be optimized include but are not limited to the thickness and reinforcement ratio of the bridge deck, the concrete strength of the bridge deck, the height of the steel main beam, the width and thickness of the steel main beam flange, and the thickness of the steel main beam web;

[0180] 3.2) Establishment of constraint mathematical model, including but not limited to the constraint that the width-to-thickness ratio of the steel main beam flange does not exceed the limit, the height-to-thickness ratio of the steel main beam web does not exceed the limit, the bending bearing capacity of the section does not exceed the bending bearing capacity limit, the shear bearing capacity of the section does not exceed the shear bearing capacity limit, the member deflection does not exceed the deflection limit, and the crack width does not exceed the crack width limit;

[0181] The steel main beam flange width-to-thickness ratio does not exceed the limit constraint as follows:

[0182] B f / t f ≤rf lim (1)

[0183] In the formula, B f is the flange width of the steel main beam, t f is the thickness of the steel main beam flange, rf lim It is the limit value of the width-to-thickness ratio of the steel main beam flange.

[0184] The constraint that the height-to-thickness ratio of the web of the steel main beam does not exceed the limit is as follows:

[0185] h w / t w ≤rw lim (2)

[0186] In the formula, h w is the web width of the steel main beam, t w is the web thickness of the steel main beam, rw lim It is the limit value of the height-to-thickness ratio of the web of the steel main beam.

[0187] The section bending capacity shall not exceed the section bending capacity limit constraint as follows:

[0188] M≤M lim (3)

[0189] Where M is the bending bearing capacity of the section, M lim is the limit of the section's bending bearing capacity.

[0190] The section shear bearing ratio does not exceed the section shear bearing limit constraint as follows:

[0191] V≤V lim (4)

[0192] Where V is the shear bearing capacity of the section, V lim is the shear bearing capacity limit of the section.

[0193] The member deflection does not exceed the deflection limit constraint as follows:

[0194] δ≤δ lim (5)

[0195] In the formula, δ is the member deflection, δ lim is the member deflection limit.

[0196] The crack width does not exceed the crack width limit constraint as follows:

[0197] w cr ≤w crlim(6)

[0198] In the formula, w cr is the crack width, w crlim is the crack width limit.

[0199] 3.3) Establish the optimization model f of material cost, that is:

[0200]

[0201] Where i is the component material number, i = 1, 2, 3…, n, n is the total number of component materials, A i is the cross-sectional area of ​​the component material, L i is the component material length, P i The unit price of component materials.

[0202] 3.4) Use the optimization algorithm to solve the optimization model f, obtain the optimization parameters of each component, and then construct the intelligent model of the steel-concrete composite beam bridge.

[0203] Embodiment 11:

[0204] A method for intelligent modeling and optimization integration of a steel-concrete composite beam bridge based on human-machine collaboration, the technical content of which is the same as any one of Embodiments 2-10, and further, the optimization algorithm includes but is not limited to: a genetic algorithm, a particle swarm algorithm, and a differential evolution algorithm;

[0205] In the process of solving the optimization model f, the parameter adaptive adjustment strategy is used to optimize the population quality and avoid generating invalid individuals, including the following steps:

[0206] a) Fix the width of the upper flange of the steel main beam and make a judgment based on the limit constraint of the width-to-thickness ratio of the steel main beam flange. If the requirement is met, the upper flange thickness will not be changed. If it is not met, it will be automatically updated to the minimum flange thickness that meets the limit constraint of the width-to-thickness ratio of the steel main beam flange.

[0207] b) Fix the width of the lower flange of the steel main beam and make a judgment based on the limit constraint of the width-to-thickness ratio of the steel main beam flange. If the requirement is met, the thickness of the lower flange will not be changed. If it is not met, it will be automatically updated to the minimum flange thickness that meets the limit constraint of the width-to-thickness ratio of the steel main beam flange.

[0208] c) The height of the web of the steel main beam is fixed, and the judgment is made based on the constraint that the height-to-thickness ratio of the web of the steel main beam does not exceed the limit value. If the requirement is met, the web thickness will not be changed. If it is not met, it will be automatically updated to the minimum web thickness that meets the limit value constraint of the height-to-thickness ratio of the web of the steel main beam.

[0209] Embodiment 12:

[0210] An integrated method for intelligent modeling and optimization of a steel-concrete composite beam bridge based on human-machine collaboration includes the following steps:

[0211] 1) Determine the structural system, span and hole distribution scheme based on the use function, natural conditions and prior knowledge, and draw the initial condition diagram.

[0212] 2) Mark the piers, bridge centerline and sidelines in the initial condition plan of the bridge.

[0213] 3) Mark the top lines of the piers and bridge deck in the initial condition elevation drawing of the bridge.

[0214] 4) Use the layer analysis algorithm to read various curve information, determine the plane coordinates and elevation of the bottom center of the pier, the plane coordinates of the bridge centerline, the elevation of the bottom center of the bridge support, and the elevation information of the top of the bridge deck.

[0215] 5) Determine the cross-sectional form, preliminary cross-sectional dimensions, material information, and support information of the pier system (piers, tie beams, cap beams) and composite beams.

[0216] 6) Combine the size information of step 5) and the reading result of step 4) to perform spatial information reasoning to determine the size and start and end coordinates of each component.

[0217] 7) Establish unit division criteria, determine the number of units for each component, automatically number the nodes in the order of abutment support bottom, pier, tie beam, cap beam, steel main beam, cross beam, and bridge deck, and then complete the unit numbering.

[0218] 8) Define the fiber cross-section of each component.

[0219] 9) Write the node and element numbers and section information into the file to achieve automatic generation of components.

[0220] 10) Extract the nodes at the bottom of the pier and the bottom of the abutment support and define the boundary conditions.

[0221] 11) Extract the nodes at the bottom of the support at the top of the cap beam and the abutment and the nodes of the steel main beam at the corresponding positions, and automatically generate the support units at the top of the cap beam and the abutment.

[0222] 12) Automatically generate the rigid arm unit between the steel main beam and the bridge deck.

[0223] 13) Write the boundary conditions, support units and rigid arm unit information into the file to achieve automatic definition of various constraints.

[0224] 14) The self-weight of various components, asphalt pavement and railings are applied in the form of uniformly distributed line loads. The concentration of line loads is g. L It is calculated through the component material density, and the calculation formula is as follows:

[0225] g L =A×ρ(1)

[0226] Where A is the cross-sectional area and ρ is the material density.

[0227] 15) The lane load is calculated according to the specification requirements by reading the span information of the composite beam and applied in the form of uniformly distributed load and concentrated load.

[0228] 16) Select the variables to be optimized for each component and determine the value range of the decision variables.

[0229] 17) Establish a constraint mathematical model.

[0230] 18) The optimization model f of material cost is established as follows:

[0231]

[0232] Where i is the component material number, i = 1, 2, 3…, n, n is the total number of component materials, A i is the cross-sectional area of ​​the component material, L i is the component material length, P i The unit price of component materials.

[0233] 19) Use the optimization algorithm to solve the variables to be optimized and obtain the optimization parameters of each component.

[0234] Embodiment 13:

[0235] An integrated method for intelligent modeling and optimization of a steel-concrete composite beam bridge based on human-machine collaboration includes the following steps:

[0236] 1) Determine the structural system, span and hole distribution scheme based on the use function, natural conditions and prior knowledge, and draw the initial condition diagram.

[0237] 2) Intelligent modeling of steel-concrete composite beam bridges.

[0238] 3) Intelligent optimization of steel-concrete composite beam bridges.

[0239] The initial condition diagram of step 1) includes information such as the plane positioning and elevation of the bridge piers, the plane positioning of the bridge centerline and sideline, and the bridge deck elevation.

[0240] The intelligent modeling process described in step 2) is implemented by finite element software that can read and write command streams.

[0241] Step 2) includes the following sub-steps:

[0242] 2.1) Use layer analysis algorithm to read the initial design information.

[0243] 2.2) Automatically generate each component unit using the read initial design information.

[0244] 2.3) Automatically generate constraints.

[0245] 2.4) Automatically input various loads, such as the deadweight of various components, the deadweight of asphalt pavement, the deadweight of railings, lane loads, etc.

[0246] 2.5) Define the output content of the result.

[0247] Step 2.1) includes the following sub-steps:

[0248] 2.1.1) Mark the piers, bridge centerline and sidelines in the initial condition plan of the bridge.

[0249] 2.1.2) Mark the top lines of the piers and bridge deck in the initial condition elevation drawing of the bridge.

[0250] 2.1.3) Use the layer analysis algorithm to read various curve information, determine the plane coordinates and elevation of the bottom center of the pier, the plane coordinates of the bridge centerline, the elevation of the bottom center of the bridge support, and the elevation information of the top of the bridge deck.

[0251] Step 2.2) includes the following sub-steps:

[0252] 2.2.1) Determine the cross-sectional form, preliminary cross-sectional dimensions, material information, and support information of the pier system (piers, tie beams, cap beams) and composite beams.

[0253] 2.2.2) Combine the size information of step 3.1) and the reading result of step 2.3) to perform spatial information reasoning to determine the size and start and end coordinates of each component.

[0254] 2.2.3) Establish unit division criteria, determine the number of units for each component, and automatically number the nodes in the order of abutment support bottom, pier, tie beam, cap beam, steel main beam, cross beam, and bridge deck, thereby completing the unit numbering.

[0255] 2.2.4) Define the fiber cross-section of each component.

[0256] 2.2.5) Write the node and element numbers and section information into the file to achieve automatic generation of components.

[0257] Step 2.2.4) includes the following steps:

[0258] 2.2.4.1) Establish fiber cross-section meshing criteria.

[0259] 2.2.4.2) Define the constitutive relationship of each material and establish a constitutive relationship library.

[0260] 2.2.4.3) Automatically generate cross-sectional dimensions, divide fiber cross sections, and read constitutive relations based on material information.

[0261] Step 2.3) includes the following sub-steps:

[0262] 2.3.1) Extract the nodes at the bottom of the pier and the bottom of the abutment support and define the boundary conditions.

[0263] 2.3.2) Extract the nodes at the bottom of the support at the top of the cap beam and the abutment and the nodes of the steel main beam at the corresponding positions, and automatically generate the support units at the top of the cap beam and the abutment.

[0264] 2.3.3) Automatically generate the rigid arm unit between the steel main beam and the bridge deck.

[0265] 2.3.4) Write the boundary conditions, support units and rigid arm unit information into the file to achieve automatic definition of various constraints.

[0266] Step 2.4) includes the following sub-steps:

[0267] 2.4.1) The self-weight of various components, asphalt pavement and railings are applied in the form of uniformly distributed line loads. The concentration of line loads is g L It is calculated through the component material density, and the calculation formula is as follows:

[0268] g L =A×ρ(1)

[0269] Where A is the cross-sectional area and ρ is the material density.

[0270] 2.4.2) The lane load is calculated according to the specification requirements by reading the span information of the composite beam and applied in the form of uniformly distributed load and concentrated load.

[0271] Step 3) includes the following sub-steps:

[0272] 3.1) Select the variables to be optimized for each component and determine the value range of the decision variables.

[0273] 3.2) Establish constraint mathematical model.

[0274] 3.3) The optimization model f of material cost is established as follows:

[0275]

[0276] Where i is the component material number, i = 1, 2, 3…, n, n is the total number of component materials, A i is the cross-sectional area of ​​the component material, L i is the component material length, P i The unit price of component materials.

[0277] 3.4) Use the optimization algorithm to solve the variables to be optimized and obtain the optimization parameters of each component.

[0278] The variables to be optimized in step 3.1) include but are not limited to: bridge deck thickness and reinforcement ratio, bridge deck concrete strength, steel girder height, steel girder flange width and thickness, and steel girder web thickness.

[0279] The constraint mathematical models involved in step 3.2) include but are not limited to: the steel main beam flange width-to-thickness ratio does not exceed the limit constraint, the steel main beam web height-to-thickness ratio does not exceed the limit constraint, the section bending bearing capacity does not exceed the section bending bearing capacity limit constraint, the section shear bearing capacity does not exceed the section shear bearing capacity limit constraint, the component deflection does not exceed the deflection limit constraint, and the crack width does not exceed the crack width limit constraint.

[0280] The steel main beam flange width-to-thickness ratio does not exceed the limit constraint as follows:

[0281] B f / t f ≤rf lim (3)

[0282] In the formula, B f is the flange width of the steel main beam, t f is the thickness of the steel main beam flange, rf lim It is the limit value of the width-to-thickness ratio of the steel main beam flange.

[0283] The constraint that the height-to-thickness ratio of the web of the steel main beam does not exceed the limit is as follows:

[0284] h w / t w ≤rw lim (4)

[0285] In the formula, h w is the web width of the steel main beam, t w is the web thickness of the steel main beam, rw lim It is the limit value of the height-to-thickness ratio of the web of the steel main beam.

[0286] The section bending capacity shall not exceed the section bending capacity limit constraint as follows:

[0287] M≤M lim (5)

[0288] Where M is the bending bearing capacity of the section, M lim is the limit of the section's bending bearing capacity.

[0289] The section shear bearing ratio does not exceed the section shear bearing limit constraint as follows:

[0290] V≤V lim (6)

[0291] Where V is the shear bearing capacity of the section, V lim is the shear bearing capacity limit of the section.

[0292] The member deflection does not exceed the deflection limit constraint as follows:

[0293] δ≤δ lim (7)

[0294] In the formula, δ is the member deflection, δ lim is the member deflection limit.

[0295] The crack width does not exceed the crack width limit constraint as follows:

[0296] w cr ≤w crlim (8)

[0297] In the formula, w cr is the crack width, w crlim is the crack width limit.

[0298] The optimization algorithm involved in step 3.4) may be selected from but not limited to: genetic algorithm, particle swarm algorithm, differential evolution algorithm.

[0299] The optimization algorithm uses a parameter adaptive adjustment strategy to optimize the population quality when generating individuals to avoid generating invalid individuals, including the following steps:

[0300] a) Fix the width of the upper flange of the steel main beam, and judge according to the limit constraint of the width-to-thickness ratio of the steel main beam flange described in step 3.2). If the requirement is met, the upper flange thickness will not be changed. If it is not met, it will be automatically updated to the minimum flange thickness that meets the limit constraint of the width-to-thickness ratio of the steel main beam flange.

[0301] b) Fix the lower flange width of the steel main beam, and judge according to the limit constraint of the steel main beam flange width-to-thickness ratio described in step 3.2). If the requirement is met, the lower flange thickness will not be changed. If it is not met, it will be automatically updated to the minimum flange thickness that meets the limit constraint of the steel main beam flange width-to-thickness ratio.

[0302] c) Fix the height of the web of the steel main beam, and judge according to the constraint that the height-to-thickness ratio of the web of the steel main beam does not exceed the limit value in step 3.2). If the requirement is met, the web thickness will not be changed. If it is not met, it will be automatically updated to the minimum web thickness that meets the limit value constraint of the height-to-thickness ratio of the web of the steel main beam.

Claims

1. An integrated method for intelligent modeling and optimization of steel-concrete composite beam bridges based on human-machine collaboration, characterized in that: The following steps are involved: 1) Determine the structural system, span and hole distribution scheme of the steel-concrete composite beam bridge, and draw the initial condition diagram; the initial condition diagram includes the initial condition plan diagram and the initial condition elevation diagram of the bridge; 2) Based on the initial condition diagram, construct the initial intelligent model of the steel-concrete composite beam bridge; 3) Optimize the initial intelligent model of the steel-concrete composite beam bridge to obtain the optimization scheme of the steel-concrete composite beam bridge; The steps to construct the initial intelligent model of the steel-concrete composite beam bridge include: 2.1) Using the layer analysis algorithm to read the initial design information in the initial condition diagram; 2.2) Generate component elements of the steel-concrete composite beam bridge using the read initial design information; 2.3) Generate constraint information of steel-concrete composite beam bridge; 2.4) Input various loads, including but not limited to the deadweight of various components, deadweight of asphalt pavement, deadweight of railings, and lane load; 2.5) Output the initial intelligent model of steel-concrete composite beam bridge; The steps for optimizing the initial intelligent model of the steel-concrete composite beam bridge include: 3.1) Select the variables to be optimized for each component and determine the value range of the decision variables; the variables to be optimized include but are not limited to the thickness and reinforcement ratio of the bridge deck, the concrete strength of the bridge deck, the height of the steel main beam, the width and thickness of the steel main beam flange, and the thickness of the steel main beam web; 3.2) Establishment of constraint mathematical model, including but not limited to the constraint that the width-to-thickness ratio of the steel main beam flange does not exceed the limit, the height-to-thickness ratio of the steel main beam web does not exceed the limit, the bending bearing capacity of the section does not exceed the bending bearing capacity limit, the shear bearing capacity of the section does not exceed the shear bearing capacity limit, the member deflection does not exceed the deflection limit, and the crack width does not exceed the crack width limit; 3.3) Establish the optimization model f of material cost, that is: Where i is the component material number, i = 1, 2, 3…, n, n is the total number of component materials, A i is the cross-sectional area of ​​the component material, L i is the component material length, P i is the unit price of component materials; 3.4) The optimization model f is solved by using the optimization algorithm to obtain the optimization parameters of each component, and then the optimization scheme of the steel-concrete composite beam bridge is constructed.

2. The integrated intelligent modeling and optimization method for steel-concrete composite beam bridge based on human-machine collaboration according to claim 1 is characterized in that: The initial condition diagram records the plane positioning and elevation of the bridge piers, the plane positioning of the bridge centerline and sideline, and the bridge deck elevation information.

3. The integrated intelligent modeling and optimization method for steel-concrete composite beam bridge based on human-machine collaboration according to claim 1 is characterized in that: The steps of using the layer analysis algorithm to read the initial design information in the initial condition diagram include: Mark the piers, bridge centerline and sideline of the initial condition plan of the bridge, and read the initial condition plan of the bridge using a layer analysis algorithm; Mark the piers and top lines of the bridge deck in the initial condition elevation drawing of the bridge, and use the layer analysis algorithm to read the initial condition elevation drawing of the bridge The line type information is read using the layer analysis algorithm to determine the plane coordinates and elevation of the center of the pier bottom, the plane coordinates of the bridge centerline, the elevation of the center of the bridge support bottom, and the elevation information of the bridge deck top.

4. The integrated intelligent modeling and optimization method for steel-concrete composite beam bridge based on human-machine collaboration according to claim 1 is characterized in that: The steps to generate member elements for steel-concrete composite beam bridges include: 2.2.1) Determine the cross-sectional form, preliminary cross-sectional dimensions, material information and support information of the pier system and composite beam; the pier system includes pier columns, tie beams and cap beams; 2.2.2) Based on the initial design information, section form, preliminary section size, material information and support information, determine the size and start and end coordinates of each component unit; 2.2.3) Formulate unit division criteria, determine the number of each component unit, and number the nodes in the order of abutment support bottom, pier, tie beam, cap beam, steel main beam, cross beam, and bridge deck; 2.2.4) Define the fiber cross-section of each component unit; 2.2.5) Combine the information from steps 2.2.2)-2.2.4) to generate the component elements of the steel-concrete composite beam bridge.

5. The integrated intelligent modeling and optimization method for steel-concrete composite beam bridge based on human-machine collaboration according to claim 4 is characterized in that: The steps to define the fiber cross-section of each component element include: 2.2.4.1) Establish fiber cross-section meshing criteria; 2.2.4.2) Define the constitutive relations of each material and establish a constitutive relation library; 2.2.4.3) Automatically generate cross-sectional dimensions, divide fiber cross sections, and read constitutive relations based on material information.

6. The integrated intelligent modeling and optimization method for steel-concrete composite beam bridge based on human-machine collaboration according to claim 3 is characterized in that: The steps to generate restraint information for a steel-concrete composite beam bridge include: 2.3.1) Extract the nodes at the bottom of the pier and the bottom of the abutment support, and define the boundary conditions of the bottom of the pier and the bottom of the abutment support; 2.3.2) Extract the nodes at the bottom of the support at the top of the cap beam and the abutment and the nodes of the steel main beam at the corresponding positions, and automatically generate the support units at the top of the cap beam and the abutment; 2.3.3) Automatically generate the rigid arm unit between the steel main beam and the bridge deck; 2.3.4) Write the boundary conditions, support units and rigid arm unit information into the file to achieve automatic definition of various constraints.

7. The integrated intelligent modeling and optimization method for steel-concrete composite beam bridge based on human-machine collaboration according to claim 3 is characterized in that: The deadweight of various components, asphalt pavement and railings are applied in the form of uniformly distributed line loads; Lane loads are applied in the form of uniformly distributed loads and concentrated loads.

8. The integrated intelligent modeling and optimization method for steel-concrete composite beam bridge based on human-machine collaboration according to claim 1 is characterized in that: The steel main beam flange width-to-thickness ratio does not exceed the limit constraint as follows: B f / t f ≤rf lim (1) In the formula, B f is the flange width of the steel main beam, t f is the thickness of the steel main beam flange, rf lim is the limit of the width-to-thickness ratio of the steel main beam flange; The constraint that the height-to-thickness ratio of the web of the steel main beam does not exceed the limit is as follows: h w / t w ≤rw lim (2) In the formula, h w is the web width of the steel main beam, t w is the web thickness of the steel main beam, rw lim is the limit of height-to-thickness ratio of web of steel main beam; The section bending capacity shall not exceed the section bending capacity limit constraint as follows: M≤M lim (3) Where M is the bending bearing capacity of the section, M lim is the limit of the section bending bearing capacity; The section shear bearing ratio does not exceed the section shear bearing limit constraint as follows: V≤V lim (4) Where V is the shear bearing capacity of the section, V lim is the shear bearing capacity limit of the section; The member deflection does not exceed the deflection limit constraint as follows: δ≤δ lim (5) In the formula, δ is the member deflection, δ lim is the member deflection limit; The crack width does not exceed the crack width limit constraint as follows: In cr ≤in crlim (6) In the formula, w cr is the crack width, w crlim is the crack width limit.

9. The integrated intelligent modeling and optimization method for steel-concrete composite beam bridge based on human-machine collaboration according to claim 1 is characterized in that: The optimization algorithms include but are not limited to: genetic algorithm, particle swarm algorithm, differential evolution algorithm; In the process of solving the optimization model f, the parameter adaptive adjustment strategy is used to optimize the population quality and avoid generating invalid individuals, including the following steps: a) Fix the width of the upper flange of the steel main beam, and make a judgment based on the constraint that the width-to-thickness ratio of the steel main beam flange does not exceed the limit. If the requirement is met, the upper flange thickness will not be changed. If it is not met, it will be automatically updated to the minimum flange thickness that meets the limit constraint of the width-to-thickness ratio of the steel main beam flange; b) The width of the lower flange of the steel main beam is fixed, and the judgment is made based on the constraint that the width-to-thickness ratio of the steel main beam flange does not exceed the limit. If the requirement is met, the thickness of the lower flange will not be changed. If it is not met, it will be automatically updated to the minimum flange thickness that meets the limit constraint of the width-to-thickness ratio of the steel main beam flange; c) The height of the web of the steel main beam is fixed, and the judgment is made based on the constraint that the height-to-thickness ratio of the web of the steel main beam does not exceed the limit value. If the requirement is met, the web thickness will not be changed. If it is not met, it will be automatically updated to the minimum web thickness that meets the limit value constraint of the height-to-thickness ratio of the web of the steel main beam.

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