A building structure intelligent design method based on human-computer interaction
By establishing intelligent building design models through human-computer interaction, and combining optimization algorithms and expert knowledge, the problem of low efficiency in traditional building design is solved, and efficient building structure optimization and automatic modeling are achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHONGQING UNIV
- Filing Date
- 2023-07-31
- Publication Date
- 2026-07-21
AI Technical Summary
Existing building structural design methods are inefficient, involve a lot of repetitive work, have high labor costs, and lack direct consideration of expert knowledge and building codes, making it impossible to effectively optimize multi-story buildings.
Using a human-computer interaction-based approach, the system acquires building floor plan information through a host computer, establishes an intelligent building design model, and optimizes it by combining genetic algorithms, ant colony algorithms, and tabu search algorithms. It also takes into account expert knowledge and building codes to automatically model and optimize building components.
It has enabled automated modeling and optimization of building structure models, improved design efficiency, reduced labor costs, reduced design errors, and promoted the digitalization and intelligentization of architectural design.
Smart Images
Figure CN116933368B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of intelligent structural modeling, and more specifically to an intelligent design method for building structures based on human-computer interaction. Background Technology
[0002] The construction industry is one of the pillar industries of my country's economy and an important component of national economic development. Digitalization and intelligentization are significant development trends in the construction industry today. In the design phase, digital and intelligent design can improve design efficiency, optimize design quality, reduce design costs, enhance design innovation, and improve design sustainability, thereby driving the construction industry towards a more efficient and intelligent direction.
[0003] Currently, in traditional structural design models, building structure modeling, load modeling, and adjustments and optimizations all require designers to manually complete these tasks based on their experience. During the design phase, multiple adjustments to the building structure are often necessary. Each time the structure is adjusted, a load model needs to be manually created. Therefore, the structural modeling and optimization process consumes a significant amount of manpower and time, involves a lot of repetitive work, is inefficient, has high labor costs, and is prone to human error.
[0004] Currently, both domestic and international research and product implementations have made some progress in intelligent structural design. However, most existing intelligent structural design methods only involve modeling structural components from architectural drawings and optimizing the shear wall layout of a single standard floor. They do not consider the case of multiple standard floors or the optimization of other building components. Furthermore, in the entire optimization process, existing design methods lack direct consideration and use of expert knowledge and building codes. Summary of the Invention
[0005] The purpose of this invention is to provide an intelligent building structure design method based on human-computer interaction, comprising the following steps:
[0006] 1) The host computer obtains the building floor plan with building annotation information and building design parameters.
[0007] The building labeling information includes room labeling information.
[0008] The room labeling information includes the room name and the corresponding load value.
[0009] 2) Read the building entity element information, building annotation information, and building design parameters from the building floor plan respectively, match the building entity element information of each room with the corresponding building design parameters and building annotation information, and establish a building intelligent design model based on the matching results.
[0010] The intelligent building design model includes building components and the corresponding loads of the building components.
[0011] 3) Based on the intelligent building design model, establish a building structural parameter optimization model.
[0012] 4) Solve the building structure parameter optimization model to obtain the optimization parameters of each building component.
[0013] 5) Compare the information of each building component in the current building intelligent design model with the building code. If there are building components that do not meet the building code, optimize the size of these building components.
[0014] Furthermore, the building labeling information and building design parameters are input by the user into the host computer through a human-computer interaction interface.
[0015] The building labeling information also includes special line load labeling information, as well as drawing information for shear wall indicator lines and structural beam indicator lines.
[0016] The special line load labeling information includes the name of the special line load and the corresponding load value.
[0017] The architectural design parameters include: material information, basic structural information, and seismic design parameters.
[0018] Furthermore, in step 2), the step of matching the architectural entity element information of each room with the corresponding architectural design parameters and architectural annotation information includes:
[0019] 2.1.1) Draw a circle with radius d centered on the location of the room label information.
[0020] 2.1.2) Connect the endpoints of the baselines of each building within the circle to form an undirected graph.
[0021] 2.1.3) Detect closed loops in the undirected graph. If there is a unique closed loop that completely contains the room label information, then the closed loop is the identified room.
[0022] 2.1.4) Match the identified rooms with the corresponding building entity element information and building design parameters.
[0023] Furthermore, the building entity element information includes walls, doors, and windows.
[0024] Furthermore, the steps for establishing the intelligent building design model include:
[0025] 2.2.1) Construct building components. The building components include shear walls, structural beams, and floor slabs.
[0026] 2.2.2) Apply loads to building components.
[0027] Furthermore, in step 2.2.1), the step of constructing the shear wall includes: arranging the shear wall indicator lines in the building baseline as shear walls.
[0028] The shear wall prompt line is input by the user through the human-computer interaction interface.
[0029] The steps for constructing structural beams include: arranging all building baselines without shear walls as structural beams.
[0030] The steps for constructing the floor slab include:
[0031] 2.2.1.1) For each room, iterate through all shear walls and structural beams. If the distance from both ends of the current shear wall or structural beam to the boundary of the corresponding room is less than a preset threshold, then the current shear wall or structural beam is located on the boundary of this room.
[0032] 2.2.1.2) Connect all shear walls and structural beams on the boundary of the current room end to end to obtain a set of arranged nodes.
[0033] 2.2.1.3) Write the coordinates of the node set into a file in sequence to achieve automated modeling of the floor slab.
[0034] Furthermore, the loads on the building components include the self-weight of the building components, beam line loads, and floor surface loads.
[0035] The self-weight of the building component is calculated using the density of the building component material.
[0036] The beam line load q L The calculation formula is as follows:
[0037] q L =η L ×h×(f1+f2+max{γ1,γ2}×w) (1)
[0038] In the formula, f1 and f2 are the surface loads of the left and right sides of the wall above the beam, respectively, and γ1 and γ2 are the unit weights of the wall core. h and w are the height and width of the wall above the beam, respectively.
[0039] Among them, the beam line load reduction factor η L As shown below:
[0040] η L =1-S L / S T (2)
[0041] In the formula, S L and S TThese are the area of the doorway on the wall above the beam and the area of the entire wall, respectively.
[0042] The floor load is taken as the load marked in the room to which the current floor belongs.
[0043] Furthermore, the building structure parameter optimization model includes an optimization model for material costs and an optimization model for ease of construction.
[0044] The optimization model f3 for the material cost is shown below:
[0045]
[0046] In the formula, i is the floor number, i = 1, 2, ..., n, and n represents the total number of floors. i This represents the total length of the shear wall segment on the i-th floor.
[0047] The optimization model f4 for ease of construction is shown below:
[0048]
[0049] In the formula, M i This represents the total number of shear wall segments on the i-th floor.
[0050] The constraints of the building structure parameter optimization model include: the length of the upper shear wall is less than the length of the lower shear wall at the same position, the inter-story drift angle does not exceed the limit, the torsion ratio does not exceed the limit, the period ratio does not exceed the limit, the story stiffness ratio does not exceed the limit, the inter-story shear capacity ratio does not exceed the limit, and the shear wall length is constrained.
[0051] The constraint that the length of the upper shear wall is less than the length of the lower shear wall at the same location is as follows:
[0052] l jk <l ik (5)
[0053] In the formula, j is the floor number, j = i+1, i+2, ..., n. k is the location of the shear wall. jk l ik Let be the lengths of the shear walls at position k on the j and i floors, respectively.
[0054] The inter-story drift angle does not exceed the following limit constraint:
[0055]
[0056] In the formula, This is the inter-story drift angle. This is the limit value for the inter-story drift angle.
[0057] The torsion ratio is not subject to the following limit constraints:
[0058] r d ≤r dlim (7)
[0059] In the formula, r d R is the torsion ratio. dlim This is the limit for the torsion ratio.
[0060] The period ratio is subject to the following limit constraint:
[0061] r p ≤r plim (8)
[0062] In the formula, r p This represents the period ratio. r plim This is the period ratio limit.
[0063] The layer stiffness ratio is subject to the following limit constraints:
[0064] r f ≥r flim (9)
[0065] In the formula, r f R is the layer stiffness ratio. flim This is the limit value for the layer stiffness ratio.
[0066] The inter-story shear capacity ratio is not subject to the following limit constraints:
[0067] r s ≥r slim (10)
[0068] In the formula, r s This represents the ratio of inter-story shear capacity. slim This is the limit value for the inter-story shear capacity ratio.
[0069] The shear wall length constraint is as follows:
[0070] 1. The length of the shear wall connected to the corner of the exterior wall and the shear wall located at both ends of the partition wall is greater than 0.
[0071] 2. The length of shear walls whose endpoints are not located on exterior walls, partition walls, or public areas is 0.
[0072] 3. Shear walls of the same type share parameters.
[0073] Furthermore, methods for solving the optimization model of building structural parameters include: genetic algorithm, ant colony algorithm, and tabu search algorithm.
[0074] The optimized parameters for the building components include the optimized parameters for the length and number of shear wall segments on each floor of the building.
[0075] Furthermore, the comparison of the information of each building component in the current intelligent building design model with the building code involves comparing the axial compression ratio and decompression ratio of the building components with the axial compression ratio and decompression ratio specified in the building code.
[0076] The optimization of the dimensions of these building components that do not meet building codes includes automatic optimization and prompt-based optimization.
[0077] The automatic optimization of the dimensions of these building components that do not meet building codes aims at minimizing material consumption. The optimization steps include: firstly, merging the dimensions of the building components to obtain a dimension set S. beam Then, the cross-sectional dimensions of the oversized components are sorted, and the feasible regions S after sorting are determined. beam Trial calculations were performed sequentially to meet the constraints of overall structural indicators and component limits, and the optimal solution was obtained with the least amount of material.
[0078] The optimization suggestions for the dimensions of these building components that do not meet building codes are performed iteratively based on the building components and their size range.
[0079] The technical effects of this invention are undeniable. This invention proposes an intelligent building structure design method based on human-computer interaction, combining expert knowledge and building regulations. Through interaction with designers, it achieves automatic modeling of structural components including shear walls, structural beams, slabs, and structural loads. The generated parameter space can be optimized using any optimization algorithm. This invention promotes the integration of artificial intelligence and architectural design, improving the technological level, work efficiency, and design quality of architectural design.
[0080] Furthermore, this invention proposes a method for structural modeling based on architectural drawings, which allows designers to complete component modeling and load modeling of the structural model through simple human-computer interaction.
[0081] Furthermore, the human-computer interaction method adopted in this invention can provide designers with a convenient working platform and give full play to their design experience and inspiration.
[0082] Furthermore, the method proposed in this invention for designers to draw shear wall hint lines and structural beam hint lines can effectively provide the algorithm with the designer's design experience and provide a reliable initial solution for structural optimization.
[0083] Furthermore, the room identification method proposed in this invention has a significant advantage in speed at the second level.
[0084] Furthermore, this invention proposes a structural parameterization method based on expert knowledge matrices and building codes, which can reduce the search space of variables and can be combined with various optimization algorithms to quickly achieve the optimized design of structural models.
[0085] Furthermore, the automatic component optimization and prompting optimization method proposed in this invention can quickly obtain the designer's design requirements.
[0086] Furthermore, the optimization method proposed in this invention simultaneously considers multiple building standard floors and multiple structural standard floors.
[0087] Furthermore, the overall structural optimization method proposed in this invention not only considers the arrangement of shear walls, but also the cross-sectional dimensions of the shear walls and the grade of concrete.
[0088] Furthermore, this invention, through the intersection of structural design and artificial intelligence, forms a human-computer interaction-based intelligent structural design method, which effectively improves the efficiency of structural design and solves the problems of low efficiency, long cycle and strong subjectivity in traditional structural design, thus promoting the digital and intelligent development of building structural design in my country. Attached Figure Description
[0089] Figure 1 This is a flowchart of the overall solution of the present invention;
[0090] Figure 2 This is the human-computer interaction interface of the present invention;
[0091] Figure 3 This is a schematic diagram of the structural intelligent modeling process of the present invention;
[0092] Figure 4 This is a schematic diagram of the human-computer interaction processing of the present invention;
[0093] Figure 5 This is a schematic diagram of the data structure of the present invention. Figure 5 (a) is a schematic diagram of wall segment information reading. Figure 5 (b) is a schematic diagram of reading room label information. Figure 5 (c) is a schematic diagram of special load information reading;
[0094] Figure 6 This is a schematic diagram of the room recognition algorithm of the present invention. Figure 6 (a) is a schematic diagram of an undirected graph structure. Figure 6 (b) is a schematic diagram of the detection area structure. Figure 6 (c) is a schematic diagram of loop 1. Figure 6 (d) is a schematic diagram of polygon 1. Figure 6 (e) is a schematic diagram of loop 2. Figure 6 (f) is a schematic diagram of the polygon 2 structure. Figure 6 (g) is a schematic diagram of loop 3. Figure 6 (h) is a schematic diagram of the polygon 3 structure;
[0095] Figure 7 This is a schematic diagram of the room identification results of the present invention;
[0096] Figure 8 This is a schematic diagram of the shear wall parameterization and beam parameterization of the present invention. Figure 8 (a) is a parametric schematic diagram of the wall. Figure 8 (b) is a parametric schematic diagram of the beam;
[0097] Figure 9 This is a schematic diagram of the automatic plate arrangement of the present invention. Figure 9 (a) is a schematic diagram of the floor slab boundary. Figure 9 (b) is a schematic diagram of the node set formation, in which the Shear wall is the shear wall and the Beam is the structural beam;
[0098] Figure 10 This is a schematic diagram of the automatic load arrangement of the present invention. Figure 10 (a) is a schematic diagram of the beam line load arrangement. Figure 10 (b) is a schematic diagram of the floor slab load arrangement;
[0099] Figure 11 This is the encoding method for the parameterized components of this invention;
[0100] Figure 12 This is a flowchart of the fine-tuning process for over-limit components of the present invention, taking a beam as an example. Detailed Implementation
[0101] The present invention will be further described below with reference to embodiments, but it should not be construed that the scope of the present invention is limited to the following embodiments. Various substitutions and modifications made based on ordinary technical knowledge and common practices in the art without departing from the above-described technical concept of the present invention should be included within the scope of protection of the present invention.
[0102] Example 1:
[0103] See Figures 1 to 12 A method for intelligent building structure design based on human-computer interaction includes the following steps:
[0104] 1) The host computer obtains the building floor plan with building annotation information and building design parameters.
[0105] The building labeling information includes room labeling information.
[0106] The room labeling information includes the room name and the corresponding load value.
[0107] 2) Read the building entity element information, building annotation information, and building design parameters from the building floor plan respectively, match the building entity element information of each room with the corresponding building design parameters and building annotation information, and establish a building intelligent design model based on the matching results.
[0108] The intelligent building design model includes building components and the corresponding loads of the building components.
[0109] 3) Based on the intelligent building design model, establish a building structural parameter optimization model.
[0110] 4) Solve the building structure parameter optimization model to obtain the optimization parameters of each building component.
[0111] 5) Compare the information of each building component in the current building intelligent design model with the building code. If there are building components that do not meet the building code, optimize the size of these building components.
[0112] Example 2:
[0113] A building structure intelligent design method based on human-computer interaction is described in Embodiment 1. Further, the building annotation information and building design parameters are input by the user to the host computer through the human-computer interaction interface.
[0114] The building labeling information also includes special line load labeling information, as well as drawing information for shear wall indicator lines and structural beam indicator lines.
[0115] The special line load labeling information includes the name of the special line load and the corresponding load value.
[0116] The architectural design parameters include: material information, basic structural information, and seismic design parameters.
[0117] Example 3:
[0118] A human-computer interaction-based intelligent building structure design method, the main technical contents of which are described in either Embodiment 1 or 2, further comprising, in step 2), matching the building entity element information of each room with the corresponding building design parameters and building annotation information, including:
[0119] 2.1.1) Draw a circle with radius d centered on the location of the room label information.
[0120] 2.1.2) Connect the endpoints of the baselines of each building within the circle to form an undirected graph.
[0121] 2.1.3) Detect closed loops in the undirected graph. If there is a unique closed loop that completely contains the room label information, then the closed loop is the identified room.
[0122] 2.1.4) Match the identified rooms with the corresponding building entity element information and building design parameters.
[0123] Example 4:
[0124] A building structure intelligent design method based on human-computer interaction, the main technical contents of which are described in any one of embodiments 1 to 3, and further, the building entity element information includes walls, doors and windows.
[0125] Example 5:
[0126] A building structure intelligent design method based on human-computer interaction, the main technical contents of which are described in any one of embodiments 1 to 4, further comprising the steps of establishing a building intelligent design model including:
[0127] 2.2.1) Construct building components. The building components include shear walls, structural beams, and floor slabs.
[0128] 2.2.2) Apply loads to building components.
[0129] Example 6:
[0130] A building structure intelligent design method based on human-computer interaction, the main technical contents of which are described in Example 5, further, in step 2.2.1), the step of building shear walls includes: arranging the shear wall indicator lines in the building baseline as shear walls.
[0131] The shear wall prompt line is input by the user through the human-computer interaction interface.
[0132] The steps for constructing structural beams include: arranging all building baselines without shear walls as structural beams.
[0133] The steps for constructing the floor slab include:
[0134] 2.2.1.1) For each room, iterate through all shear walls and structural beams. If the distance from both ends of the current shear wall or structural beam to the boundary of the corresponding room is less than a preset threshold, then the current shear wall or structural beam is located on the boundary of this room.
[0135] 2.2.1.2) Connect all shear walls and structural beams on the boundary of the current room end to end to obtain a set of arranged nodes.
[0136] 2.2.1.3) Write the coordinates of the node set into a file in sequence to achieve automated modeling of the floor slab.
[0137] Example 7:
[0138] A building structure intelligent design method based on human-computer interaction, the main technical contents of which are described in any one of embodiments 1 to 6. Further, the load of the building component includes the self-weight of the building component, the beam line load and the floor surface load.
[0139] The self-weight of the building component is calculated using the density of the building component material.
[0140] The beam line load q L The calculation formula is as follows:
[0141] q L =η L ×h×(f1+f2+max{γ1,γ2}×w) (1)
[0142] In the formula, f1 and f2 are the surface loads of the left and right sides of the wall above the beam, respectively, and γ1 and γ2 are the unit weights of the wall core. h and w are the height and width of the wall above the beam, respectively.
[0143] Among them, the beam line load reduction factor η L As shown below:
[0144] η L =1-S L / S T (2)
[0145] In the formula, S L and S T These are the area of the doorway on the wall above the beam and the area of the entire wall, respectively.
[0146] The floor load is taken as the load marked in the room to which the current floor belongs.
[0147] Example 8:
[0148] A building structure intelligent design method based on human-computer interaction, the main technical contents of which are described in any one of embodiments 1 to 7. Furthermore, the building structure parameter optimization model includes an optimization model for material cost and an optimization model for construction convenience.
[0149] The optimization model f3 for the material cost is shown below:
[0150]
[0151] In the formula, i is the floor number, i = 1, 2, ..., n, and n represents the total number of floors. i This represents the total length of the shear wall segment on the i-th floor.
[0152] The optimization model f4 for ease of construction is shown below:
[0153]
[0154] In the formula, M i This represents the total number of shear wall segments on the i-th floor.
[0155] The constraints of the building structure parameter optimization model include: the length of the upper shear wall is less than the length of the lower shear wall at the same position, the inter-story drift angle does not exceed the limit, the torsion ratio does not exceed the limit, the period ratio does not exceed the limit, the story stiffness ratio does not exceed the limit, the inter-story shear capacity ratio does not exceed the limit, and the shear wall length is constrained.
[0156] The constraint that the length of the upper shear wall is less than the length of the lower shear wall at the same location is as follows:
[0157] l jk <l ik (5)
[0158] In the formula, j is the floor number, j = i+1, i+2, ..., n. k is the location of the shear wall. jk l ik Let be the lengths of the shear walls at position k on the j and i floors, respectively.
[0159] The inter-story drift angle does not exceed the following limit constraint:
[0160]
[0161] In the formula, This is the inter-story drift angle. This is the limit value for the inter-story drift angle.
[0162] The torsion ratio is not subject to the following limit constraints:
[0163] r d ≤r dlim (7)
[0164] In the formula, r d R is the torsion ratio. dlim This is the limit for the torsion ratio.
[0165] The period ratio is subject to the following limit constraint:
[0166] r p ≤r plim (8)
[0167] In the formula, r p This represents the period ratio. r plim This is the period ratio limit.
[0168] The layer stiffness ratio is subject to the following limit constraints:
[0169] r f ≥rflim (9)
[0170] In the formula, r f R is the layer stiffness ratio. flim This is the limit value for the layer stiffness ratio.
[0171] The inter-story shear capacity ratio is not subject to the following limit constraints:
[0172] r s ≥r slim (10)
[0173] In the formula, r s This represents the ratio of inter-story shear capacity. slim This is the limit value for the inter-story shear capacity ratio.
[0174] The shear wall length constraint is as follows:
[0175] 1. The length of the shear wall connected to the corner of the exterior wall and the shear wall located at both ends of the partition wall is greater than 0.
[0176] 2. The length of shear walls whose endpoints are not located on exterior walls, partition walls, or public areas is 0.
[0177] 3. Shear walls of the same type share parameters.
[0178] Example 9:
[0179] A building structure intelligent design method based on human-computer interaction is described in Example 8. Further, the method for solving the building structure parameter optimization model includes: genetic algorithm, ant colony algorithm, and tabu search algorithm.
[0180] The optimized parameters for the building components include the optimized parameters for the length and number of shear wall segments on each floor of the building.
[0181] Example 10:
[0182] A building structure intelligent design method based on human-computer interaction, the main technical contents of which are described in any one of embodiments 1 to 9. Further, the comparison of the information of each building component in the current building intelligent design model with the building code is to compare the axial compression ratio and decompression ratio of the building component with the axial compression ratio and decompression ratio specified in the building code.
[0183] The optimization of the dimensions of these building components that do not meet building codes includes automatic optimization and prompt-based optimization.
[0184] The automatic optimization of the dimensions of these building components that do not meet building codes aims at minimizing material consumption. The optimization steps include: firstly, merging the dimensions of the building components to obtain a dimension set S. beamThen, the cross-sectional dimensions of the oversized components are sorted, and the feasible regions S after sorting are determined. beam Trial calculations were performed sequentially to meet the constraints of overall structural indicators and component limits, and the optimal solution was obtained with the least amount of material.
[0185] The optimization suggestions for the dimensions of these building components that do not meet building codes are performed iteratively based on the building components and their size range.
[0186] Example 11:
[0187] See Figures 1 to 12 A method for intelligent building structure design based on human-computer interaction includes the following steps:
[0188] 1) The host computer obtains the building floor plan with building annotation information and building design parameters.
[0189] The building labeling information includes room labeling information.
[0190] The room labeling information includes the room name and the corresponding load value.
[0191] The first line of the room label indicates the room's purpose, and the second line indicates the load value (L represents live load, D represents dead load), in kN / m. 2 .
[0192] 2) Read the building entity element information, building annotation information, and building design parameters from the building floor plan respectively, match the building entity element information of each room with the corresponding building design parameters and building annotation information, and establish a building intelligent design model based on the matching results.
[0193] The intelligent building design model includes building components and the corresponding loads of the building components.
[0194] 3) Based on the intelligent building design model, establish a building structural parameter optimization model.
[0195] 4) Solve the building structure parameter optimization model to obtain the optimization parameters of each building component.
[0196] 5) Compare the information of each building component in the current building intelligent design model with the building code. If there are building components that do not meet the building code, optimize the size of these building components.
[0197] Example 12:
[0198] A building structure intelligent design method based on human-computer interaction is described in Example 11. Further, the building annotation information and building design parameters are input by the user to the host computer through the human-computer interaction interface.
[0199] Users can click on, such as Figure 2 The selection buttons shown allow you to input the structural design information required for modeling.
[0200] Users input architectural floor plans and can annotate rooms and special line loads through clicks and input on the interactive interface. Figure 2 The completed architectural drawings are as follows: Figure 3 As shown.
[0201] The building labeling information also includes special line load labeling information, as well as drawing information for shear wall indicator lines and structural beam indicator lines.
[0202] The special line load labeling information includes the name of the special line load and the corresponding load value.
[0203] The special line loads include wind loads.
[0204] For special line loads, the first line is the load name, and the second line is the load value in kN / m.
[0205] The architectural design parameters include: material information, basic structural information, and seismic design parameters.
[0206] The material information includes the core material of the infill wall and the load on the surface layer of the infill wall.
[0207] The basic structural information includes floor height, number of floors, etc.
[0208] The seismic design parameters include site analogy and seismic fortification intensity.
[0209] Example 13:
[0210] A human-computer interaction-based intelligent building structure design method, the main technical contents of which are described in either embodiment 11 or 12, further comprising, in step 2), matching the building entity element information of each room with the corresponding building design parameters and building annotation information, including:
[0211] 2.1.1) Draw a circle with radius d centered at the location of the room label information. d is greater than 0.
[0212] 2.1.2) Connect the endpoints of the baselines of each building within the circle to form an undirected graph.
[0213] 2.1.3) Detect closed loops in undirected graphs (e.g., ... Figure 6 (d) Figure 6 (f) Figure 6 (h) If there exists a unique closed loop that completely contains the room labeling information, then that closed loop is the identified room. For example... Figure 6(g) is the only closed loop that satisfies the requirement.
[0214] 2.1.4) Match the identified rooms with the corresponding building entity element information and building design parameters.
[0215] Example 14:
[0216] A building structure intelligent design method based on human-computer interaction, the main technical contents of which are described in any one of embodiments 11 to 13, further wherein the building entity element information includes walls, doors and windows.
[0217] The building entity element information is obtained from the TGL file generated by TArch. The drawing annotation information is obtained from the DXF file generated by AutoCAD software.
[0218] Example 15:
[0219] A building structure intelligent design method based on human-computer interaction, the main technical contents of which are described in any one of embodiments 11 to 14, further comprising the following steps for establishing a building intelligent design model:
[0220] 2.2.1) Construct building components. The building components include shear walls, structural beams, and floor slabs.
[0221] 2.2.2) Apply loads to building components.
[0222] Example 16:
[0223] A building structure intelligent design method based on human-computer interaction, the main technical contents of which are described in Example 15, further, in step 2.2.1), the step of building shear walls includes: arranging the shear wall indicator lines in the building baseline as shear walls.
[0224] The shear wall prompt line is input by the user through the human-computer interaction interface.
[0225] The steps for constructing structural beams include: arranging all building baselines without shear walls as structural beams.
[0226] Parametric designation of shear walls and structural beams is performed based on the designer's hint lines, such as... Figure 8 As shown. Based on the length of the architectural guidance lines drawn by the designer and the length of the building's baseline, guidance lines can be divided into four types, such as... Figure 8 As shown in (a). The structural beam lines are first modeled according to the beam lines provided by the designer.
[0227] The steps for constructing the floor slab include:
[0228] 2.2.1.1) For each room, iterate through all shear walls and structural beams. If the distances from both endpoints of the current shear wall or structural beam to the boundary of the corresponding room are both less than a preset threshold, then the current shear wall or structural beam is located on the boundary of this room. Figure 9 (a)).
[0229] 2.2.1.2) Connect all shear walls and structural beams on the boundary of the current room end to end to obtain a set of arranged nodes. Figure 9 (b)).
[0230] 2.2.1.3) Write the coordinates of the node set into a file in sequence to achieve automated modeling of the floor slab.
[0231] Example 17:
[0232] A building structure intelligent design method based on human-computer interaction, the main technical contents of which are described in any one of embodiments 11 to 16. Further, the load of the building component includes the self-weight of the building component, the beam line load and the floor surface load.
[0233] The self-weight of the building component is calculated using the density of the building component material.
[0234] The beam line load q L The calculation formula is as follows:
[0235] q L =η L ×h×(f1+f2+max{γ1,γ2}×w) (1)
[0236] In the formula, f1 and f2 are the surface loads of the left and right sides of the wall above the beam, respectively, and γ1 and γ2 are the unit weights of the wall core, both of which are determined according to the type of closed areas on both sides of the wall above the beam. h and w are the height and width of the wall above the beam, respectively.
[0237] Among them, the beam line load reduction factor η L As shown below:
[0238] η L =1-S L / S T (2)
[0239] In the formula, S L and S T These are the area of the doorway on the wall above the beam and the area of the entire wall, respectively.
[0240] The floor load is taken as the load marked in the room to which the current floor belongs (see Figure (4)).
[0241] Example 18:
[0242] A human-computer interaction-based intelligent building structure design method, the main technical contents of which are described in any one of embodiments 11 to 17, further includes a structural intelligent optimization algorithm that considers both the structural and architectural multi-standard floors based on the designer's prompts. The optimization objective is to reduce material usage, minimize the presence of irregular columns and short-limb shear walls, and consider construction convenience while meeting code requirements. The search space for shear wall length is set based on the user's prompts, and key factors such as shear wall length, thickness, and concrete strength grade are comprehensively optimized using expert knowledge. The building structure parameter optimization model includes both a material cost optimization model and a construction convenience optimization model.
[0243] The building structure parameter optimization model also includes optimization models for wall length, wall thickness, and concrete material grade.
[0244] The optimization model f3 for the material cost is shown below:
[0245]
[0246] In the formula, i is the floor number, i = 1, 2, ..., n, and n represents the total number of floors. i This represents the total length of the shear wall segment on the i-th floor.
[0247] The aforementioned ease of construction aims to avoid the formation of numerous short shear walls. The optimization model f4 for ease of construction is shown below:
[0248]
[0249] In the formula, Mi represents the total number of shear wall segments on the i-th floor.
[0250] The parameter space for shear wall length (L) is shown in the figure; the parameter space for shear wall thickness (ThichWall) is the commonly used thickness {200mm, 250mm, 300mm, 350mm}; the parameter space for shear wall concrete grade (GradConcrete) is the commonly used concrete grade from C30 to C60.
[0251] The constraints on the overall indicators of the building structure model include: the length of the upper shear wall is less than the length of the lower shear wall at the same position; the inter-story drift angle does not exceed the limit; the torsion ratio does not exceed the limit; the period ratio does not exceed the limit; the story stiffness ratio does not exceed the limit; the inter-story shear capacity ratio does not exceed the limit; and the shear wall length is constrained.
[0252] The constraint that the length of the upper shear wall is less than the length of the lower shear wall at the same location is as follows:
[0253] l jk<l ik (5)
[0254] In the formula, j is the floor number, j = i+1, i+2, ..., n. k is the location of the shear wall. jk l ik Let be the lengths of the shear walls at position k on the j and i floors, respectively.
[0255] The inter-story drift angle does not exceed the following limit constraint:
[0256]
[0257] In the formula, This is the inter-story drift angle. The inter-story drift angle limit is determined according to the "Code for Seismic Design of Buildings" GB50011-2010.
[0258] The torsion ratio is not subject to the following limit constraints:
[0259] r d ≤r dlim (7)
[0260] In the formula, r d R is the torsion ratio. dlim The torsional ratio limit is determined according to the "Code for Seismic Design of Buildings" GB50011-2010.
[0261] The period ratio is subject to the following limit constraint:
[0262] r p ≤r plim (8)
[0263] In the formula, r p This represents the period ratio. r plim The period ratio limit is determined according to the "Code for Seismic Design of Buildings" GB50011-2010.
[0264] The layer stiffness ratio is subject to the following limit constraints:
[0265] r f ≥r flim (9)
[0266] In the formula, r f R is the layer stiffness ratio. flim The story stiffness ratio limit is determined according to the "Code for Seismic Design of Buildings" GB50011-2010 and the "Technical Specification for Concrete Structures of High-Rise Buildings" JGJ3-2010.
[0267] The inter-story shear capacity ratio is not subject to the following limit constraints:
[0268] r s ≥r slim (10)
[0269] In the formula, r s This represents the ratio of inter-story shear capacity. slim The inter-story shear capacity ratio limit is determined according to the Technical Specification for Concrete Structures of High-Rise Buildings (JGJ3-2010).
[0270] The shear wall length constraint is as follows:
[0271] 1. The length of the shear wall connected to the corner of the exterior wall and the shear wall located at both ends of the partition wall is greater than 0.
[0272] 2. The length of shear walls whose endpoints are not located on exterior walls, partition walls, or public areas is 0.
[0273] 3. Shear walls of the same type share parameters.
[0274] Example 19:
[0275] A human-computer interaction-based intelligent building structure design method, the main technical content of which is described in Example 18. Furthermore, the optimization process of this invention can employ various optimization methods, by encoding the parameterized structural components (…). Figure 11 Various optimization algorithms can be used to optimize the model, including genetic algorithm, ant colony algorithm, and tabu search algorithm.
[0276] The optimized parameters for the building components include the optimized parameters for the length and number of shear wall segments on each floor of the building.
[0277] The optimization parameters for the building components also include optimization parameters for wall length, wall thickness, and concrete material grade.
[0278] Example 20:
[0279] A building structure intelligent design method based on human-computer interaction, the main technical contents of which are described in any one of embodiments 11 to 19. Further, the step of comparing the information of each building component in the current building intelligent design model with the building code is to compare the axial compression ratio and decompression ratio of the building component with the axial compression ratio and decompression ratio specified in the building code.
[0280] The optimization of the dimensions of these building components that do not meet building codes includes automatic optimization and prompt-based optimization.
[0281] The automatic optimization of the dimensions of these building components that do not meet building codes aims at minimizing material consumption. The optimization steps include: firstly, merging the dimensions of the building components to obtain a dimension set S.beam Then, the cross-sectional dimensions of the oversized components are sorted, and the feasible regions S after sorting are determined. beam Trial calculations were performed sequentially to meet the constraints of overall structural indicators and component limits, and the optimal solution was obtained with the least amount of material.
[0282] Taking Liang as an example, such as Figure 12 As shown, the dimensions of the components are first merged to obtain a size set Sbeam. Then, the feasible regions Sbeam, after sorting the cross-sectional dimensions of the over-limit components, are sequentially calculated to satisfy the overall structural indicators and component limits, with the optimal solution being the one that minimizes material usage. If no feasible solution exists in the Sbeam region, sequential calculations are performed in the feasible region Ibeam set by the designer until the optimal solution Ibest is obtained and added to Sbeam. The optimization order for over-limit components is from the periphery to the interior of the structure.
[0283] The optimization suggestions for the dimensions of these building components that do not meet building codes are performed iteratively based on the building components and their size range.
[0284] Example 21:
[0285] A building structure intelligent design method based on human-computer interaction, the main technical contents of which are described in any one of Examples 11 to 20. Furthermore, this method also adopts an expert knowledge matrix.
[0286] The expert knowledge matrix is used in the structural parameterization stage to reduce the search space, and in the structural optimization stage to generate feasible structural designs.
[0287] Example 22:
[0288] See Figures 1 to 12 A method for intelligent design of building structures based on human-computer interaction, the contents of which are as follows:
[0289] A human-computer interaction-based intelligent building structure design method comprises four parts: (1) human-computer interaction and drawing information extraction; (2) model parameterization and automatic modeling; (3) overall model optimization; and (4) optimization of oversized components. The process is as follows: Figure 1 As shown.
[0290] I. The first part of this invention: Human-computer interaction and drawing information extraction. Users only need to import architectural floor plans and input design parameters to achieve structural parameterization. The specific implementation process is as follows:
[0291] Image preprocessing: The user inputs a building floor plan, and then adds room annotations and special line load annotations through clicks and input on the interactive interface. Figure 2The room label should include the room's purpose in the first line and the load values in the second line (L represents live load, D represents dead load), in kN / m. 2 The first line of the special line load label is the load name, and the second line is the load value in kN / m. Simultaneously, on architectural drawings, structural designers can draw shear wall and structural beam indicator lines based on design requirements or experience, accelerating the subsequent structural optimization process.
[0292] User inputs design parameters: Users can input parameters by clicking on options such as... Figure 2 The selection buttons shown allow you to input the structural design information needed for modeling, mainly including material information, basic structural information, and seismic design parameters.
[0293] Data reading: such as Figure 5 The data will be read in two parts: building entities and drawing annotations. Building entity information, such as walls, doors, and windows, will be obtained from TGL files generated by TArch. Drawing annotation information will be obtained from DXF files generated by AutoCAD software.
[0294] Parameter matching: The parameter matching process implements the matching between entity parameters and room names. Figure 6 This is a diagram illustrating room identification. Figure 7 For room identification results, first draw a circle with a certain radius centered on the room label. Figure 6 (b) Create an undirected graph based on whether the base points inside the circle are connected by a baseline. Figure 6 (a)). Detecting closed loops in the graph ( Figure 6 (d) Figure 6 (f) Figure 6 If the closed loop uniquely contains the room label (h), then the match is successful. Figure 6 (g) is the only closed loop that satisfies the requirement.
[0295] II. The second part of this invention: Model parametricization and automatic modeling. Model construction is divided into component construction and load arrangement, as detailed below:
[0296] Parametric and automatic placement of shear walls and structural beams: Parametric placement of shear walls and structural beams is performed based on the designer's prompt lines, such as... Figure 8 As shown. Based on the length of the architectural guidance lines drawn by the designer and the length of the building's baseline, guidance lines can be divided into four types, such as... Figure 8 As shown in (a). The structural beam lines are first modeled according to the beam lines indicated by the designer, and all building baselines without shear walls are arranged as structural beams.
[0297] Automated slab layout: The key to automated slab layout is determining the wall and beam segments at the boundaries of each enclosed zone. Specific steps include:
[0298] 1) For each room, iterate through all wall segments and beam segments. If the distances from both endpoints of the current wall segment or beam segment to the boundary of the closed area are less than a preset threshold, then the current wall segment or beam segment is located on the boundary of this closed area. Figure 9 (a));
[0299] 2) Connect all wall segments and beam segments on the currently closed boundary end to end to obtain a set of arranged nodes. Figure 9 (b));
[0300] 3) Write the coordinates of the node set into a file in sequence to achieve automated modeling of the board.
[0301] Automatic load distribution: Figure 9 A schematic diagram of the automated load arrangement is provided. The structural loads include the self-weight of structural members, beam line loads, and floor surface loads. The self-weight of the members is calculated by the software based on the input material density (reference). Figure 2 The beam line load qL is automatically calculated. The beam line load qL is calculated according to formula (1):
[0302] q L =η L ×h×(f1+f2+max{γ1,γ2}×w) (1)
[0303] In the formula, f1 and f2 are the surface loads of the left and right sides of the wall above the beam, respectively; γ1 and γ2 are the unit weights of the wall core, both of which are determined according to the type of closed areas on both sides of the wall above the beam; h and w are the height and width of the wall above the beam, respectively; η L The reduction factor for beam line load is expressed as shown in equation (2), and is calculated according to formula (2):
[0304] η L =1-S L / S T (2)
[0305] In the formula, S L and S T These represent the area of the door opening on the wall above the beam and the area of the entire wall. The surface load of the slab is taken as the load marked in the room to which the current slab belongs (see Figure (4)).
[0306] Thirdly, the automatic optimization of the overall model indicators. The intelligent structural optimization algorithm of this invention, based on the designer's prompts, considers both the structural and architectural multi-story structures. The optimization objective is to reduce material usage, minimize irregular columns and short shear walls, and ensure ease of construction while meeting code requirements. The search space for shear wall length is set based on the user's prompts, and expert knowledge is used to comprehensively optimize key factors such as shear wall length, thickness, and concrete strength grade. Specific optimization details are as follows:
[0307] Optimization objective: The objective function f1 for material cost can be calculated according to equation (3), where n represents the number of floors; L i This represents the total length of the shear wall segment at floor i.
[0308]
[0309] Convenience of construction is to avoid the occurrence of short and numerous shear walls. The objective function f2 can be calculated according to equation (4), where M i This represents the total number of shear wall segments on floor i.
[0310]
[0311] Parameter space: The parameter space for shear wall length (L) is shown in the figure; the parameter space for shear wall thickness (ThichWall) is the commonly used thickness {200mm, 250mm, 300mm, 350mm}; the parameter space for shear wall concrete grade (GradConcrete) is the commonly used concrete grade from C30 to C60.
[0312] Constraint establishment: First, different structural standard floors need to satisfy the following condition: the length of the shear wall at position k of the upper standard floor j must be less than the length of the lower standard floor i at the same position k (l jk <l ik This ensures the structure's validity. In addition, the constraints on the structural performance indicators of this invention mainly include the inter-story drift angle 1 / δ and the torsional ratio r. d and the period ratio r p Layer stiffness ratio r f The ratio of interstory shear capacity to r s The specific expression for the corresponding constraint is:
[0313] 1 / δ≤1 / δ lim (5)
[0314] r d ≤r dlim (6)
[0315] r p ≤r plim (7)
[0316] r f ≥r flim (8)
[0317] r s ≥r slim (9)
[0318] In the formula, 1 / δ lim r dlim and rplim These are the limits for displacement angle, torsional ratio, and period ratio, respectively, determined according to the "Code for Seismic Design of Buildings" GB50011-2010; r flim This indicates the limit value for the lateral stiffness ratio, which is determined according to the "Code for Seismic Design of Buildings" GB50011-2010 and the "Technical Specification for Concrete Structures of High-Rise Buildings" JGJ3-2010; r slim The limit value for the shear capacity ratio is determined according to the "Technical Specification for Concrete Structures of High-Rise Buildings" JGJ3-2010. In addition to the aforementioned mandatory constraints, this invention applies additional constraints to the parameter space based on the designer's design experience, as follows:
[0319] 1) For shear walls connected to the corners of the exterior walls and shear walls located at both ends of the partition walls, their length is greater than 0;
[0320] 2) For shear walls whose endpoints are not located on exterior walls, partition walls, or public areas, their length is taken as 0;
[0321] 3) Shear walls of the same type share parameters.
[0322] The optimization process of this invention can employ various optimization methods, including encoding the parameterized structural components. Figure 11 Various optimization algorithms can be used for optimization, such as genetic algorithms, ant colony algorithms, and tabu search algorithms.
[0323] III. The fourth part of this invention is the automatic optimization and prompting optimization of out-of-limit components in the model.
[0324] Automatic optimization of oversized components follows the principle of fine-tuning, while also considering types that do not increase cross-sectional dimensions, with the goal of minimizing material consumption. Taking a beam as an example, such as... Figure 12 As shown. First, the dimensions of the components are merged to obtain the dimension set S. beam Then, the feasible region S after sorting the cross-sectional dimensions of the oversized components... beam Perform trial calculations sequentially to meet the constraints of overall structural indicators and component limits, and obtain the optimal solution with the least amount of material; if S beam If no feasible solution exists in the region, then the feasible region I defined by the designer will be used. beam Perform trial calculations sequentially until the optimal solution I is obtained. best Add it to S beam The optimization order for oversized components is from the periphery to the interior of the structure.
[0325] The optimization of prompts for out-of-limit components is iteratively optimized based on the component and size range selected by the user in the interactive interface.
[0326] This invention proposes an intelligent building structure design method based on human interaction. Combining expert knowledge and building regulations with interaction with designers, it achieves automatic modeling of structural components including shear walls, structural beams, slabs, and structural loads. The generated parameter space can be optimized using any optimization algorithm. This invention promotes the integration of artificial intelligence and architectural design, improving the technological level, work efficiency, and design quality of architectural design.
[0327] This invention proposes a method for structural modeling based on architectural drawings, which allows designers to complete component modeling and load modeling of the structural model through simple human-computer interaction.
[0328] The method proposed in this invention for designers to draw shear wall hint lines and structural beam hint lines can effectively convey the designer's design experience to the algorithm and provide a reliable initial solution for structural optimization.
[0329] This invention proposes a structural parameterization method based on expert knowledge matrices and building codes, which can reduce the search space of variables and can be combined with various optimization algorithms to quickly achieve the optimized design of structural models.
[0330] The optimization method proposed in this invention takes into account multiple building standard floors and multiple structural standard floors.
[0331] The structural optimization method proposed in this invention not only considers the arrangement of shear walls, but also the cross-sectional dimensions of the shear walls and the grade of concrete.
[0332] The automatic component optimization and prompting optimization method proposed in this invention can quickly obtain the designer's design requirements.
[0333] The room identification method proposed in this invention has a significant advantage in speed at the second level.
[0334] The human-computer interaction method used in this invention can provide designers with a convenient working platform and give full play to their design experience and inspiration.
[0335] This invention, through the intersection of structural design and artificial intelligence, forms a human-computer interaction-based intelligent structural design method, which effectively improves the efficiency of structural design and solves the problems of low efficiency, long cycle and strong subjectivity in traditional structural design, thus promoting the digital and intelligent development of building structural design in my country.
Claims
1. A building structure intelligent design method based on human-computer interaction, characterized in that, Includes the following steps: 1) The host computer acquires the building floor plan containing building annotation information and building design parameters; The building labeling information includes room labeling information; The room labeling information includes the room name and the corresponding load value of the room; 2) Read the building entity element information, building annotation information, and building design parameters from the building floor plan respectively, match the building entity element information of each room with the corresponding building design parameters and building annotation information, and establish a building intelligent design model based on the matching results; The intelligent building design model includes building components and the corresponding loads of the building components; The step of matching the architectural entity element information of each room with the corresponding architectural design parameters and architectural annotation information includes: 2.1.1) Draw a circle with radius d centered on the location of the room label information; 2.1.2) Connect the endpoints of the baselines of all buildings within the circle to form an undirected graph; 2.1.3) Detect closed loops in the undirected graph. If there is a unique closed loop that completely contains the room label information, then the closed loop is the identified room. 2.1.4) Match the identified rooms with the corresponding building entity element information and building design parameters; 3) Based on the intelligent building design model, establish a building structural parameter optimization model; 4) Solve the building structure parameter optimization model to obtain the optimized parameters of each building component; 5) Compare the information of each building component in the current intelligent building design model with the building code. If there are building components that do not meet the building code, optimize the size of these non-compliant building components.
2. The intelligent building structure design method based on human-computer interaction according to claim 1, characterized in that the building annotation information and building design parameters are input by the user to the host computer through the human-computer interaction interface; The building annotation information also includes special line load annotation information, as well as drawing information for shear wall indicator lines and structural beam indicator lines; The special line load labeling information includes the name of the special line load and the corresponding load value of the special line load. The architectural design parameters include: material information, basic structural information, and seismic design parameters.
3. The intelligent building structure design method based on human-computer interaction according to claim 1, characterized in that, The building entity element information includes walls, doors, and windows.
4. The intelligent building structure design method based on human-computer interaction according to claim 1, characterized in that, The steps for establishing a building intelligent design model include: 2.2.1) Constructing building components; the building components include shear walls, structural beams, and floor slabs; 2.2.2) Apply loads to building components.
5. The intelligent building structure design method based on human-computer interaction according to claim 4, characterized in that, In step 2.2.1), the steps for constructing shear walls include: arranging the shear wall marker lines in the building baseline as shear walls; The shear wall indicator line is input by the user through the human-computer interaction interface; The steps for constructing structural beams include: designating all building baselines without shear walls as structural beams; The steps for constructing the floor slab include: 2.2.1.1) For each room, iterate through all shear walls and structural beams. If the distance from both ends of the current shear wall or structural beam to the boundary of the corresponding room is less than a preset threshold, then the current shear wall or structural beam is located on the boundary of this room. 2.2.1.2) Connect all shear walls and structural beams on the boundary of the current room end to end to obtain a set of arranged nodes; 2.2.1.3) Write the coordinates of the node set into a file in sequence to achieve automated modeling of the floor slab.
6. The intelligent building structure design method based on human-computer interaction according to claim 1, characterized in that, The loads on the building components include the self-weight of the building components, beam line loads, and floor surface loads. The self-weight of the building component is calculated using the density of the building component material; The beam line load q L The calculation formula is as follows: （1） In the formula, f1 and f2 are the surface loads of the left and right sides of the wall above the beam, respectively. and , respectively, represent the unit weight of the wall core; h and w are the height and width of the wall above the beam, respectively; Among them, the beam line load reduction factor As shown below: （2） In the formula, S L and S T These are the area of the doorway on the wall above the beam and the area of the entire wall, respectively. The floor load is taken as the load marked in the room to which the current floor belongs.
7. The intelligent building structure design method based on human-computer interaction according to claim 1, characterized in that, The building structure parameter optimization model includes an optimization model for material costs and an optimization model for ease of construction. The optimization model f3 for the material cost is shown below: （3） In the formula, i is the floor number, i=1,2,…,n, and n represents the total number of floors; L i This represents the total length of the shear wall segment on the i-th floor; The optimization model f4 for ease of construction is shown below: （4） In the formula, M i This represents the total number of shear wall segments on the i-th floor; The constraints of the building structure parameter optimization model include: the length of the upper shear wall is less than the length of the lower shear wall at the same position, the inter-story drift angle does not exceed the limit, the torsion ratio does not exceed the limit, the period ratio does not exceed the limit, the story stiffness ratio does not exceed the limit, the inter-story shear capacity ratio does not exceed the limit, and the shear wall length is constrained. The constraint that the length of the upper shear wall is less than the length of the lower shear wall at the same location is as follows: （5） In the formula, j is the floor number, j=i+1,i+2,…,n; k is the location of the shear wall; , Let be the lengths of the shear walls at position k on the j-th and i-th floors, respectively; The inter-story drift angle does not exceed the following limit constraint: （6） In the formula, This refers to the inter-story drift angle; This refers to the inter-story drift angle limit; The torsion ratio is not subject to the following limit constraints: （7） In the formula, r d r is the torsion ratio. dlim For torsional ratio limits; The period ratio is subject to the following limit constraint: （8） In the formula, The period ratio; The period ratio limit; The layer stiffness ratio is subject to the following limit constraints: （9） In the formula, The layer stiffness ratio; The limit for the layer stiffness ratio; The inter-story shear capacity ratio is not subject to the following limit constraints: （10） In the formula, The ratio of inter-story shear capacity; This is the limit value for the inter-story shear capacity ratio; The shear wall length constraint is as follows:
1. The length of the shear wall connected to the corner of the exterior wall and the shear wall located at both ends of the partition wall is greater than 0; 2. The length of shear walls whose endpoints are not located on exterior walls, partition walls, or public areas is 0.
3. Shear walls of the same type share parameters.
8. The intelligent building structure design method based on human-computer interaction according to claim 7, characterized in that, Methods for solving building structural parameter optimization models include: genetic algorithm, ant colony algorithm, and tabu search algorithm; The optimized parameters for the building components include the optimized parameters for the length and number of shear wall segments on each floor of the building.
9. The intelligent building structure design method based on human-computer interaction according to claim 1, characterized in that, The comparison of the information of each building component in the current intelligent building design model with the building code involves comparing the axial compression ratio and decompression ratio of the building components with the axial compression ratio and decompression ratio specified in the building code. The optimization of the dimensions of these building components that do not meet building codes includes automatic optimization and prompt-based optimization. The automatic optimization of the dimensions of these building components that do not meet building codes aims at minimizing material consumption. The optimization steps include: firstly, merging the dimensions of the building components to obtain a dimension set S. beam Then, the cross-sectional dimensions of the oversized components are sorted, and the feasible regions S after sorting are determined. beam Trial calculations were performed sequentially to meet the constraints of overall structural indicators and component limits, and the optimal solution was obtained with the least amount of material. The optimization suggestions for the dimensions of these building components that do not meet building codes are performed iteratively based on the building components and their size range.