JP6408885B2 - Frame design method - Google Patents

Description

  The present invention relates to a method for designing a structural frame of a building, and more particularly to a method for designing a structural body that can obtain an optimal solution for the arrangement or cross-section of structural members constituting the structural body in a short time.

  For example, a building frame by a shaft construction method is assembled using structural members including columns, beams, bearing walls, and the like. These structural members have their arrangement, cross-sectional shape, and the like determined according to the shape and load conditions of the building. Conventionally, for example, an arrangement or a cross section of a structural member capable of increasing the strength of a frame body and the like while satisfying the shape and load condition of a building is obtained using a computer.

  The following patent document proposes a method for calculating an optimal solution for arrangement of structural members based on a genetic algorithm. In the genetic algorithm, genetic operations such as crossover are repeatedly performed on a plurality of pieces of chromosome information expressed by genes that define the arrangement of structural members. As a result, chromosome information can be evolved in a time series from a small number of samples, so that an optimal solution can be obtained efficiently.

JP 2014-153994 A JP 2014-153959 A

  In the crossover, for a pair of chromosome information, for example, a group of genes delimited by one or a plurality of crossover points is replaced, and the chromosome information is evolved. In the conventional crossover, the genes were randomly replaced regardless of the superiority or inferiority of the genes included in each group. For this reason, an excellent group is not necessarily taken into crossed chromosome information. Therefore, the conventional method cannot reliably evolve the chromosome information, and thus it takes a lot of time to obtain the optimum solution.

  The present invention has been devised in view of the above-described circumstances, and the chromosomal information is surely obtained on the basis of generating new chromosome information by combining excellent groups included in each pair of chromosome information. The main object of the present invention is to provide a frame structure design method capable of obtaining an optimal solution for the arrangement or cross-section of structural members constituting the frame structure of a building in a short time.

The present invention is a method for designing a building frame including at least one structural member using a computer, the computer using numerical information for specifying an arrangement or a cross section of the structural member. A step of generating a population composed of a plurality of types of certain chromosome information, and an optimization calculation step in which the computer uses the population to calculate an optimal solution of an arrangement or a section of the structural member based on a genetic algorithm The optimization calculation step includes a crossover step of generating a new chromosome information by replacing a gene that defines the arrangement or cross section of each structural member for the pair of chromosome information selected from the population, The crossover step is a first classification step of classifying the pair of chromosome information into a plurality of groups each including the gene to be replaced, For each group of a pair of chromosomes information, combined first evaluation step for evaluating the excellence groups manufacturing cost is low, and an excellent group included in each of the pair of chromosomes information, to generate the new chromosome information A generation step is included.

  In the frame design method according to the present invention, the first classification step classifies the gene group having a relatively large influence on a constraint condition for evaluating superiority or inferiority of the chromosome information as the group. It is desirable to do.

  In the frame structure designing method according to the present invention, it is desirable that the first evaluation step evaluates the superiority or inferiority of each group based on at least a constraint condition having a large influence on each group.

  In the frame structure designing method according to the present invention, the structural member includes a plurality of large beams that horizontally connect between columns, a plurality of small beams that further divide a horizontal frame surface surrounded by the large beams, and the large beams. A load-bearing element disposed on a vertical frame surrounded by the pillar, and the group includes at least one load-bearing element gene that defines an arrangement of the load-bearing element and at least one that defines a cross section of the beam. It is desirable to include a load-bearing element / girder group including a girder gene and a girder group including at least one girder gene that defines the arrangement of the girder.

  In the frame structure designing method according to the present invention, the crossover step further includes a first determination step of determining whether the excellent group is included in the pair of chromosome information prior to the generation step. In the first determination step, the generation step is preferably performed when it is determined that each of the pair of chromosome information includes the excellent group.

  In the frame structure designing method according to the present invention, the crossover step may be performed when the first determination step determines that only one of the pair of chromosome information includes the excellent group. Preferably, the method further includes a step of performing crossover or mutation performed randomly by the computer.

  In the frame structure designing method according to the present invention, the crossover step may be performed when the first determination step determines that only one of the pair of chromosome information includes the excellent group. A second classification step of classifying each of the chromosome information into a plurality of groups subdivided from the group of the first classification step; and evaluating the superiority or inferiority of each of the subdivided groups for the pair of chromosome information It is preferable that the method further includes a second evaluation step, and the second classification step and the second evaluation step are performed prior to the generation step.

  In the frame design method according to the present invention, whether the crossover step includes the excellent group in the pair of chromosome information after the second evaluation step and before the generation step. A second determination step for determining the information, and when the second determination step determines that the excellent group is included in each of the pair of chromosome information, the generation step is performed. desirable.

  In the frame structure designing method according to the present invention, when the crossover step is determined in the second determination step that only one of the pair of chromosome information includes the excellent group. Preferably, the method further includes a step of performing crossover or mutation performed randomly by the computer.

  The frame structure designing method of the present invention is the arrangement or section of each structural member for a pair of chromosome information selected from a group consisting of a plurality of types of chromosome information, which is numerical information for specifying the arrangement or section of the structural member. The crossover process of generating new chromosome information by replacing the gene defining

  The crossover step is a first classification step for classifying each of a pair of chromosome information into a plurality of groups including genes to be replaced, a pair of chromosome information, a first evaluation step for evaluating superiority or inferiority of each group, and A generation step of generating new chromosome information by combining excellent groups included in each pair of chromosome information is included.

  Unlike the conventional method in which the groups are randomly replaced, the frame design method of the present invention generates new chromosome information by combining excellent groups. Therefore, the frame structure design method of the present invention can surely evolve chromosome information, so that an optimal solution for the arrangement or cross section of structural members can be obtained in a short time.

It is a perspective view of the computer which performs the design method of this embodiment. It is a perspective view which shows the frame of a building. (A) is a top view which shows a part of horizontal frame surface in which the 1st small beam is arrange | positioned, (b) is a top view which shows a part of horizontal frame surface in which the 2nd small beam is arrange | positioned. It is a flowchart which shows an example of the process sequence of the design method of this embodiment. It is a flowchart which shows an example of the process sequence of the basic information input process of this embodiment. It is a perspective view of a frame without a load bearing element and a small beam. (A) is a perspective view showing an X-axis vertical frame surface, (b) is a perspective view showing a Y-axis vertical frame surface. (A) is a perspective view which shows the large beam arrange | positioned on a X-axis vertical frame surface, (b) is a perspective view which shows the large beam arrange | positioned on a Y-axis vertical frame surface. It is a top view which shows the node of a horizontal frame. It is a flowchart which shows an example of the process sequence of the allele group input process of this embodiment. It is a front view which shows the allele of a strength element. It is a diagram which shows a proof stress element gene group and a proof stress element gene. It is a diagram which shows a large beam gene group and a large beam gene. It is a conceptual diagram of the group comprised by multiple types of chromosome information. It is a flowchart which shows an example of the process sequence of the group production | generation process of this embodiment. (A) is a diagram which shows the direction information and position information of a 1st small beam, and the 1st small beam arrange | positioned on a horizontal frame. (B) is a diagram showing the direction information and position information of the second beam, and the second beam arranged on the horizontal frame. It is a flowchart which shows an example of the process sequence of the optimization calculation process of this embodiment. It is a flowchart which shows an example of the process sequence of the gene operation process of this embodiment. It is a diagram which shows the total value of the volume Fj of the structural member after penalty addition of each chromosome information. It is a flowchart which shows an example of the process sequence of the next generation group production | generation process of this embodiment. It is a flowchart which shows an example of the process sequence of a crossover process. It is a diagram which shows the group classified at the 1st classification | category process. (A) is a diagram which shows a pair of selected chromosome information, (b) is a diagram explaining a production | generation process. It is a flowchart which shows an example of the process sequence of the 1st evaluation process of this embodiment. It is a flowchart which shows an example of the process sequence of the 1st step | paragraph evaluation process of this embodiment. It is a flowchart which shows an example of the process sequence of the 2nd step | paragraph evaluation process of this embodiment. It is a diagram which shows the group which subdivided the proof stress element and the girder group. It is a diagram which shows the group which subdivided the beam group. It is a flowchart which shows an example of the process sequence of the 2nd evaluation process of this embodiment. It is a flowchart which shows an example of the process sequence of the 3rd step | paragraph evaluation process of this embodiment. It is a flowchart which shows an example of the process sequence of the 4th step | paragraph evaluation process of this embodiment. It is a graph which shows the relationship between the index | exponent of the objective function of Example 1, the comparative example 1, and the comparative example 2, and a generation. It is a graph which shows the relationship between the index | exponent of the objective function of Example 2, the comparative example 3, and the comparative example 4, and a generation.

Hereinafter, an embodiment of the present invention will be described with reference to the drawings.
The frame structure design method of the present embodiment (hereinafter sometimes simply referred to as “design method”) is a method for designing a building frame including at least one structural member using a computer.

  FIG. 1 is a perspective view of a computer that executes the design method of the present embodiment. The computer 1 includes a main body 1a, a keyboard 1b, a mouse 1c, and a display device 1d. The main body 1a is provided with, for example, an arithmetic processing unit (CPU), a ROM, a working memory, a storage device such as a magnetic disk, and disk drive devices 1a1, 1a2. The storage device stores in advance processing procedures (programs) of the design method of the present embodiment. The processing procedure is executed by the arithmetic processing unit of the computer 1. Therefore, the computer 1 is configured as a design apparatus 1A for implementing the design method of the present invention.

  FIG. 2 is a perspective view showing a building frame of a building. The frame 2 of the building B is a structural member including, for example, a pillar 3, a beam 4, and a load bearing element (a structural element that resists a horizontal load such as an earthquake or wind. In this embodiment, a load bearing wall) 5. 6. The beam 4 includes a plurality of large beams 7 that horizontally connect the columns 3 and 3, and a small beam 8 that further divides the horizontal frame 9 surrounded by the large beams 7. The large beam 7 and the small beam 8 are configured as a floor beam for supporting a floor (not shown) on the second floor or higher, or a roof beam for supporting a roof (not shown). The girder 7 includes, for example, a web, an upper flange, and a lower flange, and is configured of a substantially horizontal H-shaped steel or the like. Similarly to the large beam 7, the small beam 8 is also made of a substantially horizontal H-shaped steel or the like.

  FIG. 3A is a plan view showing a part of the horizontal frame 9 on which the first beam is arranged. FIG. 3B is a plan view showing a part of the horizontal frame 9 on which the second beam is arranged. As shown in FIG. 3A, each horizontal frame 9 has two small beams by arranging a first beam (hereinafter, also referred to as “first beam”) 8a. It is divided into a frame surface 13. Further, as shown in FIG. 3 (b), the horizontal frame 9 has a second beam (hereinafter also referred to as “second beam”) 8 b of the two beams 13. By being arranged in any one, it is divided into three small frames 13. Thus, the horizontal frame 9 is divided into a plurality of small frames 13 by arranging the small beams 8.

  Further, in the drawing, the small frame 13 is the first small frame 13A closest to the reference point 14a, which is the lower left apex of the horizontal frame 9, and the first small frame 13A next to the reference point 14a next to the first small frame 13A. The second small structure surface 13B and the third small structure surface 13C next to the reference point 14a are included after the second small structure surface 13B. Each small frame 13 is provided with a horizontal brace (not shown) for keeping the horizontal frame 9 on a rigid floor.

  The frame 2 has a vertical frame surface 10 surrounded by a pair of columns 3 and 3 and at least one large beam 7 connecting the columns 3 and 3. A load bearing wall 5 is disposed on the vertical frame surface 10. The bearing wall 5 of the present embodiment includes two columns 5a and 5a and two diagonal members 5b and 5b that are connected between the columns 5a and 5a and are inclined in opposite directions.

  FIG. 4 is a flowchart illustrating an example of a processing procedure of the design method according to the present embodiment. In the design method, the optimal solution of the arrangement or cross section of the structural member 6 constituting the frame 2 shown in FIG. 2 is calculated. In the present embodiment, the structural members 6 for which the optimum solution is calculated are the bearing wall 5, the large beam 7, and the small beam 8. The frame 2 and the building B are manufactured based on the optimum solution of the arrangement of the bearing walls 5, the cross section of the large beam 7, and the arrangement of the small beams 8.

  In the design method of this embodiment, first, basic information of a building is input to the computer 1 (basic information input step S1). FIG. 5 is a flowchart showing an example of the processing procedure of the basic information input step S1 of the present embodiment.

  In the basic information input step S1 of the present embodiment, first, the shape of the frame 2 of the building B is input to the computer 1 (step S11). FIG. 6 is a perspective view of the frame 2 from which the bearing wall 5 and the small beam 8 are omitted.

  In step S <b> 11, for example, the shape and arrangement of the pillar 3, the large beam 7, the horizontal frame surface 9, the vertical frame surface 10, and the roof (not shown) of the building B are input to the computer 1. The arrangement, dimensions, and the like of the pillars 3, the beams 7, the horizontal frame 9, the vertical frame 10, and the roof (not shown) are set based on a predetermined horizontal module or vertical module. In addition, parameters necessary for structural calculation such as the cross-sectional shape and the secondary moment of the cross-section are set in the column 3. The arrangement and parameters of the pillar 3, the large beam 7, and the roof (not shown) and the like are all stored in the computer 1 as numerical data. In step S11, the bearing wall 5, the large beam 7, and the small beam 8 (shown in FIG. 2) for which the optimal solution for the arrangement is calculated are not input.

  The horizontal frame 9 of the present embodiment includes a first horizontal frame 9a to a fourth horizontal frame 9d that supports the floor on the second floor, and a fifth horizontal frame 9e to an eighth horizontal frame that supports the roof (not shown). Surface 9h.

  The vertical frame surface 10 according to the present embodiment includes an X-axis vertical frame surface 11 disposed along the X-axis direction and a Y-axis vertical frame surface 12 disposed along the Y-axis direction.

  FIG. 7A is a perspective view showing the X-axis vertical frame surface 11, and FIG. 7B is a perspective view showing the Y-axis vertical frame surface 12. As shown in FIG. 7A, the X-axis vertical frame surface 11 is disposed on the second floor, the first vertical frame surface 11A to the sixth vertical frame surface 11F disposed on the first floor of the frame body 2. The seventh vertical frame surface 11G to the twelfth vertical frame surface 11L are included. As shown in FIG. 7 (b), the Y-axis vertical frame 12 is arranged on the second floor, the 13th vertical frame 12A to the 16th vertical frame 12F arranged on the first floor of the frame 2. The 17th vertical frame surface 12G-the 24th vertical frame surface 12L are comprised. These vertical frame surfaces 11 </ b> A to 12 </ b> L are stored in the computer 1.

FIG. 8A is a perspective view showing the large beam 7 arranged on the X-axis vertical frame 11 . FIG. 8B is a perspective view showing the large beam 7 arranged on the Y-axis vertical frame 12. One large beam 7 of this embodiment is arranged for each pair of columns 3 and 3. As shown in FIG. 8A, the girder 7 of the present embodiment is a first girder 7A to a sixth girder 7F arranged on the upper side of the first vertical frame surface 11A to the sixth vertical frame surface 11F on the first floor. And the seventh large beam 7G to the twelfth large beam 7L arranged on the upper side of the seventh vertical frame surface 11G to the twelfth vertical frame surface 11L on the second floor. Further, as shown in FIG. 8B, the 13th large beam 7M to the 18th large beam 7R arranged on the upper sides of the 13th vertical frame 12A to the 16th vertical frame 12F on the first floor, The 19th large beam 7S to the 24th large beam 7X arranged on the upper side of the 19th vertical frame 12G to the 24th vertical frame 12L are included.

  Next, in the basic information input step S1 of the present embodiment, the load condition of the building B (shown in FIG. 2) is input to the computer 1 (step S12). The load condition is information regarding an external force acting on the building B. The load condition is input based on, for example, various specifications of the building B, for example, outer wall specifications, floor specifications, roofing materials, fire resistance specifications, earthquake resistance grades, wind resistance grades, and the like. Such a load condition is also numerical data and is stored in the computer 1.

  The basic information of the building B can be set using software such as general CAD and a consistent structure calculation system, for example. In the present embodiment, the two-story building B is shown as an example. However, for example, a one-story building or a three-story or more building may be used.

  Next, in the design method of this embodiment, the structural members (bearing walls 5 and small beams 8 in this embodiment) are arranged on the horizontal frame 9 and vertical frame 10 of the frame 2 in the computer 1. Design constraint conditions are input (design constraint condition input step S2).

  As a design constraint condition for arranging the bearing wall 5, similarly to the above-mentioned Patent Document 1, in each of the vertical frame surfaces 11 </ b> A to 12 </ b> L (shown in FIG. 7) It may also be referred to as “wall-arrangeable area”). The load bearing wall possible area 16 (shown in FIG. 11) is set as an area excluding the openings 15 (shown in FIG. 11) such as windows and doors on the vertical frame surfaces 11A to 12L.

  The design constraint conditions for arranging the beam 8 include information on the longitudinal direction of the beam 8, the position where the beam 8 can be arranged, and the number of the horizontal frame surfaces 9 a to 9 h (shown in FIG. 6). . The longitudinal direction of the small beam 8 of the present embodiment is limited to, for example, “parallel to the X-axis direction” or “parallel to the Y-axis direction”.

  FIG. 9 is a plan view showing the nodes of the horizontal frame. The position at which the small beam 8 of the present embodiment can be arranged is an end portion (hereinafter simply referred to as “reference point”) of the pair of end portions 8t, 8t of the small beam 8 that is closest to the reference point 14a of each horizontal frame 9a-9h. It is sometimes referred to as an “end”. The fixed position of the reference end 18 is set with reference to the nodes 19 defined on the horizontal frame surfaces 9a to 9h. Further, the node 19 is arranged in accordance with the horizontal module in a region excluding openings (not shown) of the horizontal frame surfaces 9a to 9h. The number of beams that can be arranged can be appropriately set based on the rigidity and cost required for the building B. In the present embodiment, for example, “3” is set.

  Furthermore, the design constraint conditions of this embodiment further include a constraint condition for evaluating the superiority or inferiority of the chromosome information 21 (shown in FIG. 22). The constraint condition includes a constraint condition related to the strength of the bearing wall 5 and the large beam 7 and a constraint condition related to the strength of the small beam 8.

  The constraint conditions related to the strength of the bearing wall 5 and the girder 7 are, for example, the interlayer deformation angle of the frame 2, the bearing strength (tensile, compression, bending, shear, combined stress) of the pillar 5 a of each bearing wall 5, and each bearing strength. The proof stress (tension, compression) of the diagonal member 5b of the wall 5, the proof strength of the beam (bending, shearing, combined stress), the deflection of each timber, the anchor bolt (tensile, shearing) and the like are included. As these constraint conditions, allowable values required for the frame 2 to be designed are set. The allowable value can be appropriately set according to the scale of the building B to be designed, for example. In addition, the constraint conditions related to the strength of the bearing wall 5 and the large beam 7 may be appropriately selected from these conditions, or new conditions may be added.

  The constraints related to the strength of the beam 8 are, for example, the strength of each beam (bending, shearing, combined stress), the deflection of each beam, the horizontal brace strength (tensile), and the deformation in the floor (rigid floor conditions). And beam spacing (floor support). As these constraint conditions, allowable values required for the frame 2 to be designed are set. The allowable value can be appropriately set according to the scale of the building B to be designed, for example. Note that the constraint condition related to the strength of the small beam 8 may be appropriately selected from these conditions, or a new condition may be added. These design constraint conditions are stored in the computer 1 as numerical data.

  Next, in the design method of the present embodiment, an allele group including a plurality of alleles representing the type of the structural member 6 is input to the computer 1 (allele group input step S3). In the present embodiment, allele groups of the bearing wall 5 and the large beam 7 are input. FIG. 10 is a flowchart showing an example of the processing procedure of the allele group input step S3 of the present embodiment.

  In the allele group input step S3 of this embodiment, first, the load bearing element (bearing wall) 5 is defined in the computer 1 (step S31). FIG. 11 is a front view showing alleles of the bearing wall 5. In step S31, for example, based on the horizontal module L3 of the building B, a plurality of types of bearing walls 5 having different widths W1 are set. The bearing wall 5 of the present embodiment includes, for example, a small bearing wall 5A having a width W1a of 450 mm, a medium bearing wall 5B having a width W1b of 600 mm, and a large bearing wall 5C (not shown) having a width W1c of 900 mm. Yes. Each of the bearing walls 5A, 5B, and 5C is numerical data including the cross-sectional shape of the column 5a and the diagonal member 5b, the cross-sectional secondary moment, and the like, and is stored in the computer 1.

  Next, in the allele group input step S3 of this embodiment, an allele group (hereinafter, simply referred to as “strength element gene”) 28 is included in the computer 1 in the computer 1. , Sometimes simply referred to as “bearing element gene group”) 27 is input (step S32). The load bearing element gene 28 of the present embodiment is arranged in the same manner as in Patent Document 1, with respect to each of the vertical frame surfaces 11A to 12L (shown in FIG. 7), the arrangement pattern of the load bearing walls 5A to 5C satisfying the load bearing wall arrangement possible region 16. Is expressed.

  FIG. 12 is a diagram showing the proof stress element gene group 27 and the proof stress element gene 28. In the plurality of load-bearing element genes 28 of the present embodiment, all the arrangement patterns in which at least one of the load-bearing walls 5A to 5C is arranged are set for each of the vertical frame surfaces 11A to 12L (shown in FIG. 7). In addition, the strength element gene group 27 is provided with an index (for example, “0001”) for uniquely identifying the strength element gene 28. Such a plurality of load-bearing element genes 28 and load-bearing element gene groups 27 are stored in the computer 1 as numerical data.

  Next, in the allele group input step S3 of the present embodiment, the computer 1 causes the allele group (hereinafter simply referred to as “large beam gene”) 30 of the large beam 7 to be stored in the computer 1 (hereinafter simply referred to as “large beam gene”). 29 may be input (step S33). FIG. 13 is a diagram showing the large beam gene group 29 and the large beam gene 30.

  The large beam gene 30 of the present embodiment represents a cross section of each large beam 7A to 7X. For the girder gene 30, for example, a plurality of types in which the shape of the girder 7, the web, the upper flange, or the lower flange are different in thickness are defined. Each of these large beam genes 30 is numerical data including a cross-sectional secondary moment and the like. The large beam gene group 29 is provided with an index (for example, “0001”) for uniquely identifying each large beam gene 30. The plurality of large beam genes 30 and large beam gene group 29 are stored in the computer 1 as numerical data.

  Next, in the design method of the present embodiment, the computer 1 generates a group composed of a plurality of types of chromosome information for defining the arrangement or cross section of the structural member 6 (group generation step S4). FIG. 14 is a conceptual diagram of a group composed of a plurality of types of chromosome information. The chromosome information 21 of the present embodiment is numerical information for specifying the arrangement or cross section of the structural member 6 shown in FIG. Each chromosome information 21 includes at least one gene locus 22 and a gene stored in the gene locus 22.

  The gene locus 22 of the present embodiment defines the arrangement of the load-bearing element gene locus 23 for defining the arrangement of the load-bearing walls 5, the girder gene locus 24 for defining the cross section of the large beam 7, and the arrangement of the small beams 8. Of the small beam gene locus 25.

  One strength element locus 23 of this embodiment is set for each of the vertical frame surfaces 11A to 12L shown in FIG. The proof stress gene locus 23 stores a proof stress element gene 28 (shown in FIG. 12). One large beam locus 24 of the present embodiment is set for each of the large beams 7A to 7X shown in FIG. The large beam gene locus 24 stores a large beam gene 30 (shown in FIG. 13).

  As shown in FIG. 14, the trabecular locus 25 of this embodiment includes a direction locus 25a that can store direction information and a position locus 25b that can store position information. Direction loci 25a and position loci 25b are defined by the same number as the upper limit value (for example, 3) of the small beams 8 for each of the horizontal frame surfaces 9a to 9h. Accordingly, the trabecular gene locus 25 is a directional gene for each of the trabeculars 8 (in the present embodiment, the first traversal beam 8a, the second traversal beam 8b, and the third traverse beam 8c) on each horizontal frame 9a to 9h. A locus 25a and a position locus 25b are assigned.

  FIG. 15 is a flowchart illustrating an example of a processing procedure of the group generation step S4 of the present embodiment. In the group generation step S4, first, the strength element gene 28 (shown in FIG. 12) is stored in the strength element locus 23 (step S41). As shown in FIG. 12, each strength element locus 23 stores one strength element gene 28 randomly selected from the strength element gene groups 27 corresponding to the vertical frame surfaces 11A to 12L. . Thereby, in each vertical frame surface 11A-12L, arrangement | positioning of the bearing wall 5 which satisfy | filled the bearing wall arrangement | positioning possible area | region 16 is defined. Each strength element locus 23 of the present embodiment stores only an index for specifying the strength element gene 28. In addition, it is not necessarily limited to the load-bearing element gene locus 23 and the load-bearing element gene 28 as in the present embodiment, and may be appropriately changed.

  Next, in the group generation step S4, the large beam gene 30 (shown in FIG. 13) is stored in the large beam locus 24 (step S42). As shown in FIG. 13, each girder locus 24 stores one girder gene 30 randomly selected from the girder gene group 29 corresponding to each girder 7A to 7X (shown in FIG. 8). Is done. Thereby, the cross section of each large beam 7A-7X is each defined. In each of the large beam loci 24 of the present embodiment, only an index for specifying the large beam gene 30 is stored. Note that the present invention is not limited to the large beam gene locus 24 and the large beam gene 30 as in the present embodiment, and may be appropriately changed.

  Next, in the group generation step S4, the beam beam gene 33 is stored in the beam beam locus 25 (step S43). FIG. 16A is a diagram showing the direction information and position information of the first beam and the first beam arranged on the horizontal frame, and FIG. 16B is the direction information and position of the second beam. It is a diagram which shows information and the 2nd small beam arrange | positioned at a horizontal frame.

The beam beam 33 includes direction information 33a and position information 33b. In step S43, the direction information 33a is stored in the direction locus 25a. The direction information 33a defines the orientation of the small beam 8 in the longitudinal direction. In the present embodiment, for example, any one of the following numerical values (0 to 2) is set based on the design constraint condition of the small beam 8 as described in Patent Document 2 above.
0: No beam arrangement 1: Parallel to X-axis direction 2: Parallel to Y-axis direction

  The number of digits of the direction information 33a of each beam 8 in the present embodiment is set to be the same as the number of horizontal frame surfaces 9 or frame frames 13 on which the beam 8 can be arranged. As shown in FIG. 16 (a), the first beam 8a can be arranged on only one of the horizontal frames 9, so the direction information 33a is set to one digit. As shown in FIG. 16 (b), the second beam 8b can be arranged on either of the two frames 13A, 13B divided by the first beam 8a. It is set to two digits. Further, the numerical value of the first digit of the direction information 33a defines whether or not the second beam 8b is arranged on the first small frame 13A. Further, the numerical value of the second digit of the direction information 33a defines whether or not the second small beam 8b is arranged on the second small frame 13B. Similarly, the third beam 8c (not shown) can be arranged on any of the three small frame surfaces divided by the first beam 8a and the second beam 8b. Set to a digit.

  In step S43, position information 33b is stored in the position locus 25b. The position information 33b defines a fixed position of the reference end portion 18 of the small beam 8 on the horizontal frame 9 or the small frame 13 on which the small beams 8 are arranged. As shown in FIGS. 16A and 16B, the position information 33b of the present embodiment is the length of the side 26 with which the reference end 18 abuts on each of the horizontal frame surface 9 and the small frame surface 13. It is defined by the ratio of the distance L1 between the reference points 14a, 14b and the reference end 18 of the beam 8 to the length L2. Therefore, the numerical value that the position information 33b of the present embodiment can take is 0.0 to 1.0.

  As shown in FIG. 16A, when the direction information 33a of the first beam 8a is “2” and the position information 33b is “0.4”, the reference end portion of the first beam 8a. The fixed position 18 is such that the distance L1x (X-axis direction) from the reference point 14a of the horizontal frame 9 is 0.4 with respect to the length L2x of the side 26 (X-axis direction) with which the reference end 18 abuts. Is defined as node 19.

  As shown in FIG. 16B, when the direction information 33a of the second beam 8b is “10” and the position information 33b is “0.5”, the reference end portion 18 of the second beam 8b. The distance L1y (Y-axis direction) from the reference point 14b of the second small frame 13B is 0. with respect to the length L2y of the side 26 (Y-axis direction) with which the reference end 18 abuts. It is defined at node 19 which is 5. As described above, the position information 33b of the present embodiment can define the fixed position of the reference end portion 18 of the beam 8. Therefore, the position information 33b is used together with the direction information 33a, so that the arrangement of the small beams 8 can be uniquely defined. Details of the direction information 33a and the position information 33b are as described in Patent Document 2.

  In step S43, the computer 1 causes the direction beam locus 25a and the position locus 25b to include direction information 33a and position information 33b for the first beam 8a, the second beam 8b, and the third beam 8c. Each of the beam genes 33 is stored. The storage of the direction information 33a and the position information 33b is randomly determined according to a random number. Thereby, in each horizontal frame surface 9a-9h, arrangement | positioning of the small beam 8 for the number below an upper limit is defined. Note that the beam beam locus 25 and the beam beam gene 33 that define the arrangement of the beam beams 8 are not limited to such an embodiment, and may be appropriately changed.

  As described above, in the group generation step S4, the load-bearing element gene locus 23, the large beam gene locus 24, and the small beam gene locus 25 of the chromosome information 21, the load-bearing element gene 28 (shown in FIG. 12), and the large beam gene 30 (shown in FIG. 13). And a beam beam 33 (shown in FIG. 16) are respectively stored (shown in FIG. 22). Thereby, one frame 2 (design sample) in which the bearing wall 5, the large beam 7, and the small beam 8 shown in FIG. Then, as shown in FIG. 14, a group 20 is set by setting a plurality of types of chromosome information 21. Such chromosome information 21 is stored in the computer 1 as numerical data.

  Next, in the design method of the present embodiment, the computer 1 uses the group 20 to calculate an optimal solution for the arrangement or cross section of the structural members 6 based on a genetic algorithm (optimization calculation step S5).

  A genetic algorithm is a learning algorithm that mimics the process by which a living organism adapts to the environment and evolves. In this genetic algorithm, genetic operations such as crossover or mutation are repeated for a plurality of chromosome information expressed by genes. Thereby, in a genetic algorithm, chromosome information can be evolved in time series from a small number of samples, and an optimal solution can be obtained in a short time. FIG. 17 is a flowchart illustrating an example of a processing procedure of the optimization calculation step S5 of the present embodiment.

  In the optimization calculation step S5 of the present embodiment, first, the result value of the frame 2 for the constraint condition is calculated based on the chromosome information 21 of the group 20 (step S51). In the present embodiment, the constraint condition for calculating the result value is the constraint condition related to the strength of the bearing wall 5 and the large beam 7 input in the design constraint condition input step S2 and the constraint condition related to the strength of the small beam 8. Conditions are included. In each chromosome information 21, a structural analysis (structural simulation) is performed using a strength element gene 28 (shown in FIG. 12), a large beam gene 30 (shown in FIG. 13), and a small beam gene 33 (shown in FIG. 16). Thus, the result value Ri (x) is calculated for each constraint condition (for example, the interlayer deformation angle of the frame 2, the tension, compression, bending, etc. of the column 5 a of each bearing wall 5). In the result value Ri (x), the suffix i is for distinguishing each constraint condition. The subscript x is for distinguishing each chromosome information 21 included in the group 20. These result values Ri (x) are stored in the computer 1.

  Next, in the optimization calculation step S5 of this embodiment, the volume is calculated for each structural member 6 in each chromosome information 21 (step S52). The volume Fj (x) of each structural member 6 (for example, the bearing walls 5A, 5B, etc.) is the bearing wall 5 defined by the bearing element gene 28, the large beam gene 30 and the small beam gene 33 stored in each chromosome information 21. It can be calculated based on the dimensions of the large beam 7 and the small beam 8. As the volume Fj (x) of each structural member 6 decreases, the cost of the frame 2 decreases. The subscript j is for distinguishing each structural member (for example, the bearing walls 5A, 5B, etc.). The subscript x is for distinguishing each chromosome information 21 included in the group 20. These volumes Fj (x) are stored in the computer 1.

  Next, in the optimization calculation step S5, it is determined whether or not the group 20 satisfies the optimization calculation end condition (determination step S53). As an optimization calculation termination condition, for example, whether or not there is chromosome information 21 that satisfies both “all design constraint conditions and“ manufacturing cost conditions ”(hereinafter, sometimes simply referred to as“ optimal solution ”). Or the like can be adopted as appropriate. In the present embodiment, it is determined whether the group 20 satisfies the optimization calculation end condition according to such a condition.

For determining whether or not each design constraint condition is satisfied, a constraint function Gi (x) defined by the following equation (1) is used.
Gi (x) = Ri (x) / Ci-1 (1)
Here, Ci is an allowable value of each constraint condition (for example, an interlayer deformation angle etc.) distinguished by the subscript i. The allowable value Ci is determined in advance in the design constraint condition input step S2.

  In the chromosome information 21 of the subscript x, when the constraint function Gi (x) is 0 or less, the result value Ri (x) is the allowable value Ci or less, so the constraint condition of the subscript i (for example, the interlayer deformation angle, etc.) It shows that it meets. On the other hand, when the constraint function Gi (x) is larger than 0, it indicates that the constraint condition of the subscript i is not satisfied.

  Whether or not the manufacturing cost condition is satisfied is determined based on the volume Fj (x) calculated for each structural member 6 (in this embodiment, the bearing wall 5, the large beam 7, and the small beam 8) in each chromosome information 21. Judgment based on. In the present embodiment, when the total value of the volumes Fj (x) of the structural members 6 is equal to or less than a predetermined target value, it is determined that the chromosome information 21 satisfies the manufacturing cost condition. In addition, about a target value, it can set suitably according to the building B to be designed.

  In the determination step S53, when it is determined that an optimal solution exists among all the chromosome information 21 constituting the group 20 (“Y” in the determination step S53), a series of processes in the optimization calculation step S5 is completed. Next step S6 is executed. On the other hand, when it is determined that there is no optimal solution ("N" in the determination step S53), genetic operations such as crossover and mutation are performed on at least a part of the chromosome information 21, and the chromosome information 21 is restored. The gene manipulation step S54 to be configured is performed. And based on the chromosome information 21 reconfigure | reconstructed by gene operation, process S51-judgment process S53 are performed again. Thereby, in the optimization calculation process S5, an optimal solution can be obtained reliably.

  In step S6, the frame 2 and the building B are manufactured based on the chromosome information 21S having the highest degree of optimization in the group 20 of the last generation. Thereby, in the design method of this embodiment, the frame 2 and the building with the lowest manufacturing cost can be easily manufactured while ensuring a predetermined strength. In the present embodiment, the chromosome information 21S having the highest degree of optimization is, for example, that all constraint functions Gi (x) are 0 or less and the total value of the volumes Fj (x) of the respective structural members 6 Is not more than the target value and the total value of the volume Fj (x) of each structural member 6 is determined to be the smallest chromosome information 21, but is not limited thereto.

FIG. 18 is a flowchart showing an example of the processing procedure of the gene manipulation step S54 of the present embodiment. In the genetic manipulation step S54 of the present embodiment, first, the penalty P of each chromosome information 21 is calculated (step S541). The penalty P is the largest constraint function (hereinafter, simply referred to as “maximum constraint function”) Gmax (x) among the constraint functions Gi (x) of each chromosome information 21 (that is, the constraint P Only when the condition is not satisfied) is calculated using the following equation (2). The maximum constraint function Gmax (x) has the lowest degree of satisfaction with respect to the allowable value Ci of the constraint condition in each chromosome information 21. Note that a penalty P is not calculated for excellent chromosome information 21 in which the maximum constraint function Gmax (x) is 0 or less.
P = Cp × Gmax (x) (2)
Here, Cp is a penalty coefficient. The penalty coefficient Cp can be set as appropriate.

  The penalty P of each chromosome information 21 calculated by the above formula (2) is added to the total value of the volumes Fj (x) of the respective structural members 6 of the chromosome information 21. Therefore, as the maximum constraint function Gmax (x) increases, the apparent manufacturing cost increases.

  Next, in the genetic manipulation step S54 of the present embodiment, the computer 1 determines that the plurality of chromosome information 21 belonging to the group 20 is in ascending order of the total value of the volumes Fj (x) of the structural members 6 to which the penalty P is added. Ranking is performed (step S542). FIG. 19 is a diagram showing the total value of the volumes Fj (x) of the structural members 6 after the penalty addition of each chromosome information 21.

  Next, the computer 1 classifies the group 20 into an elite group 35 and a non-elite group 36 (step S543). The elite group 35 is configured by chromosome information 21 having a relatively low rank among all the chromosome information 21 belonging to the group 20. On the other hand, the non-elite group 36 includes chromosome information 21 having a relatively higher rank than the chromosome information 21 of the elite group 35. The ratio of the elite group 35 can be set as appropriate. For example, it can be determined to be about 5% to 20% of all chromosome information 21 constituting the group 20.

  Next, in the gene manipulation step S54 of the present embodiment, a new population 20 of chromosome information 21 is generated (next generation population generation step S544). FIG. 20 is a flowchart illustrating an example of the processing procedure of the next generation group generation step S544 of the present embodiment.

  In the next generation population generation step S544, first, the chromosome information 21 of the elite group 35 is included in the new population 20 without being crossed or mutated (step S71). Thereby, it can suppress that the best solution of the chromosome information 21 which comprises the group 20 deteriorates. The chromosome information 21 of the elite group 35 is stored in the computer 1 as the chromosome information 21 constituting the new group 20.

  Next, in the next generation population generation step S544, a pair of chromosome information 21 and 21 for performing crossover is selected from the chromosome information 21 constituting the population 20 (step S72). In the present embodiment, a pair of chromosome information 21 and 21 is randomly selected from all the chromosome information 21 (including the elite group 35 and the non-elite group 36) constituting the group 20.

  Next, in the next generation population generation step S544, crossover is performed on the pair of chromosome information 21 and 21 (crossover step S73). In the crossover step S73, with respect to the pair of chromosome information 21 selected in the step S72, genes defining the arrangement or cross section of each structural member 6 are replaced, and new chromosome information 21 is generated. FIG. 21 is a flowchart showing an example of the processing procedure of the crossover step S73.

  In the crossover step S73, first, the pair of chromosome information 21 and 21 is classified into a plurality of groups 41 including genes to be exchanged (first classification step S731). In the first classification step S731, a group of genes having a relatively large degree of influence on the constraint condition is classified as a group 41. FIG. 22 is a diagram showing the groups classified in the first classification step.

  In the present embodiment, based on the constraint condition related to the strength of the load bearing wall 5 and the large beam 7 input in the design constraint condition input step S2 and the constraint condition related to the strength of the small beam 8, It is classified into two groups including a load-bearing element / beam group 42 including the large beam gene 30 and a beam group 43 including the small beam gene 33.

  Next, in the crossover step S73, the superiority or inferiority of each group 41 (in this embodiment, the strength element / large beam group 42 and the small beam group 43) is evaluated for the pair of chromosome information 21 and 21 (first evaluation). Step S732). Evaluation of superiority or inferiority of each group 41 is performed based on a constraint condition that has a large influence on each group 41. The superiority or inferiority of each group 41 is evaluated based on the constraint function Gi (x) calculated in the determination step S53. FIG. 23A is a diagram showing a selected pair of chromosome information 21 and 21. FIG.

  The constraint condition having a large influence on the load bearing element / beam group 42 is a constraint condition related to the strength of the bearing wall 5 and the beam 7. As described above, the constraint condition related to the strength of the bearing wall 5 and the large beam 7 includes the inter-layer deformation angle of the frame 2 or the proof stress (tensile, compression, bending, shearing, and Combination stress) and the like.

  In the present embodiment, among the constraint functions Gi (x) of the constraint conditions related to the strength of the load bearing element / beam group 42, the load bearing element / beam group is based on the largest constraint function (maximum constraint function) Gmax (x). 42 superiority or inferiority is evaluated. The maximum constraint function Gmax (x) has the lowest satisfaction with respect to the allowable value Ci of the constraint condition in each proof stress element / beam group 42. Therefore, the superiority or inferiority of the load bearing element / beam group 42 can be easily and reliably evaluated by such maximum constraint function Gmax (x).

  The constraint condition having a large influence on the beam group 43 is a constraint condition related to the strength of the beam 8. As described above, the constraint condition related to the strength of the beam 8 includes the proof stress (bending, shearing, combined stress) of each beam, or the deflection of each beam. In the present embodiment, in the same manner as the proof stress element / large beam group 42, the largest constraint function (maximum constraint function) Gmax (x) among the constraint functions Gi (x) of the constraint conditions related to the strength of the beam group 43 is set. Based on this, the superiority or inferiority of the beam group 43 is evaluated. FIG. 24 is a flowchart illustrating an example of a processing procedure of the first evaluation step S732 in the present embodiment.

  In the first evaluation step S732 of the present embodiment, first, one group classified in the first classification step S731 is selected (step S811). In the present embodiment, either the strength element / large beam group 42 or the small beam group 43 is selected. In this example, a case where the load bearing element / beam group 42 is selected will be described.

  Next, in the first evaluation step S732, the maximum constraint function Gmax (x) of the group 41 selected in step S811 for the pair of chromosome information 21 and 21 (in this example, the load-bearing element / beam group 42) is It is determined whether it is 0 or less (step S812).

  When it is determined in step S812 that the maximum constraint function Gmax (x) of the group 41 of the pair of chromosome information 21 and 21 is both 0 or less (“Y” in step S812), each chromosome information 21 and 21 is The tolerance value Ci of the constraint condition of the selected group 41 (in this example, the load-bearing element / beam group 42) is satisfied. In this case, in the present embodiment, the next first stage evaluation step S813 is performed.

  On the other hand, when it is determined that the maximum constraint function Gmax (x) of the group 41 of the pair of chromosome information 21 and 21 is not less than 0 (at least one is greater than 0) (“N” in step S812), at least one of them The group of chromosome information 21 (in this example, the load-bearing element / beam group 42) does not satisfy the allowable value Ci of the constraint condition. In this case, the next second stage evaluation step S814 is performed.

  FIG. 25 is a flowchart showing an example of the processing procedure of the first stage evaluation step S813. As a result of the determination in step S812, the chromosome information 21 and 21 satisfy both the allowable values Ci of the constraint conditions of the selected group 41 (in this example, the load-bearing element / beam group 42). Therefore, in order to bring the chromosome information 21 closer to the optimal solution (that is, to make the total value of the volumes Fj (x) of the respective structural members 6 equal to or less than the target value), the volume (manufacturing cost) Fj (x) of the structural members is set. It is important to make it smaller.

  In the first stage evaluation step S813 of the present embodiment, first, the total value of the volumes Fj (x) of the group 41 of one chromosome information 21 (in this example, the load-bearing element / beam group 42) is the chromosome information 21 of the other. It is determined whether or not the volume is smaller than the total value of the volumes Fj (x) of the group 41 (step S815). The smaller the total value of the volume Fj (x), the smaller the manufacturing cost of the bearing wall 5 and the large beam 7 is. Accordingly, when it is determined that the total value of the group 41 of the one chromosome information 21 is smaller than the total value of the group 41 of the other chromosome information 21 (“Y” in step S815), The group 41 is evaluated as being excellent, and the group 41 of the other chromosome information 21 is evaluated as being inferior (step S816).

  When it is determined that the total value of the volume Fj (x) of the group 41 of one chromosome information 21 and the total value of the volume Fj (x) of the group 41 of the other chromosome information 21 are the same (in step S815) , “N”) and the total value of the volume Fj (x), the superiority or inferiority of the group 41 of the pair of chromosome information 21 and 21 cannot be evaluated. Therefore, the superiority or inferiority of each group 41 is evaluated based on the magnitude of the maximum constraint function Gmax (x) of the group 41 of the pair of chromosome information 21 and 21 (step S817).

  In step S817, the maximum constraint function Gmax (x) of the group 41 of one chromosome information 21 (in this example, the load bearing element / beam group 42) is the maximum constraint function Gmax (x) of the group 41 of the other chromosome information 21. Or less is determined. When it is determined that the maximum constraint function Gmax (x) of the group 41 of one chromosome information 21 is smaller than the maximum constraint function Gmax (x) of the group 41 of the other chromosome information 21 (“Y” in step S817) ), The group 41 of one chromosome information 21 is evaluated as being excellent, and the group 41 of the other chromosome information 21 is evaluated as being poor (step S818).

  When it is determined that the maximum constraint function Gmax (x) of the group 41 of one chromosome information 21 is the same as the maximum constraint function Gmax (x) of the group 41 of the other chromosome information 21 (in step S817). , “N”), the superiority or inferiority of the group 41 cannot be evaluated in the pair of chromosome information 21 and 21. Therefore, it is determined that there is no excellent group 41 in the pair of chromosome information 21 and 21 (step S819).

  FIG. 26 is a flowchart illustrating an example of a processing procedure of the second stage evaluation process S814. As a result of the determination in step S812, at least one group of chromosome information 21 (in this example, the strength element / large beam group 42) does not satisfy the allowable value Ci of the constraint condition. Therefore, the maximum constraint function Gmax (x) of the group 41 of one chromosome information 21 (in this example, the load-bearing element / beam group 42) is greater than the maximum constraint function Gmax (x) of the group 41 of the other chromosome information 21. Is also smaller (step S820).

  When it is determined that the maximum constraint function Gmax (x) of the group 41 of one chromosome information 21 is smaller than the maximum constraint function Gmax (x) of the group 41 of the other chromosome information 21 ("Y" in step S820) ), The group 41 of one chromosome information 21 is evaluated as excellent, and the group 41 of the other chromosome information 21 is evaluated as poor (step S821).

  Further, the maximum constraint function Gmax (x) of the group 41 of one chromosome information 21 (in this example, the load-bearing element / beam group 42) and the maximum constraint function Gmax (x) of the group 41 of the other chromosome information 21 are obtained. When it is determined that they are the same ("N" in step S820), superiority or inferiority cannot be evaluated with the maximum constraint function Gmax (x) of the group 41. For this reason, the superiority or inferiority of each group 41 is evaluated based on the total value of the volumes Fj (x) of the respective structural members 6 defined in each group 41 (step S822).

  In step S822, the total value of the volumes Fj (x) of the group 41 of one chromosome information 21 (in this example, the load-bearing element / beam group 42) is the volume Fj (x) of the group 41 of the other chromosome information 21. When it is determined that the value is smaller than the total value (“Y” in step S822), it is evaluated that the group 41 of one chromosome information 21 is excellent and the group 41 of the other chromosome information 21 is poor. Is evaluated (step S823).

  When it is determined that the total value of the volume Fj (x) of the group 41 of one chromosome information 21 and the total value of the volume Fj (x) of the group 41 of the other chromosome information 21 are the same (in step S822) , “N”), the superiority of the group 41 cannot be evaluated. Therefore, it is determined that there is no excellent group 41 in the pair of chromosome information 21 and 21 (step S824).

  Next, in the first evaluation step S732, the superiority or inferiority of all the groups 41 (strength element / large beam group 42 and small beam group 43) classified in the first classification step S731 is evaluated for the pair of chromosome information 21 and 21. It is determined whether or not (step S825). When it is determined that the superiority or inferiority of all the groups 41 has been evaluated (“Y” in step S825), the series of processes in the first evaluation step S732 is completed. On the other hand, when it is determined that the superiority or inferiority of all the groups 41 has not been evaluated (“N” in step S825), a new group 41 (for example, the beam group 43) is selected (step S826), and step S812. Step S825 is performed again. Thereby, in 1st evaluation process S732, the superiority or inferiority of all the groups 41 (strength element / large beam group 42 and small beam group 43) can be determined for the pair of chromosome information 21 and 21. The determination of superiority or inferiority of the small beam group 43 can be performed by the same procedure as the determination of superiority or inferiority of the load bearing element / large beam group 42 shown in this example.

  Next, in the crossover step S73, it is determined whether or not the excellent group 41 is included in each of the pair of chromosome information 21 and 21 (first determination step S733). In FIG. 23A, the group 41 determined to be excellent is shown in color. As shown in FIG. 23A, in the first determination step S733, when it is determined that each of the pair of chromosome information 21 and 21 includes an excellent group 41 (in the first determination step S733, “ Y "), the next generation step S734 is performed. On the other hand, in the first determination step S733, when it is determined that only one of the pair of chromosome information 21 and 21 includes the excellent group 41 (“N” in the first determination step S733), the pair of chromosome information Even if the groups 41 included in 21 and 21 are combined (crossed), the chromosome information 21 cannot be evolved. In the present embodiment, the second classification step S735 is performed prior to the generation step S734.

  FIG. 23B is a diagram for explaining the generation step S734. In the generation step S734, new chromosome information evolved by crossing (combining) excellent groups (groups colored in FIG. 23 (a)) 41 included in each of the pair of chromosome information 21 and 21. 21 g can be produced. The newly generated chromosome information 21g is stored in the computer 1 as the chromosome information 21 constituting the new group 20 (shown in FIG. 14). The deteriorated chromosome information 21w combined with the inferior group (the group not colored in FIG. 23A) 41 is discarded without being added to the new group 20. Thereby, since the new group 20 is comprised only by the evolved chromosome information 21g, the arrangement | positioning of the structural member 6 or the optimal solution of a cross section can be calculated | required in a short time.

  Thus, unlike the conventional method in which the groups 41 and 41 are randomly replaced, the design method of the present embodiment can generate new chromosome information 21 by combining excellent groups 41. Therefore, since the chromosome information 21 can be reliably evolved in the design method of the present embodiment, the optimal solution for the arrangement or cross section of the structural member 6 can be obtained in a short time.

  Further, in this embodiment, a group of genes 28, 30 or 33 having a relatively large influence on the constraint condition is classified as a group 41, and these excellent groups 41 are combined to generate new chromosome information 21. Is done. Therefore, the new chromosome information 21 is directly inherited without destroying the combination of the genes 28, 30 or 33 constituting the excellent group 41. Therefore, the chromosome information 21 can be efficiently evolved.

  In the present embodiment, since the load bearing element / beam group 42, which is a combination of the bearing wall 5 and the large beam 7 having a common influence on the constraint condition, is directly inherited by the new chromosome information 21, the chromosome information 21 is further efficiently evolved. Can be made.

  In the second classification step S735, the pair of chromosome information 21 and 21 is classified into a plurality of groups subdivided from the group 41 in the first classification step S731. In this embodiment, the strength element / large beam group 42 and the small beam group 43 are subdivided.

  FIG. 27 is a diagram showing a group obtained by subdividing the bearing element / large beam group 42. In the proof stress element / girder group 42, the proof stress element gene 28 and the girder gene 30 are subdivided according to, for example, the direction of the vertical frame surface 10 and the rank of the frame body 2. The strength element / large beam group 42 of the present embodiment is subdivided into a first group 42a to a fourth group 42d.

  The first group 42a includes the load-bearing element genes 28 defined in the vertical frame planes 11A to 11F (shown in FIG. 7) in the X-axis direction on the first floor. The second group 42b includes the load-bearing element genes 28 defined on the second floor vertical frame planes 11G to 11L (shown in FIG. 7), and the first floor and second floor vertical frame planes 11A. ˜11L (shown in FIG. 7), which includes a girder gene 30 defined as girder 7A-7L respectively arranged on the upper side. The first group 42a and the second group 42b are defined by the bearing walls 5 or the large beams 7 (shown in FIG. 2) directly arranged on the vertical frame surfaces 11A to 11L (shown in FIG. 7) in the X-axis direction. Therefore, the degree of influence on the constraint condition is relatively large.

  The second group 42b includes the large beam gene 30 defined as the large beams 7A to 7F arranged on the upper sides of the first floor vertical frame surfaces 11A to 11F (shown in FIG. 7) in the X-axis direction. As a result of the inventors' diligent research, the girder 7 (shown in FIG. 2) has a higher strength than the load-bearing wall 5 (shown in FIG. 2) disposed below the beam-bearing wall 5 (shown in FIG. 2). This is due to the fact that the degree of influence on the constraint conditions is greater. Thereby, the superiority or inferiority of the first group 42a and the second group 42b can be accurately evaluated. In addition, since the top floor of the frame body 2 of the present embodiment is the second floor, the second group 42b is defined as the upper side of the vertical frame surfaces 11G to 11L (shown in FIG. 7) in the X-axis direction of the second floor. The large beam gene 30 is included.

  Similar to the first group 42a and the second group 42b, the third group 42c includes the strength element genes 28 defined on the vertical frame surfaces 12A to 12F (shown in FIG. 7) in the Y-axis direction on the first floor. . The fourth group 42d includes the load-bearing element genes 28 defined on the vertical framing planes 12G to 12L (shown in FIG. 7) on the second floor in the Y-axis direction, and the vertical framing plane 12A on the first and second floors in the Y-axis direction. ˜12L (shown in FIG. 7) includes a girder gene 30 defined as girder 7M-7X arranged on the upper side, respectively. The third group 42c and the fourth group 42d are defined by the bearing walls 5 or the large beams 7 (shown in FIG. 2) directly arranged on the vertical frame surfaces 12A to 12L (shown in FIG. 7) in the Y-axis direction. Therefore, the degree of influence on the constraint condition is relatively large.

  FIG. 28 is a diagram showing a group obtained by subdividing the beam group 43. In the beam group 43, the beam beam 33 is subdivided according to the rank of the frame 2. The beam group 43 of the present embodiment includes a first-floor beam group 43a including the respective beam beams 33 on the first-floor horizontal frame planes 9a to 9d (shown in FIG. 6) and the second-floor horizontal frame surfaces 9e to 9h. And a second-floor beam group 43b including each beam beam gene 33 (shown in FIG. 6). Since the first-floor beam group 43a and the second-floor beam group 43b define the beam 8 (shown in FIG. 2) on each floor, the degree of influence on the constraint condition is relatively large.

  Next, in the crossover step S73, the superiority or inferiority of each of the subdivided groups 42a to 42d, 43a and 43b is evaluated for the pair of chromosome information 21 and 21 (second evaluation step S736). In the second evaluation step S736 of this embodiment, the superiority or inferiority of each of the groups 42a to 42d, 43a, and 43b is evaluated based on at least a constraint condition that has a large influence on each of the groups 42a to 42d, 43a, and 43b. In the present embodiment, the superiority or inferiority of each group 42a to 42d, 43a and 43b is evaluated based on the constraint condition and the total value of the volumes Fj of the groups 42a to 42d, 43a and 43b.

  The constraint condition having a large influence on the first group 42 a to the fourth group 42 d is a constraint condition related to the strength of the load bearing wall 5 and the large beam 7, similarly to the strength element / large beam group 42. The constraint conditions are limited to those related to the structural members 6 (bearing walls 5 and large beams 7) defined by the groups 42a to 42d. For example, since the first group 42a defines the bearing walls 5 of the vertical frame surfaces 11A to 11F (shown in FIG. 7) in the X-axis direction on the first floor, the constraint condition of the first group 42a is the first floor X It is limited to the yield strength (tensile, compression, bending, shearing, combined stress) of the pillar 5a of each bearing wall 5 in the axial direction, the yield strength (tensile, compression) of the diagonal member 5b, and the like. In the present embodiment, for each group 42a to 42d, the largest constraint function (maximum constraint function) Gmax (x) among the constraint functions Gi (x) of the constraint conditions related to the strength of the bearing wall 5 and the large beam 7 is used. Based on the above, superiority or inferiority is evaluated.

  The constraint condition having a large influence on the first-floor beam group 43a and the second-floor beam group 43b is a constraint condition related to the strength of the beam 8 like the beam group 43. The constraint conditions are limited to those related to the structural member 6 (the small beam 8) defined by the groups 43a and 43b. For example, since the first-floor beam group 43a defines the first beam of horizontal frames 9a to 9d (shown in FIG. 6) on the first floor, the constraint condition of the first-floor beam group 43a is each of the first-floor beam groups 43a. It is limited to the yield strength (bending, shearing, combined stress), etc. of the beam 8. In the present embodiment, for each of the groups 43a and 43b, the constraint function Gi (x) of the constraint condition related to the strength of the beam 8 is based on the largest constraint function (maximum constraint function) Gmax (x). , Dominance is evaluated. FIG. 29 is a flowchart illustrating an example of a processing procedure of the second evaluation step S736 of the present embodiment.

  In the second evaluation step S736 of the present embodiment, first, one group subdivided in the second classification step S735 is selected (step S831). In step S831 of the present embodiment, first, among the first group 42a to the fourth group 42d, the first floor beam group 43a, or the second floor beam group 43b shown in FIGS. Group 42a is selected. In this example, a case where the first group 42a is selected will be described.

  Next, in the second evaluation step S736, for each chromosome information 21, 21, the maximum constraint function Gmax (x) of the group 41 (in this example, the first group 42a) selected in step S831 is both 0 or less. It is determined whether or not there is (step S832).

  If it is determined in step S832 that the maximum constraint functions Gmax (x) are both 0 or less (“Y” in step S832), the selected group (in this example, the first Both of the allowable values Ci for the constraints of the one group 42a) are satisfied. In this case, the next third stage evaluation step S833 is performed.

  On the other hand, when it is determined that both of the maximum constraint functions Gmax (x) are not less than 0 (at least one is greater than 0) (“N” in step S832), at least one group of chromosome information 21 (in this example, The first group 42a) does not satisfy the allowable value Ci for the constraint condition. In this case, the next fourth stage evaluation step S834 is performed.

  FIG. 30 is a flowchart showing an example of the processing procedure of the third stage evaluation step S833 of the present embodiment. As a result of the determination in step S832, both the allowable values Ci for the constraint conditions of the selected group (in this example, the first group 42a) are satisfied. For this reason, in order to bring the chromosome information 21 closer to the optimal solution, it is important to reduce the volume (manufacturing cost) Fj (x) of the structural member.

  In the third stage evaluation step S833 of the present embodiment, first, the total value of the volumes Fj (x) of the group of one chromosome information 21 (in this example, the first group 42a) is the group of the other chromosome information 21 ( In this example, it is determined whether or not it is smaller than the total value of the volumes Fj (x) of the first group 42a) (step S835). When it is determined that the total value of the group of one chromosome information 21 is smaller than the total value of the group of the other chromosome information 21 (“Y” in step S835), the group of one chromosome information 21 is excellent. It is evaluated that there is one, and the other group of chromosome information 21 is evaluated to be inferior (step S836).

  Further, the total value of the volumes Fj (x) of one chromosome information 21 group (in this example, the first group 42a) and the volume Fj of the other chromosome information 21 group (in this example, the first group 42a). When it is determined that the total value of (x) is the same (“N” in step S835), the total value of the volume Fj (x) cannot evaluate the superiority or inferiority of the pair of chromosome information 21 and 21 groups. . Therefore, the superiority or inferiority of each group is evaluated based on the magnitude of the maximum constraint function Gmax (x) (step S837).

  In step S837, the maximum constraint function Gmax (x) of one chromosome information 21 group (first group 42a in this example) is the maximum of the other chromosome information 21 group (first group 42a in this example). It is determined whether or not it is smaller than the constraint function Gmax (x). When it is determined that the maximum constraint function Gmax (x) of one chromosome information group 21 is smaller than the maximum constraint function Gmax (x) of the other chromosome information group 21 ("Y" in step S837), One group of chromosome information 21 is evaluated as excellent, and the other group of chromosome information 21 is evaluated as inferior (step S838).

  Further, the maximum constraint function Gmax (x) of one chromosome information 21 group (in this example, the first group 42a) and the maximum constraint function of the other chromosome information 21 group (in this example, the first group 42a). When it is determined that Gmax (x) is the same (“N” in step S837), the superiority or inferiority of the group of the pair of chromosome information 21 and 21 cannot be evaluated. Therefore, it is determined that there is no excellent group in the pair of chromosome information 21 and 21 (step S839).

  FIG. 31 is a flowchart showing an example of the processing procedure of the fourth stage evaluation step S834. According to the determination in step S832, at least one group of chromosome information 21 (in this example, the first group 42a) does not satisfy the allowable value Ci of the constraint condition. Therefore, in the fourth stage evaluation step S834 of the present embodiment, first, the maximum constraint function Gmax (x) of one chromosome information 21 group is more than the maximum constraint function Gmax (x) of the other chromosome information 21 group. Is also smaller (step S840).

  The maximum constraint function Gmax (x) of one group of chromosome information 21 (in this example, the first group 42a) is the maximum constraint function Gmax (xmax) of the other group of chromosome information 21 (in this example, the first group 42a). x) is judged to be smaller (“Y” in step S840), one group of chromosome information 21 is evaluated as excellent, and the other group of chromosome information 21 is evaluated as poor. (Step S841).

  Further, the maximum constraint function Gmax (x) of one chromosome information 21 group (in this example, the first group 42a) and the maximum constraint function of the other chromosome information 21 group (in this example, the first group 42a). When it is determined that Gmax (x) is the same ("N" in step S840), the maximum constraint function Gmax (x) of the group 41 cannot evaluate the superiority or inferiority of the group of the pair of chromosome information 21 and 21. . Therefore, the superiority or inferiority of each group 41 is evaluated based on the total value of the volumes Fj (x) of the respective structural members 6 defined in each group (in this example, the first group 42a) (step S842).

  In step S842, as in step S835 of the third stage evaluation step S833, the total value of the volumes Fj (x) of the group of one chromosome information 21 (in this example, the first group 42a) is the chromosome information 21 of the other stage. Group (in this example, the first group 42a) is determined to be smaller than the total value of the volume Fj (x) (“Y” in step S842), one of the groups of chromosome information 21 is excellent. And the other group of chromosome information 21 is evaluated to be inferior (step S843).

  The total value of the volumes Fj (x) of the group of one chromosome information 21 (in this example, the first group 42a) and the volume Fj (x of the group of the other chromosome information 21 (in this example, the first group 42a) ) Is determined to be the same (“N” in step S842), the superiority or inferiority of the group of the pair of chromosome information 21 and 21 cannot be evaluated. Therefore, it is determined that there is no excellent group 41 in both the pair of chromosome information 21 and 21 (step S844).

  Next, the second evaluation step S736 is performed for all the subdivided groups (in this example, the first group 42a to the fourth group 42d, the first-floor beam group 43a, and the second-floor beam group 43b). It is determined whether or not superiority or inferiority has been evaluated (step S845). When it is determined that the superiority or inferiority of all the groups has been evaluated (“Y” in step S845), the series of processes in the second evaluation step S736 ends. On the other hand, when it is determined that the superiority or inferiority of all the groups has not been evaluated (“N” in step S845), a new subdivided group (for example, the second group 42b to the fourth group 42d, the first floor small) The beam group 43a or the second-floor small beam group 43b) is selected (step S846), and the steps S831 to S845 are performed again. Thereby, in 2nd evaluation process S736, all the groups 41 (1st group 42a-4th group 42d, 1st floor beam group 43a, which were classified by 2nd classification process S735 about a pair of chromosome information 21 and 21, And the superiority or inferiority of the second-floor beam group 43b) can be determined.

  Next, in the crossover step S73, an excellent group (in this example, the first group 42a to the fourth group 42d, the first floor beam group 43a) is included in the pair of chromosome information 21 and 21 (shown in FIG. 23A). Or the second-floor beam group 43b) is determined (second determination step S737). In the second determination step S737, when it is determined that an excellent group is included in each of the pair of chromosome information 21 and 21 (“Y” in the second determination step S737), the generation step S734 described above is performed. The In the generation step S734, the first group 42a to the fourth group 42d, the first-floor beam group 43a, and the second-floor beam group 43b out of the pair of chromosome information 21 and 21 are combined and evolved. New chromosome information 21g can be generated. In addition, when it is determined that there is no excellent group among some of the first group 42a to the fourth group 42d, the first-floor beam group 43a, and the second-floor beam group 43b, about the group Cannot be replaced.

  In this way, in the group 41 classified in the first classification step S731, even though the excellent group 41 is not included in each of the pair of chromosome information 21 and 21, the group 41 is further subdivided, so It becomes easier to judge superiority. Thereby, since the chromosome information 21 can be reliably evolved, the arrangement | positioning of the structural member 6 or the optimal solution of a cross section can be calculated | required in a short time.

  In addition, since the combination of genes constituting the excellent group 41 is not destroyed, it is inherited as it is to the new chromosome information 21, so that the chromosome information 21 can be efficiently evolved. In addition, since the first group 42a to the fourth group 42d, the first-floor beam group 43a, and the second-floor beam group 43b that have a relatively large influence on the constraint conditions are inherited as they are by the new chromosome information 21. The chromosome information 21 can be further efficiently evolved.

  In the second determination step S737, when it is determined that only one of the pair of chromosome information 21 and 21 includes the excellent group 41 (“N” in the second determination step S737), the pair of chromosome information 21 and Even if the groups 41 included in the group 21 are combined, the chromosome information 21 cannot be evolved. For this reason, in this embodiment, crossover or mutation performed by the computer 1 at random (randomly) is performed on the pair of chromosome information 21 and 21 (step S738).

  The crossover may be, for example, a single point crossover or a multipoint crossover as in the past, or may be carried out by combining these. One-point crossover and multipoint crossover include, for example, those in which genes are replaced based on a crossover position fixed in advance or a crossover position selected at random. For the mutation, first, the locus 22 of the chromosome information 21 is randomly selected. Then, all the genes that can be stored in the selected gene locus 22 (for example, alleles included in the allele group) are replaced with a randomly selected gene. In such a mutation, unlike crossover, the chromosome information 21 is reconstructed with a new gene without being limited to the genes preset in the chromosome information 21. Therefore, the mutation can prevent falling into a local optimal solution.

  In step S738, even if an excellent group does not exist in the pair of chromosome information 21 and 21, new chromosome information 21 can be reconstructed by random crossover or mutation performed by the computer 1. Information 21 can be effectively evolved. The newly generated chromosome information 21g is stored in the computer 1 as the chromosome information 21 constituting the new group 20 (shown in FIG. 14).

  In the step S738 of the present embodiment, when it is determined in the second determination step S737 that only one of the pair of chromosome information 21 and 21 includes the excellent group 41 (“N” in the second determination step S737). However, the present invention is not limited to this. For example, in the first determination step S733, when it is determined that only one of the pair of chromosome information 21 and 21 includes the excellent group 41 (“N” in the first determination step S733), the second classification The step S738 may be performed without performing the step S735 to the second determination step S737. Thereby, after the first determination step S733, since the crossover or mutation performed randomly by the computer 1 is performed from an early stage, it is possible to prevent falling into a local optimum solution.

  In the crossover step S73 of the present embodiment, when it is determined in the second determination step S737 that only one of the pair of chromosome information 21 and 21 includes the excellent group 41 (in the second determination step S737, “N”) exemplifies a mode in which crossover or mutation performed randomly by the computer 1 is performed, but is not limited thereto. For example, a third classification step (not shown) for classifying each of the plurality of groups further subdivided than the group of the second classification step S735 may be performed. In this case, a third evaluation step (not shown) for evaluating the superiority or inferiority of each further subdivided group, and a first determination of whether or not the excellent group 41 is included in each of the pair of chromosome information 21 and 21. It is desirable that three determination steps (not shown) are performed. Such subdivision of the group makes it easier to judge an excellent group, so that an optimal solution for the arrangement or cross section of the structural member 6 can be obtained in a short time.

  Next, in the next generation population generation step S544, it is determined whether or not crossover has been performed for all chromosome information 21 to be crossed (step S74). The chromosome information 21 to be crossed is appropriately selected. In the present embodiment, the number obtained by subtracting the number of chromosome information 21 constituting the elite group 35 and the number of chromosome information 21 generated in the next mutation step S76 from the number of chromosome information 21 constituting the group 20. Crossover is performed until the minute chromosome information 21 is generated.

  When it is determined that crossover has been performed for all chromosome information 21 to be crossed (“Y” in step S74), steps S75 to S77 for performing the next mutation are performed. On the other hand, when it is determined that crossover has not been performed for all chromosome information 21 to be crossed (“N” in step S74), step S72 and crossover step S73 are performed again. Thus, since all the chromosome information 21 to be crossed is crossed, the group 20 can be constituted by the plurality of evolved chromosome information 21.

  Next, in the next generation population generation step S544, the chromosome information 21 to be mutated is selected from the chromosome information 21 constituting the population 20 (step S75). In the present embodiment, one chromosome information 21 is randomly selected from all the chromosome information 21 (including the elite group 35 and the non-elite group 36) constituting the group 20.

  Next, in the next generation population generation step S544, mutation is performed on the selected chromosome information 21 (mutation step S76). The mutated new chromosome information 21 is stored in the computer 1 as the chromosome information 21 constituting the new group 20 (shown in FIG. 14).

  Next, in the next generation population generation step S544, it is determined whether or not mutation has been performed for all chromosome information 21 to be mutated (step S77). When it is determined that mutation has been performed for all the chromosome information 21 to be mutated (“Y” in step S77), the series of processes in the next generation population generation step S544 ends. On the other hand, when it is determined that mutation has not been performed for all the chromosome information 21 to be mutated (“N” in step S77), step S75 and mutation step S76 are performed again. Thereby, since mutation is performed for all chromosome information 21 to be mutated, a group 20 can be constituted by a plurality of evolved chromosome information 21.

  As mentioned above, although especially preferable embodiment of this invention was explained in full detail, this invention is not limited to embodiment of illustration, It can deform | transform and implement in a various aspect.

[Example A]
In accordance with the processing procedure shown in FIG. 4, a two-story frame structure (shown in FIG. 6) with the bearing walls and small beams omitted is set in the computer, and the arrangement of the bearing walls and the small beams and the cross section of the large beam are optimized. Solutions were determined (Example 1, Comparative Example 1, Comparative Example 2).

  In the crossover process of Example 1, a pair of chromosome information was classified into a plurality of groups according to the procedures shown in FIGS. 21, 24 to 26, and 29 to 31. The superiority or inferiority of each group was evaluated, and excellent chromosomes were combined to generate new chromosome information. In the crossover process of Comparative Example 1, random multipoint crossover was performed. In the crossover process of Comparative Example 2, one-point crossover was performed.

For Example 1, Comparative Example 1 and Comparative Example 2, the simulation as described above was performed 10 times each. And the objective function (total value of the volume of each structural member after penalty addition) of the chromosome information (for 10 pieces) with the highest degree of optimization was obtained for each generation, and the average value thereof was calculated. The index of the average value of Example 1, Comparative Example 1 and Comparative Example 2 was determined with the average value of the 0th generation objective function being 100. The types of bearing walls, large beams, and small beams are as described in the specification, and the common specifications are as follows.
Number of chromosome information constituting the group: 50
Number of chromosome information in the elite group: 1
Number of chromosome information generated by crossover: 35
Number of chromosome information generated by mutation: 14

  The test results are shown in FIG. In FIG. 32, test results from the 0th generation to the 100th generation are extracted and displayed. As a result of the test, compared with Comparative Example 1 and Comparative Example 2, Example 1 was able to reduce the objective function (total value of the volume of each structural member after penalty addition) at an early stage. Therefore, in Example 1, it was possible to obtain the optimum solution for the arrangement of the bearing walls and the small beams and the cross section of the large beam in a short time.

[Example B]
In accordance with the processing procedure shown in FIG. 4, a three-story frame structure (not shown) with the bearing walls and small beams omitted is set in the computer, and the arrangement of the bearing walls and the small beams and the optimal solution for the cross section of the large beam are determined. It was determined (Example 2, Comparative Example 3, Comparative Example 4).

  In the crossover process of Example 2, as in the crossover process of Example 1, a pair of chromosome information is classified into a plurality of groups according to the procedures shown in FIGS. 21, 24 to 26, and FIGS. 29 to 31. It was done. The superiority or inferiority of each group was evaluated, and excellent chromosomes were combined to generate new chromosome information. In the crossover process of Comparative Example 3, random multipoint crossover was performed. In the crossover process of Comparative Example 4, one-point crossover was performed.

For Example 2, Comparative Example 3, and Comparative Example 4, the simulation as described above was performed 10 times each. And the objective function (total value of the volume of each structural member after penalty addition) of the chromosome information (for 10 pieces) with the highest degree of optimization was obtained for each generation, and the average value thereof was calculated. The index of the average value of Example 2, Comparative Example 3, and Comparative Example 4 was determined with the average value of the objective function of the 0th generation being 100. The types of bearing walls, large beams, and small beams are as described in the specification, and the common specifications are as follows.
Number of chromosome information constituting the group: 50
Number of chromosome information in the elite group: 1
Number of chromosome information generated by crossover: 35
Number of chromosome information generated by mutation: 14

  The test results are shown in FIG. In FIG. 33, test results from the 0th generation to the 100th generation are extracted and displayed. As a result of the test, compared with Comparative Example 3 and Comparative Example 4, Example 2 was able to reduce the objective function (total value of the volume of each structural member after penalty addition) earlier. Therefore, Example 2 was able to obtain the optimum solution for the arrangement of the bearing walls and the small beams and the cross section of the large beams even in the 3rd-floor frame.

2 Frame 6 Structural member 21 Chromosome information 41 group

Claims (9)

A method for designing, using a computer, a building frame including at least one structural member,
A step of generating a group consisting of a plurality of types of chromosome information, which is numerical information for specifying the arrangement or cross section of the structural member;
The computer includes an optimization calculation step of calculating an optimal solution of an arrangement or a cross section of the structural member based on a genetic algorithm using the population;
The optimization calculation step includes a crossover step of generating a new chromosome information by replacing a gene that defines an arrangement or a cross section of each structural member for the pair of chromosome information selected from the group,
The crossover step is a first classification step of classifying each of the pair of chromosome information into a plurality of groups including the gene to be replaced,
For each group of the pair of chromosome information , a first evaluation step for evaluating a group with a low manufacturing cost as excellent , and combining the excellent group included in each of the pair of chromosome information to generate the new chromosome information A structure design method including a generating step.
  The frame design method according to claim 1, wherein the first classification step classifies the group of genes having a relatively large influence on a constraint condition for evaluating superiority or inferiority of the chromosome information as the group.   The frame design method according to claim 2, wherein the first evaluation step evaluates superiority or inferiority of each group based on at least a constraint condition having a large influence on each group. The structural member is disposed on a plurality of large beams that horizontally connect between the columns, a plurality of small beams that further divide the horizontal frame surrounded by the beams, and a vertical frame that is surrounded by the large beams and the columns. Bearing strength element,
The group defines a load-bearing element / beam group including at least one load-bearing element gene that defines an arrangement of the load-bearing elements and at least one beam beam gene that defines a cross-section of the beam. The frame structure designing method according to any one of claims 1 to 3, comprising a beam group including at least one beam beam gene. The crossover step further includes a first determination step of determining whether or not each of the excellent groups is included in the pair of chromosome information prior to the generation step,
5. The frame structure according to claim 1, wherein in the first determination step, the generation step is performed when it is determined that each of the pair of chromosome information includes the excellent group. Design method. In the crossover step, in the first determination step, when it is determined that the excellent group is included only in one of the pair of chromosome information,
6. The frame design method according to claim 5, further comprising a step of performing crossover or mutation performed randomly by the computer with respect to the pair of chromosome information. In the crossover step, in the first determination step, when it is determined that the excellent group is included only in one of the pair of chromosome information,
A second classification step of classifying the pair of chromosome information into a plurality of groups subdivided from the group of the first classification step; and A second evaluation step for evaluating,
The frame structure designing method according to claim 5, wherein the second classification step and the second evaluation step are performed prior to the generation step. The crossover step further includes a second determination step of determining whether the excellent group is included in the pair of chromosome information after the second evaluation step and before the generation step,
The frame design method according to claim 7, wherein in the second determination step, the generation step is performed when it is determined that the excellent group is included in each of the pair of chromosome information. In the crossover step, when it is determined in the second determination step that only one of the pair of chromosome information includes the excellent group,
The frame design method according to claim 8, further comprising a step of performing crossover or mutation performed randomly by the computer on the pair of chromosome information.

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