Building structure estimation device and building structure estimation method

Abstract

To decide a suitable structural frame from a frame volume quantity of a building.SOLUTION: A building frame estimation device comprises a model storage part for storing a building frame estimation model constructed based on learning data having a plurality of assembly data including at least a joint part number vector inner product value, a frame volume quantity and a building frame of the building, wherein the building frame estimation model estimates the building frame of the building from the joint part number vector inner product value, which is an inner product value of a first joint part number vector which is a vector having factors as a number of each type of joint parts connecting a column and a beam of the building in a first direction parallel to a horizontal surface of the building, and a second joint part number vector which is a vector having factors as a number of each type of joint parts in a second direction parallel to the horizontal surface and different from the first direction, and a frame volume quantity of the building, and also comprises a building frame estimation part for estimating the building frame of the building from the building frame estimation model stored by the model storage part, a joint part number vector inner product value and the frame volume quantity of the building.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a building frame estimation device and a building frame estimation method. [Background technology]

[0002] Conventionally, in the early stages of structural design of a building, when determining the structural frame from the building volume, which is the volumetric capacity of the building's frame, the designer individually makes the decision while incorporating engineering judgment based on past experience and knowledge, past similar cases, etc. Note that the technology described in Patent Document 1 is known as a technology for designing building frames. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2016-110298 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, for example, if the designer is inexperienced, they may lack past experience and knowledge, which may result in reworking in the next design stage, resulting in inconsistent design accuracy for the structural frame, and making it difficult to determine an appropriate structural frame based on the building's structural volume.

[0005] The present invention has been made to solve the above-mentioned problems, and its object is to provide a building frame estimation device and a building frame estimation method that can determine an appropriate structural frame from the frame volume of a building. [Means for solving the problem]

[0006] In order to solve the above problem, one aspect of the present invention is to provide a joint number vector inner product value that is the inner product value of a first joint number vector, which is a vector whose elements are the numbers of each type of joint connecting a column and a beam of the building in a first direction parallel to a horizontal plane of the building, and a second joint number vector, which is a vector whose elements are the numbers of each type of the joint in a second direction parallel to the horizontal plane and different from the first direction, and a frame volume of the building. Basic design quantities to be considered in structural design from 、 Estimate the building frame of the building For Building frame estimation model of a model storage unit that stores the building frame estimation model, the joint number vector inner product value, and a frame volume of the building, , the basic design quantities and and a building frame estimation unit for estimating a building frame of the building from the The building frame estimation model is constructed based on learning data having a plurality of sets of data including at least the joint number vector inner product value, the frame volume, the basic design quantities, and the building frame of the building. The building frame estimation device is characterized by the above.

[0007] In one aspect of the present invention, a joint number vector inner product value is a value obtained by inner product of a first joint number vector, which is a vector having as its elements the number of each type of joint connecting a column and a beam of the building in a first direction parallel to a horizontal plane of the building, and the first joint number vector, which is a vector having as its elements the number of each type of the joint in a second direction parallel to the horizontal plane and different from the first direction; and a frame volume of the building. Basic design quantities to be considered in structural design from 、 Estimate the building frame of the building For Building frame estimation model of A model storage unit for storing the model , Building Frame Estimation Department and A building frame estimation method for a building frame estimation device comprising: The building frame estimation unit, The building frame estimation model stored in the model storage unit, the joint number vector inner product value, and the frame volume of the building. , the basic design quantities and A building frame estimation step of estimating a building frame of the building from the The building frame estimation model is constructed based on learning data having a plurality of sets of data including at least the joint number vector inner product value, the frame volume, the basic design quantities, and the building frame of the building. The building frame estimation method is characterized by the above. Effect of the Invention

[0008] According to the present invention, an appropriate structural frame can be determined based on the frame volume of a building. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is a block diagram showing an example of a building frame estimation device according to an embodiment of the present invention. [Diagram 2] 1 is a diagram illustrating an example of a building frame and a joint in this embodiment. FIG. [Diagram 3] 10A to 10C are diagrams illustrating an example of a process for constructing a building frame estimation model in this embodiment. [Figure 4] 10 is a flowchart showing an example of a building frame estimation model construction process of the building frame estimation device according to the present embodiment. [Diagram 5] 11 is a flowchart showing an example of a building frame estimation process of the building frame estimation device according to the present embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, a building frame estimation device and a building frame estimation method according to an embodiment of the present invention will be described with reference to the drawings.

[0011] FIG. 1 is a block diagram showing an example of a building frame estimation device 1 according to this embodiment. 1, the building frame estimation device 1 is, for example, a computer device such as a personal computer or a server device. The building frame estimation device 1 acquires, for example, a building frame volume and basic design quantities considered in structural design (for example, a standard floor area, an XY span length, and a floor height) as input data, estimates the building frame of the building based on the input data, and outputs the building frame. The building frame estimation device 1 includes an input unit 11, a display unit 12, a storage unit 13, and a control unit 14.

[0012] The input unit 11 is an input device such as a keyboard or a mouse, and receives various information such as input data and outputs it to the control unit . The display unit 12 is, for example, a display device such as a liquid crystal display, and displays various information such as the building frame of a building, which is output data (output information). The display unit 12 is an example of an output unit that outputs output information. When using software, the building frame estimation device 1 displays the building frame of a building on the display unit 12 using a three-dimensional modeling tool or the like.

[0013] The storage unit 13 stores various information used by the building frame estimation device 1. The storage unit 13 includes a learning data storage unit 131, a model storage unit 132, an input information storage unit 133, an inner product information storage unit 134, and a building frame storage unit 135.

[0014] The learning data storage unit 131 stores learning data for constructing a building frame estimation model described later. The learning data storage unit 131 stores learning data having a plurality of sets of data that associate, for example, the frame volume of a building, the area of ​​a reference floor of the building (reference floor area), the span length in the X direction, the span length in the Y direction, the floor height, a joint number vector inner product value described later, and the building frame. Here, the building frame and the joint number vector inner product value will be described.

[0015] FIG. 2 is a diagram illustrating an example of a building frame and a joint in this embodiment. Fig. 2(a) shows an example of a building frame. In Fig. 2(a), the directions parallel to the bottom surface (horizontal surface) of the standard floor of the building are the X direction and the Y direction, and the X direction and the Y direction are orthogonal or nearly orthogonal to each other.

[0016] As shown in Figure 2(a), the building frame is made up of columns and beams, and is divided into three sections: the top floor, the middle floor, and the bottom floor. FIG. 2(b) shows a cross section of another example of a building frame. As shown in Fig. 2(b), in the building frame, columns and beams are joined by four types of joints, including an L-shaped joint K1 (first joint), a T-shaped joint K2 (second joint), a T-shaped joint K3 (third joint), and a cross-shaped joint K4 (fourth joint).

[0017] The L-joint K1 is an L-shaped joint that connects a column and a beam at the corner of a building. The T-joint K2 is a joint that connects a column and a beam on the periphery of a building. The T-joint K3 is a T-shaped joint that connects a beam of a building to a column on either the top or bottom of the beam. The cross-shaped joint K4 is a cross-shaped joint that connects a column and a beam of a building in a cross shape.

[0018] In this embodiment, a vector whose elements are the numbers of L-shaped joints K1, T-shaped joints K2, T-shaped joints K3, and cross-shaped joints K4 in the X direction is defined as a joint number vector in the X direction (first joint number vector). Also, a vector whose elements are the numbers of L-shaped joints K1, T-shaped joints K2, T-shaped joints K3, and cross-shaped joints K4 in the Y direction is defined as a joint number vector in the Y direction (second joint number vector).

[0019] Furthermore, a vector (a junction number vector) having the numbers of the above-mentioned four types of junctions as elements is generated for each of the top floor, intermediate floors, and bottom floor. In this embodiment, the inner product value of the junction number vector in the X direction and the junction number vector in the Y direction is set as the junction number vector inner product value.

[0020] Here, the junction number vector UVx in the X direction, the junction number vector UVy in the Y direction, and the junction number vector inner product value UIP on the top floor are expressed by the following formula (1).

[0021]

number

[0022] Here, ULx, UTx, UWx, and UPx indicate the number of L-shaped joints K1, T-shaped joints K3, T-shaped joints K2, and cross-shaped joints K4 in the X direction of the top floor. Also, ULy, UTy, UWy, and UPy indicate the number of L-shaped joints K1, T-shaped joints K3, T-shaped joints K2, and cross-shaped joints K4 in the Y direction of the top floor. In addition, there are no T-shaped joints K2 and cross-shaped joints K4 on the top floor of the building, and the number thereof is "0".

[0023] Furthermore, the junction number vector MVx in the X direction, the junction number vector MVy in the Y direction, and the junction number vector inner product value MIP on the intermediate floor are expressed by the following formula (2).

[0024]

number

[0025] Here, MLx, MTx, MWx, and MPx indicate the number of L-shaped joints K1, T-shaped joints K3, T-shaped joints K2, and cross-shaped joints K4 in the X direction of the intermediate floor. Also, MLy, MTy, MWy, and MPy indicate the number of L-shaped joints K1, T-shaped joints K3, T-shaped joints K2, and cross-shaped joints K4 in the Y direction of the intermediate floor.

[0026] Moreover, the junction number vector BVx in the X direction, the junction number vector BVy in the Y direction, and the junction number vector inner product value BIP on the lowest floor are expressed by the following formula (3).

[0027]

number

[0028] Here, BLx, BTx, BWx, and BPx indicate the number of L-shaped joints K1, T-shaped joints K3, T-shaped joints K2, and cross-shaped joints K4 in the X direction of the lowest floor. Also, BLy, BTy, BWy, and BPy indicate the number of L-shaped joints K1, T-shaped joints K3, T-shaped joints K2, and cross-shaped joints K4 in the Y direction of the lowest floor. In addition, there are no T-shaped joints K2 and cross-shaped joints K4 on the lowest floor of the building, and the number thereof is "0".

[0029] Returning to the explanation of FIG. 1, the learning data includes the above-mentioned inner product values ​​of the joint number vector (the inner product value UIP of the top floor, the inner product value MIP of the intermediate floor, and the inner product value BIP of the bottom floor). In addition, the building frame included in the learning data may be generated from the area of ​​the standard floor of the building (standard floor area), the span length in the X direction, the span length in the Y direction, and the floor height, for example, using an existing three-dimensional modeling tool or the like, or may be previously designed. In other words, each set of data in the learning data is data generated using an existing three-dimensional modeling tool or the like according to various shapes, frame volumes, and the like, or data previously designed.

[0030] The model storage unit 132 stores a building frame estimation model that estimates the building frame of a building from the joint number vector inner product value, the building frame volume of the building, and basic design quantities considered in structural design. The building frame estimation model is a model constructed based on the learning data stored in the above-mentioned learning data storage unit 131. The building frame estimation model is a model that classifies the building frame into a plurality of groups by machine learning such as clustering using the frame volume and the joint number vector inner product value as feature quantities, and outputs labels (group identification information) indicating the classified groups from the frame volume and the joint number vector inner product value, the details of which will be described later.

[0031] The input information storage unit 133 stores various information (for example, standard floor area, span length in the X direction, span length in the Y direction, and floor height) input from outside when estimating the building frame.

[0032] The inner product information storage unit 134 stores the inner product values ​​of the joint number vector (the inner product value UIP of the top floor, the inner product value MIP of the intermediate floor, and the inner product value BIP of the bottom floor) used as input data when estimating the building frame. The inner product information storage unit 134 may store the joint number vector in the X direction and the joint number vector in the Y direction.

[0033] The building frame storage unit 135 is, for example, a database of building frames, and stores the above-mentioned labels (group identification information) in association with the building frames. The building frames stored in the building frame storage unit 135 are, for example, building frames used to construct a building frame estimation model, building frames of buildings designed in the past, etc. The building frame storage unit 135 may be configured to add building frames of buildings designed in the past as know-how whenever necessary.

[0034] The control unit 14 is a processor including, for example, a CPU (Central Processing Unit) and generally controls the building frame estimation device 1. The control unit 14 mainly executes a process of constructing a building frame estimation model and a process of estimating the building frame. The control unit 14 includes a model construction unit 141, a data acquisition unit 142, an inner product generation unit 143, a building frame estimation unit 144, and a display control unit 145.

[0035] The model construction unit 141 performs machine learning based on the learning data stored in the learning data storage unit 131 to construct a building frame estimation model. The model construction unit 141 stores the constructed building frame estimation model in the model storage unit 132. Here, the construction process of the building frame estimation model by the model construction unit 141 will be described with reference to Fig. 3.

[0036] FIG. 3 is a diagram illustrating an example of the process of constructing a building frame estimation model in this embodiment. 3(a) shows a multidimensional chart in which each group of learning data stored in the learning data storage unit 131 is classified by machine learning such as clustering using the frame volume and the inner product value of the joint number vector as feature values. In FIG. 3(a), the dashed line portion F1 corresponds to the frame volume and the inner product value of the joint number vector of the building.

[0037] 3(a), classifying the buildings into groups (G1, G2, G3, ...), and assigning a label to each group as group identification information. The model construction unit 141 classifies the building structures of a building into a plurality of groups by clustering processing using the structure volume and the joint number vector inner product value as feature quantities.

[0038] Next, the model construction unit 141 constructs an estimation model that estimates each group (group identification information) classified by clustering processing from the frame volume and joint number vector inner product value of the learning data, for example, by deep learning such as a neural network. The model construction unit 141 constructs, for example, a neural network NN1 as shown in Fig. 3(b) as a building frame estimation model by machine learning. The neural network NN1 estimates a group of building frames (a label to be assigned to the group) from the frame volume, basic design quantities considered in structural design, and the joint number vector inner product value. The model construction unit 141 stores the constructed building frame estimation model in the model storage unit 132.

[0039] Returning to the explanation of Fig. 1, the data acquisition unit 142 acquires input data (input information) for estimating the building frame. For example, the data acquisition unit 142 acquires the frame volume, the area of ​​the standard floor of the building (standard floor area), the span length in the X direction, the span length in the Y direction, and the floor height indicating the height of the floor of the building as input data via the input unit 11. The data acquisition unit 142 stores the acquired input data in the input information storage unit 133.

[0040] The inner product generating unit 143 generates a junction number vector inner product value based on the area of ​​the reference floor, the span length in the X direction, the span length in the Y direction, and the floor height acquired by the data acquiring unit. The inner product generating unit 143 calculates the number of each type of junction based on the input data stored in the input information storing unit 133, and generates the junction number vector inner product value (the inner product value UIP of the top floor, the inner product value MIP of the intermediate floors, and the inner product value BIP of the bottom floor) using the above-mentioned formulas (1) to (3). The inner product generating unit 143 stores the generated junction number vector inner product value (the inner product value UIP of the top floor, the inner product value MIP of the intermediate floors, and the inner product value BIP of the bottom floor) in the inner product information storing unit 134.

[0041] The building frame estimation unit 144 estimates the building frame of the building from the building frame estimation model stored in the model storage unit 132, the joint number vector inner product value, the building frame volume and basic design quantities considered in structural design. The building frame estimation unit 144 estimates the building frame of the building based on, for example, the inner product value of the top floor, the inner product value of the intermediate floor, and the inner product value of the bottom floor stored in the inner product information storage unit 134, and the building frame volume and basic design quantities considered in structural design stored in the input information storage unit 133.

[0042] Specifically, the building structure estimation unit 144 acquires group identification information based on the building structure estimation model, and estimates the building structure belonging to the group corresponding to the acquired label (group identification information) as the building structure. For example, the building structure estimation unit 144 acquires the building structure corresponding to the acquired label from the building structure storage unit 135, and outputs the acquired building structure as the building structure of the estimation result.

[0043] The display control unit 145 (an example of an output control unit) outputs output information indicating the building structure of the building estimated by the building structure estimation unit 144 to the display unit 12 (output unit). That is, the display control unit 145 causes, for example, a three-dimensional model or an image of the building structure to be displayed on the display unit 12. Note that the display control unit 145 causes the display unit 12 to display a plurality of building structures belonging to a group corresponding to the label (group identification information) estimated by the building structure estimation unit 144. In this case, the display control unit 145 may, for example, preferentially display on the display unit 12 a building structure close to the reference building structure (average value building structure) of the estimated group.

[0044] Next, the operation of the building frame estimation device 1 according to this embodiment will be described with reference to the drawings. FIG. 4 is a flowchart showing an example of a building structure estimation model construction process of the building structure estimation device 1 according to this embodiment.

[0045] 4, in the process of constructing a building frame estimation model, the building frame estimation device 1 first acquires learning data (step S101). The model construction unit 141 of the building frame estimation device 1 acquires the learning data stored in the learning data storage unit 131.

[0046] Next, the model construction unit 141 classifies the building structures by clustering processing or the like using the structure volume and the joint number vector inner product as features, and assigns labels (step S102). The model construction unit 141 executes clustering processing as shown in Fig. 3(a) to classify the buildings into groups (G1, G2, G3, ...), and assigns labels to each classified group as group identification information.

[0047] Next, the model construction unit 141 constructs an estimation model that estimates a label from the frame volume, basic design quantities, and the inner product value of the joint number vector (step S103). The model construction unit 141 constructs, by machine learning, for example, a neural network NN1 as shown in Fig. 3(b) as a building frame estimation model. The neural network NN1 uses basic design quantities considered in structural design as input values, and estimates (the label to be assigned to) the group of the building frame using the frame volume and the inner product value of the joint number vector as judgment criteria.

[0048] Next, the model construction unit 141 stores the constructed estimated model as a building frame estimation model in the model storage unit 132 (step S104). After the process of step S104, the model construction unit 141 ends the construction process of the building frame estimation model.

[0049] Next, the building frame estimation process of the building frame estimation device 1 according to this embodiment will be described with reference to FIG. FIG. 5 is a flowchart showing an example of the building frame estimation process of the building frame estimation device 1 according to this embodiment.

[0050] 5, in the building frame estimation process, the building frame estimation device 1 first acquires input data (step S201). The data acquisition unit 142 of the building frame estimation device 1 acquires, for example, the frame volume, the area of ​​the reference floor of the building, the span length in the X direction, the span length in the Y direction, and the floor height indicating the height of the floor of the building as input data via the input unit 11. The data acquisition unit 142 stores the acquired input data in the input information storage unit 133.

[0051] Next, the inner product generating unit 143 of the building frame estimation device 1 generates a joint number vector inner product value from the input data (step S202). The inner product generating unit 143 calculates the number of each type of joint based on the input data stored in the input information storage unit 133, and generates a joint number vector inner product value (the inner product value UIP of the top floor, the inner product value MIP of the intermediate floors, and the inner product value BIP of the bottom floor) using the above-mentioned formulas (1) to (3). The inner product generating unit 143 stores the generated joint number vector inner product value in the inner product information storage unit 134.

[0052] Next, the building frame estimation unit 144 of the building frame estimation device 1 estimates a label of a group from the building frame estimation model, the frame volume of the building, and the dot product value of the joint number vector, etc. (step S203). The dot product value of the joint number vector, etc., includes, for example, basic design quantities to be considered in structural design. The building frame estimation unit 144 estimates a label of a group from the dot product value of the top floor, the dot product value of the intermediate floor, and the dot product value of the bottom floor stored in the dot product information storage unit 134, and the frame volume of the building and basic design quantities to be considered in structural design stored in the input information storage unit 133, using the building frame estimation model (neural network NN1) stored in the model storage unit 132.

[0053] Next, the building frame estimation device 1 outputs the building frame of the group corresponding to the label (step S204). The building frame estimation unit 144 acquires the building frame corresponding to the estimated label from the building frame storage unit 135. The display control unit 145 of the building frame estimation device 1 causes, for example, a three-dimensional model or image of the acquired building frame to be displayed on the display unit 12. After the process of step S204, the building frame estimation device 1 ends the building frame estimation process.

[0054] As described above, the building frame estimation device 1 according to the present embodiment includes a model storage unit 132 and a building frame estimation unit 144. The model storage unit 132 stores a building frame estimation model that estimates the building frame of a building from the joint number vector inner product value and the frame volume of the building, and is constructed based on learning data having a plurality of sets of data including at least the joint number vector inner product value, the frame volume, and the building frame of the building. Here, the joint number vector inner product value is an inner product value of an X-direction joint number vector (first joint number vector) that is a vector having the number of each type of joint that connects the columns and beams of the building in the X-direction (first direction) parallel to the horizontal plane of the object as an element, and a Y-direction joint number vector (second joint number vector) that is a vector having the number of each type of joint in the Y-direction (second direction different from the first direction) that is parallel to the horizontal plane and perpendicular or nearly perpendicular to the X-direction as an element. The building frame estimation unit 144 estimates the building frame of the building from the building frame estimation model stored in the model storage unit 132, the joint number vector inner product value, and the frame volume of the building.

[0055] As a result, the building frame estimation device 1 according to the present embodiment estimates suitable building frame candidates from the building frame volume and the joint number vector inner product value, so that even if the designer has little experience, for example, it is possible to determine a suitable structural frame. In addition, the building frame estimation device 1 according to the present embodiment uses the inner product value of the joint number vector in the X direction and the joint number vector in the Y direction as a feature value, so that the feature value can be reduced and processing can be simplified compared to the case where the joint number vector is used as it is as a feature value. For example, each of the joint number vector inner product value UIP of the above-mentioned formula (1), the joint number vector inner product value MIP of the above-mentioned formula (2), and the joint number vector inner product value BIP of the above-mentioned formula (3) contracts a total of eight feature values ​​of the XY directions and four patterns of joints into a one-dimensional numerical value.

[0056] In this embodiment, the scalar product of the joint number vector includes the scalar product UIP of the top floor of the building, the scalar product MIP of the middle floor of the building, and the scalar product BIP of the bottom floor of the building. The building frame estimation unit 144 estimates the building frame of the building based on the scalar product UIP of the top floor, the scalar product MIP of the middle floor, and the scalar product BIP of the bottom floor, and the frame volume.

[0057] As a result, the building frame estimation device 1 of this embodiment can estimate a more appropriate building frame according to the shape by dividing the dot product value UIP of the top floor, the dot product value MIP of the middle floor, and the dot product value BIP of the bottom floor, since the types and numbers of joints on the top floor, middle floors, and bottom floor tend to differ depending on the shape of the building.

[0058] In this embodiment, the building frame estimation model classifies the building frames into a plurality of groups by clustering based on the frame volume and the inner product value of the joint number vector, and outputs a label (group identification information) indicating the classified group from the frame volume and the inner product value of the joint number vector. The building frame estimation unit 144 acquires the group identification information based on the building frame estimation model, and estimates the building frames belonging to the group corresponding to the acquired label (group identification information) as the building frames of the building.

[0059] As a result, the building frame estimation device 1 according to the present embodiment estimates groups classified by clustering, so that the user can select an appropriate building frame from among multiple candidates belonging to a group.

[0060] Moreover, the building frame estimation device 1 according to this embodiment includes a data acquisition unit 142, an inner product generation unit 143, and a display control unit 145 (output control unit). The data acquisition unit 142 acquires the frame volume, the area of ​​the reference floor of the building, the span length in the X direction, the span length in the Y direction, and the floor height indicating the height of the floor of the building. The inner product generation unit 143 generates an inner product value of the joint number vector based on the area of ​​the reference floor, the span length in the X direction, the span length in the Y direction, and the floor height acquired by the data acquisition unit 142. The display control unit 145 outputs output information indicating the building frame of the building estimated by the building frame estimation unit 144 to the display unit 12 (output unit).

[0061] As a result, the building frame estimation device 1 according to this embodiment calculates the joint number vector inner product value from the input data, which are the area of ​​the reference floor of the building, the span length in the X direction, the span length in the Y direction, and the floor height of the building, and therefore the user does not need to generate the joint number vector inner product value, thereby improving convenience. Also, in the building frame estimation device 1 according to this embodiment, the display control unit 145 outputs output information indicating the building frame of the estimated building to the display unit 12 (output unit), so that the user can, for example, visually confirm the building frame, making it easier for the user to select a more appropriate building frame.

[0062] In this embodiment, the types of joints include an L-shaped joint (first joint) that connects a column and a beam at the corner of a building, a T-shaped joint (second joint) that connects a column and a beam on the periphery of the building, a T-shaped joint (third joint) that connects a beam of the building to a column on either the top or bottom of the beam, and a cross-shaped joint (fourth joint) that connects a column and a beam of the building in a cross shape. The joint number vector in the X direction and the joint number vector in the Y direction include the number of L-shaped joints K1, T-shaped joints K2, T-shaped joints K3, and cross-shaped joints K4 as vector elements.

[0063] As a result, the building frame estimation device 1 according to this embodiment is able to appropriately capture the characteristics of a building by using a joint number vector, since the type and number of joints tend to vary depending on the shape of the building.

[0064] Moreover, the building frame estimation device 1 according to this embodiment includes a model construction unit 141 that constructs a building frame estimation model based on the learning data. As a result, the building frame estimation device 1 according to this embodiment can reconstruct a building frame estimation model with updated learning data, for example, periodically.

[0065] The building frame estimation method according to the present embodiment is a building frame estimation method of the building frame estimation device 1 equipped with the model storage unit 132, and includes a building frame estimation step. The model storage unit 132 stores a building frame estimation model that estimates the building frame of the building from a joint number vector inner product value and a frame volume of the building, and is constructed based on learning data having a plurality of sets of data including at least the joint number vector inner product value, the frame volume, and the building frame of the building. In the building frame estimation step, the building frame estimation device 1 estimates the building frame of the building from the building frame estimation model stored in the model storage unit 132, the joint number vector inner product value, and the frame volume of the building. As a result, the building frame estimation method according to this embodiment has the same effects as the building frame estimation device 1 described above, and can determine an appropriate structural frame even if the designer has little experience, for example.

[0066] The present invention is not limited to the above-described embodiment, and can be modified without departing from the spirit of the present invention. For example, in the above embodiment, a configuration example in which the building frame estimation device 1 includes the model construction unit 141 has been described, but the present invention is not limited to this, and the building frame estimation model may be constructed outside the building frame estimation device 1. In this case, for example, the model construction device may include the model construction unit 141 and construct the building frame estimation model.

[0067] In the above embodiment, the model construction unit 141 constructs a model for estimating the labels (group identification information) of classified groups using clustering, but the present invention is not limited to this. The model construction unit 141 may construct a model for directly estimating the building structure instead of the labels (group identification information) of the groups.

[0068] In the above embodiment, the building frame estimation device 1 is configured as one device, but the present invention is not limited to this and may be configured as a plurality of devices (for example, a plurality of server devices). In addition, the building frame estimation device 1 may include a part or the whole of the storage unit 13 externally.

[0069] In addition, in the above embodiment, an example was described in which the output unit was the display unit 12, but this is not limited to this and the output unit may be another output unit, such as a printer or a storage device such as a file server. In the above embodiment, an example of the second direction different from the first direction is described in which two directions (X direction and Y direction) that are perpendicular or nearly perpendicular to each other are used. However, the present invention is not limited to this example, and different directions having another relationship may also be used.

[0070] In addition, in the above embodiment, an example of using clustering has been described as an example of a machine learning method for classifying building structures into multiple groups, but this is not limited to this, and other methods such as dimensionality reduction and principal component analysis may also be used.

[0071] Each component of the building frame estimation apparatus 1 described above has a computer system inside. A program for realizing the function of each component of the building frame estimation apparatus 1 described above may be recorded in a computer-readable recording medium, and the program recorded in the recording medium may be read into a computer system and executed to perform processing in each component of the building frame estimation apparatus 1 described above. Here, "reading a program recorded in a recording medium into a computer system and executing it" includes installing the program into a computer system. The "computer system" referred to here includes hardware such as an OS and peripheral devices.

[0072] Furthermore, a "computer system" may include multiple computer devices connected via a network, including the Internet, a WAN, a LAN, a dedicated line, and a communication line for a virtual desktop. Furthermore, a "computer-readable recording medium" refers to portable media such as a flexible disk, an optical magnetic disk, a ROM, a CD-ROM, and a storage device such as a hard disk built into a computer system. In this way, the recording medium storing the program may be a non-transitory recording medium such as a CD-ROM.

[0073] The recording medium also includes a recording medium installed inside or outside and accessible from a distribution server to distribute the program. The program may be divided into a plurality of parts, downloaded at different times, and then combined by each component of the building frame estimation device 1, or each divided program may be distributed by a different distribution server. Furthermore, the "computer-readable recording medium" includes a recording medium that holds a program for a certain period of time, such as a volatile memory (RAM) in a computer system that becomes a server or a client when a program is transmitted via a network. The above program may be a recording medium for realizing part of the above-mentioned functions. Furthermore, the recording medium may be a so-called difference file (difference program) that can realize the above-mentioned functions in combination with a program already recorded in the computer system.

[0074] In addition, some or all of the above-mentioned functions may be realized as an integrated circuit such as LSI (Large Scale Integration). Each of the above-mentioned functions may be individually processed, or some or all of the functions may be integrated into a processor. The integrated circuit method is not limited to LSI, and may be realized by a dedicated circuit or a general-purpose processor. In addition, if an integrated circuit technology that can replace LSI appears due to advances in semiconductor technology, an integrated circuit based on that technology may be used. [Explanation of symbols]

[0075] 1...building frame estimation device, 11...input unit, 12...display unit, 13...storage unit, 14...control unit, 131...learning data storage unit, 132...model storage unit, 133...input information storage unit, 134...sinner product information storage unit, 135...building frame storage unit, 141...model construction unit, 142...data acquisition unit, 143...sinner product generation unit, 144...building frame estimation unit, 145...display control unit

Claims

1. a model storage unit that stores a building frame estimation model for estimating a building frame of the building from a first joint number vector, which is a vector whose elements are the numbers of each type of joint connecting columns and beams of the building in a first direction parallel to a horizontal plane of the building, and a second joint number vector, which is a vector whose elements are the numbers of each type of joint in a second direction parallel to the horizontal plane and different from the first direction, a frame volume of the building, and basic design quantities taken into consideration in structural design; a building frame estimation unit that estimates a building frame of the building from the building frame estimation model stored in the model storage unit, the joint number vector inner product value, a frame volume of the building, and the basic design quantities; Equipped with The building frame estimation model is constructed based on learning data having a plurality of sets of data including at least the joint number vector inner product value, the frame volume, the basic design quantities, and the building frame of the building. A building frame estimation device characterized by:

2. The junction number vector inner product value includes an inner product value of a top floor of the building, an inner product value of an intermediate floor of the building, and an inner product value of a bottom floor of the building; The building frame estimation unit estimates a building frame of the building based on the inner product value of the top floor, the inner product value of the intermediate floor, and the inner product value of the bottom floor, the frame volume, and the basic design quantities. The building frame estimation device according to claim 1 .

3. the building frame estimation model is a model for classifying the building frame into a plurality of groups by clustering based on the frame volume and the joint number vector inner product value, and outputting group identification information indicating the classified groups from the frame volume, the basic design quantities, and the joint number vector inner product value; The building frame estimation unit acquires the group identification information based on the building frame estimation model, and estimates the building frame belonging to the group corresponding to the acquired group identification information as a building frame of the building.

3. The building frame estimation device according to claim 1 or 2.

4. a data acquisition unit that acquires the frame volume, the area of ​​a reference floor of the building, the span length in the first direction, the span length in the second direction, and a floor height indicating a height of a floor of the building; an inner product generating unit that generates the inner product value of the junction number vector based on the area of ​​the reference floor, the span length in the first direction, the span length in the second direction, and the floor height acquired by the data acquiring unit; an output control unit that outputs, to an output unit, output information indicating the building frame of the building estimated by the building frame estimation unit; The building frame estimation device according to claim 1 , further comprising:

5. The types of the joints include a first joint that connects a column and a beam at a corner of the building, a second joint that connects a column and a beam on the outer periphery of the building, a third joint that connects a beam of the building to a column at either the top or bottom of the beam, and a fourth joint that connects the column and the beam of the building in a cross shape; The first junction number vector and the second junction number vector each include the number of the first junction, the second junction, the third junction, and the fourth junction as vector elements. The building frame estimation device according to any one of claims 1 to 4.

6. A model construction unit that constructs the building frame estimation model based on the learning data. The building frame estimation device according to any one of claims 1 to 5.

7. a joint number vector inner product value that is an inner product value of a first joint number vector, the first joint number vector being a vector whose elements are the numbers of each type of joint connecting columns and beams of the building in a first direction parallel to a horizontal plane of the building, and the first joint number vector, the first joint number vector being a vector whose elements are the numbers of each type of joint in a second direction parallel to the horizontal plane and different from the first direction, a model storage unit that stores a building frame estimation model for estimating a building frame of the building from a frame volume of the building and basic design quantities taken into consideration in structural design; and a building frame estimation unit, the building frame estimation unit includes a building frame estimation step of estimating a building frame of the building from the building frame estimation model stored in the model storage unit, the joint number vector inner product value, a frame volume of the building, and the basic design quantities, The building frame estimation model is constructed based on learning data having a plurality of sets of data including at least the joint number vector inner product value, the frame volume, the basic design quantities, and the building frame of the building. A building frame estimation method comprising:

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