Generative Design 4x4 cubic space

The file in the video is as below, and it can be executed from any template in 2023.

Spatial Configuration Information and Basic Concepts

Preparation and Information Organization for a 3x3 Cubic Rectangular Arrangement

Spatial arrangement is based on a 3x3 cube of points. The arrangement allows for a total of 9 spaces in a 3x3 square.

The size of each rectangular space is determined by the WIDTH and HEIGHT values in the Excel information below. The QUANTITY value determines the shape of the rectangle.

ADJACENT SPACES indicates the accessibility of each space. The more spaces are adjacent, the more usable the space is in practice. DEPARTMENT classifies spaces into three types: Public, Privacy, and (SP)Semi-Public. The closer these departments are to each other, the more clustered they are, and the more usable the space is evaluated to be.

Cubic Space Arrangement and Definition

Cubic Space Arrangement and Variable Confirmation

The arrangement of these spaces is carried out within a 3x3 cubic space, with the center of each rectangle as the reference point.

The arrangement method prioritizes Random (random placement).

Subsequently, Generative Design will be executed, and the position values will be recorded and confirmed through the Slide Bar in Dynamo.

3x3 Cubic Space Formation

This section outlines the process of forming and arranging rectangular spaces within a 3x3 cubic structure.

Process:

3x3 Cubic Space Formation: A 3x3 cubic space is established as the framework for the arrangement. This provides a defined volume within which the rectangular spaces will be placed.
Space Formation based on QUANTITY: The QUANTITY value, likely referring to the number of rectangular units, dictates the formation of spaces within the cubic structure. This value determines how many individual rectangular units will be created and placed.
Placement of Available Spaces within the 3x3 Cube: This step involves positioning the generated rectangular spaces within the cubic framework. The arrangement considers factors like adjacency and department types (as mentioned in previous translations) to optimize space utilization and flow.

Output:

[count]: This variable records the total number of spaces successfully placed within the 3x3 cube after the arrangement process. It provides a quantitative measure of space utilization.
[Angle 1, 2]: These variables likely represent the two possible 90-degree rotations of each rectangular space within the cubic structure. This suggests an evaluation of different orientations to potentially find optimal placements based on adjacency and department clustering.

A situation has arisen where it is impossible to place all the spaces within the 3x3 cubic structure. This is due to insufficient space based on the given QUANTITY and instances where the arrangement exceeds the boundaries of the 3x3 space. Therefore, further examination is required to explore approaches for maintaining the 3x3 space and potentially expanding the size of the cubic structure.

3x3, 4x4 Spatial Arrangement Review

Securing Spatial Arrangement Diversity through Expansion to a 4x4 Cubic Space

4x4 Cubic Space Expansion: The existing 3x3 cubic space is expanded to a 4x4 configuration, increasing the overall volume and potential placement options.
Forming Cubic Space Boundaries: Boundaries are defined for the 4x4 cubic space to establish the limits within which spaces can be arranged.
Checking the List of Available Spaces: A comprehensive list of available spaces, including their dimensions and properties (potentially from the Excel data mentioned earlier), is reviewed to understand the elements to be placed within the expanded cubic space.
Confirming Maximum and Minimum Placement Cases: The maximum and minimum number of spaces that can be placed within the 4x4 cubic space are determined. This likely involves analyzing different arrangements and rotations to identify the upper and lower limits of space utilization.

By forming a boundary around the 4x4 cubic space, instances of spaces exceeding the cubic space are identified. Despite expanding the space to 4x4, the phenomenon of rectangular spaces exceeding the boundaries persists. This necessitates a review of the rectangular space placement method and the angle modification method. Since simply expanding the space does not resolve the issue, it is necessary to explore alternative methodologies.

Error Review and Solution Application

Updating the Space Arrangement Algorithm to Stay Within the Boundary (Outline)

Ensuring spaces stay within the 4x4 cubic space: Situations where spaces exceed the 4x4 cubic space are prevented by fixing the [boxedIn] output to a value of 0. This is achieved through the Generative Design _ Optimize function, which sets the boxedIn target value to 0.
Checking for overlaps: The DoesIntersect node is used to identify any overlaps between the boundary and the spaces. It assigns a value of true if an overlap occurs and false if there is no overlap.

To identify spaces that do not overlap with the boundary, the Randomize (random placement) results are first examined. The Generative Design _ Optimize function is then executed, with the boxedIn output value fixed at 0. This ensures that Generative Design only identifies results where spaces do not overlap with the boundary.

Algorithm for Identifying Adjacent Spaces

Adding an ADJACENT SPACES Algorithm

This algorithm leverages the adjacency information in the Excel data to connect related spaces within the cubic space. Here's how it works:

Extract Center Points: The center point of each space within the cubic structure is identified and extracted.
Connect with Lines: Based on the adjacency information in the Excel data, lines are drawn to connect the center points of related spaces. This creates a visual representation of the connections between spaces, as shown in the image above.
Calculate Total Line Length: The total length of all the lines connecting the spaces is calculated and used as a GENERATIVE DESIGN OUTPUT. This value serves as a metric for adjacency.
Interpreting the Results: A shorter total line length indicates that spaces are closer together and more adjacent. Conversely, a longer total line length signifies that spaces are more dispersed.
Utilizing in Generative Design: This adjacency metric can be used in GENERATIVE DESIGN to optimize the arrangement of spaces, prioritizing solutions with higher adjacency (shorter total line length). This promotes efficient spatial configurations where related spaces are clustered together.

In essence, this algorithm translates the adjacency information from the Excel data into a quantifiable metric (total line length) that can be used to guide and optimize space arrangement in GENERATIVE DESIGN.

Stacking and Adding Fixed Points

The Need for Automated Design Algorithms in Architecture

This section explores the application of the developed algorithm to a multi-story structure and introduces the concept of fixed points.

Process:

Multi-story Application: The algorithm used for the single-story arrangement is applied to a multi-story structure. This involves replicating the core logic and adapting it to accommodate the additional levels.
ADJACENT SPACES Implementation: The ADJACENT SPACES algorithm is applied to each floor to ensure that related spaces are positioned near each other, optimizing spatial organization on every level.
Staircase Integration: A staircase space is introduced and placed in the same location on each floor to provide vertical circulation throughout the structure.
Staircase Placement on the First Floor: The staircase is initially placed on the first floor, serving as a reference point for its placement on subsequent floors.
Point Extraction: The center point of the staircase space is extracted.
Point Exclusion in Multi-story: The extracted point is excluded from the possible placement locations on all floors above the first floor. This ensures the staircase maintains a consistent position throughout the building.
Remaining Space Allocation: The remaining spaces are then arranged on each floor, considering the fixed position of the staircase and the ADJACENT SPACES algorithm to optimize their placement.

Fixed Points:

The concept of fixed points is introduced for multi-story structures. These points represent spaces that must remain in a consistent location across all floors. In this case, the staircase serves as a fixed point. By identifying and excluding the fixed point locations, the algorithm ensures that other spaces are arranged around these essential elements, maintaining structural integrity and functional flow.

This approach demonstrates the adaptability of the algorithm to more complex architectural designs, incorporating multi-story structures and fixed points to address practical considerations in building design.

Guiding Fixed Core Placement (North and West Sides)

Placement and Input Conditions for Fixed Core (North, West)

This section focuses on guiding the placement of the fixed core (likely referring to essential service areas like stairs, elevators, or restrooms) towards the north and west sides of the building.

Process:

Reduced Floor Count: The experiment is conducted with a reduced number of floors (4 floors) to simplify the analysis and focus on the core placement strategy.
Guiding Core Placement: The algorithm is modified to guide the placement of the core towards the north and west sides of the building. This might involve assigning weights or priorities to those locations during the optimization process.
Adding ADJACENT and DEPARTMENT Conditions: The ADJACENT SPACES and DEPARTMENT algorithms are incorporated to ensure that related spaces are clustered together and that the overall spatial organization is functional and efficient.
Enhancing Practicality: By combining the ADJACENT, DEPARTMENT, and CORE placement algorithms, the system aims to generate more practical and usable spatial arrangements.
Output Value Configuration: The process considers how to set target values and configure output values for these combined criteria to effectively guide the optimization process and achieve the desired core placement and spatial arrangement.

This approach demonstrates a more nuanced control over the spatial arrangement, guiding the placement of critical building elements while considering adjacency and departmental relationships to achieve a more refined and functional design.

Section-by-Section Dynamo Explanation

Definition of the Entire Dynamo File

This Dynamo file orchestrates the automated placement of cubic spaces across multiple floors, starting with importing data from an Excel file. After arranging the spaces on each floor, the arrangement is reviewed and evaluated. Various metrics are calculated, such as the placement of spaces on the north and west sides, the core placement location, and the total length of lines representing adjacency. These results are then displayed as output for analysis.

Final Output Configuration and Generative Design Optimization Review

Reviewing Optimization Goals and Constraints in Optimize

This section defines the optimization goals and constraints used in the Generative Design process.

Core: [Core Score] > The core location is determined by assigning scores based on its position. The outermost north and west positions receive the lowest score (-1 point), and the next line inwards receives a slightly higher score (-0.5 points). This encourages the core to be placed in the desired locations.
Adjacent: [Average Length] > A shorter average length of lines connecting adjacent spaces on each floor indicates a more desirable arrangement where related spaces are closer together.
Space Group (Department): [Department] > A lower average area for each department on each floor suggests a better distribution and clustering of related spaces.

Running Generative Design

When running Generative Design, select the Optimize option and input the minimum and maximum values from the image above into the target values. These values represent the upper and lower limits of the Input values (Min/Max) and define the range within which the optimization algorithm will search for solutions. This process ensures that only optimized results are generated, meeting the defined criteria for core placement, adjacency, and department clustering.

( If you have a request for more than 20 people, I will upload that file as well :) )

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