AAD_SU21_013

Portfolio

Selected Works 2020 - 2021

Master of Science in Advanced Architectural Design

Columbia University GSAPP

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Generative Design Danil Nagy Fall 20'

Small, irregularly shaped lots are abundant in New York City, and in residential districts they are often left vacant due to the awkward corners, narrow spaces, and limited square footage (less than 2000 square feet). Often, too, these irregular lots are located directly adjacent to existing buildings, restricting a designer’s opportunity to implement proper light and air.

We asked ourselves, if we had to design the floor plan for a lot like this, what would we want to be solved or automated for us? One design factor we discussed was the issue of natural light and ventilation. New York City building code states that “every habitable room shall be provided with natural light”(NYC 1968 Code, Vol 1, Subchapter 12, Article 3). Therefore, we knew we wanted to find a way for the computer to optimize a program plan according to each room’s need for access to light and/or air, while maintaining certain adjacencies. Our project uses Grasshopper, Discover, and Python to generate these steps of the design process computationally and to identify ideal scenarios.

in collaboration with Sierra Heckman, Seongguen Hur, Aditi Shetye

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Generative Design

Methodology

Comprehensive overview of the computational design process

Designated six plots for the test

Manual Inputs We first determined what components of the design process would be handled by a person, and which components would be handled by the computer. We concluded that it would be easiest for a person to create the lot boundary in Rhino with a simple polyline, and also to choose the type of egress that best suits the plot shape (e.g. straight stair versus switchback stair) — we consider these “manual inputs” that will then be referenced in Grasshopper.

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Spatial Optimization for Irregular Plots

Discover optimizations for all six plots Parameterization Once we had our manual inputs in place, we then used Grasshopper to idealize the plot shape for rooms by identifying angles of each plot corner, and chamfering any given corner that does not meet the minimal threshold of 50 degrees. This step allows for more ordinarily shaped rooms, mitigating awkward and unusable space.

We also knew that distinguishing party walls versus exterior walls would be important for us in order to place programs with respective adjacencies for window openings. Because the plots we are dealing with hover around 1,700 square feet, we created a set of five programs to implement a two-bedroom apartment for each plot: living room, bedroom 1, bedroom 2, kitchen, and bathroom. The living room and bedrooms are all considered “habitable” spaces and thus need to be located along exterior walls.

In Grasshopper we were able to shatter the plot boundary using the context polylines in order to create the distinction between which parts of the plot we can have window openings on and which parts are solid party walls.

Next we placed the egress, which involved the consideration of two things: centralizing the location of the stairs for safest and easiest path of egress, then shifting the stairs against a party wall so that we don’t lose valuable space along exterior walls. We located the stairs centrally by establishing a connection between the starting point of the stairs which we had defined, and the geometric center of the plot. From there, we moved the stairs along a party wall by ensuring the exterior points of the stairs moved to points along the nearest party wall curve.

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Generative Design

Program breakdown and regulations of light and air according to NYC Building Code

Subdivision The next step in setting up the computational process was to bring the boundaries of the resulting floor areas into Python for subdivision. As mentioned earlier, we defined a set of five rooms for each plot to have a two-bedroom apartment. We also assigned these programs fixed ratios so that they always maintain livable and occupiable proportions within the plot’s square footage.

We split the floor areas using four curves, knowing that the number of spaces created would be one more than this number, giving us our five rooms. We used Python to run this split recursively according to our set of parameters, while Discover would work to optimize three things: room ratios, façade adjacencies, and squareness of rooms.

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Spatial Optimization for Irregular Plots

Optimized floor plans from Discover

Results At the end of our computational process, we were able to produce a set of six subdivided plots which optimize access to light for habitable rooms, room area ratios, squareness of rooms, and corner angles of the building. It was our intent that if the computer could generate a diagrammatic plan having solved the issues mentioned earlier, then the designer could take this and apply it to be a more realistic plan. In sum, we are using computation as a schematic step of design, then the designer can jump straight into a more developed stage of design.

The resulting floor plans pleasantly surprised us, as they achieved everything we set out to achieve; most importantly, the spaces are, for the most part, all occupiable and clean-cut, which was the biggest problem we faced during the development process.

We noticed that Discover seemed to have an easier time implementing squareness of plots than mitigating area difference in most cases, as indicated by the vertical trend of dots in the Discover images below.

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Generative Design

Discover optimizations for all six plots

Optimization In order to achieve our targeted room ratios, we minimized the difference between targeted room areas by dividing the two; the overarching objective is that this ratio would be equal to 1, though realistically this is not a constraint — we are only trying to get as close to 1 as possible.

As discussed earlier, from the beginning it was important to us to make sure that the living rooms and bedrooms all were able to receive natural light in compliance with the New York City Building Code.

This meant that these rooms would need to share at least one side with an exterior wall curve. The way we addressed this issue was by extracting room areas from our subdivided spaces, then constraining the three largest room areas to be adjacent to the exterior wall curves. This works because we have defined the three largest room ratios as our “habitable” rooms, so without stating that a living room needs light and bedrooms need light, we still always end up with the living room and bedrooms having access to the exterior due to them having the largest room areas.

One issue we ran into in the process of subdivision is that we were getting rooms which technically met our requirements for ratios and façade adjacencies, but they were not occupiable spaces; for example, we would get rooms that were 3ft x 20ft, which is clearly not realistic. Thus, we knew we needed to control the “squareness” of rooms. By minimizing the difference in lengths for each segment along the room boundary curve, we are able to ask Discover to optimize this parameter as an objective.

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Spatial Optimization for Irregular Plots

Floor plans drawn by designer based off of Discover’s optimized floor plans

Conclusion Optimizing floor plans for irregular plots computationally proved to be rewarding in automating a step of the design process, as shown through the six floor plans we were able to generate using Rhino, Grasshopper, Python, and Discover. For designers in New York City especially, there are bound to be encounters with awkwardly-shaped lots like the ones we’ve explored, and by taking the time to solve this step of the design process once, we save several iterative steps in the future. However, as designers we must also know to take these resultant floor plans with a grain of salt; in the end, it is the architect who is designing space in terms of atmosphere, touch, and emotion. This is why we chose to leave the final steps of the design process, the design of spatial quality, in the designer’s hand.

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Universal NYC Urban Design for Equality

X Information Modeling Luc Wilson Fall 20'

in collaboration with Keonyoung Jang

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X Information Modeling

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Goal

Analysis of Metrics & Types of isabilities

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Input

Urban Grid Type (4) FAR Options (4) 256 options

Size of the Nodes (4) Urban Grid Type(4)

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Universal NYC

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Procedual Building Types

Market Hall Tower on Podium

1. Street Grid

4. Inner Streets

2. Intersection Nodes

5. Bridges

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3. Podiums & Markethalls

6. Towers

X Information Modeling

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Analysis Tools & Metrics

ACCESSIBILITY

SAFETY

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Universal NYC

MENTAL HEALTH

MUTUAL CARE

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X Information Modeling

4 Site

Less than $35,000

Manufacturing and Logistics

$70,000 - $100,000

Income

+

Occupation

+

Population with disability

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High School

15% to 20%

Retail, Hospitality, and Other Services

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Middle School

< 25%

$35,000 - $70,000

Education Level

Universal NYC

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SCOUT

METRICS IN ORDER OF PRIORITIES 1. CPTED = MUTUAL CARE 2. VIEW RANGE = SAFETY 3. 2 MINUTES DISTANCE OPEN SPACES = ACCESSIBILITY 4. DAY LIGHT ON STREETS = MENTAL HEALTH 5. DAY LIGHT ON BUILDINGS = MENTAL HEALTH

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Optimal Result

Street option 1 Far option 4 Podium height 35ft Node offest distance 30ft

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Hyper-Striated Reterritorization The Truck Stop in the Era of Automation and Computer Vison Advanced Architectural Design V Everything Must Scale (5) Michael Bell Spring 21'

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Hyper-Striated Reterritorization

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The Truck Stop in the Era of Automation and Computer Vison

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Death Lab Death as a Social Experiment

Unsettled Landscapes Civic-Sacred Coexistence as Design Activism Karla Rothstein Summer 21' in collaboration with Sungmin Kim

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Death Lab

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Death as a Social Experiment

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AVERY SPOT Temporary Pavilion Structure for GSAPP Events

The Outside in Seminar Laurie Hawkinson and Galia Solomonoff Spring 21'

This design-build seminar is a collaborative initiative to design, build, and program a temporary pavilion structure that was erected by the students in the area between Avery, Fayerweather and Schermerhorn Halls during the Spring semester of 2021.

The pavilion is being programmed for events outside of GSAPP beginning with 2021 graduation for both on-site and remote event participation.

in collaboration with Zina Berrada, Eleanor Lucas Birle, Marie Christine Karim Dimitri, Anays Mical Gonzalez Sanchez, Lin Hou, Nanjia Jiang, Blake Matthew Kem, Cecile Kim, Kim Cherutai Langat, Kassandra Shuen Lee, Xinyi Qu, Vera Montare Savory, Tristan Schendel, Lauren Brooke Scott, Kaeli Alika Streeter, Taylor James Urbshott, Xindi Wang, Eunjin Yoo, and Elie Zeinoun

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Balloon Saloon Safely Back Together

Outside In Pavilion: The Body in Space Today Charette Selected Finalists Spring 21'

The flexible design of Balloon Saloon allows people to create and adjust their own space depending on their individual or collective needs. The system utilizes 6-feetdiameter balloons inflated with helium and draped with a soft fabric curtain.

Balloons are contained within a honeycomb wire-mesh pavilion. Users can easily move, shuffle, or pull the balloons to customize a variety of spaces, from an individual enclosed capsule to a bigger gathering plaza.

in collaboration with Jun Ito and Reem Yassin

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An Atlas of Dust Reading Builings as Long Term Environmental Sensors

An Atlas of Dust Jorge Otero-Pailos & CyArk Foundation Summer Workshop

Our objective is that people see the dust in Avery North facade as a physical record of the environmental pollution that inhabited New York City in the past, an architectural layer of environmental history that in our opinion deserves to be studied in collaboration with the Climate School. From the micro, to meso and macro levels of analysis of this facade, we have used different methods of observation to collect some evidence that enabled us to raise such an argument. our methodology, which has consisted in lidar scanning, photogrammetry, XRF, lupe observation, color separation analysis, and wind simulations, all together accompanied with historical and archival research, will be useful for future endeavours in that direction.

in collaboration with Adam Vosburgh, Andrea Molina, Aya Abdallah, Jiyong Chun, Konstantina Marinaki, Nupur Roy, Preme Chaiyatham, Seonggeun Hur, Xindi Wang, and Ziming Wang

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