Data Management Proposal

Data Management in Architecture zach soflin

a look into how data systems can improve the architectural process

As software encompasses more of the architectural process, forms become more complex, and architectural systems become more integrated, Data management is becoming more integral to a successful architectural product. Digital architectural models as we know them are dead. The idea that points lines and planes exist only as geometry and nothing else is now considered inefficient.

Architecture is behind the pack when it comes to the use of technology but are quickly catching up with products like Revit and Grasshopper becoming standard. While very different in nature and application, both softwares provide the ability to track entities so that groups of points, lines, and planes can become walls, ceilings, and floors, Which allow users to track and test building information like never before. The necessity for employees that can visualize and implement systems are becoming more and more desired and a students ability to do this gives them a head above the rest. Below are a few case studies in my practice and study that I found to be a catalyst for this class. They are interesting examples of how data management and integrated systems can help architects become better and more informed designers.

1 | Haymarket Arena The Haymarket Arena has exactly 21,834 Z-loc metal panels. The paneled surface is made of exactly 51,093 linear feet of 21� wide rolled metal. Each panel’s dimension, color, position, orientation, and tolerance is stored along with the geometry itself in the digital model. The panel geometry itself is controlled by 4 ellipses at different elevations. The program is given these 4 simple ellipses and their corresponding elevations and the calculations begin.

Without getting into too many specifics, the definition first calculates a surface between the 4 ellipses, it then divides those into the desired panel pattern (The final is a running bond). After all of the panels are accounted for the subtractions begin: This process trims the panels around any stair towers, curtain walls, etc. and leaves only the panels that will actually be there in reality. Then the punched linear windows are subtracted from the panels, the mullions are created along with the window glazing. The inner wall is next offset 18 inches from the panel surface, creating an interior wall that contains the same punched openings and trimmings. Next is the structure for the panels. It consists of metal stud trusses spaced at a given distance, These are constructed and trimmed based on the surrounding obstructions. Finally the color is applied using a random algorithm to distribute 25% light grey 50% mid grey and 25% darker grey.

In under 241 seconds (depending on your computer) after introducing the simple 4 ellipses the definition returns the modelled arena panels complete with all panel dimensions (including trimmed panels),color, position, orientation, and tolerances. One might say that this is a waste of time because the building never changes once built. Which is true, but if one were to calculate the cost to rebuild this model 51 times over as footprints, elevations, and sections changed over a 1 year period, the benefit could be seen quite clearly. In this case, parametric data management not only saved money by automatically producing the geometry each time the building changed, but it also provided accurate material use for a more accurate cost estimate, provided data which helped evaluate which sizes and layouts where most cost effective, and automatically checked items like tolerances between panels to ensure that the system was working.

2 | The Gateway Arch The Gateway Arch is monument to the western expansion in the United States and stands as one of the most well known national monuments in the US. But being built in 1965, it is in need of some repair. One of the central problems in the repair of the Gateway arch though is in the survey of it. Data management plays a large part in how one can find correlation between variables like humidity, structure, welds, elevation, etc. Calculations again play a large part firstly in how the model for the arch is made, as it is based on a catenary curve.

Eero Saarinen (original architect) constructed this through a table of point coordinates, and from this table one can also construct the model. But with 141 stations each with 18 uniquely shaped panels, constructing this using a definition that reads the table is much easier and cost effective. This process begins with automatically placing the points in space. Then, using other given dimensions on the table, the stations can be constructed and divided into the 2,338 unique panels. Every station to station crease is welded with either a field weld or a shop weld (shop welds tend to last longer) so this is mapped onto the creases. The walls of the arch are also filled with concrete up to a certain height so this is also modeled. The inner wall of the arch is constructed the same as the arch was by offsetting each panel a given distance. As one moves up the arch, the walls actually get thinner depending on the elevation. This thickness is also taken automatically from the table. Once the model is complete it can be put to use mapping surveys of each panel and their condition to the surface of the arch.

Data like humidity sensors, temperature sensors and others are also mapped to the model giving the user the ability to find correlations between the variables and determine the conditions that are leading to some panels deterioration and conditions that are leading to others preservation.

3 | North Dakota State Capitol Data Management in architecture finds an unexpected usefulness in preservation aspect of architecture which can be seen in the previous example.

Another example of said usefulness is the North Dakota State Capitol. The Capitol, clad in limestone panels was built in 1883. Bvh, tasked with its restoration, ran into issues when planning the survey process. The capitol had hundreds of limestone panels each with different conditions in spalling, structure, cracking and displacement. Using Android a system was created that surveyors could use to track the condition of each panel. The system allows surveyors to submit any spalling on the panel, what the condition of the structure was, how displaced the panel was, and if there was any cracking. They could also take pictures of the panel and write any notes about each panel. The location and altitude of the device was also used to verify the panel being surveyed was the right panel. All of this information was instantly uploaded to a online database that organized the info according to the panel. This database was connected to the model of the building which meant that as data was entered in the field, the model was immediately updated with that information. The application for this type of program is extensive. From simply organizing objects like photos and notes according to panel, to identifying commonalities in conditions to find potential problems, this could be applied to many projects.

4 | Data Driven Architecture With the ever expanding web, data is becoming available faster now, than ever before. We now have access to details of all properties within a city, terrain data for the entire earth, Detailed Census Data, economic statistics, crime statistics and human opinion. We now have the ability to see every intimate detail of hundreds of millions of peoples lives. We can see images of almost anywhere on the earth, whether it be through satellite images or images taken on a family vacation. We can access statistical data that tells us what people are thinking on politics and other issues. We can find the answer to almost any medical question imaginable. So what do we do with it?

This project aimed to find a direct connection between data and building and how the two can interact. This resulted in a revision to the definition of an architect as one who designs frameworks rather than buildings. A Framework is defined as a basic structure underlying a system. This definition can be applied to architecture in that rather than producing a form as a result, architects produce a framework or basic structure and allow context and data to drive the actual form working within the given framework. An analogy that works well with this concept is that of a automobile. All automobiles start with a basic unchanging framework that includes steering, wheels, and axles. It also has a specific size limitation (depending on the regulation for that type of vehicle). All of these factors could be considered a framework, or something to work within. It allows for changes while maintaining basic elements. Vehicles forms are then produced based on user needs, market demand and other data. So, while the basic framework remains unchanged, the form and shape of the vehicle can vary quite drastically.

This project focuses on a site in downtown Chicago. It lies in a dense area populated by residential towers and a few office buildings. The program for this building is for a base that serves as a hub for the tower’s residents and the surrounding 20,000 residents. The tower would serve as residences. This resulted in the need of two frameworks one for the retail base and one for the residential tower.

4 | Data Driven Architecture The base:

The program for any retail environment is tied mostly to its context. Who will use your space? What is near your space? How do people get to your space.

All of these questions go into the decision for program size, type, etc. This is typically done manually.

The goal here was to develop a system where this is done automatically. Based on a single locational point and parameters for size limitations, program preferences, etc. The process begins calculating. It starts by first gathering input about its potential users by way of mobile apps that survey for answers to questions. The basics of these questions ask surrounding users how far they are willing to walk for specific programs. For example, one question asks users how far they are willing to walk for a bite to eat. The user answers in number of minutes. The answers to these questions are instantly synced with a online database and thus begins the calculation process. Once answers start flowing in the program begins by getting walking directions from the given site to all surrounding buildings. The distance is calculated and compared to the answers to the surveys. If the survey says people are willing to walk 4 minutes with a load of groceries, then only buildings within a 4 minute walk are included in the results. After determining which buildings are within walking distance for the given program, the population of those buildings are determined using GIS information including square footage, number of units, etc. Adding these determines the total customer base for the given program. Additionally according to each building’s location the census data for each is queried and down-

loaded. Returning detailed demographic data to be used later. The result is an accurate picture of each program’s customers and their demographics. Each program customer base is different because people are willing to walk longer for certain programs than for others. For example a person may only be willing to walk 4 minutes to grab a load of groceries but would walk 9 minutes to get a bite to eat. Thus the number of buildings and therefore people, is less for the grocery program than for the restaurant program.

From each programs industry leaders a sqft/capita is determined (ex. National Grocers association). Using this, combined with the customer population a desired square footage can be determined.

Next the program uses Google to search the area for similar programs and uses a GIS database to find their sizes. Their sizes are then subtracted from the desired square footage for the area and the result is the additional square feet that location would be able to support for the specific program.

Using a combination of this square footage data and the demographics of the programs customers, the application begins to find a good fit for the space.

For example, if an area could support 18000 sqft of grocery space, and the customers have an above average income, a Trader Joe’s would be a good fit. This is because the average size of a Trader Joe’s store is around 16000 sqft. The stores also provide a higher quality product but at a higher price. Alternatively if the customers made a relatively low income, a urban Walmart may be a better fit. The application pulls these stores from a database ranking each on quality, cost, and size. Demographics like income, family size, gender, and others are considered by the application and the decision made. Then considering site constraints, the programs are laid out. The application considers points of access to the site and other factors when optimizing the locations of different programs. One of the main organizing factors that the application considers is a spaces relative size and its proximity to an anchor space. Anchor spaces are places that draw people in like a grocery store, a nice restaurant or a well known retail space. The application places smaller spaces at prime locations between where users enter the building, and the anchor spaces.

4 | Data Driven Architecture The Tower:

Residential towers seem to grow anywhere and everywhere in an urban context. Stacked floors with similar floor plans for every unit. But what if you could layout your own unit? What if you paid for exactly what you wanted and nothing else? The goal was to develop a system which assigned cost to square footage, views, elevator proximity, height, and other parameters, and allowed its users to lay out their unit based on a predefined 12x12 module system. The system would then take all of the units of the tower and optimize their location so everyone’s priorities are met. The first step was thus establishing the limits. Each floor has 8 units: 4 corner units and 4 standard units. Each unit begins with a utilities module which contains all of the utilities for the unit and the entrance to the unit. Vertically, this module is in the same location for all units allowing for easy vertical transport of HVAC and other utilities. 5 other modules make up the base of each unit as this is the minimum size that is allowed. This is equivalent to 864 sqft. The actual process of laying out the unit is where this system comes into play. An app was developed so users could do this. The app begins with collecting information about the user and what their priorities are. This includes showing the user views from different sides and heights of the building and asking their opinion, finding how important a quick elevator ride is to you, and many others. Since you are not buying a specific unit on the tower, this first portion of the app is meant to find the users overall preferences when it comes to their unit location on the tower.

The second portion of the app is a visual layout of a typical unit (either corner or standard depending on which you prefer). Here the user begins to program their unit by placing living rooms, dining rooms, kitchens, bedrooms, patio space, etc. and choosing their sizes. As stated before users begin with 6 modules (5 open ones and 1 unchanging utility module). They can then move outward depending on how much space they need. While all of these design decisions are being made in the app, the program is keeping a running total of cost which is assigned to all aspects of this process. For example, if a user wants to move outward 4 modules they can, but the further they move out, the more expensive each square foot becomes. This is to cover the additional cost for structure that is a result of the cantilever. The run-

ning total is always in view of the user so they can keep tabs on their cost and make sure they are staying within their budget.

The question then becomes how does one interact with their neighbors? If you are choosing to move outward from the tower but your neighbor wants a view in your direction, how is this negotiated? When the user specifies a priority that may come in conflict with a neighbors, a max bid is request from the user. When placing the unit on the tower, the programs number one goal is satisfying every users priority. But while this is sufficient in 90% of the cases, it isn’t possible for all of them. Therefore the next override is the bid. The User willing to pay the most takes priority.

Once all of the units for the tower are laid out and sold, the tower begins its optimization process. This process works by placing all units at a default location. Each priority given by the user is tested and given a score. The units are then moved randomly at first to give the program an idea of the possible solutions. Through an evolutionary optimization process the units are moved, priorities tested, and scored then repeated. This continues until the program reaches a solution it believes scores the best possible. This becomes the towers final geometry and the construction process can begin.

Fusing these two frameworks delivers a unique building tightly and directly connected to its context and to its users. This program can take any given location and data from users and lay out a unique form based directly on its context and its users in a matter of minutes. Therefore this same system could be applied with minimal effort anywhere in the world and produce a unique contextual product.