Research Journal | IAAC Studio III

Studio 1 / Deep Skins Faculty / Alexandre Dubor, Aldo Sollazzo IaaC, 19/12/2019

MRAC Master in Robotics and Advanced Construction

Tutors / Alexandre Dubor, Raimund Krenmueller

Localize, Collate and Design Towards Circular Building Environment

Anna Batallé İrem Yağmur Cebeci Matt Gordon Roberto Vargas

Studio 1 / Deep Skins IaaC, 19/12/2019

MRAC Master in Robotics and Advanced Construction

Tutors / Alexandre Dubor, Raimund Krenmueller

Content Framework The need to create a more sustainable and circular building environment

Proposal Research goals through the construction process

Research Definition of the challenges and opportunities and identification of the digital methods that can be applied to enhance existing workflows

Case Study Proof of concept of the new methodology.

Open Up Future Vision of the project

This research aims to understand how the digitalization of the construction sector is going to lead us to a more sustainable building environment. It presents a series of technological advancements that can be applied in the deconstruction, design and fabrication phase to transform and optimize material ows.

FRAMEWORK

Deconstruction

Demolition Building Linear Life Cycle

Optimize the workflow to sort and locate construction material Demolition

Site demolition produces a significant amount of waste. All material that is derived from from demolished sites ends up in landfills, without any attempt to be sorted on reusable and non-reusable pieces which can later be used in new structures.

Resources optimization Construction and demolition waste in the EU accounts for approximately 25% to 30% of all waste generated in the EU. Therefore, responsible management of waste is an essential aspect of sustainable building as well as rationalizing the use of new material. https://ec.europa.eu/environment/waste/construction _demolition.htm

Waste Generation

Deconstruction

Resources

Economic challenge and sustainable impact Carbon Footprint

In construction, four materials are commonly used: concrete, masonry, wood and metal, these will also be the focus throughout this project. The graph below showcases the current life cycle of the materials as well as how much of them are being wasted by land-ďŹ lling or down-cycling.

Material Uniqueness > Added value + 10% to 20%

Manufacturer Manufacture Transport

The aim is to increase the amount of material recycled and reused, which will also lead in decreasing the quantity that is being down-cycled or resulting in the landďŹ lls.

Transport

Material Retrieval Raw Material

Waste Management

Raw Material

Recycled

Reused

PROPOSAL

Building Circular Life Cycle

tion

truc

ns eco

Stages

nst

ruc

tion

Pre

-de con

stru

ctio n

D

Co

Service Plan

Services

Demolition Waste

GHG Emission Production

Embodied Energy

Lifetim

e

Structure

Skin

Building Owner

Building Layers Services > 7 - 15 years Space Plan > 3 - 30 years Skin > 20 years Structure > 30 - 300 years

Material Shape and Data

03

Deconstruction Company

tion

Co nst

truc

Material Location and Data

ns eco

ruc

D

tion

Pre

01 | 02

-de con

stru

ctio n

Building Life Cycle Actors Involved Technologies

03 Technologies

02 Machine Learning

e Lifetim

01 Computer Vision

Structure

Design

Service Plan

Designer Architect Material Shape and Data

Services

03 Computational Design 04 Robotics

Skin

Building Owner

Proposed Products

Collaborative Design Tool

Assistive Design Workow

Digital Material Dataset

Designer - Generic User

Architect

Demolition Company

Social Awareness Material Resources

Responsible Design

Resources Management

RESEARCH

Siftsite The production of materials needed to satisfy the demand for new architectures generates a great impact on our environment. Meanwhile, every year resources are lost during building demolitions. Almost thirty percent of the waste generated in the European Union comes from the construction industry.

The term urban mining is born from the perspective of environmental concerns and aims for more eďŹƒcient use of the construction materials available in the shape of building stock. The purpose is to reduce the use of virgin sources, generating savings through improving secondary resource use and lowering the negative environmental impacts.

The sifting of buildings to obtain material is something that has recurrently happened in history but the tools used to perform these tasks haven’t evolved. At SIFTSITE we are developing a new workow to sort and locate material from pre-demolition sites to create a more reliable report of the resources available. http://www.iaacblog.com/programs/digitalizing-mate rial-collation-predomolition-sites-studio-ii/

Digitalize and Automate the process of creating the material dataset

Building Inspection

01

ClassiďŹ cation and Localization

02

Geometry Reconstruction

Material Report

03

04

Building Inspection

01

Manual Drone Flight

Capture Images of the Demolition

Classification and Localization

Brick

Concrete

Metal

Wood

None

Classification and Localization 02

Training Data

Images from the building Inspection

Analyze by Larger Sliding Kernel

Classification and Localization

Material Classification

Classification and Localization 02

Finding Features by Corners

Describing Regions Around Features

Clustering Descriptions into codewords

Comparing Histograms of codewords counts

Geometry Reconstruction

02

Material Localization

03

Dense Point Cloud

Geometry Reconstruction

ClassiďŹ cation and Localization

03

Segmentation

Geometry Extraction

Colored Mesh

ASSISTIVE DESIGN WORKFLOW

Material Language Best Fitting Function for Element based in Material Properties

Adobe wall made of soil + 10% granulated old bricks

Metal Beam

Brick A Transition from grey-orange

Assembly Rules

Construction System

Brick B Grey Brick

Fabrication Methods Concrete Beam

Brick C Concrete Brick Deconstruction System Stone

Design Exploration Case Study

Design Exploration Stacking System

Design Exploration Stacking System

The generation of the design is done through 3 steps. First a simple design space is created allowing to focus the research in the definition of the matching algorithm. The optimization solver takes places in order to find the best solution given various parameters (cost, material waste…) http://www.iaacblog.com/programs/post-collation-design-exploration/

Generative Design

Generat ive Design

Matching Algorithm

Optimization Solver

Design Exploration Stacking System In order to relate the material with the design, we need to build a language that understands the construction constraints. A grid is created and the length of the elements is the one that is taken to process in the matching algorithm.

We have the length of the elements coming from the design and the material dataset as inputs for the matching algorithm as well as a tolerance of 10 percent. The outputs that we are getting from the matching algorithm are then used for the optimization and the shape reconstruction of the element. And the matching is working mainly through the lengths and ids of the pieces in design and the dataset.

The algorithm is taking the length of the needed piece from the design and it is matching it with the length in the dataset to first use it in the design structure but also to delete the element from the left materials in the dataset. For the ones that is not exactly matching but can fit in the design, the algorithm will cut the batten and place the left part back to the material dataset.

For the materials that there was no match, a new element, from outside the material dataset, is going to be used. With the optimization solver we are minimizing the amount of cuts and outer materials.

First matching algorithm that we had can be seen below. There was material that it could potential be reused in the material dataset but the design was already asking for new material.

We improved the algorithm by allowing to cut material in case none was founded. As we can see, the design asks for new material when the one from the dataset has finished. To improve its performance we could start relating the quality of the material to the structural dependencies. Meaning that new material will be place where higher forces appear and less quality material will be used as a filler.

Design Exploration Stacking System

There were three primary factors that we chose to judge the designs by. Firstly of course we want to make the deepest use of the available material, so we want to reduce the number of unmatched parts in the design. Secondly, we want to require as little additional labor at construction time as possible, so we want to reduce the total amount of material cuts needed.

Finally, the construction should be mindful of its carbon impact, so we want to reduce the needed transporation distance for all the included elements. All of these factors are normalized, weighted, and combined as a ďŹ tness function to be minimized.

Design Exploration Stacking System

COLLABORATIVE DESIGN TOOL

Collaborative Design Tool Human-Machine Interaction

Visualization

Hand detection

Remote Controller Research

Ideal Outcome

Collaborative Design Tool Human-Machine Interaction

Collaborative Design Tool Human-Machine Interaction http://www.iaacblog.com/programs/courses/mrac/2019-2020-mrac/h-3-hardware-iii-seminar-mrac-2019-2020-3rt/

Collaborative Design Tool Human-Machine Interaction

CASE STUDY

Case Study

Material Dataset

Potential Process Transformation Through Digitalisation

SITE I DECONSTRUCTION SITE

Iaac IAAC Pujades 102

Inspection, Organization and, Fabrication Automation

Design Strategy

Deconstructing wood framing

SITE III CONSTRUCTION

Plug in Barcelona SITE I AUTOMATED WAREHOUSE Storing and modifying of elements

Constructing wood facade design

Potential Process Transformation Through Digitalisation

>>

>>

Site Scanning for Digital Material Dataset Creation

DeďŹ nition and Extraction of Material Properties for Construction and Reuse

Material Dataset

>>

Intuitive design of a large set of discrete elements

>>

Best ďŹ tting function for each element based on its properties

Design Strategy

Develop Fabrication Methods that support new construction solutions for repurposed materials Automation

Companies Interviews and Takeaways

Pre-Deconstruction Site

Deconstruction

Design

Fabrication

% 10-20

Cost & Carbon Footprint comparison

Load Testing for structural performance

Material Informed Design to minimize the labor and extra material

Avoiding Storage Time directly from deconstruction to construction

Adding New Elements around %10-20 for structural assurance

MATERIAL DATASET

Digital Material Dataset

Material Dataset Data acquisition | IaaC Main Building

1 IAAC Main Building Laser scan

2 Selected Area

3 Selected Area Segmentation by Dimensionality and Planes

Colored by ID

Digital Material Dataset

Material Dataset Data acquisition | IaaC Main Building

Canupo Classifier Classification Parameter: Dimensionality

Classifier 1: Roof board

Confidence Threshold: 0.95

Confidence Threshold: 0.95

Classifier 2: Rafters

Confidence Threshold: 0.6

Digital Material Dataset

Material Dataset Data acquisition | IaaC Main Building

Quality by texture | Variation on each piece Visual Assessment

Final Dataset 220 Elements

Properties DeďŹ nition

Properties DeďŹ nition Data acquisition | IaaC Main Building

Quality by texture | Variation on each piece Visual Assessment

Name

Iaac beams

Location

Category

Material type

Material specific

Quantity

Carrer de Pujades, 102

Harvest

Wood

Pine

189

Availability

Length

Width

Height

Quality

From June 2028

1000-3000mm

100-500mm

100-500mm

Good

Density

Fiber Stress

E

Compressive Strength

Shear Strength

470 kg/m3

79.5 MPa

11,1Gpa

41.15 Mpa

8.25 Mpa

Description

Several beams from an old warehouse transformed to a research facility. They were used on the ceiling. They are believed to be 60 years old. The parts are available in different sizes.

100%

0%

Properties DeďŹ nition

Initial Structural Testing

All Elements Tested to Max Calculated Load

BUILDING A

BUILDING B

BUILDING C

BUILDING D

Sampling From Each Site Tested to Failure

Computer vision detection for knots and splits Example Test Machine : Admat Expert 2600 Example Defect CV : A Multiple Systems Approach to Wood Defect Detection, Xiangyu Xiao, 1998

DESIGN STRATEGY

Design Strategy

Design workflow

GENERATIVE DESIGN

ELEMENT MATCHING ALGORITHM Selecting from MATERIAL DATASET via ranges

OPTIMIZATION SOLVER EVOLUTIONARY ALGORITHMS

Design Strategy

Material Dataset Design Shape Fitness Dimensions Material Structural Analysis Structural Data Cost Analysis Distance to material resource Characteristics Quality

CASE STUDY PARAMETERS Length Range: 100mm to 10000mm Width Range: 50mm Height Range: 100mm Minimal Quality: 0 - 1 Maximum Distance: 100km Number of Items: 800-1500

Design Strategy

Discrete Wood Elements Plug in Building Barcelona Current Building

http://www.miasarquitectes.com/port folio/plug-in-building-barcelona/ Mias Architects Project Size Area: 7.600 sqm Use: Offices

Proposal - Front

Proposal - Interior

Design Strategy

Design Strategy Solar Performance

mer

Sum 72ยบ Wi

nte

r2

6ยบ

Fitting Function

Construction System Tolerances and adaptability

Design for Disassembly.

Dry joints allow for and easy disassembly of the structure

Tolerance x: 40 mm

Tolerance y: 40 mm

Tolerance z: 100mm

Adaptability. Depending on the material available the system can adapt (x,y,z direction) so that less transformations need to be done in the material

Fitting Function

Matching Algorithm Best Fitting

Design

Elements Needing Material Cuts

Fully Matched Elements

Material Dataset

Materials not matched Elements Needing New Material

Matched materials

Fitting Function

Matching Algorithm Sorted Elements

Scenario A

Scenario B

Scenario C

[...]

DATASET ITEM MATCHES WITHIN TOLERANCE

CUTOFF ADDED BACK TO DATASET

[...]

ELEMENT CUT TO MATCH DESIGN

UNMATCHED ELEMENT WILL NEED NEW MATERIAL

Fitting Function

Optimization Solver Parameters Design Space

Controllers

Controls Openings Scale Move Distance of influence

Optimized Parameters Degree of Use of Reused Materials Unmatched Element Count Cut Element Count

Carbon Cost of Chosen Materials Average Element Carbon Cost

Controls Facade Density

Structural Analysis

Distance of influence

Structural Maximum Utilization Structural Total Deflection

Fitting Function

Design Analysis Structural Analysis via Karamba

Material Cost and Embodied Carbon

Recovered Material

New Material

+ Transport Cost for 100km Shipping : Truck : 4.0 kg CO2 / m3 Material Rail : 2.7 kg CO2 / m3 Material

- Sequestered Carbon: Softwood Average : 0.34 kg CO2/ kg Material Varies by Age

+ Scanning, Analysis and Data Storage Electrical Costs Supports at Building Face

Total Beam Deection and Worst-Case Utilization are Calculated

+ New Lumber Processing and Disruption of Forest System

Transport Data from : Acoya Timber Transport Calculator, Example Material Pine

Fitting Function

Design Optimization

Fitting Function

Design Optimization Design Results From Dataset Choice

Dataset Slice 1 (1000 Elements) Maximum Distance : 200km

Dataset Slice 2 (850 Elements) Maximum Distance : 100km

Dataset Slice 3 (650 Elements) Maximum Distance : 50km

Cut Elements : 265 / 391 Unmatched Elements : 0

Cut Elements : 303 / 443 Unmatched Elements : 0

Cut Elements : 311 / 409 Unmatched Elements : 1

AUTOMATION

Material Properties

Transformation

Unloading and Sorting

Milling and Assembly

Prototype Fabrication : Checking Dimensions and Sorting by Order of Construction

Prototype Fabrication : Cutting to Size, Milling for Joinery, and Position

Properties Definition

Warehouse

4

From Iaac IAAC Pujades 102 to Plug in Barcelona

3

ARRANGEMENT

2

INSPECTION AND MARKING

REPLACING automated gantry crane bots move bundles around the storage system

5

ORGANIZATION bundles are organized roughly by length again and the bots just fill out the closest positions first and the database stores the location.

pieces are arranged in bundles by site of origin and roughly by length (e.g. <3m, <6m, <9m)

6

dimensions and visual information is measured and database entries are created/qr codes are marked

7 1

ARRIVING OF ELEMENTS elements arrived without organization from the demolition site

STRUCTURAL TESTING according to order specifications, bundles are retrieved and opened, individual elements are structurally tested

LOADING FOR SHIPPING specific and structurally tested pieces are loaded to be shipped

Properties DeďŹ nition

Storage and Logistics

200615_6bae4d51-1ab7-4fe0-a0f0-ea1ef32ba7d9

PROCESSING DATE + ELEMENT UUID

OPEN UP

References

Iterative Aggregation

Spatial Frames

Robotic Timber Construction Complex Timber Structures

HG-A Live components_Part to Whole

Prostho Museum Research, Kengo Kuma

The Sequential Structure 2, ETH Zurich

https://www.archdaily.com/544023/part-to-whole-hg-a-live-components

http://www.archtalent.com/projects/gc-prostho-museum-research-c enter

https://gramaziokohler.arch.ethz.ch/web/e/lehre/187.html

Others MAS DFAB: Gradual Assemblies https://ethz.ch/en/news-and-events/eth-news/news/2018/08/progra mming-for-perfect-shade.html

Facade Possibilities Design Strategy Morphogenesis Unit Principle | Rules | Direction

Interlocking by Aggregation 3D Multiplicity of dimensions is encourage

Joining Segments into Larger Beams Across Structure

9 Unique Orientations Coming from Cube Face Strategy

Facade Possibilities Design Strategy

Recursive Subdivision based on Performance Criteria

Pattern Generation | Point Selection for Grow Algorithm

Grow Algorithm (Depth) | Wood Location

Structural VeriďŹ cation

Facade Possibilities Design Explorations

Non Planar Surfaces | Solar Radiation

Karamba Simulation with Supports at Wall and Gravity Load

Attractor Points Position | Visibility

Attractor Points Radius | Visibility

Pattern Generation | Density

Structural Optimization

Final Product

Facade Possibilities Simple Interlocking system | Performance & Adaptability Density Based on Solar Radiation Cluster | Iterative Assembly