Jorge Tubella Portfolio 2019 by Jorge Tubella

2 0 19 PORTFOLIO - SELECTED WORKS

JORGE D. TUBELLA

CONTENTS SELECTED WORKS

Architectural Studies

A.I. | Software Research

Robotics Research

Comprehensive Studio

Master Thesis

Establishing RDF Lab

Graduate Design Studio 7

Reinforcement Learning

Inauguration Scissors

Graduate Design Studio 8

Revit API Studies

Robot Drums

Dynamic Shade System

Cyborgs Future

Water Wand

69 3

1 Architecture Studies

Architecture has transformed many times since man created their first occupied space and will continually evolve till man has become extinct. We are in a very special time in that evolution. The technology that will impact is not only bounded to architecture but to every industry imaginable. We are in the midst of the fourth industrial revolution and I am extremely excited to experience this new architectural age. My architectural studies were always focused on how to integrate some new method or technology into the design or design process. The selected work are example of how I was able to achieve those goals.

1.1

Comprehensive Design Studio Workforce Housing

Comprehensive studioâ&#x20AC;&#x2122;s focus was to incorporate building systems into our design while following local zoning, codes, and ADA. The program of the design is a multi use building, Affordable housing with commercial program at the ground level. The site is located in Indian Creek & 27th Miami Beach, Florida. The studio generated drawings such as, framing plans, foundation, plumbing, HVAC, life safety, and electrical throughout the course to demonstrate competency in general system design.

ARC ARC 5361 5483

Integrated Comprehensive Design Integrated Building Systems Fall Fall2017 2017

Project ProjectName Name

Project Address:

Yerba Buena Lofts Exercise 0 : Title Block

855 Folsom St, San Francisco, CA

Yerba Buena Lofts Exercise 0 : Title Block

855 Folsom St, San Francisco, CA

ARC ARC 5361 5483

Integrated Comprehensive Design Integrated Building Systems Fall Fall2017 2017

Project ProjectName Name

Project Address:

Yerba Buena Lofts Exercise 0 : Title Block

855 Folsom St, San Francisco, CA

Name

Drawn By:

Test Sheet

Date

Stephanie

Stephanie Alvarez Alvarez Jorge Tubella Drawing

8/24/17

Date:

Scale

09-18-2017

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855 Folsom St, San Francisco, CA

Stephanie

Stephanie Alvarez Alvarez Jorge Tubella Drawing

09-18-2017

Rendering of Final Design

Yerba Buena Lofts Exercise 0 : Title Block

Name

Drawn By:

Test Sheet

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Stephanie Alvarez Alvarez Jorge Tubella Drawing

Date

8/24/17

Date:

09-18-2017

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Stephanie Alvarez Alvarez Jorge Tubella Drawing

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Sheet No.

Materials, Finishes, General Aesthetics

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Integrated Comprehensive Design Integrated Building Systems Fall Fall2017 2017

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ARC ARC 5361 5483

Integrated Comprehensive Design Integrated Building Systems Fall Fall2017 2017

Sheet No.

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Project ProjectName Name

ARC 5361 ARC 5483Lofts Yerba Buena Exercise 0 :Systems Integrated Comprehensive Design Integrated Building Fall Fall2017 2017 Title Block

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Project Address:

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ARC ARC 5361 5483 Integrated Comprehensive Design Integrated Building Systems Fall Fall2017 2017

Project ProjectName Name Francisco, Integrated Comprehensive DesignCA Integrated Building Systems Yerba Buena Lofts Fall Fall2017 2017

ARC ARC 5361 5483

Exercise 0 : Title Block

Integrated Comprehensive Design Integrated Building Systems Fall Fall2017 2017

Project Address:

855 Folsom St, San Francisco, CA of a home can be a time consuming Finishing the interior

Project Address:

ARC ARC 5361 5483

Project Address:

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Yerba Buena Lofts Exercise 0 : Title Block

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855 Folsom St, San Project Address: Project ProjectName Francisco, CASt, San 855 Name Folsom Yerba Buena Lofts Yerba Buena Lofts Francisco, Exercise 0 : Exercise 0 CA : Title Block Title Block

Project ProjectName Name

ARC ARC 5361 5483

Integrated Comprehensive Design Integrated Building Systems Fall Fall2017 2017

855 Folsom St, San

Integrated Comprehensive Design Integrated Building Systems Fall Fall2017 2017

ARC ARC 5361 5483

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Project Address:

Integrated Comprehensive Design Integrated Building Systems Fall Fall2017 2017

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Project ProjectName Name

ARC ARC 5361 5483 Yerba ARC 5361 ARC 5483Lofts Project Name Project Name Buena Exercise 0 : Lofts Yerba Buena Title Block0 : Exercise Project Address: Title Block

ARC 5361 ARC 5483Lofts Yerba Buena Exercise 0 :Systems Integrated Comprehensive Design Integrated Building Fall Fall2017 2017 Title Block

Materials, Finishes, General Aesthetics

YERBA BUENA LOFTS YERBA BUENA LOFTS 855 FOLSOM STREET 855 FOLSOM STREET 855 FOLSOM STREET Structures│Systems, Spacing & Details Structures│Systems, Spacing & Details YERBA BUENA LOFTS YERBA BUENA LOFTS Environmental│Cross Environmenta Light & Sha Spacing & Details Structures│Systems, Spacing & Details Structures│Systems, Spacing & Details Environmental│Cross Ventilation, Natural Light Environmental│Cross & Shading Systems Ventilation, Natural Light Environmental│Cross & Shading Systems Ventilation, Natural Light & Shading Systems Ventilation, Natural Structures│Systems, Structures│Systems, & Details JORGE TUBELLASpacing Structures│Systems, & Details JORGEStructures│Systems, & Details JORGE TUBELLASpacing │ STEPHANIE ALVAREZ │ STEPHANIE ALVAREZ TUBELLASpacing │ STEPHANIE ALVAREZ ARC ARC ARC 5361 5483 ARC 5361 5483 ARC 5361 5483 ARC ARC ARC 5361 5483 ARC 5361 5483 855P2.1 FOLSOM STREET 855 FOLSOM STREET ARC P2.1 P2.1 A101 A101 A101 Structures│Systems, Spacing & Details Structures│Systems, Spacing & Details Environmental│Cross Ventilation, Natural Light Environmental│C & Shading Structures│Systems, Structures│Systems, & Details JORGE TUBELLASpacing & Details JORGE TUBELLASpacing │ STEPHANIE ALVAREZ │ STEPHANIE ALVAREZ ARC ARC 5361 5483 09-18-2017

Materials, Finishes, General Aesthetics

Structures│Systems, Spacing & Details Structures│Systems, Spacing & Details Structures│Systems, SpacingNatural & Details 1/8" = 1'-0" = 1'-0" Systems YERBA BUENA LOFTS Environmental│Cross Ventilation, Environmental│Cross Light1/8" & Shading Ventilation, Natural Environmental│Cross Light & Shading Systems Ventilation, Natural Light & Shading Systems

Project ProjectName Name Project Address:

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09-18-2017 Sheet No.

Integrated Comprehensive Design Integrated Building Systems Fall Fall2017 2017

structural grid. with a deep plan and tight structural grid.

Name

45’ Stephanie Alvarez Alvarez 60’ Drawn By: Jorge Tubella Drawing 50’ Stephanie Alvarez 35’ 60’ Test Sheet Sheet Name: Jorge Tubella Drawing 20’ 20’Units & Typologies

15’

Couples Stephanie

YERBA BUENA LOFTS 855P2.1 FOLSOM STREET A101

P2.10 A101

Exercise 0: P2.14 Miami Beach A101 Title BlockHousing Workforce

Single

15’

1/8" = 1'-0"

Name

Sheet No.

Integrated Comprehensive Design Integrated Building Systems Fall Fall2017 2017

855 Folsom St, SanProject Address: Francisco, CA 855 Folsom St, San

15’

09-18-2017

15’

45’ Stephanie Alvarez 15’ Alvarez Drawn By: Jorge Tubella Drawing 50’ Stephanie Alvarez 35’ 60’ Test Sheet Sheet Name: Jorge Tubella Drawing 20’ Units & Typologies 35’

60’

45’

Date

20’

8/24/17

Drawn By:

50’

35’

37’

20’

Date

15’

35’

Project Address: 45’

45’

Sheet No.

Typology :

37’ 20’

Francisco, CA

Scale

Couples

45’

Target Activities

15’

60’

Sheet No. Project Project Name Sheet No.Name

Project Address:

20’

Date: 15’

15’ Integrated Comprehensive Design Integrated Building Systems Fall Fall2017 2017

15’

Sheet No.

Young Single Roommates Young Young Single Couples Women

15’

1/8" = 1'-0"

Sheet No.

Project ProjectName Name

60’

45’

Single

Sheet No.

15’

35’

8/24/17

Scale

09-18-2017 15’ 15’

Sheet No.

60’

Project ProjectName Name

35’

Sheet No.

Integrated Comprehensive Design Integrated Building Systems Fall Fall2017 2017

Project Address: 45’

20’

Single

Date:

1/8" = 1'-0"

Typology : P2.14 P2.14 ARC ARC 5361 5483 loft or warehouse design A101 A101 ARC 5361 ARC ARCwith 5483 ARC 5361 5483 warehouse design a deep plan loft andor tight

Yerba Buena Lofts Project ProjectName Name 60’ Exercise 0: Residents: 35’ Yerba Buena Lofts Title Block Exercise 0 : Project Address:

60’

Date

to install.

Scale

09-18-2017

P2.1 A101

Date:

Francisco, CA

15’

45’

60’

Residents: 20’

Single

15’

Young Young Young Single Single Couples Women Men Name

20’

Fall 2017 Fall

Target Activities 1/8" = 1'-0"

Single

Couples

JORGE TUBELLA │ STEPHANIE ALVAREZ P2.10 P2.10 A101 A101 P2.1 A101 1/8" = 1'-0"

35’

60’

Sheet No.

45’

1/8"50’= 1'-0"Sheet No.Date:

No.

Scale Sheet No.TUBELLA │ STEPHANIE ALVAREZ JORGE 09-18-2017 20’

P2.1 A101

50’

35’

855 Folsom St, SanProject Address: Francisco, CA 855 Folsom St, San

Single

1/8" = 1'-0"

15’

Exercise 0 : Title Block

Project Address:

Residents:

15’

45’

Project Address:

20’

1/8" = 1'-0"

Project ProjectName Name

60’

Francisco, CA

Scale

15’

15’ 15’

P2.14 A101

20’

09-18-2017

15’ 15’

15’

45’

20’

Date:

1/8" = 1'-0"

Scale

Residents:

20’

Date

Sheet No.

20’

Scale Sheet15’ No.

Sheet No.

37’

09-18-2017

Sheet No.

15’

Typology : ARC 5361 ARC 5483 Sheet No. loft or warehouse design Integrated Comprehensive Design Integrated Building Systems warehouse design with a deep plan loft andor tight Fall Fall2017 2017 structural grid. with a deep plan and tight structural grid.

60’

Yerba45’Buena Lofts Project ProjectName Name 60’ Exercise 0 : 45’ 35’ Buena Lofts Yerba Title Block

37’

P2.10 A101

Integrated Comprehensive Design Integrated BuildingAlvarez Systems

P2.1 A101

Project ProjectName Name

Date: Sheet No.

15’

Sheet No.

60’

45’

Date:

Scale

Date:

Scale Date

15’

Sheet No. Integrated Comprehensive Design Integrated Building 15’ Systems Sheet No. Fall Fall2017 2017 Integrated Comprehensive Design Integrated Building Systems Fall Fall2017 2017

60’

37’

20’

Scale

15’

15’ Typology :

P2.10 A101

Project ProjectName Name

37’

60’

20’ 35’

Units & Typologies Date

Scale

35’Sheet

35’

15’

35’

Date:

Date

Date: Sheet Name:

Scale Drawing

Sheet No. 15’

15’

20’

to install.

Sheet Name: Drawn By:

Date

Date:

Scale

Sheet No.

Project Address:

1/8" = 1'-0"

Project Address:

Date Name

Sheet Name:

Sheet No.

Project Name Project Name Target Demographics

1/8" = 1'-0"

Sheet No. Integrated Comprehensive Design Integrated Building Systems 15’ Sheet No. Fall Fall2017 2017 Integrated Comprehensive Design Integrated Building Systems Fall Fall2017 2017

15’

20’

855 Folsom St, San

Date: Lofts Yerba Buena Francisco, CA Exercise 0: Scale 09-18-2017 Title Block

Drawing

Sheet No.

45’

60’

Drawn By:

Drawing

Date:

15’

Sheet No.

45’

37’

20’

Scale

60’

37’

Name

Drawn By:

Sheet No.

15’

Integrated Comprehensive Design Integrated Building Systems Fall Fall2017 2017

20’

Scale

Sheet No.

structural grid.

Test Sheet

Residents:

8/24/17

Young Young Single Single Women Men

Test Sheet

Date:

09-18-2017

60’

15’

Drawing

60’

45’45’

Sheet Name:

8/24/17

Sheet No.

Jorge Tubella Drawing

60’

Scale

YERBA BUENA LOFTS YERBA BUENA LOFTS 8/24/17 855 FOLSOM STREET 855 FOLSOM STREET Date

P2.10 A101

45’45’

37’

Date:

09-18-2017

15’

15’ Units & Typologies

50’

P2.10 A101

20’

Date:

Drawn By:

Sheet Name:

Young Single Men

NIE ALVAREZ

20’

8/24/17

Name

Drawing

15’

LOFTS STREET

Couples Stephanie 09-18-2017 Stephanie 1/8" 1'-0" AlvarezAlvarez=Stephanie Jorge Tubella Stephanie Alvarez Alvarez Test Sheet Drawn By:

09-18-2017 15’

60’

Drawing Date

Sheet No.

45’

37’

1/8" = 1'-0"

Date:

09-18-2017

Sheet No.

Alvarez

Target Demographics

Sheet No.

15’

8/24/17

20’

Date

Scale

15’

Drawn By:

Sheet No.

Finish Costs

37’ 60’

Date

20’

Date:

15’

Sheet Name:

Test Sheet

Form Evolution Name

Scale

Sheet No.

Name 15’

15’

Test Sheet

Drawing

60’

45’

Scale

Sheet No.

15’

Drawing

20’

15’

Date:

Drawing

Project Address:

Scale

YERBA BUENA LOFTS YERBA BUENA LOFTS ARCSTREET 5361 5483 855 FOLSOM 855 FOLSOM STREET Stephanie Stephanie

P2.10 A101

Date:

Sheet No.

Drawn By: 15’

15’

Drawn By:

15’

Sheet No.

Project Address:

20’

15’

Name

15’

Sheet No.

37’

45’

Scale

Sheet No.

StephanieP2.10 P2.10 A101 A101 : StephanieStephanie Stephanie AlvarezAlvarez Typology Typology : P2.14 ARC ARC 5361 5483 loft or warehouse design A101 Jorge Tubella ARC ARC 5361 5483 Stephanie Alvarez Stephanie Alvarez or warehouse design with a deep plan loft and tight P2.14 Alvarez Alvarez A101 with a deep plan and tight structural Jorge Tubella Jorge grid. Tubella

Sheet No.

35’

15’

Drawn By: Sheet Name: Date

Integrated Comprehensive Design Integrated Building Systems Fall Fall2017 2017

Project Address:

Project ProjectName Name

1/8" = 1'-0"

Date:

Scale

Integrated Comprehensive Design Integrated Building Systems Fall Fall2017 2017 Sheet No.

Exercise 0 : Stephanie Alvarez Residents: Stephanie Alvarez Yerba Buena Lofts Title Block Alvarez Stephanie Drawn Alvarez Exercise 0: the costTubella of interior Byfinishes severely ofBy:reducing theResidents: the cost of Jorge interior finishes of the Jorge Tubella Block Drawing Drawing Title Alvarez 855 the Folsom St, San ng into the finishes units which andStephanie increase only investing into finishes which increase Alvarez Francisco, CA 855 Folsom St, San Jorge Tubella P2.10 Drawing e most for the cost; the it lowers aesthetic the total value the most for the cost; it lowers the total A101 Test Sheet Test Sheet Francisco, CA Sheet Name: Sheet Name: e cost. This makes overall the units square more footage cost. This makes the units more Single Test Sheet Finishmaterials Costs and Finishand Costs dering affordable labor needed when considering materials labor needed Single Date Date to install. Date 8/24/17 8/24/17 8/24/17 Couples 60’

Name

Drawing Sheet Name:

Scale

15’

855 Folsom St, San Buena Lofts Francisco, CA Exercise 0 : Scale 09-18-2017 Date: Title Block

YERBA BUENA LOFTS 855 FOLSOM STREET Stephanie

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Integrated Comprehensive Design Integrated Building Systems Fall Fall2017 2017

Date:

Scale

Sheet No.

Integrated Comprehensive Design Integrated Building Systems Fall Fall2017 2017

45’

Date:

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Which you a shelland andonly save $25 to $60 square foot. Which you a shelland andonly save $25 to $60 square foot. Which you Date No. No. Name Name Name JORGESheet TUBELLA │ STEPHANIE ALVAREZ JORGESheet TUBELLA │ STEPHANIE ALVAREZ Folsom St, San Folsom St, San Folsom St, San Date: 20’ Jorge Tubella 20’ the aesthetic the most855 fortothe cost; it lowers the total theName: aesthetic the most855 fortothe cost; it lowers the total choose the aesthetic the most855 fortothe cost; it lowers the total Name Name Name Jorge Tubella JORGE TUBELLA │ STEPHANIE ALVAREZ Jorge Tubella Test Sheet Test Sheet Test Sheet choose to do value is completely up you. choose to do value is completely up you. to do value is completely up you. Drawing Drawing Drawing 20’ Sheet Sheet Name: Sheet Name: 8/24/17 Scale 35’ 35’ Stephanie Stephanie Stephanie By: Drawn By: Drawn By: square footage cost. This makes the overall square footage cost. This makes the units more -overall square footage cost. This makes the units more Drawn 09-18-2017 Francisco, CAunits more Francisco, CA Francisco, CA -overall homebuildingsmart.com homebuildingsmart.com homebuildingsmart.com Stephanie Stephanie Stephanie Drawn By: Costs Drawn By: Costs Drawn By: Costs Finish Finish Finish 1/8" = 1'-0" when considering materials and labor needed affordable when Alvarez Stephanie Alvarez Stephanie Alvarez affordable when considering materials and labor needed affordable considering materials and labor needed Stephanie Test Sheet Test Sheet Test Sheet 20’ 20’ Sheet Name: Sheet Name: Date: Date Date Date Sheet Name: Alvarez Alvarez Alvarez Stephanie Alvarez Stephanie Alvarez Alvarez Scale Jorge TubellaStephanie Jorge Tubella Jorge Tubella to install. to install. to install. Alvarez Alvarez Alvarez Drawing Drawing Drawing By severely reducing the cost of interior finishes of60’the By severely By severely reducing the cost of interior finishes of60’the 09-18-2017 Natural Light Natural Light Natural Light 8/24/17 8/24/17 8/24/17 FLOOR DEPTH FLOOR DEPTH FLOOR DEPTH Jorge Tubellareducing the cost of interior finishes of60’the Jorge Tubella Jorge Tubella Drawing Drawing Drawing 1/8" = 1'-0" Date Date Date units and only investing into the finishes which increase units and only investing into the finishes which increase units and only investing into the finishes which increase Test Sheet Test Sheet Test Sheet Sheet Name: Sheet Name: the aesthetic value the most for the cost; it lowers the total theName: aesthetic the most for the cost; it lowers the total Sheet Name: Date: Date: Date: 8/24/17 8/24/17 8/24/17 Test Sheet value the most for the cost; it lowers the total the aesthetic value Test Sheet Test Sheet Sheet Sheet Name: Sheet Name: Scale Natural Light Natural Light overall square footage cost. This makes the units more overall square footage cost. This makes the units more footage cost. This makes the units more 60’ FLOOR DEPTH 60’ FLOOR DEPTH overall squareScale 60’ FLOOR DEPTH Natural Light Scale 09-18-2017 09-18-2017 09-18-2017 Date Date Date Finish Costs Finish Costs Finish Costs considering materials and labor needed affordable when considering materials and labor needed affordable when considering of a15’ homeaffordable can bewhen aFinishing time consuming the interior of a home can beSheet aDate time consuming 1/8" = 1'-0" 1/8" = 1'-0" materials and labor needed 1/8" = 1'-0" Date: Date: Date Date Date: No. 8/24/17 8/24/17 8/24/17 Scale Scale Scale to install. toNo.install. to install. Sheet 15’ 09-18-2017 09-18-2017 09-18-2017 ss, however if youand feelpainstaking comfortable process, however if you feel comfortable 8/24/17 8/24/17 8/24/17 Typology :

Name

Drawn NameBy:

Date:

Materials, Finishes, General Aesthetics

Integrated Comprehensive Design Integrated Building Systems Fall Fall2017 2017

Project ProjectName Name

Jorge Stephanie Tubella Alvarez Drawing Drawing

Test Sheet Research, Precedents, and, Parti Diagrams Date

Date

8/24/17

12/12/17 Scale

1/8" = 1'-0"

Address

Addres

2660 Collins Ave. 2660 Collins Ave. 266 Miami Beach Fl Miami Beach FlMia 33140 33140 331 Name

Name

Nam

Jorge Stephanie Jorge StephanieJo S Tubella Tubella Tu Alvarez Alvarez A Drawing Drawing

Drawing Drawing

Drawin Draw

Test Sheet Test Sheet Te Research, Precedents,Research, Precedents Res and, Parti Diagrams and, Parti Diagrams and Date

Date

12/12/17

12/1

8/24/17

8/2

Scale

Sca

1/8" = 1'-0"

1/8

Sheet No.

A101 A001 Sheet No.

Sheet No.

A101 A001 Sheet No.

Sheet No.

She

A101 A001 A Sheet No.

Sheet

RRATIVE + INSPIRATION IMAGES (KEEP THEM ABSTRACT) Site Plan NTS

Ground Plan NTS

” = 20’

Second Level Floor Plan NTS A

Fourth Level Floor Plan NTS A

ARC 5483 5361 Integrated Comprehensive Design Integrated Building Systems Fall 2017 Fall

Project Name Project Name

Exercise 0: Miami Beach Title BlockHousing Workforce

Site Plan 1/32” = 1’ Address

2660 Collins Ave. Miami Beach Fl 33140

Site Plan 1/32” = 1’

Name

Jorge Stephanie Tubella Alvarez

Drawing Drawing

Third Plan TestFloor Sheet

Ground Level 1” = 20’

Date

8/24/17

12/12/17 Scale Scale

1”1/8" = 20’ = 1'-0"

Sheet No.

A101 A103 Sheet No.

Fifth Level Floor Plan NTS A

ARC 5483 5361 Integrated Comprehensive Design Integrated Building Systems Fall 2017 Fall

Project Name Project Name

Exercise 0: Miami Beach Title BlockHousing Workforce

Address

2660 Collins Ave. Miami Beach Fl 33140 Name

Name

Jorge Stephanie Tubella Alvarez Drawing Drawing

TestFloor Sheet Fifth Plan Date

Date

8/24/17

12/12/17 Scale Scale

1”1/8" = 20’ = 1'-0"

Sheet No.

A101 A105 Sheet No.

North Elevation NTS

West Elevation

1” = 20’

North Elevation

Section B

1” = 20’

1” = 20’ 11

North Elevation

1” = 20’

Roof 81' - 0"

Residential 5 70' - 0"

Residential 4 59' - 0"

Residential 3 48' - 0"

Residential 2 37' - 0"

Residential 1 26' - 0" Auditorium Entrance 18' - 0"

Pedestal 9' - 0" Street Level 5' - 0" Base Grade 0' - 0"

West 1"C=NTS 20'-0" Section 1

Section C

Section B

1” = 20’

West Elevation

1” = 20’

Roof 81' - 0"

Residential 5 70' - 0"

Residential 4 59' - 0"

Residential 3 48' - 0"

Residential 2 37' - 0"

Residential 1 26' - 0" Auditorium Entrance 18' - 0"

Pedestal 9' - 0" Street Level 5' - 0" Base Grade 0' - 0"

West

Section A

1" A=NTS 20'-0" Section

1” = 20’ 13

Water Proof Membrane Roof Board Rigid Insulation Concrete

Gravel Water Proof Membrane Primary Flashing Secondary Flashing

1'-8"

Steel Reinforcement Concrete Shear Studs Girder I-Beam Joist I-Beam

1'-8"

Gravel Water Proof Membrane Primary Flashing Secondary Flashing

Metal Deck

1. conceptual diagrams / axons Parapet

Girder: 1.5' Exposed Structural System Concrete Partition Wall

Gravel Water Proof Membrane Primary Flashing Secondary Flashing

1'-8"

Wood Finish in interior Floor 8" Door to balcony

Josit: 10"

Steel Reinforcement Concrete Shear Studs Smooth ConcreteGirder Finsih I-Beam Mullion Joist I-Beam Steel Reinforcement Concrete Shear Studs Girder I-Beam Joist I-Beam

Mullion Steel Reinforcement Concrete Shear Studs Girder I-Beam Joist I-Beam

Wooden Lovre

11 '

Exposed Girder

Railing

Mullion Steel Reinforcement Concrete Shear Studs Girder I-Beam Joist I-Beam

Insulated Window

Balcony

Inside unit

Parapet

17 '

Girder: 1.5'

Joist: 10"

Insulated Glass Pane Glass Door Mullion

Slab Concrete Insulation Air Pocket

Column

Concrete Finish

Footing

Detailed Section NTS

Grade Beam

ems

Site Plan 1/32” = 1’

M W

2 M 3

J T

D S

Water Proofing Membrane Roof Board Vapor Retarder Rigid Insulation Light Weight Concrete Steel Metal Deck

Parapet

Girder: 1.5' Wood Louvre / Facade Elements

Note: Wall System chosen is from Hunter Douglas specifically the TERRART Bauguette facade system. This system incorporates wooden vertical elements. In my design I have taken these elements to accentuate the language of building. Additionally the louvre provides shading from the glare coming from the West.

Railing

Interior Of Unit

Water Proof Membrane

Roof Board Balcony Vapor Retarder Wood Finish

Rigid Insulation

Concrete

Steel Deck

Parapet

Lobby Space

Double Glazed Curtain Wall

Concrete Plaster Exposed Concrete Floor

Mullion

Air Pocket Insulation

Concrete

Column Retail Space Footing

Grade Beam

Wall Axon Sectrion Facade Detail NTS

ARC

Integrated Comp Integrated Bu Fall Fall

Project Name Project Name

Exercise Miami Bea Title Bloc Workforce

Address

2660 Col Miami Be 33140 Name

Name

Jorge Steph Tubella Alvare Drawing Drawing

Test She Foundation Date

Date

8/24/17

12/12/17 Scale Scale

1/16” 1/8" =1’ = 1'-

Sheet No.

A1 S100 Sheet No.

Foundation Plan NTS

North Facade: Residents Tower

20'

W33X152

4. 59

24' - 6"

W12X16

W16X40

W12X16

4. 63

W12X16

W16X40

W24X131

W8X31

W24X131

W12X16

W24X131

W16X40

W12X16

W8X31

W16X40

W12X16

W6X12

W12X16

W6X12

W12X16

W8X31

W18X158

W12X16

4. 43

Drains W12X16

Slab Proposed slab system is Corrugated Steel Deck - 8" thick

W12X16

W16X40

W12X16

W16X40

Egress W18X158

2660 Collins Ave. Miami Beach Fl 33140 Name

Name

Jorge Stephanie Designer Tubella Alvarez

W12X16

Address

Return

Drawing Drawing Drawing

Test Framing Sheet Ground Structuralplan Res 1 Floor Plan

W18X158

W16X40

4. 58

IAN IND

Notes

Joist Joist are spaced every 6' at Habitable Levels and every 8' on Roof Level.

W6X12

Supply

22' - 6"

W6X12

W16X40

W12X16

W16X40 ?

W16X40

W12X16

W16X40

W12X16

26' - 0"

W6X12

W12X16

W16X40

W6X12

W12X16

Cooling Tower

Date

W18X158

W12X16

W16X40

W18X158

W12X16

W16X40

6' - 6"

22' - 0"

W24X131

W16X40

40' - 0"

50' - 6"

1/16” 1’1'-0" = 1'-0" 1/8" ==1/16"

Exhaust Fan 5

Sheet No.Sheet No.

A135 A101 S101 Sheet No.

22' - 0"

294.32'

300.00'

W33X152

31' - 0"

Scale

Scale Scale

7.5'

W18X158

W16X40

W12X16

10/09/17 8/24/17

12/12/17

33' - 6"

W16X40

W12X16

4. 80

' 25

W12X16

W8X31

W12X16

4. 90

W12X16

W18X158

4. 90

W6X12

4. 32 4. 31

4. 22

4 A .70 VG .

W6X12

W18X158

W24X131

W6X12

W16X40

W24X31

W12X16

4. 54

W33X152

W6X12

W33X152

° 3641 110.

4. 45

4. 30

- 0"

53'

3X1

Edge of Slab

G. C.& 3.5'

W12X16

W33X152

Date

Columns C1 Dimensions: 1' x 2' Concrete

4. 69

' R (50

W6X12

4. 65

MEP Shaft

4. 07

Miami Exercise 0 :Beach Miami Beach Workforce Title Block Workforce Housing Housing

W12X16

23' - 0"

W24X131

W16X40

W12X16

W16X40

W12X16

W16X40

W8X31

4. 67

4. 63

W6X12

4. 40

W6X12

W8X31

W6X12

4. 73

C1 26' - 0"

W16X40

W24X131

W8X31

W24X131

W8X31

' .00 160

22' - 0"

Elevator Shaft

Egress

Integrated Building Integrated Comprehensive DesignSystems Integrated Building Systems Fall 2017 Fall 2017 Fall

Project Name Project Name Project Name

9' - 0"

10' - 6" W8X31

W6X12

20'

ARC ARC 53615483 5483

W12X16

TYP.

350.00'

40' - 0"

26' - 0"

W12X16

4. 75

25' - 0"

W16X40

4. 35

DR.

4. 66

W24X131

W12X16

.6 9 E RIV K D E E CR /W)

31' - 0"

W16X40

359° 69.6

4. 40

4. 17 4. 12

22' - 0"

0.5' CURB

4. 21

DROP CURB

20' Setback Front

W12X16

4. 46

TYP.

5. 4. AV 20 26 G .

25'

3. 65

W12X16

4. 15

.1 3

W16X40

3.5' C.&G. 4

Drains

' 25

72.00'

30'

F 4. 4. TC L= 44 20 = 3 3.

3. 83

Fan Coil Unit

4. 46

TYP.

4. 40

350.00'

20' Setback Front

4. 35 D

DR.

4. 59

4. 63

W24X131

23' - 0"

W16X40

W12X16

W16X40

W12X16

W16X40

W12X16

W16X40

W12X16

37' - 0" W12X16

23' - 6"

4. 73

Chiller Boiler

11' - 6"

6' - 0"

W8X31

W6X12

4. 65

Date

10/09/17 8/24/17

12/12/17 W12X16

Scale Scale 33' - 6"

W24X131

W12X16

Framing Plan: Third Level NTS

14' - 6"

30' - 6"

26' - 0"

W12X16

W24X131

Scale

1/16” 1’1'-0" = 1'-0" 1/8" ==1/16"

Retail

20' - 0"

W24X131 W12X16

W12X16

W24X131

37' - 0"

Sheet No.Sheet No.

A136 A101 S102 Sheet No.

22' - 0"

4. 90

22' - 0"

4. 90

4. 80

300.00'

Date

W12X16 W6X12

W12X16

4. 54

4. 32 4. 31

4 A .70 VG .

plan Test Framing Sheet Second Floor ResPlan 2 Structural

9' - 0" ' 25

Name

Jorge Stephanie Designer Tubella Alvarez Drawing Drawing Drawing

W24X131

Egress

2660 Collins Ave. Miami Beach Fl 33140 Name

W6X12

W16X40

4. 22

4. 69

W6X12 W6X12

W24X131 W6X12

W24X131

W6X12

14' - 0"

W6X12

4. 45

W12X16

4. 58

W16X40

W24X131

W16X40

W12X16 W12X16

° 3641 110.

W6X12

W24X131

W6X12

W24X131

W6X12 W12X16

4. 43

W12X16

W24X131

W6X12

W8X31

W6X12

13' - 0"

30' - 6"

Drains

10' - 0"

17' - 6"

7' - 0"

W6X12

Egress

Address

Name

W6X12

Miami Exercise 0 :Beach Miami Beach Workforce Title Block Workforce Housing Housing

22' - 6"

W16X40

W12X16

W16X40

W12X16

W16X40

W12X16

W12X16 W24X131

4. 67

4. 63

4. 40

W6X12

4' - 0"

Edge of Slab

W8X31

MEP Shaft

4. 07

4. 30

Columns C1 Dimensions: 1' x 2' Concrete

31' - 0"

W24X131

W6X12

G. C.& 3.5'

Slab Proposed slab system is Corrugated Steel Deck - 8" thick

Elevator Shaft

31' - 0"

W24X131

ARC ARC 53615483 5483 Integrated Building Integrated Comprehensive DesignSystems Integrated Building Systems Fall 2017 Fall 2017 Fall

Project Name Project Name Project Name

37' - 0"

W6X12

' R (50

Joist Joist are spaced every 6' at Habitable Levels and every 8' on Roof Level.

31' - 0"

W24X131

W16X40

20'

IAN IND

Notes

22' - 6"

TYP.

W6X12

W12X16

W8X31

W6X12

W12X16

' .00 160

11' - 0"

.6 9 E RIV K D E E CR /W)

4. 21

4. 75

359° 69.6

4. 75

0.5' CURB

7.5'

5. 4. AV 20 26 G .

25'

DROP CURB

20'

' 25

3. 65

4. 66

4. 15

.1 3

4. 17

3.5' C.&G. 4

Drains

4. 12

30'

72.00'

3. 83

F 4. 4. TC L= 44 20 = 3 3.

Framing Plan: Ground Level NTS

294.32'

HVAC System Axonometric

Note This building is equipped with hydraulic elevators so does not require bulkheads. Maintenance is done through a ladder.

AR ARC

Integrated

Integrated CoB Integrated FF

Project Na

Project Name Project Name

Miam Exercis Work Miami Be Title Blo Hous Workforc

Address

Joist

Address

2660 2660 Co Miam 33140 Miami B 33140 Name

Des

Name

Girder

Jorge Step Tubell Alvar Drawing

Drawing Drawing

Struc

Test Sh Structura

Shear Wall

Date

10/14 8/24/17 Date

Date

12/12/17

Column

Scale Scale Scale

1/16” 1/8" ==1’1

Grade Beam Footing Strip Footing

Structural Axonometric

Sheet No.

A A1 S30 Sheet No.

Sheet No.

1.2

Graduate Design Studio 7 Drastic Sea Level Rise The studioâ&#x20AC;&#x2122;s focus was to design for a post apocalyptic sea-level rise scenario where the sea level had risen 30â&#x20AC;&#x2122;. The site was located in the Biscayne Slip in Downtown Miami. The program is a University / Research Center specializing in cause and adaptability of sea level rise. Utilizing Maya + Unreal Engine 4 I was able to simulate my design in a digital environment and explore it as I have never done before. This allowed me to understand different aspects of my design. Further use of this program can help with site analysis such as sun penetration in the structure and how to react to it or using its physics engine. Additionally, when combined with virtual reality, these simulations are unmatched; the only thing more accurate would be the building itself.

Flooded Site

Green Space

Program

Once the area has flooded the program distribution evolves. Allowing sections that were formerly safe from the water, beach front; and areas that were too shallow to use water vehicles deep enough to use. Water sports, exterior of building

Stadium Seating

Linear park on top deck

Rooftop Park

Class rooms

Under Water Program CROSS SECTION

Class Rooms Stadium Seating

Rooftop Park

ArtiďŹ cial Beach

Under Water Program Overall view of design in Downtown Miami Using Unreal Engine 4

The main interesting thing about this project is that it I was able to visualize my design within a video game software, Unreal Engine 4. This allowed me to get a unique first person perspective of my site and design, which was model 1:1. By having this technology integrated into my workflow I was able to make crucial design choices in proportion and presumed comfort.

Final Rendering of design and use

1.3

Graduate Design Studio 8 Responsive Wind Canopy The studioâ&#x20AC;&#x2122;s focus was to design a structure in Miami Beach that would re-purpose a service alley. The program of the design is a roof structure that can harness the wind and redirect it into the alley to create a micro-climate, enjoyable by the occupants in the alley. Using the Arduino board I programed all the functions required to make the model operational and programmed in Java. Some of the coding included was linking the app from the phone through the IR sensor, controlling and timing of motors, and attaching all motors to the code so it could recognized by the board. By using motors, the panels were able to move to a into a position to take advantage of the prevailing winds. Three motors were used. One to move on the Z axis, one to rotate 180 degrees, and one to tilt the panel accordingly.

SHIFT TO SOURCE

Wind Effect

Shift up to respond to source and gather its resources.

Panel Design The driving factor for this project was from the prevailing winds

SHIFT TO SOURCE Paneling System Paneling System Paneling System

STANDARD FORM

on the site and the micro conditions formed by the wind as a SHIFT TO SOURCE

SHIFT TO SOURCE

result.

RCE

Panels Reacting to wind

By harnessing the wind and redirecting it to the site we are able to create an enjoyable microclimate for the occupants.

STANDARD FORM

Shift up to respond to source and gather its resources.

STANDARD FORM

The system would be composed of an array of panels that would automatically orient themselves to the prevailing winds and shift in order to redirect the air, creating an artificial breeze.

Panels Redirecting wind

SHIFT TO SOURCE

Panel Experiments The experiments on the right where exploring different forms

STANDARD FORM

that can be generated from the shifting panels, creating an infinite amount of variations for the array above. We worked with several primitive shapes and generated artificial wind conditions in grasshopper. To fabricate the system we used a CNC to mill high density foam, then took the milled foam to a vacuum form that laid down a melted sheet of acrylic which took the form of the milled â&#x20AC;&#x153;windâ&#x20AC;?. One that was complete we laid an array of panels over the produced piece to study the forms that were generated.

EAST

SOUTHEAST

NORTH

WEST

MULTIPOINT ATTRACTOR SOUTH

SOUTHWEST

NORTHWEST

NORTHEAST

Pattern Studies in Grasshoper using attractors

Rendering of the main gathering area with panel system above

The panel experiments were used to build a model that would test the panel movement system on a small scale. Below is the model, which is controlled by an Arduino Mega. There are 12 actuators total including DC motors and servo motors. The system is controlled from a cellphone with an IR sensor to allow the wind direction to be simulated. This project has been exhibited several times in the University and at Maker Faire that Miami Dade College hosted.

Re-purposing an app that uses IR signal to activate the model

Selecting the North function sends a IR signal to a receiver in the model and activates the motors to shift to programmed position

Alternate position close up

Arduino board with all the motors wires attached.

1.4

Dynamic Shade System Independent Study 17â&#x20AC;&#x2122;

The focus of this study was to learn how to create kinetic facades by exploring basic mechanical engineering concepts incorporated into design. Creating a window installation that can go from 40% transparency to 85% transparency. In order to find a form with many different iterations in real time, I modified a program that uses Islamic Star Patterns. The Islamic Star Patterns follow a system called Hankins method, which uses a simple rule of extrusions based on intersecting lines, underlying trigonometry as the math for the shapes. There are several variables in the forms of sliders that can be manipulated to change the pattern.

Form Finding The beginning of the process was to generate symmetrical geometry that was repeatable. To achieve this I re purposed a tool online developed by Daniel Schiffman. The modifications to the code allowed me to have a program with sliders that would generate different geometries by adjusting the variables in a technique called the Hankin Method, which is the root of many Islamic patterns.

Mechanism Development

Preliminary Design

The next step was to create a mechanism that would allow a panel system to shift based on one motion. The result was to convert a linear motion to rotary motion to expand the panels. By having a slider on a tri-armed system and two fixed arms, I was able to get the desired effect.

The depth of the arm system is based on the distance between each of the 5 panels. The center arm, which is considered the â&#x20AC;&#x153;key armâ&#x20AC;? is what would be actuated in order to rotate

Linear Motion

Rotary Motion

the panels on either side.

Mechanism Function

Experiment of mechanism design

To the left is the experimentation done with the mechanism design. The initial panel design was hexagonal because the geometry was easier to work with. There were several on the fly changes that needed to be made to have the panel functioning. The result of the studies produce a 5 layer panel system, as seen below. The first panel is a stationary panel that has the full design, this panel serves to cover the mechanism and additional layers in the back as a mask. The following 3 panels are fabricated in a form that the arm system can function and when the panels are fully actuated, non break out of the bounding box the first stationary panel has set.

Axon of Panel System

This axon shows the assembly order of the panels as well as where the tri-arm mechanism order. There are several spacers located inbetween each layer so that each panel has enough space to move with out collision.

Below it the final product of the study installed on a window. The final dimensions were approximately 4â&#x20AC;&#x2122; x 8â&#x20AC;&#x2122; and functioned as intended. Future work to this project would focus on the material of the panels (to reduce deflection in the sun), adding bearing to make the mechanism smoother, and adding a linear actuator with sensors to have the shading device move based on sensory input, such as light.

2 Artificial Intelligence and Software Research

Artificial Intelligence will effect every industry in the next three to five years. Many industries are already experiencing the symptoms of what is being call the fourth industrial revolution. Architecture is an industry that can benefit tremendously from this technology. I believe that automation will replace many repetitive task that take place in the industry and as result allow the architects to have more time to develop thoughtful designs. My research with Artificial Intelligence and Software is in anticipation of this change. The early adopters of this technology will become pioneers in the industry and generate new work-flows previously inconceivable. These technologies will become a necessary tool in the industry.

2.1 Master Thesis

Autonomous Construction Systems In recent years there has been great strides in technology such as, robotics and artificial intelligence (AI). Additionally, the amount of data being generated is unprecedented in human history, which is needed for AI training. The research conducted for my thesis was focused on exploring the viability of automating construction. The results from the research conducted not only validated the plausibility of autonomous construction robots but also revealed that there is a need to automate the field as well. Augmenting modern day machines with advance sensors controlled by AI will create the necessary â&#x20AC;&#x153;cyber-physical ecosystemâ&#x20AC;? for autonomous construction to exist.

Automotive Automation - 1991

Industry Automation Investigation Findings

Construction - 2018

J - Office Silk wall | Phillip Yuan 2010

Below: Jig for workers

Many fields have transition into automation, with the automotive industry being pioneers since the mid 70â&#x20AC;&#x2122;s. Yet, construction is largely human based labor. In an interview with Phillip Yuan about his Silk Wall and Chi She projects he explained that in 2010 robotic arms were not readily available and the work flow were not developed so he had to create a jig system to assist laborers in constriction of facade of the Silk Wall facade. By 2016 he had acquired robots and developed a work flow to build the facade for Chi She. Although the technology was not readily available in 2010, similar facade styles were constructed in using different methods. The work flow used in Chi She still required two workers to operate the robot, ruling it non autonomous. However it showed proof of a larger idea, that robots have long been able to build complex structures. But it is not until now that we are researching systems for automating construction. Robot stacking bricks for Chi She facade

Chi She | Phillip Yuan 2016

SAM100 Semi Automated Masonry Unit

Random Construction Site

Tesla Factory Automated Assembly Line

SAM100 is a Semi Automated Masonry robot that is capable of installing 5x to 6x more bricks than a human. This is one of the most advance construction robots, yet it still requires a person to feed the bricks into the load bay and another to clean the excess mortar. Furthermore, it can only install bricks in a straight line, making it very limited with it’s workload. The most pressing issue with automating construction is the ability to have a system functioning in a dynamic environment. Automated factories such as Tesla, are able to function because they have a highly structured system where most variables are accounted for. Material location, tasks, and tool paths are all pre-programmed and function only when conditions are an almost exact match to the work cells parameters. A construction site, can be highly organized but it will never be as structured as a factory, and unaccounted variables are inevitable in a open environment like a construction site. The task in factories are structured around a system of sensors that monitor the work area and factory; and the task are only executed if the expected value on the sensors is returned. This disqualifies these systems as “smart systems” regardless of the advance sensor array and machinery. They are not adaptable to situations that they are not explicitly programmed for. Subsequently, these same systems augmented with AI become smart systems that can learn from their interactions with the environment. Systems like these can work in dynamic environments such as construction sites. There is a crippling shortage of laborers in the construction sector. The shortage of laborers fluctuate but is on a downward trend. The new labor force generation are interested in other professions besides construction and it is having a impact on housing. By 2024 we are expected to need 790,000 new jobs in this sector with no one to fill those positions. Bureau of Labor Statics

2017 Study Conducted by the National Association for Home Builders

endeavor and my research was focused on one aspect of it. Dynamic Input. If you imagine autonomous task as a modular

k Tas

Automating Task is a complicated

s ou om ton Au

A.I. Research and Experimentation Dynamic Input Robotic Feedback Loop Execution of Task

block and its meta goals within, you can break down the problem of automation. Three major barriers to solve autonomous task

It requires Dynamic input to rationalize the environment and the status of the given task, Robotic Feedback loop in

s ou om ton Au

order to interpret the dynamic input in real time, Execution of task, this involves environment and mitigating failures

k Tas

dealing with unknown variables in the

Dynamic Input

while enhancing solutions. There are several forms of Dynamic input; I chose to focus my research on computer vision utilizing object detection.

Focused on Dynamic Input for research

Object Detection is a form of AI that

to Au

allows an algorithm to utilize a con-

volutional neural network to classify

pre-trained parameters in real time.

k Tas

Object Detection

Object Detection is a sub-category of Dynamic Input

Establish Sensor Needs Proper AI Platform Acquisition of Data Training Algorithm Extraction of Useful Data Distribution of Data

Requirements for dynamic input / object detection

Proper AI Platform

Inception v3 Deep Convolutional Neural Network

An important part of creating a dynamic input system is the selection of the proper AI platform. For computer vision, specifically Object Detection, you need a convolutional neural network; and a large data set to conduct supervised machine learning while training the network. I selected a model from Google called Inception v3. This is a deep convolutional neural network that is able to function in real time. At the time of my research this model was the most advance model available for use and was well documented which made experimentation possible.

Data Acquisition After the selection of the proper AI platform is made, in the case of this research it was Googleâ&#x20AC;&#x2122;s Inception V3 model, it is necessary to understand how the model functions so it can be properly trained. Inception v3 is written in python using the Tensor Flow Library. The model is pre trained on COCO data set which is composed of over 200,000 labeled images of common objects. Having a pretrained model allows the user to bypass a lot of extra work that is needed because the model already has a lot of features and parameters from the initial training saved, for example, edge detection, distance,color, shapes, ect.. Leveraging the pre-trained model allowed me to focus on developing my own data set to train on. As a result, I focused my efforts on developing a data set of images of objects that can be found on a construction site. This included a brick, damaged brick, bolt, screw, nail, pliers, and a piece of wood. I created a data set of approximately 1000 images of these objects in different areas and settings. The purpose of having a data set of your objects in different environments is to not create a bias while training and have the model thing the environment is classifier of the object.

Tensor Flow Library

COCO Dataset

Training the Algorithm

Labeling Objects

Once the all the training data was gathered it needed to be prepared for a supervised machine learning environment. This required using a program that allowed the user to outline and label each object in the image. The location of the objects within each photo are saved to a CSV file that is later used as an input for training. The purpose for labeling the objects is so the network can extract the features of each object in different environments and finds the similarities, which servers and that objects identifying features.

Sample of Data set

Training Algorithm Cont. Once the data is labeled it is ready to use for

Training Set

Validation Set

training. It needs to be split it to a training set and validation set (with appropriate labeled CSV files). You then set your folder path in your code and then execute the training. The network will use the training set to do feature extraction and sort the objects and then it will test against the validation set and back propagate accordingly

Training Results This is a sample of the output from the trained net-

Feature Extraction Example

work on my data set. It was able to detect with high accuracy a nail and screw and correctly plot the center point to return the value of the pixel coordinates. With these values, a system equipped with a camera and AI for Object Detection can locate items by parsing the values to real world coordinates. After the algorithm has reached the amount

Sample Output for Object Detected

Sample Syntax for Center Point

of epochs specified, the training is complete. Using Tensor Board the results are observable. The large graph is displaying the loss on the model. The lower the loss the better the model has trained. In this particular example you can see the model did converge despite it having spikes in loss. For the purpose of my research it was acceptable to continue since it recognized 4 of the 7 objects successfully.

Tensor Board Results

Extraction of Useful Data Work Area Cartesian Plane

0,0

1200px

xmax

xmin x

ymax

ymax Height

Height

Mastered Cartesian Plane

Width

0,0

120cm

Units: Metric (CM) 120CM x 120CM

ymin xmax

ymin

120cm

120cm Units: Metric (CM) 120CM x 120CM

Units: Pixels 1200PX x 1200PX

0,0

1200px

120cm

xmin

0,0

Mastered Cartesian Plane

Edges

Camera Vision Cartesian Plane

Top Left

: X(xmin), Y(ymin)

Top Right

: X(xmax), Y(ymin)

Bottom Right : X(xmax), Y(ymax) Bottom Left : X(xmin), Y(ymax) Center

: X((xmax+xmin)/2), Y((ymax+ymin)/2)

Center Point: Tool / Material Selection

Return: Item :

Screw

Uses :

Task A Task C

Tools Compatible:

Tool E

Tool H

Position:

(X,Y)

Return: Item :

Screw

Uses :

Task B Task E

Reconciling the returned Pixel value with real world coordinates is the first step to extracting useful data for positioning a robot. Being able to find the center point allows a system to know

Tools Compatible:

Tool R

Position:

(X,Y)

Tool Q

exactly where an object is within its frame regardless of positioning. The position is then relayed to a robot that can retrieve an object such as a tool or building component. The objects also have meta data embedded, therefore an AI system would be able to know what task and

Return: Object A Item : Brick Condition: Not Aligned Pos: (Xmin, Xmax Ymin, Ymax) Solution: Remove; Re-install Object B Item : Brick Condition: Not Aligned Pos: (Xmin, Xmax Ymin, Ymax) Solution: Remove; Re-install

compatible components work for each object. Edge Position: Site Monitoring Edge detection is in beginning of site monitoring. In this example, by knowing the edges of the bricks the system would be able to understand if something is not aligned and provide a stored solution as to the next task. In this case, remove the misplaced brick and reinstall. 37

Distribution of Data Vision Sensor

The sensor feedback loop is describing how the vision sensor would work in real time with a robotic arm. There is a constant pull push between the robot and controller. This is relayed to a server that is receiving the information from the vision sensor. Therefore, the robot requests information when needed and receives dynamic information from the camera augmented with AI and in return, determines the best process based on the given

Information Received

environment.

Information Request

Having a feedback loop with cameras focused on a material gathering station and a material installation station gives the system information in real time in an unstructured environment to function properly. Material Installation

Material Gathering

Example of system building wall This system would be given G-Code to construct a wall and the tools and materials to build. It would commence with task A. Which is to create a block wall. As it is building the wall it is going through

Materials and Tools needed

the sensor feedback loop that is augmented with material and installation AI cameras tracking the

If (doTaskA == True && siteClearA == true) { complete task if (taskCompA == true) {move to siteB else complete task} }

progress with solutions if and unknown variable is introduced; allowing the system to work in an unstructured environment. In task B the robot will be looking for things such as adherence of the furring to the concrete block. If there is an issue, a possible solution can be to move slightly and install a new screw. The feedback loop strengthens with

When task A is completed begin task B

the amount of sensors added. Thus, the robot can stress test the screws on the furring to see if it has properly attached. 38

Task A | Create Block Wall

Sensor Feedback loop With the AI trained to detect objects coordinates and edges while having Data of Object Detected

embedded meta data the system has the functionality to select the correct tool and material for the task needed cally checking for location, status, and

Object Detection / Computer Server

Robot Controller

While work_condition_valid(true): if task_A: check_tool() check_material() retrieve_tool() retrive_material() execute_task() else if task_B: check_tool() check_material() retrieve_tool() retrive_material() execute_task() else if task_C: check_tool() check_material() retrieve_tool() retrive_material() execute_task() else wait()

to be completed. All while dynami-

Stored on server as compatible language

Process Selection

In task B the robot will be looking for

Material Data

things such as adherence of the furring to the concrete block. If there is an issue, a possible solution can be to move slightly and install a new screw.

Nail

Screw

Block

Sheet Rock

Furring

The feedback loop strengthens with the amount of sensors added. Thus, the robot can stress test the screws

Tool Data

on the furring to see if it has properly attached. In task C the system can be looking for discrepancies in the dry wall.

Power Drill

Nail Gun

Gripper

Concrete Extruder

There could be a portion of the wall that is protruding and it would have a solution to remedy the situation. Materials and Tools needed

Materials and Tools needed

If (doTaskB == True && siteClearB == true) { complete task if (taskCompB == true) {move to siteC else complete task} }

When task B is completed begin task C

Task B | Install Furring

Task C | Install Drywall

Transition from Human Labor to Autonomous Construction

Architecture has always been a profession we have always relied on Contractors to build our designs. Therefore, we have had to transcribe our work to documents they could understand. In the modern age we do most our modeling and design on a computer. Programs such as Revit have Building information Modeling (BIM) data that can be re purposed to develop G-Code for robots. By creating an optimize version of BIM we can send our model information to a robot to be built and not devote time to creating drawings for another person to interpret and inevitably question. Speeding the time and accuracy from design to construction. The transition from a human labor force to a fully autonomous one will take time yet it is inevitable. We are in the beginning stages of the change. We already use robots but more as a tool rather than a building system. As time continues there will be more robots introduce with higher capabilities and that can replace several men for one particular task.

programmers will disrupt the normal way construction has been carried out. There will be gathering of data and used to train AI for the robots as outlined in my thesis. At this point the systems will still be very reliant on human collaboration but as time progresses and there are more operators and programmers introduced the more data and less training we will have to assist the robotic systems with. Finally there will be a point were the robots are able to optimize their own learning from the data is has and develop new techniques to complete task. Reinforcement learning will help with the development of these techniques.

Time

The introduction of advance robotic operators and

2.2

Reinforcement Learning Expansion on Master Thesis Research At the completion of my thesis I understood that the autonomous construction systems needed several forms of AI to function properly. Besides the dynamic input to rationalize unstructured environments, the system also needs to be able to learn from introduction of unknown variables and site conditions. This requires the system to adaptively learn from task in different conditions and how to increase the methods used to execute said task. Reinforcement Learning is the tangent of Machine Learning that can begin to handle these issues. This was one focus of my research during my post professional degree.

Reinforcement Learning Experimentation

Reinforcement Learning (RL) is a form of Machine Learning (ML) but with a different use and approach. The system functions abstractly on a very simple concept. There is an environment of some sort (video game, robotic simulation, actual robot, ect..) , and an agent. The goal of the agent is to reach a reward or maximize the reward it’s receiving. The way the agent progresses is based on the agents actions in any given state. The agent learns how to

Fig 1: Basic visualization of how RL conceptually functions

navigate in the environment to maximize its reward and it reinforces its actions in a given state by lowering the probability of it moving to a negative reward and maximizing its probability of moving to a positive reward (Fig 1.). The goal in reinforcement learning is to learn a policy which maximizes the expected The environment that I used to learn RL was from OpenAI Gym’s Robotic Environment. This environment was created to test the deployment of ta trained RL model, thus the robot in the environment was designed exactly as the robot in the physical world, mimicking all its limitations. The physical was controlled by a third party program called MujoCo to make sure that the environment was within the tolerances of the physical world. My goal was to achieve the recorded convergence baseline for two of the robotic environments, FetchandPush and FetchPickAndPlace, which in my case is simple convergence of the model. The purpose of achieving the baseline results for my research would be to get an understanding of how to train other models in the future for similar task defined by the environment I need to train. In order to navigate these scripts needed to train and test the model. I was able to locate a Github repository by Sandeep Gogadi. Fortunately enough, I was able to have a phone conversation with him where he explained the steps to me in order to execute the code and some research papers to read to understand the codes functionality.

The goal of the simulations are both to use the robot to carry out a task. The robots move in the space based on a cartesian system and the joints move in tandem to find the correct position in XYZ. The environment has a margin for error around the target location for both simulations. In figure 2 and 3 you can see a still image of the robot completing the simulation successfully. For the FetchPickAndPlace I had a 90% success rate in the test and FetchAndPush 100% success rate. This model is based on a new Figure 3: Testing of FetchandPush Environment after training. The goal being for the robot to move the block to the target(red sphere) by sliding/pushing it

RL architecture labeled as HER (Hindsight Experience Replay). The basic idea of HER is to use the robots failed attempts as successful attempts. The idea being, that if the robot moves the object to the incorrect location, which is highly likely do to sparse rewards, the algorithm registers it as successful by changing the goal parameter. This allows the training to take full advantage of training times by maximizing the rewards. The normal convention for creating successful RL models where sparse rewards is eminent is to do reward shaping. This is an unsustainable practice though because it requires domain knowledge and specific

Fig 2: Testing of the FetchPickAndPlace environment after training. The goal is for the robot to locate the cube and use it’s grippers to pick and move to the target location (red sphere)

directions in the program to “guide” the agent to the reward.

Open AIâ&#x20AC;&#x2122;s Deployment to Robot Case Study When Open AI was developing the robotics environment the ultimate goal was to deploy the model to an actual robot in the physical world to see if it could carry out the task successfully. They modeled the robots in the simulation (digital environment) with same specifications and limits as the physical robots. Open AI had a high rate of success without any tuning of the model to transfer into the physical world. The target location was digital coordinates and the location of the target object was given through coordinates from a camera.

The first test was a 40% accuracy, the reason for this was because there was an issue reconciling the physical and the digital worlds. In the digital world everything is exact and in the physical it is not. To increase the tolerance a technique they call gaussian noise was applied, they gave the robot a small range to move around in the target object location in order to pick up the object. In figure 4 you can see the photos of their test.

After training the models and testing them to verify convergence I wanted to understand on some level as to why the model works and how it deals with the complexity of an environment such as the ones trained on and still be able to converge. Knowledge in Figure 3: Test to see if they could deploy the model trained with a digital version of the robot to the physical version. The experiment was successful. Findings of how RL functions in trained mod

this domain is critical to continue research for environments that are not in the Open AI Gym. My interpretation of the information is outlined in the subsections below.

Conclusion: Future Steps and Implementation The following step with the RL branch of research into this macro ACR research is to create a digital environment that a KUKA robot can work in, preferably in an integrated program such as Mujoco. If not the alternative is to use a program such as Unity or Unreal Engine 4 because they both have built in physics simulators. After the creation of the digital environment I would train the robot for different construction related task in our workcell with a vacuum gripper. When the model converges I would then deploy it to the KUKA robot. There are technical issues to overcome in all of the steps but it is definitely plausible with the implementation of todayâ&#x20AC;&#x2122;s state of the art technology for a proof of concept to be completed. Ultimately the research I did into RL has only begun, but the understanding of how to implement the system has been achieved. The larger goal is to create Autonomous Construction Robotic (ACR) systems that can function in dynamic environments. RL is one of the realms that have to be solved for in order to have functioning autonomous robots in addition to having a well informed feedback loop with the environment and creation/implementation of the tools needed for the robot to succeed. 43

2.3 Revit API Studies

Study into the development of plugins for Revit

Over the years Revit has grown to become on of the most popular 3D modeling tools in the AEC industry. As a result, there is huge potential for growth creating customized solutions for the program based on individual firmâ&#x20AC;&#x2122;s needs. With the rise of A.I. there is a unique opportunity that has appeared to integrate these technologies together in order to generate extraordinary results.

Zoning and Code Interpretation Architectural design is dictated by zoning and building codes that need to be satisfied. The typical method for the design manifestation is through the interpretation of an Architect (at minimum) of these requirements. As anything that involves interpretation there is an error factor. This error factor inevitably requires the designers to go back and correct over several iterations. As the development of the design continues, these corrections cost more and more and delay the project. As technology advances there are growing opportunities to integrate them into our work flows. An example of this could be to automate the process of designing/correcting with zoning and coding. The method that this can happen is to use modern AI techniques such as Natural Language Processing, to interpret zone and code data on a national level down to a municipality level. The values that are returned can then be fed into a custom plug in for Revit that creates boundaries or warning when there a design change that is not satisfying the requirements for that specific site.

Diagrams depicting barrier restrictions on active design

The diagrams above depict how this process would be executed. The green model is what the zoning and code requirements allow. The following red diagram is what the architect has design. The plug in would generate barriers and warning explaining what to change and why it is in violation. Similarly, this would work for ADA as seen on the diagram to the right.

The warning could manifest as call outs for the Architect or Firm to review. Missing Parapet X Balcony Overhang X Restrict Occupant Use X Restrict Vehicles X Need to Raise X Score = 76% Failed

The process would be live throughout the life of the design. From schematic to construction documents. By having a system such as this in place, it is possible to work directly with the government to have this plug in as a requirement for expedited permit services. Plans are reviewed by Architects working for the city and are typically returned with some modifications needed. However, this

Parapet

system would remove those timely

Balcony

and costly errors by guiding the designer(s) to a law abiding design

Restricted Valid Entrance Raised Score = 98% Passed

before submission.

Plug in pipeline

This system would require extensive resources, research, and time to complete. Therefore, I focused my research on a series of small plug ins and scripts for Revit rather than the natural language processing or integration portion. The plug ins were written in C# in the Visual Studio IDE. I wrote a series of incremental scripts that would allow me to investigate different portions of how Revit worked â&#x20AC;&#x153;behind the curtainsâ&#x20AC;? the final culminating into a custom ribbon with two custom buttons that were programmed to create a zone barrier and then to check for errors.

Custom Plug in buttons and Icon made for ribbon created

These two scripts inside the plug in are the result of my short research period. I was able to create a static barrier that would server as the zoning + code layer in my proposition. This is done by clicking the create zone barrier button, once that is selected a geometry appears. This geometry is not optimized yet, but it is an element that can provide a collision warning. The second portion was to check for errors. Once you select that button the new environment (different from the zone barrier previously generated)

Create zone barrier script

will be able to detect a collision between two elements. In this example the roof of this structure is acting as a zone barrier and the column is acting as a element protruding and that needs remedy. This project was a great exercise in gaining a preliminary understanding of the Revit API and will assist in creating plug ins to automate task.

Check for errors script

2.4 Cyborg Futures

A.I. Workshop Shanghai, China | Tongji University 2018 One week workshop at Tongji University for Cyborg Futures 2018. Applied binary image classification with mentors Wanyu He and Chun Li, co-founders of XKOOL, an AI Urban Planning Startup in bashed in Shanghai. The workshop was focused on comparing facade types for classification. In my teamâ&#x20AC;&#x2122;s case it was New York Art Deco and Miami Art Deco. We were then ask to find an application for this type of AI. I created a program that would assist in training engineers at XKOOL on facade types so they could identify different types of architectural styles easier while labeling their data sets.

Data Generation The workshop was focused on binary image classification. The group was asked to find to facade styles to compare. My team, composed of Zalman Meyer and myself, New York Art Deco and Miami Art Deco to compare. We began the workshop with a lecture on image classification and how it functions using the Keras library. Miami Art Deco Sample

New York Art Deco Sample

Sample Data Set

When the introductory lecture concluded we jumped right in and began gathering data from Google on our facades. This required a conversion from HTML files to JPG, labeling in sequential order, and re size all the images to 256 x 256 pixels. We wrote a script to handle this process. After the files were processed we cleaned the data set to include relevant images. To clean the data set we had to look at each image individually to increase the accuracy of the proper image being included in the appropriate data set and remove any non relevant phots.

Part of the Script to automate cleaning process

Model Development and Training

Neural Network Layers

After the data was processed completely we began building our convolutional neural network. Because this was an introductory course to AI and image classification we used a simple 3 layer fully connected neural network. In between each convolutional layer we applied a technique called max pooling. This technique allows the network to save time by approximating a small portion of pixels at a time and returning the highest valued pixel in the small array. Each combination of convolutional layer with max pool goes from larger sample to smaller samples with the ultimate goal of extracting features from the labeled data sets.

Diagrammatic example of how the CNN functions. The small squares are called kernels and defined in the networkâ&#x20AC;&#x2122;s parameters. Inside the kernel there is a function that takes the feature with the

highest score and extracts that feature. It repeats the process till classification.

INPUT LAYER

CONVOLUTION LAYER 1

66 66

Visualization of feature extraction from an image Label: New York

MAX POOL LAYER 2

66 68 CONV LAYER 2

669

4 MAX POOL LAYER 2

CONV LAYER 3

FULLY CONNECTED LAYER WITH DROPOUT

Score 0.997047

Training Results To the left is the visualization of the training results. The model was able to achieve convergence based on the data sets we gave it and accurately classify each facade based on Architectural Label: Miami

Score 0.044029

style. The chromatic images depict the feature extraction process that the CNN is going through as it is identifies specific parameters that make each facade unique to either style. Some of these features are edge detection, depth, and general shapes.

The first trained model returned with a bias because the feature

Visualization of data set cluster.

map was being trained on images of Miami Art Deco having blue skies and sun while New York was typically dark or cloudy. So we introduced images that had Miami at night and New York on sunny days. The new model trained correctly and was able to accurately label new photos introduced. On the right is a visualization of the data and how it was clustered. Teal is Miami photos and Magenta is New York. There was a clear convergence of the model. There are a total of 16,000 images / data points that the model was trained on. A large data set is necessary for accurately training models.

Custom Training Program After the model has been trained the user will be able to access it from the model library. Once a model from the library is selected it opens a window that allows the user to learn by asking them to select the correct style from an image on the screen.

Data upload and parameter selection

Welcome Screen

Model Training UI

During the workshop, XKOOL expressed the issued they were having with their engineers not knowing how to label the photos needed for the supervised models being trained. On the last day of the workshop we were ask to create an application. l styles for labeling data sets. As a result, I developed the front end of a training tool for AI engineers that need to increase their domain knowledge of architecture. The program functions by allowing

User model selection and learning UI

the user to either train a model. If the user selects to train a model they will located a labeled data set generated by domain experts. They would then input the model parameters and click train and the model would execute.

3 Robotic Research

When speaking about automation it is common to think of Artificial Intelligence as the primary contributor; but, robotics plays an equally important role. Robots have been a part of the human fabric for decades now, however, it was not till the rise of A.I. that they have become quite capable of manipulating the material world with minimal instructions successfully. These two technologies combined are capable of extraordinary feats. My research in this area is focused on two branches of robotics, industrial and small. The work that was conducted was an exploration of different systems that could potentially utilized (either from what was learn or directly) into our industry. By leveraging this technology we can transform how construction sites are manipulated, data is retrieved, and countless other possibilities.

3.1

Establishing RDF Lab Robotics and DIgital Fabrication Lab (RDF) Automation is quickly becoming an part in our everyday lives. Education on this topic is of paramount importance. I was fortunate enough to be at the core of a change in our University education with automation. In 2017 I was tasked with the creation of the Robotics and Digital Fabrication lab. Through the next year I learned about robotic systems and advance fabrication techniques to understand how to build the lab. The lab was funded by a University Technology grant award of $375,000. Its only been a short while but I am seeing the effects of having technology such as automation and advance digital fabrication is having on the student body.

Construction Process for KR30 HA The overall process to create the lab was extremely educational, not only by learning how to manage multiple projects simultaneously, but being exposed to the automation industry and the significant impact it will have on all industries. The project began by doing research on all the components needed to create a robotics lab and ordering the proper equipment; which was no simple task. Once the orders were made the preparation for the rooms began. For the large KR30 robot I had to contract workers to cut a section of the existing slab to install an isolation slab that was 12â&#x20AC;? deep; in order to have the robot properly anchored. Once the concrete fully cured we installed the base mounts for the robot and moved it inside inch by inch using a engine life for a car since out forklift would not fit through the door. After successfully installing the robot I contracted electricians to install the 480v triple phase power the robot required and did cosmetic work to the room so it was presentable. The following night I installed all the cables and safety components, mastered the robot, and manually jogged the first KUKA robot at a university in Florida.

The final result can be seen in this photo. The KR30 was successfully installed to the ground and power in the room. The robot was then mastered to reconcile the real world axis position with the digital position on the software. It was stress tested with a few programs that I wrote to make sure the bolts would hold and passed successfully. This was a major accomplishment for the University, firstly, because we are the only University in Florida to have such equipment available and secondly, during my research I discovered that it took most universities approximately 5 years to get the robot operational, we were able to complete the task in less than two years. Since the installation of the robotic arm we have had many students interested in using it and successfully completed our safety and operations workshop. I am proud to have been able to work closely with the Lab Director, Shahin Vassigh, to make this vision a reality.

Preparation Process for KR10 R1100 The preparation work for are smaller KR10 robot was a smoother process. I contracted an electrician to install the necessary power (220v) and worked with students to fabricate to base of the robot and the work cell. We bolted the frame to the ground to make sure the robot was stationary while being used. The work cell was made with acrylic walls to observe the work area and a portion that could expand out into the room using a wheel system on the bottom. This was allow for larger projects using the robot while simultaneously not occupying a large amount of space in the room.

Our KR10 is the robot that is the most used in the lab. It is the robot we train on and the easiest to prototype due to the scale. Since the robot has been installed there have been a range of experiments done by students ranging from light studies from custom 3D printed end effectors to drawing exercises. On the right is a vacuum gripper that I integrated to the robotic arm for pick and place operations . I used a air solenoid valve that controlled a compressor into a venturi mechanism. The room that the robot is housed in is equipped with an array of 3D printers, ranging from different print styles and sizes. These printers are used for rapid prototyping of end effectos, or end of arm

KUKA College While the robots were being manufactured in Germany, I traveled to Michigan to KUKA Headquarters to train with the robots for 1 week. The course I taught robotic safety, robotic operations, and basic robotic programming. From this course I built a workshop to teach faculty and students how to use the robotic arm for their work.

Inauguration During September 2018, the Robotics and Digital Fabrication Lab had itâ&#x20AC;&#x2122;s Inaugural event, with over 150 attendees, including the President and Provost of FIU, robotic industry partners, architecture firms, students, faculty, and community members. We had approximately 15 projects that were demonstrated, three using the robotic arms, during the event that work worked on by over 40 students. The projects were curated and manage by a colleague and I.

3.2

Inauguration Scissors Automated Ribbon cutting for the RDF Labâ&#x20AC;&#x2122;s Inagural event. Creation of a scissor end effector and programmed simulation using the KR10 for the President of the University to automatically cut the ribbon for the Robotics and Digital Fabrication Labâ&#x20AC;&#x2122;s inaugural event held on September 20th 2018. Worked with the students from the sculpture department to fabricate the steel frame attached to the robot that the scissors were mounted to. 3D scanned the scissors to create the grip the pneumatic linear actuator was fixed to. When the end effector was complete, programmed the scissor for the President to cut the inaugural ribbon.

FIU President executing program to cut ribbon Left to Right: Dean Brian Schriner, Provost Kenneth Furton, President Mark Rosenberg, Director Shahin Vassigh, Dean John Volakis, Prof. Iyengar, Director Shu-Ching Chen

Design and Fabrication Process The beginning process was to sketch out a design that could firmly mount the inaugural scissors to the KR10 robotic arm. Afterwards the sketch was transformed into a cardboard model and attached to the scissor to see if there were any collisions between the frame and the function of the scissor. A few modifications were made, then it was ready for fabrication with our colleagues in the sculpture department whom were familiar with steel work.

A 1/8â&#x20AC;? thick sheet of steel was used to create the frame for the scissors. A rough cut was done initially, then grind to the size of the mock up frame. The holes were then drilled through and finally it was polished. This process was happening simultaneously with a mount piece I designed to go from the frame of the scissors to the flange of the robot. As the scissor frame was being fabricated, I developed the system for the linear pneumatic actuator to mount to so that the scissor would actuate. This process involved 3D scanning the eyelid of the scissor to get the exact contours the create a holder that would be fixed to the scissor and accept the actuator forces.

Testing and Programming Simulation After the scissor frame and pneumatic pump mount were completed, the whole end effector was assembled and mounted to the KR10, where I had previously completed the Digital Mapping of the I/O to control the solenoid that provided air on demand from the pump to the pneumatic actuator. To the left are the images of the end effector actuating. The final simulation was approximately 45 seconds long and cut the ribbon flawlessly.

3.3 Robotic Drums

Inaugural drum set controlled by Arduino Creation of an automated drum set for the Robotics and Digital Fabrication Labâ&#x20AC;&#x2122;s inaugural event. Working with Computer Science students and a Professor from the Music department. We fabricated mounts for the drums that housed fast repeatability linear actuators responding to a composition created by the music department. Utilizing Arduino to control the actuators and receive input from an external program that the composition was created on. I managed the overall project and specifically worked on all fabrication + assembly and wiring for the project. This project was awarded first place for Robotic Automated Process for FIUâ&#x20AC;&#x2122;s Shellhack 2018.

Inauguration guest observes the drums playing a composition created by a Professor of Music from FIU

Prototyping Actuators The project began by doing research on how to execute the project. We had about a month to complete several projects so we decided to use a resources we found on the internet and modify them for our project. We began this process with the actuators. We used a high speed linear actuator but needed to figure out how to control them by reversing the polarity then provide power. Once that was complete the next step was to figure out communication between the Arduino and the software being used to create the music. Each note would actuate a different drum stick.

Final Assembly Once the actuators were completed, I began work on fabricating the frames that would house the actuator and drumstick. The frame would allow the drumstick to pivot and strike the drum when the actuator stroke was a maximum length. There were 4 frames, one for each drum, with two assemblies each. The final assembly for the robot drums as powered through a small 12v battery that was distributed to 4 H bridges controlled logic from the Arduino board. The drums began the event with a drum roll for the ribbon cutting ceremony completed by the KR10 robotic arm. Afterwards the drum played several musical compositions for guest to enjoy.

3.4 Water Wand

Community Scientist kit replacement

Development of a sensor kit utilizing Arduino platform that can be given to community scientist to gather information from King Tide inundation around the city of Miami. The sensor kit replaces the current kit, that has highly subjective methods of measuring samples gathered on site, which community scientist are not trained for. The sensor kit comes in the form of a stick and placed in the inundated area. It measures temperature, conductivity, depth, salinity, and GPS coordinates. The team developed a phone application that receives a blue-tooth transmission from the device and uploads the information to a data repository.

King Tide Inundation Event in the Shorecrest Community Miami, FL

Data Collection from King Tide Inundation Communities in Florida suffer from King Tide Inundation and are in need of help to mitigate such costly damage as a result from the flooding. For a few years, INWE (Institute of Water and Environment) and CREST CAChe (Center of Research Excellence in Science and Technology + Center for Aquatic Chemistry and the Environment ) have been tracking sunny flooding events due to King Tides. The method they use is to host a community event where citizens of the community are equipped with kits and given an explanation on how to retrieve data from inundated areas. The problem with this is that the data retrieved is subjective and crude at best. The goal of this project was to develop a device that could be given to a citizen scientist and objectively gather data at a push of a button at a price comparable to the original kit, which was approximately $70. Community Members taking measurements of ďŹ&#x201A;ood zones

First Water Wand Prototype with several members of original team

Data Collection from King Tide Inundation Working quickly, we assembled a preliminary team to develop a prototype. Working along side Professors from environmental science and computer science we assessed the sensor needs to satisfy the kitâ&#x20AC;&#x2122;s requirements and put them together in a cardboard box, which was the first version of the water wand. Once the process was complete we began out testing immediately, which spawned many design iterations for the wand. Each design iteration was informed in part by the current sensor system we were using and the experiment using said sensors to return the proper readings. An example of this was when we tried using a humidity sensor to return the conductivity reading of the water by having to steel probes extend from the sensor into the water. Each iteration required a calibration period where we would dip the wand into a saline solution with known values. By this point there were several students involved with the development of the project.

Application Development All the experiments till this point where done through the Arduino IDE. Once we were at a comfortable prototype for the wand we created an app for Android OS using MIT app inventor. This is the first version of the app that would allow the user to select get measurements and see the return. The data was uploaded to a database for review. The components of the first version can be observed in this rendering. Utilizing these sensors we were able to receive a baseline to test in the environment at a King Tide event. Further efforts on this design included creating a handle piece that was ergonomic yet had ample space to house all the necessary components. A constant effort was made to optimize each component and its software to receive a consistent reliable return. Eventually, these components would change to those that could satisfy the necessary requirements, such as using the phones GPS to return location, using an Arduino Uno

GPS

to provide adequate power from the board, and using a lab certified conductivity sensor.

Blue tooth

Distance Sensor

Arduino Nano

Humidity Sensor

Battery

Code Sample from MIT App Inventor

Rendering of components in earlier version of the Water Wand

King Tide Event | First Field Test After all our efforts we were ready for a live test at a King Tide community event. During this event we unveiled the Water Wand to the community and was well received. There were approximately 150 passionate people in attendance and I gave a presentation explaining the water wand and its capabilities and how we hope to replace the current kits. Furthermore, outlining the importance of the work that was being conducted during these events because the data was used to directly inform policy makers who can actually make a difference in communities like Shorecrest, where these efforts eventually allowed the government to supply water pumps to remove the unwanted and damaging salt water. The field test were a success, but there were some issues that did arise as expected. Some being connectivity issues to the blue tooth and consistent return from our hacked humidity sensor for conductivity. These issues were addressed further design iterations but these experiments and feedback from the community were of great importance to the project.

Future Versions and Progress

To the left is a rendering of the the final iteration of Version 1. This device has been submitted to FIU and the process of being reviewed to receive a patent. In future versions we are integrating sensors that can return more consistent values, this includes distance and salinity. We are also going to create custom PCBs (printed circuit board) so that we can eliminate unnecessary wires that are currently occupying a large amount a space. There are 5 students in electrical engineering who will be working on this project the next coming year to develop Version 2.

Along with the sensor updates we have also redesigned the aesthetics and ergonomics of the water want to provide the capability of branding in the future. The application has also received a face lift and animated gifs that will sever as instructions. They will show the user how to connect the device to their phone, orient the device in water, and how long to wait for readings. This way in the future we will only need to pass out the device and have them download the application. This project was excellent to work on and knowing that it will make a change in the community and how policies are created and enforced has been an amazing opportunity. 75