REEF/ booklet

Page 1

REEFF Renovation for efficiency

Andreea Bunica Abdullah Sheikh Jun Woo Lee



index

WHY

/GLOBAL PERSPECTIVE /INVESTMENT /OPTIMISATION DATA

WHAT

/BUILDING DIAGNOSTICS

HOW

/ WORKFLOW / DATA ACQUISITION / THERMAL PREDICTION / THERMAL ANALYSIS / RECOMMENDATIONS / AUTOMATED ANALYSIS


44% New construction 2015

56% Renovation construction 2015

*reconstruction market = higher than new construction market since 2010


35%

40%

36%

EU building stock

EU energy consumption

EU Co2 emissions

= over 50 years old

= lifespan of existing buildings

= lifespan of existing buildings


1% Renovation rate *on average

=

100 years / decarbonisation


RENOVATION PROCESS

BUILDING APPRAISAL

DESIGN & PLANNING

PRIVATE SERVICE PROVIDERS /Commercial operations

ANALOGUE SERVICE PROVIDERS /Private usage

EU REGULATED APPRAISAL /Public operations

ON SITE RENOVATION

/ECONOMY /LIFE QUALITY /ECOLOGY

RENOVATION

CURRENT METHODS OF APPRAISAL

/ Mechanical resistance and stability / Safety in case of fire / Hygiene, health and the environment / Safety in use / Protection against noise / Energy economy and heat retention


40%

Greenhouse reductions by 2030 2019

EU GOAL TO 2030

32%

Renewable energy by 2030 2015


“

(10)

Apart from accelerated renovation rates, a Union-wide and sustained increase in deep

renovations is necessary. (12)

It is necessary to obtain high-quality on the building stock (...)

data

�

EU commission recommendation 2019/786 of 8 May 2019 on building renovation

EU METHODOLOGY TO 2030


RENOVATION RENOVATION

DEEP RENOVATION

SUPPORTING ACTIONS

DEEP RENOVATION

Building health & energy efficiency

Envelope efficiency renovation

Internal refurbishment

Structural refurbishment

Building services refurbishment

BUILDING ITEM DATA

MATERIAL DATA


Extracting

OPTIMISATION DATA FOR DEEP RENOVATION

RENOVATION

PROPOSAL



HOW

/WORKFLOW /DATA ACQUISITION


DATA ACQUISITION

RENOVATION

STAGES

ANALYSIS

PREDICTION


CloudCompare

COLMAP

IMAGES/rgb + depth

PHOTOGRAMMETRY

3D POINT CLOUD

Grasshopper

SEGMENTED CLOUD

CONVERTED MODEL

Grasshopper

M. LIB

REAL MATERIALS Grasshopper

HONEYBEE

PREDICTIVE BUILDING PERFORMANCE

REPLACEMENT

MATERIAL PERFORMANCE OF BUILDING COMPONENTS

THERMAL DATA

SIMULATION

Grasshopper

M. LIB

MATERIAL LIBRARY

.JSON



/PHASED DEVELOPMENT HOW

/DATA ACQUISITION /DATA PROCESSING


VIDEO FRAMES

COLMAP

PHOTOGRAMMETRY RECONSTRUCTION CONVERSION

SEGMENTED POINTCLOUD

ANALYSIS USER LIBRARY

PCD ACQUISITION

`

DENSE CLOUD FULL COLOR

CLOUDCOMPARE

CLEAN CLOUD OBJECT SEGMENTATION

RGB MATERIAL


PCD ACQUISITION

`

DOWNSAMPLED CLOUD


` ` ` ` ` `

`

` ` `

`

SEGMENTATION

OVERALL ELEMENTS BREAK-DOWN


ENVELOPE

STRUCTURE

/existing

SEGMENTED POINT CLOUD

XYZ External walls RGB normals

BOUNDING BOXES

PLANE FITTED BB PHOTOGRAMMETRY

Rooftop planes

VOLUME AVERAGE

CONVERSION

Floor plate

ANALYSIS USER LIBRARY

SCALE TO FIT

TRIM OPENINGS windows

BASIC MODEL

doors ADD ELEMENTS Rooftop skylight Beam(Primary)

ELEMENT TYPE

INSULATION TYPE

Window

SEGMENTATION

ELEMENT CLASSIFICATION

Internal wall


Component segmented pcd

Plane fitted bounding boxes (element approximation)

Bounding boxes

Volume area average & scale to fit

Final model

Scale to fit

CONVERSION

PCD TO BIM PROCESS

Trim windows


Material: Wood Conductivity: 0.049 W/m-k Density: 265 kg/m3 Specific heat: 836.800 J/kg-k Thermal Absorptance: 0.9

STRUCTURE

Cross beams

Material: Brick M01 Conductivity: 0.89 W/m-k Density: 1920 kg/m3 Specific heat: 790 J/kg-k Thermal Absorptance: 0.9

Material: Concrete HW Conductivity: 1.311 W/m-k Density: 2240 kg/m3 Specific heat: 836.800 J/kg-k Thermal Absorptance: 0.9

Material: Concrete HW Conductivity: 1.729 W/m-k Density: 2242.9 kg/m3 Specific heat: 839.999 J/kg-k Thermal Absorptance: 0.9

Columns

Beams

Material: Concrete HW Conductivity: 1.311 W/m-k Density: 2240 kg/m3 Specific heat: 836.800 J/kg-k Thermal Absorptance: 0.9 Floor plate

Internal wall- solid

Fixed window U-Factor: 2.555 W/m-k Solar heat Gain coeff: 0.45 Size: 2.54 x 0.45m

Roof skylights

Panels

Material: Composite gypsum Conductivity: 0.53 W/m-k Density: 980 kg/m3 Specific heat: 810 J/kg-k Thermal Absorptance: 0.9 Internal Walls

ENVELOPE

Windows

Fixed window U-Factor: 1.9 W/m-k Solar heat Gain coeff: 0.40 Size: 0.8 x 0.6

Material: Metal roofing Conductivity: 45.00 W/m-k Density: 7680 kg/m3 Specific heat: 418.4 J/kg-k Thermal Absorptance: 0.9 Rooftop panels

CLASSIFICATION

EXTRACTED OBJECTS

Material: Metal roofing Conductivity: 45.00 W/m-k Density: 7680 kg/m3 Specific heat: 418.4 J/kg-k Thermal Absorptance: 0.9 Rooftop panels

Material: Composite Brick M01 Conductivity: 0.89 W/m-k Density: 1920 kg/m3 Specific heat: 790 J/kg-k Thermal Absorptance: 0.9 External Walls



/PCD TO SURFACE HOW

/THERMAL ANALYSIS /RECOMMENDATIONS


BUILDING ZONES DAYSIM OPEN STUDIO

BUILDING COMPONENTS PHOTOGRAMMETRY

MATERIAL ASSIGNMENT

THERMAL SIMULATION

ANALYSIS

HONEYBEE ENERGY PLUS

USER LIBRARY

.JSON FILE

WEATHER DATA

THERM

MATERIAL PROPERTIES


.JSON FILE

LIBRARY ELEMENTS

_Construction _Construction set _Materials _Climate zone _Space types _Standarts

MATERIALS

Name: Type: Roughness: Thickness: Conductivity: Density: Specific heat: Thermal absorptance: Solar absorptance: Visible absorptance: U-Factor: RGB code:

COMPONENTS CLASSIFICATION

WALL

Basic Construction Materials

Concrete Steel Wood

FLOOR

AIR

ROOF

WINDOW

Structure Climate zone: ESP - Barcelona Space Type: Interior/ Exterior Standards: Int. Average temp

COATING

Envelope

288 Materials Construction Set

WALL Mass Basic materials Steel Frames Metal Frames Wood Frames Concrete Frames

Floor Mass Basic materials Steel Frames Wood Frames

Roof

Coating

Air

Window

Mass Steel Metal Wood Concrete

Wall board Insulation board Membrane Surfaces Sidings

Clear space Wall air space Ceiling air space Wall air space

Fixed window Theoretical glass


MATERIAL CLASSIFICATION THERMAL PROPERTIES

THERMAL ANALYSIS

THERM MATERIALS

ENERGY PLUS MATERIALS

.JSON FILE

SIMULATION

Softwares: THERM OpenStudio GH + Honeybee

INSULATION IMPROVEMENTS Composite walls

ENERGY PLUS WINDOW MATERIALS


Door

Window

Roof

Siding

Beam

Column

Steel structure

Wooden frame

ENVELOPE

STRUCTURE

LIBRARY

ANALYSIS OBJECTS

Brick wall [+]


T1

ROOF

FLOOR

DE

WALL

DET 2

COMPONENTS Metal roof deck Waterproof membrane 19 mm gypsum board Wood roof insulation R17.79IP Metal structure [cross beam]

Temperature 7.013 ~1.99 AVG = 4.50 °C

COMPONENTS M11 200mm Lightweight concrete Floor insulation 200 mm Steel decking frame

Temperature 5.36 ~3.78 AVG = 4.57 °C

DET 3

Temperature 10.615 ~ 7.25 AVG = 8.932 °C

COMPONENTS M01 Brick 15 mm Insulation board Wall insulation

Barcelona (IWEC FILE) EXT

LIBRARY

COMPOSITE MATERIALS

20 degrees INT



_envelope + windows/ doors

_internal zones

_analysis objects



SURFACE TEMPERATURE

15 mm BRICK COMPOSITE +THERMAL INSULATION

EXTERNAL WALL

CONCRETE COMPOSITE + STEEL INFILL

CROSS BEAMS

WINDOWS

5 mm GLASS SANDWICH PANELS

ANALYSIS

`

SURFACE TEMPERATURE

METAL SHEET SANDWICH + THERMAL INSULATION

ROOFTOP

FLOOR

CONCRETE COMPOSITE + STEEL INFILL + THERMAL INSULATION

WOOD FRAME + GYPSUM PANELS

PARTITION

10mm BRICK + THERMAL INSULATION

BRICK WALL

GLASS WALL

5 mm GLASS SANDWICH PANELS

BRICK COMPOSITE + GYPSUM PANELS

PLASTER WALL


ANALYSIS

`

SURFACE TEMPERATURE


458 CONCRETE

GROUND FLOOR SLAB 412

STRUCTURAL

BRICK

EXTERNAL WALLS 366

METAL

ROOFTOP 320 CROSS BEAMS

CONCRETE COMPOSITE

BEAMS

274

FLOOR

229

BEAMS- small 183 WOOD

PARTITION WALL 137

GLASS

BRICK

Z

PLASTER

INTERIOR WALL 3

91

INTERIOR WALL 2

45

INTERIOR WALL

0.0 kmw

ANALYSIS

`

TOTAL THERMAL LOAD

jan

feb

mar

apr

may

jun

jul

aug

sep

oct

nov

dec



Material Library

Genome

Galapagos Fitness

Material Types

Thickness

Density

Thermal Infiltration

Thermal conductivity

Comfort

Indoor Temp Material Library Key Component


Insulation +

Components List M01 100MM BRICK G05 25MM WOOD AIR (Variable) STEEL FLANGE EXTRUDED POLYSTYRENE GYPSUM BOARD

WALL

Material Improvement + WALL LIBRARY


DATA ACQUISITION

ANALYSIS

PREDICTION

OPTIMISE PHOTOGRAMMETRY FROM VIDEO MATERIAL EFFICIENCY & REPLACEMENT RECOMMENDATION MASK R-CNN/ AUTOMATED SEGMENTATION & CLASSIFICATION STREAMLINE HONEYBEE PROCESS THROUGH PYTHON APP RGB RADIANCE/ AUTOMATED MATERIAL DETECTION & CLASSIFICATION

PREDICTED WORKFLOW

CONCLUSIONS

NEAR FUTURE WORKFLOW

RGB RADIANCE/ AUTOMATED MATERIAL DETECTION & CLASSIFICATION

SURFACE DEFECTS & PREVENTION

INTERNAL DAMAGE PREDICTION


Final video link: https://www.youtube.com/watch?v=07CJZ0Bc3OE&feature=youtu.be


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