'Build or Repair' pre-thesis @ Chalmers by Marie-Lou Valdes

BUILD 0R REPAIR 1

TABLE OF CONTENT

INTRODUCTION

page 3

I/ RESEARCH PROCESS

A) Climate B) Architecture

page 4 page 7

II/ CLASSIFICATION & CALCULATION

A) Criteria B) Formulas

page 10 pages 31

III/ PURPOSES FOR ARCHITECTS

A) Create B) Improve

CONCLUSION

page 34 page 40

page 42

INTRODUCTION

“Vernacular architecture exists for a reason”

Vernacular architecture is the design made by the population, for the population. Sometimes called “local genius”, it is composed of local materials and derived from local customs. It often answer to very specific issues that only experience can beat. Ronald Brunskill wrote :

“A building designed by an amateur without any training in design; the individual will have been guided by a series of conventions built up in his locality, paying little attention to what may be fashionable. The function of the building would be the dominant factor, aesthetic considerations, though present to some small degree, being quite minimal. Local materials would be used as a matter of course, other materials being chosen and imported quite exceptionally”

Thus vernacular architecture does exist -first of all - as a shelter. And then it endures time, weather, local politics and economy. Therefore vernacular architecture is a memory of what happened to a culture. What happened to its settlers, to the whole environment. It must be studied carefully for it reflects one’s traditions and feelings. But Brunskill tells us “ the function of the building would be the dominant factor” . It is true and not true. Indeed the function of the building is buried deep down inside the whole subjective façade. This is why vernacular architecture exists for a reason.

Whether the reason has been lost for some reason or maybe never existed, there is always a positive answer to investigate vernacular architecture : to build or to repair.

This project is aiming a reconciliation between architecture and climate with the help of vernacular architecture. It mainly consists of making a data gathering, to then use it in a tool that’ll lead to architectural answers. In the first hand I’m going to expose the research processes, then explain the objective classification and calculation, and finally, present the purposes for architects.

I/ RESEARCH PROCESS A) CLIMATE DATA

Where to seek general informations

Collecting climate related data, is quite simple when it comes to «general informations». What we’re looking for is mainly : - High / low temperatures, - Precipitations - Length of day, - Relative humidity, - Dew point, - Daily sunshine, - Sun altitude at solar noon.

Any other information besides those, could be more complicated to find even sometimes impossible.

The first helpful website is climatemp. com (fig 1). It provides a large amount of useful material BUT not all locations are available. The website seems to be aware of this lack of information (somehow) and when a position can’t be found, it present us a list of nearby cities with corresponding informations.

The other source is weatherbase. com (fig 2) which is pretty well known for having good data. Depending on the location, it shows more or less material.

The biggest mistake in seeking climate data is wanting to be 100% precise on every data. It is not possible, and doesn’t depend on ourselves : sometimes, websites can show different numbers. Even if those numbers are pretty close, we’re tempted to say “it will make a huge difference”. After trying different possibilities, the answer is no.

FIG 1.

The best we can do is trying to make an average of everything you find. But anyway, average means average. It doesn’t actually state the humidity will be 70% all year long.

General informations are the core of this project, even if more detailed informations are the reason why pro-

FIG 2.

-jects are so different around the world.

Like I said above, sometimes data for specific location are not available. There is no use to look at “general information” websites for ever, at some point it makes more sense to create yourself data, starting with monthly more detailed (to avoid) informations.

How to extract more specific data

In the case you need to create yourself some data, you can call for different kind of help.

The first case is when «general informations» are missing (temperature, precipitations, etc ... ). Meteoblue.com is a website that displays lots of different graphs. For example, “Average temperature and precipitation” (fig 3), “Cloudy, sunny and precipita -tion days» (fig 4), « Precipitation amounts» (fig 7) , «Wind speed» (fig 5) «Wind rose» (fig 6) and «Maximum temperatures» (fig 8). Here the graphs displays averages amounts per month. It is quite simple to then calculate.

FIG 3.

FIG 6.

FIG 4.

FIG 7.

FIG 5.

FIG 8.

FIG 10.

FIG 9.

FIG 13.

FIG 11.

FIG 12.

FIG 14

The software that I used to determine custom sunlight angle is DiagSol (fig 9). It needs only the latitude and it displays the sun trajectory for each month. Then, I gather the results for 12 am throughout the year, in the azimuth output.

In order to have more precise material for wind speed and direction, I work with windfinder.com (fig 12). It’s best to find some airport nearby the location; they’ll always have complete data. In case, we can’t come up with wind direction in degree, (fig 12) can help converting.

Rarely, the elevation of the city can’t be found. In this case, elevationmap.net (fig 13) is a very useful tool.

Finally, the seismic data I work with all come from geohazards.usgs. gov (fig 14). The website let you pick any location, and present different sources : red is not up to date, and green is very recent. SS and S1 are values for response acceleration. They vary with location and the site composition.

B) ARCHITECTURE DATA

What does it mean ?

In order to compare climate and vernacular architecture, at some point it’s necessary to find good information websites about vernacular. The mistake is to choose a location and them trying to find the vernacular architecture that belongs there. This is not how it works, since cities

Themselves don’t have a very specific architecture. It’s all about areas. Architecture data, is the parameters that define architecture. Structure, materials, dimensions. All of it can be quantified or else, can be classified. The main idea is to erase the style from architecture, and end up with facts.

Great buildings.com “The best vernacular architecture, as a time-tested consensus response to the environment, available materials, production methods, and cultural structures, can show beautiful fitness to purpose. In addition, neighbourhoods and cityscapes created and maintained in a consistent common vernacular style sometimes exhibit the harmony of a family of repeating forms with consistency in adaptive

variations, such that the many beautiful parts also achieve great beauty as a collective whole.”

Greatbuildings.com if a database of different architecture building, and vernacular architecture built over the year by the magazine Architecture Week. They display 3D buildings, photographies, models, commentaries, bibliographies.

FIG 8.

Natural homes .org

«Once upon a time, architecture did not need architects. The construction was based on tradition and community knowledge rather than on the know-how of one unique specialist. The alpine chalet or a bamboo home from South-East Asia are just some examples of this “vernacular” architecture. Vernacular architecture evolves over time reflecting the characteristics of the local environment, climate, culture, natural materials, technology and the experience of centuries of community building» Naturalhomes.org is a well documented website, based on a collection that grows week by week. They encourage users to share pictures and knowledge. Each vernacular design is explained with a large text and lots of pictures. They state different informations, not well structured though.

FIG 8.

Db world housing.net

â&#x20AC;&#x153;The World Housing Encyclopedia (WHE) Report Database contains reports on housing construction types in seismically active countries. Each housing report is a detailed description of a housing type in a particular country. Each report has five major categories including architectural and structural features: - Building - Materials and Construction Process - Socio-economic Issues; - Past Performance In Earthquakes, - Seismic Features â&#x20AC;&#x153;

All of the housing reports in this database have been contributed by volunteers.

FIG 8.

II/ CLASSIFICATION & CALCULATIONS A) CRITERIA

Climate data

TEMPERATURE LIGHT SUN RAIN SNOW WIND EARTHQUAKE

1. High (°C) 2. Low (°C) 3. Dew Point (°C) 4. Sunlight (h) 5. Daylight (h)

6. Sun Altitude (°)

7. Annual Rain (mm) 8. Probability of rain /d (%) 9. Humidity (%)

10. Probability of snow /d (%) 11. Speed (km/h) 12. Direction (°) 13. SS (g) 14. S1 (g)

Vernacular data

WALL ROOF WINDOW FLOOR

ROOMS STRUCTURE GENERAL

1. Material 2. Masonry 3. Porosity 4. Thickness (cm) 5. Material 6. Slope 7. Size

8. Material 9. Number 10. Size (m²) 11. Heigh (m) 12. Number

13. Element 14. Foundation

15. Inhabitants 16. Shape

The choice of classification over subjectivity

From the very beginning the choice of being objective was made in order to obtain a realistic result. What more objective than numbers ? If we look at architecture through human eyes, all we see is style. The moment I’ll talk about Japanese architecture, everyone will have in mind the exact same picture. But when I talk about Japanese architecture, I’m not talking about the very specific shape of the roof, or the way wood makes the house look like. I’m talking about the material that makes the roof appear like this, or the structure that support the house, the wood that is more than fancy looking.

So how could we possibly take out the subjective look of a building ? Will it lead us to true architecture ?

My answer is to completely forget about the “final look” of a building, but instead find out how it’s made.

And more importantly why it is like that. Is climate alone responsible for vernacular shapes ?

Not likely but they surely have a link. Thus, classification is a big part of the tool. The choice of each and every criteria is crucial.

For climate data, the selection needs to avoid similarities between data : it’s better to have one criteria instead of two really close. For architecture data, the selection needs to be wide enough to embrace all of the building’s aspects, but not too wide otherwise it won’t make sense anymore.

On the left is the selection of criteria I made based on what I believe is representative. Below is shown some of the vernacular architecture ( of the data base) explained step by step how I classified them.

2. Newtonmore // UK

WALL 1. Material 2. Masonry 3. Porosity 4. Thickness (cm) ROOF 5. Material 6. Slope WINDOW 7. Size FLOOR 8. Material 9. Number ROOMS 10. Size (mÂ˛) 11. Heigh (m) 12. Number STRUCTURE 13. Element 14. Foundation GENERAL 15. Inhabitants 16. Shape

dry-stone & earth, Lime Hermetic or due to mounting 60 Thached with cereal straw 4 Small Flagstones or packed earth 1 25 3,9 1 Walls + Frames Built on clay house (+ live stock) Rectangle

1 3

5 6

resources : documents and pictures

10 12 13 16

5. Ise // Japan

WALL 1. Material 2. Masonry 3. Porosity 4. Thickness (cm) ROOF 5. Material 6. Slope WINDOW 7. Size FLOOR 8. Material 9. Number ROOMS 10. Size (mÂ²) 11. Heigh (m) 12. Number STRUCTURE 13. Element 14. Foundation GENERAL 15. Inhabitants 16. Shape

Unfinished wood No Due to material 30 Thached with reed or hinoki cypress bark 2 No window Wood 2 59,95 2,7 1 Frame No Temple Rectangle

1 2 3

resources : documents and pictures

8 10 12 16

13 14

6. Lake toba // Indonesia

Bamboo and wood timber None Holes in the wall 10 Thached sugar palm fiber or zinc 2 Small Wood 3 47,73 3,6 3 Beams Stones 3,75 Rectangle

3 7

Resources : documents and pictures

10 15

12 16

7. Rouen // France

Stone & timber framing Adobe Hermeticly close 50 Thached & iris ( roots) 4 Big Wood 3 136 2,5 3 Frame Flint 5<x<10 Rectangle

resources : documents and pictures

9 11 12

8. Shibam // Yemen

Adobe None Hermeticly close 40 Adobe 0 Medium Tile 2 16 3,5 4 Frame & wall Stones x<5 Rectangle or square

1 5

resources : documents and pictures

9 11 13

10 12 16

9. Hodka // India

Mud or stone Clay Hermeticly close 30 Thached 4 Small Ground 1 17,5 2,85 1 Frame & wall Stone x<5 Round

1 2

5 6

2 7

resources : documents and pictures

9 11

13 14

12 16

10. Mesa Verde // United States

Hard sandstone Adobe Holes = mounting process 30 Hard sandstone 0 Small Ground 2 25 2,5 1,5 Frame & wall No x<5 U & L shape

1 2 3

5 6

4 7

resources : documents and pictures

9 11

13 14

11. San Juan // Argentina

Adobe tiles Adobe Holes = mounting process 30 Mud coat 0 Small Cane & mud on poplar logs 1 74 3 6 Frame & wall No 5<x<10 U shape

1 2 13 14

resources : documents and pictures

11 db.world-housing.net

10 12 16

15 db.world-housing.net

12. Cuzco // Perou

Adobe tiles Adobe Holes = mounting process 40 Tiles or thatched 2 Medium Ground 1 36 3 2 Frame & wall Stone x<5 Rectangle

1 2 3

resources : documents and pictures

10 12 16

9 11

13 14

II/ CLASSIFICATION & CALCULATIONS B) FORMULAS

TEMPERATURE 1er quarti 12,9 2e quarti 2 3e quarti 14,4

Why organize data into groups & how When all the data is gathered in the database, it’s necessary to find a way to deal with it. Here the tool is based on comparison ( between climate / vernacular or between locations etc ..).

There is different kind of formulas to compare. Some more complicated than others. But in this case, we only have a dozen of projects, no more. Instead of thinking about a very complicated formula, I just found a way to create 3 different groups. The A formula takes the matrice ( column of data, ex : temperature) and with quartile, divide it in 3 groups. 1st quartile, 2cd quartile and 3rd quartile numbers are displayed for both temperature and altitude.

The B formula, display whether the data is inferior or equals the 1st quartile, superior or equals the 3rd quartile ; or in between ( fig 1).

TEMPERATURE 1er quarti 12,9 2e quarti 2 3e quarti 14,4

Then, it gives a group number :

ALTITUDE 1er quarti 78 2e quarti 253 TEMPERATURE LOCATION 3e quarti 661

- 1 : x < Q1 - 0 TEMPERATURE : Q1< x <Q3 2 : Q3 < x 1er- quarti 12,9

Zawadka 12,9 HUMIDITY Newtonmore 11,4 1er quarti Reykjavík 6 54,5 2e quarti 76 Ghadamès 28 3e quarti 80 Ise 18 Lake Toba 22,92 Rouen 14,4 Shibam 32,75 Hodka 32 Mesa Verde 16,9 San Juan 23 Cuzco 18 Goteborg 11

2e quarti 2 example, here 3eFor quarti 14,4we have 3 cri-

teria : temperature, altitude and humidity. ALTITUDE The 12 projects are put into groups for each criteria. 1er quarti 78 So Zawandka belongs in temperature, 2e quarti to group 1253 group 0 in altitude & group 2. 3e quarti 661 With HUMIDITY a quick look we can see :

1er quarti 54,5 Zawadka, Newtonmore & Reykja2e quarti 76 vik are in the same group 3e quarti 80 for temper-

ature. - Zawadka, Newtonmore, Ghadames, Rouen & San Juan for altitude. - Zawadka, Reykjavik , Lake Toba, Rouen.

But there is a simple way to compare location with each other for each criteria.

LOCATION

1 1 1 2 0 0 0 2 2 0 2 0 1

=QUARTILE(MATRICE;1) Zawadka =QUARTILE(MATRICE;2) Newtonmore Reykjavík =QUARTILE(MATRICE;3)

251 253 60 356 4 1412 156 661 78 2254 648 3249 5

Ghadamès Ise Lake Toba Rouen ALTITUDE Shibam Hodka 0 Mesa Verde 0 San Juan 1 Cuzco 0 Goteborg

1 2 0 2 1 2 0 2 1

TEMPERATURE ALTITUDE 12,9 1 1er quarti 78 11,4 1 2e quarti 253 1 3e6 quarti 661 28 2 18 0 HUMIDITY 22,92 0 1er quarti 54,5 14,4 0 2e quarti 76 HUMIDITY 32,75 2 3e quarti 80 32 80 2 2 16,9 78 0 0 23 80,8 2 2 18 0 36 1 11 1

76 81,8 83 26,25 63 47 54,5 62 77,4

=IF(DATA<=QUARTILE(MATRICE;1);1)+IF(AND(DATA>=QUARTILE(MATRICE;3));2)

GROUP 1 <= QUARTILE 1 < GROUP 0 < QUARTILE 3<= GROUP 2

0 2 2 1 0 1 1 0 0

ALTITUDE 251 253 60 356 4 1412 156 661 78 2254 648 3249 5

Compare results to a referential The way to compare data very quickly is to just look up at the groups and verify if they are similar. If they are ( like Zawadka and Newtonmore for Temp) then the formula displays a 1; if not : 0. For the 3 criteria, there is 3 columns filled with 1 and 0. At the end, it add columns together : - x = 3 : 3 out of 3 criteria are similar - x = 2 : 2 out of 3 criteria are similar - ... This is a pretty easy way to see which locations are similar to the reference ( here Zawadka). For example, when we take Newtonmore for reference, we can see that Zawadka shares 2 criteria out of 3 with it, and Reykjavik, Ghadamès, Ise, Rouen, Hodka, San Juan & Cuzco only 1. This is not the final formula, it is just a very simplistic way to expose the thinking process behind the final formula. But All of the steps before are also part of the end formula.

TEMP ALTITUDE TEMP ALTITUDE Zawadka 1 0 Zawadka 1 0 Newtonmore 1 0 Newtonmore 1 0 Reykjavík 1 1 Reykjavík 1 1 Ghadamès 2 0 Ghadamès 2 0 Ise 0 1 Ise 0 1 Lake Toba 0 2 Lake Toba 0 2 Rouen 0 0 Rouen 0 0 Shibam 2 2 Shibam 2 2 Hodka 2 1 Hodka 2 1 Mesa Verde 0 2 Mesa Verde 0 2 San Juan 2 0 San Juan 2 0 Cuzco 0 2 Cuzco 0 2 Goteborg 1 1 Goteborg 1 1 =IF(DATA=REF;1;0)

HUMIDITY HUMIDITY 2 2 0 0 2 2 1 1 0 0 2 2 2 2 1 1 0 0 1 1 1 1 0 0 0 0

TEMP TEMP 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1

ALTITUDE ALTITUDE 1 1 1 1 0 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0 1 1 0 0 0 0

HUMIDITY HUMIDITY 1 1 0 0 1 1 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0

TOTAL TOTAL 3 3 2 2 2 2 1 1 0 0 1 1 2 2 0 0 0 0 0 0 1 1 0 0 1 1

Zawadka Zawadka Newtonmore Newtonmore Reykjavík Reykjavík Ghadamès Ghadamès Ise Ise Lake Toba Lake Toba Rouen Rouen Shibam Shibam Hodka Hodka Mesa Verde Mesa Verde San Juan San Juan Cuzco Cuzco Goteborg Goteborg

TEMP TEMP 1 1 1 1 1 1 2 2 0 0 0 0 0 0 2 2 2 2 0 0 2 2 0 0 1 1

ALTITUDE ALTITUDE 0 0 0 0 1 1 0 0 1 1 2 2 0 0 2 2 1 1 2 2 0 0 2 2 1 1

HUMIDITY HUMIDITY 2 2 0 0 2 2 1 1 0 0 2 2 2 2 1 1 0 0 1 1 1 1 0 0 0 0

TEMP TEMP 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1

ALTITUDE ALTITUDE 1 1 1 1 0 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0 1 1 0 0 0 0

HUMIDITY HUMIDITY 0 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0 1 1 0 0 0 0 1 1 1 1

TOTAL TOTAL 2 2 3 3 1 1 1 1 1 1 0 0 1 1 0 0 1 1 0 0 1 1 1 1 2 2

Zawadka Zawadka Newtonmore Newtonmore Reykjavík Reykjavík Ghadamès Ghadamès Ise Ise Lake Toba Lake Toba

TEMP TEMP 1 1 1 1 1 1 2 2 0 0 0 0

ALTITUDE ALTITUDE 0 0 0 0 1 1 0 0 1 1 2 2

HUMIDITY HUMIDITY 2 2 0 0 2 2 1 1 0 0 2 2

TEMP TEMP 1 1 1 1 1 1 0 0 0 0 0 0

ALTITUDE ALTITUDE 0 0 0 0 1 1 0 0 1 1 0 0

HUMIDITY HUMIDITY 1 1 0 0 1 1 0 0 0 0 1 1

TOTAL TOTAL 2 2 1 1 3 3 0 0 1 1 1 1

Example with 3 criteria

So before was just the explanation of how the formula works. Here is the grid -with still only 3 criteria- where locations are compared to each other. For example, if we take Newtonmore, and compare it with itself, we end up with a grey cell : it means that those cells are of no interest in this comparison.

If we’re comparing Ghadamès to Shibam ( following the arrows) we can see that they share 2 criteria out of 3. And we can see which one : temperature & humidity. The A formula states how much criteria is in common. The B formula explains which criteria is in common. In “real life” with more than 3 criteria, such a grid can be represented (too big). The A and B formulas are similar, except there is X «+SI(DATA=REF;1;0)» and

X «&SI(DATA=REF;CRITERIA;«»)». X being the number of criteria.

LOCATION

Newtonmore

Reykjavík

Ghadamès

Ise

Lake Toba

Rouen

Shibam

Hodka

Mesa Verde

San Juan

Cuzco

Goteborg

2 TEMP ALT

1 ALT 1

1 HUMID 0

2 ALT HUMID 1

1 ALT 1

2 TEMP HUMID 1

TEMP ALT

TEMP

ALT

HUMID

ALT

HUMID

1 TEMP 2 TEMP HUMID 2

ALT

HUMID

Zawadka

Zawadka Newtonmore

Reykjavík

Ghadamès

Ise Lake Toba Rouen Shibam Hodka Mesa Verde San Juan Cuzco Goteborg

2 TEMP HUMID 1

TEMP

ALT

HUMID

ALT

0 1 ALT 0

1 HUMID 1 HUMID 0

1 HUMID 0

1 ALT 0

1 ALT 1 HUMID 2 TEMP HUMID

1 HUMID 2 ALT HUMID 0

0 1 ALT 2

1 TEMP 1 TEMP 0

TEMP HUMID 0 0 1 ALT 0 1 TEMP

1 TEMP 1 HUMID 3 TEMP ALT HUMID 0

TEMP ALT

ALT

HUMID 0

ALT

TEMP ALT

ALT

TEMP HUMID

TEMP

HUMID

TEMP

ALT HUMID

TEMP

2 TEMP ALT 1 TEMP 2

TEMP

ALT HUMID

2 TEMP HUMID 2 TEMP HUMID 1

1 ALT 0

ALT 2 ALT HUMID 1 TEMP 0

2 TEMP ALT 0

2 TEMP HUMID 2

2 TEMP ALT 0

1 TEMP 1 ALT 1 TEMP 0

1 TEMP 2 ALT HUMID 2 TEMP HUMID 1 ALT 0

ALT HUMID

0 0 1 TEMP 1 HUMID 2 ALT HUMID

=SI(DATA=REF;1;0)+SI(DATA=REF;1;0)+SI(DATA=REF;1;0)

=SI(DATA=REF;TEMP;«»)&«»&SI(DATA=REF;ALT;«»)&«»&SI(DATA=REF;HUMID;«»)

1 HUMID 2 TEMP ALT 0

3 TEMP ALT HUMID 0

0 1 ALT 2 TEMP HUMID 1 TEMP 1 HUMID

2 TEMP HUMID 2 TEMP ALT 1 TEMP 1

0 0 0

ALT 1 HUMID 2 TEMP ALT 0

0 0

ALT HUMID

2 ALT HUMID 0 0 1 HUMID

1 HUMID

III/ PURPOSES FOR ARCHITECTS A) CREATE

Why vernacular lead to creating sustainability

“Traditional architecture is architecture is passed down from person to person, generation to generation, particularly orally, but at any level of society, not just by common people. “ Allen Noble

“Comprising the dwellings and all other buildings of the people. Related to their environmental contexts and available resources they are customarily owner- or community-built, utilizing traditional technologies. All forms of vernacular architecture are built to meet specific needs, accommodating the values, economies and ways of life of the cultures that produce them.” Encyclopedia of Vernacular Architecture of the World Definition of a sustainable building by WBDG Sustainable Committee : 1) Optimize Site Potential 2) Optimize Energy Use 3) Protect & Conserve Water 4) Optimize Building & Materials 5) Enhance Indoor Quality

Vernacular architecture could possibly lead architects to create sustainable buildings. As we can read from Allen Noble, vernacular architecture is transmitted through the time by and for people. And the Encyclopedia of VAOFW is very clear about the relation to the environment context, resources.

They exist to fit into the socio-politico context, with the means and materials already existing. Thus not everyone can have a concrete truck nearby ready to be used anytime. However with the globalization of the architectural techniques, concretebuilding - or else - end up in environments without a good reason. If we follow the WBDG definition of a sustainable building, vernacular architecture sure meets the expectations. Or at least some of them.

1) It always optimize site potential (it’s free) meaning they have a proper site selection, and reuse or rehabilitate existing buildings. It cares about location, orientation, landscaping....

2) It is “cheap” in energy use, as they always use earth materials like adobe, rocks etc... So always zero energy buildings. 3) As most of the building are at least 50 years old, they had to find ways to collect water at that time. But it’s not always included in the design.

4) The world population continues to grow and there is a need to achieve an intelligent use of materials. As said above, earth materials, wood etc are non limited when well taken care of. 5) Enhance indoor quality means making the best out of climate : maximizes daylighting, good ventilation and moisture control, optimizes acoustic performance ... Being no expect the average inhabitant will just built what suits him. Probably what could suit everyone.

This is where architects have to gain learning through vernacular technics, materials, structures, processes ....

Example : Gothenburg

What if we could pick one location, the one we want to build on, and end up with advices linked to vernacular architecture around the world ?

We’ll take Gothenburg for example. The main idea is to gather the climate data with the previous research websites , software or maps, and then put it in the excel sheet (fig 1). We obtain the different groups for each criteria (fig 2). Then it compares locations with Gothenburg groups (fig 3). The black rounds notifies when data are similar to gothenburg. At the bottom are the total. We clearly see that Newtonmore, Ise and Rouen are the mos similar location (fig 4). Then we can sort out what defines Gothenborg with words (fig 5). Finally we link previous climate data back in (fig 1), sum everything up and obtain (fig 6). In bold are the “most important” informations about gothenburg and their linked match. For example : high temperature in bold, linked to Newtonmore.

Location Location Zawadka Zawadka Rymanowska Rymanowska Newtonmore Newtonmore Reykjavík Reykjavík Ghadamès Ghadamès Ise Ise Lake Toba Toba Lake Rouen Rouen Shibam Shibam Hodka Hodka Mesa Mesa Verde Verde San Juan Juan San Cuzco Cuzco Goteborg Goteborg

High Low High Low temperature temperature temperature temperature

Sunlight aa Sunlight day day

Daylight Daylight in in aa Sun altitude Sun altitude day day

Rain Rain

Humidity Humidity

Rain % % Rain

Probability % % Probability

Elevation Elevation

Dew point point Dew

Wind Wind speed speed

Wind Wind direction direction

SS SS

S1 S1

12,9 12,9

3,1 3,1

4,04 4,04

12,8 12,8

40,2 40,2

649 649

80 80

44,08 44,08

10,26 10,26

251 251

4 4

14 14

180 180

0,2 0,2

0,09 0,09

11,4 11,4 6 6 28 28 18 18 22,92 22,92 14,4 14,4 32,75 32,75 32 32 16,9 16,9 23 23 18 18 11 11

3,5 3,5 2 2 16 16 10 10 16,83 16,83 6,5 6,5 19,25 19,25 20 20 2,6 2,6 11 11 5 5 5 5

3 3 3,26 3,26 8,48 8,48 5,53 5,53 7,07 7,07 5,2 5,2 8,3 8,3 7 7 7,33 7,33 8 8 6,26 6,26 5,3 5,3

13,1 13,1 13,6 13,6 12,6 12,6 12,6 12,6 12,5 12,5 12,9 12,9 12,2 12,2 12,5 12,5 12,6 12,6 12,5 12,5 12,5 12,5 13,2 13,2

33,4 33,4 26,2 26,2 57,6 57,6 57,7 57,7 75,1 75,1 41,5 41,5 83,29 83,29 70,95 70,95 33,38 33,38 56,7 56,7 71,9 71,9 32,7 32,7

1208 1208 810 810 27 27 1820 1820 3206,4 3206,4 670 670 125 125 340 340 461 461 105,1 105,1 640 640 791 791

78 78 80,8 80,8 36 36 76 76 81,8 81,8 83 83 26,25 26,25 63 63 47 47 54,5 54,5 62 62 77,4 77,4

61 61 58 58 5,75 5,75 27,1 27,1 48 48 44 44 9,77 9,77 6,3 6,3 8,76 8,76 20,53 20,53 42 42 51,5 51,5

8,35 8,35 8,65 8,65 0 0 5,2 5,2 0 0 5,28 5,28 0 0 0 0 17,47 17,47 1,1 1,1 0 0 3,4 3,4

253 253 60 60 356 356 4 4 1412 1412 156 156 661 661 78 78 2254 2254 648 648 3249 3249 5 5

5 5 0,66 0,66 3 3 10 10 20,7 20,7 6 6 5 5 17 17 -1 -1 7,7 7,7 4 4 5 5

13,1 13,1 19 19 16 16 17 17 10,3 10,3 17 17 12,7 12,7 12,6 12,6 13 13 17 17 17 17 15 15

213,75 213,75 90 90 270 270 315 315 22 22 225 225 130 130 200 200 76 76 157 157 90 90 247 247

0,18 0,18 0,96 0,96 0,08 0,08 1,69 1,69 1,09 1,09 0,19 0,19 0,04 0,04 0,73 0,73 0,15 0,15 1,72 1,72 1,24 1,24 0,15 0,15

0,07 0,07 0,46 0,46 0,03 0,03 0,68 0,68 0,43 0,43 0,08 0,08 0,01 0,01 0,29 0,29 0,05 0,05 0,69 0,69 0,5 0,5 0,07 0,07

Location Location Zawadka Zawadka Rymanowska Rymanowska Newtonmore Newtonmore Reykjavík Reykjavík Ghadamès Ghadamès Ise Ise Lake Lake Toba Toba Rouen Rouen Shibam Shibam Hodka Hodka Mesa Verde Verde Mesa San Juan San Juan Cuzco Cuzco Goteborg Goteborg

HTHT-

LTLT-

SUNSUN-

DAYDAY-

SUN/ALSUN/AL-

RAINRAIN-

HUMIDHUMID-

RAIN%RAIN%-

SNOW%SNOW%-

ELEVELEV-

DEW/PDEW/P-

WIND/SWIND/S-

WIND/DWIND/D-

SSSS-

S1 S1

1 1

0 0

2 2

0 0

2 2

0 0

1 1

0 0

1 1 1 1 2 2 0 0 0 0 0 0 2 2 2 2 0 0 2 2 0 0 1 1

1 1 1 1 2 2 0 0 2 2 0 0 2 2 2 2 1 1 0 0 0 0 0 0

1 1 1 1 2 2 0 0 0 0 1 1 2 2 0 0 2 2 2 2 0 0 0 0

2 2 2 2 0 0 0 0 1 1 2 2 1 1 1 1 0 0 1 1 1 1 2 2

1 1 1 1 0 0 0 0 2 2 0 0 2 2 2 2 1 1 0 0 2 2 1 1

2 2 2 2 1 1 2 2 2 2 0 0 1 1 1 1 0 0 1 1 0 0 0 0

0 0 2 2 1 1 0 0 2 2 2 2 1 1 0 0 1 1 1 1 0 0 0 0

2 2 2 2 1 1 0 0 2 2 0 0 1 1 1 1 1 1 0 0 0 0 2 2

2 2 2 2 1 1 0 0 1 1 0 0 1 1 1 1 2 2 0 0 1 1 0 0

0 0 1 1 0 0 1 1 2 2 0 0 2 2 1 1 2 2 0 0 2 2 1 1

0 0 1 1 1 1 2 2 2 2 0 0 0 0 2 2 1 1 2 2 1 1 0 0

0 0 2 2 0 0 2 2 1 1 2 2 1 1 1 1 1 1 2 2 2 2 0 0

0 0 1 1 2 2 2 2 1 1 2 2 0 0 0 0 1 1 0 0 1 1 2 2

0 0 0 0 1 1 2 2 2 2 0 0 1 1 0 0 1 1 2 2 2 2 1 1

1 1 2 2 1 1 2 2 0 0 0 0 1 1 0 0 1 1 2 2 2 2 1 1

LOCATION LOCATION

Zawadka Zawadka

Newtonmore Newtonmore

Reykjavík Reykjavík

Ghadamès Ghadamès

Ise Ise

Lake Toba Toba Lake

Rouen Rouen

Shibam Shibam

Hodka Hodka

Mesa Verde Verde Mesa

San Juan Juan San

Cuzco Cuzco

Goteborg Goteborg

3 3

8 8

5 5

4 4

6 6

2 2

6 6

3 3

4 4

2 2

4 4

15 15

SUNSUNHUMIDHUMIDELEVELEV-

SUN/ALSUN/ALRAINSSRAINSSS1 S1

LTLTSNOW%SNOW%-

LTLT- SUNSUNRAINRAINHUMIDHUMID-

Goteborg Goteborg

HTHT- RAINRAINWIND/SWIND/S-

HTHT- DAYDAYSUN/ALSUN/ALHUMIDHUMIDRAIN%RAIN%DEW/P-

HTHT- DAYDAYWIND/SWIND/SSUN/ALSUN/ALWIND/D- SSSSWIND/DRAIN%- ELEVELEVRAIN%S1

LTLT- SUNSUNHUMIDHUMIDSNOW%SNOW%ELEVELEV-

SUNSUNRAIN%RAIN%-

LTLT- DAYDAYRAINRAINSNOW%SNOW%DEW/PDEW/P-

DEW/PDEW/PSSSS- S1 S1

FIG 1.

FIG 2.

TEMPERATURE

LIGHT

4. Sunlight

RAIN

SUN

1. High

EARTHQUAKE

WIND

SNOW

ZAWADKA

NEWTONMORE

REYKJAVIK GHADAMÃ&#x2C6;S

ISE

LAKE TOBA

ROUEN

SHIBAM

HODKA

MESA VERDE

SAN JUAN

CUZCO

2. Low

3. Dew Point 5. Daylight

6. Sun Altitude 7. Annual Rain

8. Prob. rain/d 9. Humidity

10. Prob. snow/d 11. Speed

12. Direction 13. SS

14. S1

TOTAL

FIG 3.

Gothenburg // Sweden

8 NEWTONMORE // UK 6 ISE // JAPAN 6 ROUEN // FRANCE

NEWTONMORE

ISE / ROUEN

3. Dew point (°)

NEWTONMORE / ISE / ROUEN

5. Daylight (h)

13,2

NEWTONMORE / ISE /ROUEN

TEMPERATURE 1. High (°c) 2. Low (°c)

FIG 4.

LIGHT 4. Sunlight (h)

SUN 6. Sun Altitude (°)

RAIN 7. Annual Rain (mm) COLD TEMPERATURE FEW SUNLIGHT HOURS LONG DAY LOW SUN ALTITUDE SLIGHTLY HIGH RAINFALL MORE THAN 50% RAIN/DAY SLIGHTLY HIGH HUMIDITY %

8. Prob. Rain/d (%)

32,7

NEWTONMORE

791

ISE / ROUEN

51,4

NEWTONMORE

77,4

NEWTONMORE

3,4

ISE

NEWTONMORE

12. Direction (°)

247

ISE / ROUEN

14. S1 (g)

0,07

9. Humidity (%)

SNOW 10. Prob. Snow/d (%) WIND 11. Speed (km/h)

FIG 5.

5,3

EARTHQUAKE 13. SS (g)

0,15

NEWTONMORE

FIG 6.

Results and meaning

Previously, with the climate we stated that Gothenburg : - Cold temperature - Few sunlight hour - Long day - Low sun altitude - Slightly high rainfall - More than 50% rain/day - Slightly high humidity %

We decided to choose these criteria as important (fig 6) in bold. In front of them, the location corresponding. With this visual categorisation, Newtonmore is still the most important location, but then Rouen comes second, before Ise.

It’s not a very important step, but still it allows us to have in mind wich location really is closer on important matters. Now, we can finally conclude and ask yourselves :

What sustainable architecture could look like in gothenburg ?

Well of course it’ll be a mix of Newtonmore / Rouen / Ise data. But with the previous “mind scaling”, we can orientate the result for more logical answers. There is only 2 criteria where all 3 agreed : Roof material & general shape.

In regard of previous important data and new architectural answers, a wall in stone or wood, with a coat layer and hermetically close seems to be the right answer for Gothenburg. A thickness between 30 and 60 cm depends on the material of course. Four slopes and a thatched roof is probably also a good idea for high humidity, and deals nicely with rainfall. The rectangle shape could be linked to personal beliefs or as well be a good shape to heat up a house.

What architecture in Gothenburg could look like WALL 1. Material 2. Masonry 3. Porosity 4. Thickness (cm) ROOF 5. Material 6. Slope WINDOW 7. Size FLOOR 8. Material 9. Number ROOMS 10. Size (mÂ²) 11. Heigh (m) 12. Number STRUCTURE 13. Element 14. Foundation GENERAL 15. Inhabitants 16. Shape

Stone / Wood Lime / Adobe Close 30 < x < 60 Thatched 4 / Wood / 25 < x < 135 2,5 < x < 3,9 / Wall / Frame Clay / Flint / Rectangle

FIG 7.

FIG 8.

III/ PURPOSES FOR ARCHITECTS B) IMPROVE

General informations

Unfortunately, vernacular architecture is also - sometimes - synonym of failure.

Over time, the main problem of a location could change (ex : becoming more and more seismic sensible). When some types of architecture finds a way to adapt like in Indonesia in Lake Toba, some others fail to do so : San Juan, Argentina and Cuzco, Peru.

In lake Toba, they avoid earthquakes issues by adding structural vertical beams (fig 2) in their stilts and by having a really lightweight roof. But in San Juan or Cuzco, the house are directly on the ground, providing no movement possibilities. Plus, the dry adobe is easily breakable during a seism due to the mounting process (fig 1). When a vernacular building failed to fill its duty, two choice is offered : - Ignore it - Improve it

Well of course, ignoring it is not even an option. At least it will be used as a not to do example. It’s a reminder that even with the most accurate climate data in the world, the weather, context in general is in constant movement.

But anyway, what is equally -or even more- interesting. The World Housing Encyclopedia (WHE) embrace this idea, by locating neglecting vernacular. As stated above, sustainability is also to be able to “Optimize building & materials” . So it’s obvious , bad vernacular architecture needs to be repaired.

Like in Cuzco (A pictures) show how rough earthquakes can be. The tiles are easily broke, and sadly most of the house remain this way (lack of money ?). WHE went there (B pictures) and reinforced the pre-existing structure.

FIG 1.

IDENTITY

FIG 2.

RESULTS

COUNTRY

CITY

ANSWER MAIN PROBLEM ?

POLAND

Rawadka

Newtonmore

ICELAND

Reykjavik

LIBYA

GhadamÃ¨s

JAPAN

Ise

INDONESIA

Lake Toba

FRANCE

Rouen

YEMEN

Shibam

INDIA

Hodka

Mesa Verde

ARGENTINA

San Juan

PERU

Cuzco

COLD /RAIN / SNOW /

RAIN / WIND SPEED/ EARTHQUAKES RAIN / HUMIDITY / EARTHQUAKES COLD / HUMIDITY

WATER RISE / HOT

WIND / EARTHQUAKES COLD NO

FIG 3.

CONCLUSION

General informations

The research process is the foundation of the whole process. A good database can make things way easier. Furthermore, finding a database also mean creating one when there is no other option.

The tool in itself - classification and calculation - would be more like the basement. The criteria must be chosen rightly to create a link between architecture and climate. The tool holds the database for now, but these formulas can’t handle a lot of data. Finally the purpose for the architects here is to gain knowledge, and use it in two different ways. Creating or improving. In any case, it is possible to say vernacular architecture is always sustainable. Indeed when it fails to resolve earthquakes issues, it’ll answer some other engaging aspects. Compared to

globalization in ain architecture nowadays, it’s a mistake to not be involved in vernacular architecture. However to improve vernacular architecture, it’s our responsibility to promote sustainability.

Is it responsible to reinforce adobe house with concrete ? Can’t we find interesting answers in Lake Toba houses, or Ise ? In a nutshell, the outcome for the test subject Gothenburg looks correct. It could have been guessed though, but partially. It’s quite known Sweden traditional houses are made of wood, but I haven’t seen more “french cottage” in Gothenburg - yet -. The process could be widen by circling specificities in each project, what make them extreme in their own way.

MASONRY

IDENTITY N°

Country

2 3

Zawadka Rymanowska Newtonmore Reykjavík

TEMPERATURE

LIGHT

Average high Average low Average dew temperature temperature point (°C) (°C) (°C)

City

Average sunlight a day (h)

SUN

RAIN

SNOW

WIND

Sun altitude Average Probability of Probability of at solar noon Annual rain Average Wind speed daylight in a rain in a day snow in a (mm) humidity (%) (km/h) on the 21st day (h) (%) day (%) day (°)

HEIGH

Wind Elevation (m) direction (°)

EARTHQUAKES SS (g)

S1 (g)

Poland

12,9

3,1

4,04

12,8

40,2

649

44,08

10,26

180

251

0,2

0,09

United Kingdom Iceland

11,4 6

3,5 2

5 0,66

3 3,26

13,1 13,6

33,4 26,2

1208 810

61 58

78 80,8

8,35 8,65

13,1 19

213,75 90

253 60

0,18 0,96

0,07 0,46

Ghadamès

Libya

8,48

12,6

57,6

5,75

270

356

0,08

0,03

Ise

Japan

5,53

12,6

57,7

1820

27,1

5,2

315

1,69

0,68

6 7 8 9 10 11 12

Lake Toba Rouen Shibam Hodka Mesa Verde San Juan Cuzco

Indonesia France Yemen India United States Argentina Perou

22,92 14,4 32,75 32 16,9 23 18

16,83 6,5 19,25 20 2,6 11 5

20,7 6 5 17 -1 7,7 4

7,33 8 6,26

12,5 12,9 12,2 12,5 12,6 12,5 12,5

75,1 41,5 83,29 70,95 33,38 56,7 71,9

3206,4 670 125 340 461 105,1 640

48 44 9,77 6,3 8,76 20,53 42

81,8 83 26,25 63 47 54,5 62

0 5,28 0 0 17,47 1,1 0

10,3 17 12,7 12,6 13 17 17

22 225 130 200 76 157 90

1412 156 661 78 2254 648 3249

1,09 0,19 0,04 0,73 0,15 1,72 1,24

0,43 0,08 0,01 0,29 0,05 0,69 0,5

5,3

13,2

32,7

791

51,5

77,4

3,4

247

0,15

0,07

IDENTITY N° 1 2 3 4 5 6 7 8 9 10 11 12

Country

5,2 8,3

WALL City

Material

Masonry

Porosity

WINDO W

ROOF Thickness (cm)

Material

Slope

Size

FLOOR Material

Number

Zawadka Rymanowska Poland Newtonmore United Kingdom Reykjavík Iceland Ghadamès Libya Ise Japan Lake Toba Indonesia bamboo and wood timber 0 1 thached 10 sugar palm fiber or zinc 2 1 wood 3 Rouen 2,3 stone France + timber framing (Wattle and daub 2 & oak timber) 4 50 thached + iris 4 3 wood 2 Shibam Yemen adobe 2 4 40 ramad 0 2 tile 1 Hodka India mud, stone 2 4 30 thached 4 1 ground 2 Mesa Verde United States hard sandstone 1 3 30 hard sandstone 0 1 Stone San Juan Argentina Adobe 1 3 30 mud coat 0 1Cane and mud on poplar logs1 1 Cuzco Perou mud walls Wood planks 1 or beams 3 that support40 slate, metal, asbestos-cement 2 or plastic corrugated 2 sheets ground or tiles

Size (m²)

47,73 136 16 17,5 25 74 36

ROOMS Heigh (m)

3,6 2,5 3,5 2,85 2,5 3 3

Number

0 no 1 adobe 0 no clay adobe 021 no clay 1 adobe 02 no holesadobe in the wall 211 POROSITY clay material inner holes clay inwith the wall 221 material inner 1 holesporosity in with the wall 2 porosity material with inner holes in the wall small holes 21 3 porosity material with inner mounting process small holes 23 porosity mounting process small holes hermeticly close 34 mounting process small holes hermeticly close 34 mounting process small 41 hermeticly close SIZE medium smallclose 421WINDOW hermeticly big medium 123 small big 213 medium small 1 beams 3 big 2 medium frame beams 321 big 3 wall 2 frame 1 beams STRUCTURE ELEMENT 4 both wall 213 frame beams 3 wall 4 both 2 frame square 4 both 31 wall 2 rectangle square 41 both 3 U rectangle 12 square no 4 L 3 U 20 rectangle 1 square 1 adobe 5 round 4 L 3 U 2 rectangle STRUCTURE GENERAL clay round 425 L 3 U 5 round 4 L holesround in shape the wall Element Foundation N°51of inhabitants material with inner 2 porosity 3

small holes mounting process

hermeticly close

3 1 stone 3,75 2 1 small2 3 2 flint 10 2 medium 4 4 stones 5 2&1 3 big 5 1 4 yes 5 1,5 4 No 5 3&4 1 beams 6 4 No 10 3 2 frame 2Shallow Foundation: 4 Rubble stone, fieldstone 5 strip footing2 3 wall 4 both

GENERAL SHAPE 1 2 3 4 5

square rectangle U L round