Will artificial intelligence remain under human control?

How can one communicate fluently with artificial intelligence? Can one cooperate with artificial intelligence?

The existing artificial intelligence (AI) systems based on machine learning are often independent actors that inform people about their conclusions, but otherwise interact with people in a very limited scale.  AI is being increasingly introduced not only in services accessible via the internet, but also in mobile machines, such as autonomous cars and robots. We should consider how to ensure that AI will always remain under human control, and how humans can and how they should be able to interact with AI.

Verbal and non-verbal communication

In trend analyses of technology, the interactive properties of AI have been identified as the next major step in their development. Dialogical interaction does not require the user to seek and learn commands, but the correct function is negotiated through free dialogue with the machine. Interaction can be supplemented by non-verbal communication in such a manner that the machine identifies and reacts to the person’s emotional state, such as the person being confused. A machine can learn to identify individuals and adjust its operations according to which matters the person is and is not familiar with, and how he or she prefers to operate. Personal virtual assistants, such as Apple’s Siri, strive to establish a relationship with their owner and learn their preferences in such a manner that, with time, they can predict the person’s needs and offer assistance even before the person takes the initiative to ask for it.

In the internet, nowadays you often encounter chatbots. They are already relatively clever, and, when dealing with them, you may not always notice at first that you are not encountered by a real human being. A chatbot’s ability to discuss is based on the fact that it knows very well the limited service area within which it operates. It has learned to predict what kind of questions people may have. Every now and then, a chatbot may feel a little bit rude. This probably derives from the fact that they are programmed by people who transfer their own manners to the robot.

Interest towards AI solutions where a human and AI operate in collaboration with each other is increasing.  Collaborative human power can be used, for example, for collecting data or interpreting images in solutions, where a large group of people and AI form a collectively functioning entity. This kind of collective  intelligence has been used for such purposes as digitalisation of old texts. A human eye is incomparable in recognising words, even when written in strange letters. When AI carries out easy text recognition tasks and lets people deal with any unclear cases, the work will advance quickly with such collective power.

Fluent interaction requires learning and participation

Fluent interaction between humans and AI still requires a lot of development in many areas. In the future, we will see increasing amounts of work teams consisting of humans and robots. A robot can assist humans in many kinds of maintenance and service tasks. Fluent interaction is based on AI, with the help of which the robot interprets its environment and humans. Recognising the intentions of one another plays a key role: a human must be able to anticipate the robot’s actions, and, in the same way, the robot must be able to anticipate human actions. Dialogical interaction solutions are needed in this field as well.

Autonomous cars and other vehicles largely function on their own, but when they encounter a problematic situation, they may easily need human assistance. In such a situation, it is good if the machine has kept the human up to date on what is going on, so that he or she may quickly resolve the problematic situation. Indicating and recognising intentions is important also with a view to bystanders: when pedestrians encounter an autonomous car, how can they be sure that the car has seen them and stops at a pedestrian crossing to give way for them? How do you establish an eye contact with an autonomous car?

Different smart services at home and in offices strive to fulfil people’s wishes and predict their desires. Often such services remain unnoticed by people, in which case it may remain unclear why air conditioning is blowing at full blast or why the temperature does not rise. An easy interaction channel is needed, so that people can find out why things are going the way they are going, and that they can influence matters.

AI is not infallible − it can make mistakes and it may have faults. Once humans learn to understand the limitations of AI, and the way AI draws conclusions and functions, the interaction between them will become easier. When people understand the basics of the way AI functions, they can put themselves on a level with it, in the same manner as people naturally tune into the same level with the person they are talking with.  It is important to develop AI solutions in such a manner that people who will work with AI are allowed to participate in the design of the solutions.

Read more: VTT and Smart City

Kaasinen Eija
Eija Kaasinen
Senior Scientist, VTT
@eijakaasinen 
eija.kaasinen(a)vtt.fi

 

 

Pysyykö tekoäly ihmisen hallinnassa?

Miten tekoälyn kanssa voi kommunikoida sujuvasti? Voiko tekoälyn kanssa tehdä yhteistyötä?

Nykyiset koneoppimiseen perustuvat tekoälysysteemit ovat usein itsenäisiä toimijoita, jotka tuovat johtopäätöksensä ihmisten tiedoksi mutta eivät muuten paljon ole vuorovaikutuksessa ihmisten kanssa.  Tekoälyä on tulossa yhä enemmän paitsi verkon kautta saataviin palveluihin myös liikkuviin koneisiin kuten autonomisiin autoihin ja robotteihin. On syytä miettiä, miten tekoäly varmasti pysyy ihmisen hallinnassa sekä miten ihminen voi ja miten pitää voida olla vuorovaikutuksessa tekoälyn kanssa.

Sanallinen ja sanaton viestintä

Teknologian trendianalyyseissa tekoälyn vuorovaikutusominaisuudet on tunnistettu seuraavaksi merkittäväksi kehitysaskeleeksi. Keskusteleva vuorovaikutus ei vaadi käyttäjää etsimään ja opettelemaan komentoja vaan oikea toiminto neuvotellaan vapaassa keskustelussa koneen kanssa. Vuorovaikutusta voi täydentää sanaton viestintä niin, että kone tunnistaa ja reagoi ihmisen tunnetilaan, kuten esimerkiksi siihen, että ihminen on ymmällään. Kone voi oppia tunnistamaan yksilöitä ja muokata toimintaansa sen mukaan, mitkä asiat ovat tälle henkilölle tuttuja, mitkä outoja, ja miten hän mieluiten toimii. Henkilökohtaiset virtuaaliapulaiset, kuten Applen Siri, pyrkivät luomaan suhteen omistajaansa ja oppimaan hänen mieltymyksensä niin, että pystyvät ajan myötä ennakoimaan ihmisen tarpeita ja tarjoamaan apua jo ennen kuin ihminen sitä ehtii itse pyytää.

Verkossa voi useinkin törmätä keskustelurobotteihin (chatbot). Ne ovat jo kohtuullisen taitavia ja niiden kanssa asioidessa ei edes heti huomaa, että vastassa ei olekaan oikea ihminen. Keskustelukyky perustuu siihen, että keskustelurobotti tuntee hyvin rajatun palvelualueen, jolla se toimii. Se on oppinut ennakoimaan, minkä tyyppisiä kysymyksiä ihmisillä on. Keskustelurobotti voi joskus tuntua vähän töykeältä, se johtunee siitä, että niitä ohjelmoivat ihmiset, joiden omat käytöstavat siirtynevät robotille.

Kiinnostus on kasvamassa sellaisiin tekoälyratkaisuihin, joissa ihmiset ja tekoäly toimivat yhteistyössä.  Ihmisten joukkovoimaa voidaan käyttää esimerkiksi tietojen keräämiseen tai kuvien tulkitsemiseen ratkaisuissa, joissa laaja joukko ihmisiä ja tekoäly muodostavat yhdessä toimivan kokonaisuuden. Globaalia älyä on käytetty esimerkiksi vanhojen tekstien digitoinnissa. Ihmissilmä on ylivertainen tunnistamaan sanoja oudoillakin kirjasimilla kirjoitettuna. Kun tekoäly tekee helpot tekstien tunnistamiset ja antaa ihmisten tehtäväksi epäselvät tapaukset, niin joukkovoimalla työ etenee vauhdikkaasti.

Sujuva vuorovaikutus vaatii  oppimista ja osallistumista

Ihmisen ja tekoälyn sujuvassa vuorovaikutuksessa riittää kehittämistä monella alueella. Tulevaisuudessa teollisuudessa nähdään yhä enemmän ihmisten ja robottien muodostamia tiimejä. Robotti voi toimia ihmisen apurina myös monenlaisissa huolto- ja palvelutehtävissä. Sujuva vuorovaikutus perustuu tekoälyyn, jonka avulla robotti tulkitsee ympäristöään ja ihmistä. Keskeistä on aikeiden tunnistaminen puolin ja toisin: ihmisen tulee pystyä ennakoimaan robotin toimia ja samoin robotin tulee ennakoida ihmisen toimia. Keskustelevia vuorovaikutusratkaisuja tarvitaan tälläkin alueella.

Autonomiset autot ja muut kulkuneuvot toimivat suurelta osin itsenäisesti, mutta kun eteen tulee pulmatilanne, tarvitaan helposti ihmistä apuun. Silloin on hyvä, jos ihminen on pidetty koko ajan tilanteen tasalla, jotta hän voi nopeasti selvittää, miten ongelmasta päästään yli. Aikeiden ilmaisu ja tunnistaminen on tärkeää myös sivullisten kannalta: kun jalankulkija kohtaa autonomisen auton, miten hän voi varmistua, että auto on havainnut hänet ja pysähtyy suojatien eteen antamaan tietä? Miten autonomiseen autoon saadaan katsekontakti?

Erilaiset älykkäät palvelut kodissa tai toimistossa pyrkivät täyttämään ihmisen toiveet ja ennakoimaan toiveita. Usein palvelut eivät näyttäydy ihmiselle, jolloin saattaa jäädä epäselväksi, miksi ilmastointi hurisee täysillä tai miksi lämpötila ei nouse. Tarvitaan sujuva vuorovaikutuskanava, jotta ihminen saa selville, miksi asiat menevät niin kuin menevät ja että asioihin voi vaikuttaa.

Tekoäly ei ole erehtymätön, se voi tehdä virheitä ja siihen voi tulla vikoja. Kun ihmiset oppivat ymmärtämään tekoälyn rajoitteet ja tekoälyn tavan päätellä ja toimia, niin vuorovaikutuskin helpottuu. Kun ihminen ymmärtää tekoälyn toiminnan perusteita, hän osaa asettua samalle tasolle sen kanssa, samaan tapaan kuin ihminen luontevasti virittäytyy ihmiskeskustelukumppaninsa tasolle.  Tekoälyratkaisuja on tärkeää kehittää niin että ihmiset, jotka tekoälyn kanssa tulevat toimimaan, otetaan mukaan ratkaisujen suunnitteluun.


Kaasinen Eija
Eija Kaasinen
Senior Scientist, VTT
@eijakaasinen 
eija.kaasinen(a)vtt.fi

Smart buildings as part of smart cities and societies

In order to reach clean and low-carbon future in cities, buildings need to become a proactive part of the urban environment. What this means is that they need to be highly efficient and allow for flexibility in their operations. All this requires new level of integration and smartness in the buildings themselves, and in their physical and digital connections with the rest of the urban environment.

Can I make my building smart with a mobile app?

Our smart phones already have all the necessary computing power needed to operate any smart home device. With them, it is possible to control, for example, lighting, heating and cooling, monitor your energy consumption, and detect leaks in water lines. All this can help you to manage and customize the conditions in your home and improve safety. However, a set of apps and gadgets do not equal to a smart building or ease of living. A smart building integrates all the building systems work seamlessly together in an optimal way and provides you your preferred living conditions without the need of apps. There are already examples of companies, who have entered the market by providing fully integrated service solutions. You can already buy desired indoor air conditions as a service, whilst enjoying cost and energy savings.

Will smart buildings result in smart cities?

Building-level integration and intelligence can enable significant building-level savings, while improving indoor conditions. However, the biggest benefits are found on the district or city-level, when two or more buildings are connected together with smart technologies. A simple example is connecting buildings with cooling needs, such as ice hockey rinks or server centers with buildings with heating needs, like swimming halls. These types of simple pairings have shown to bring energy savings of up to 40% with relatively simple technologies. On a district-scale, there are already projects in planning, where office and residential buildings are connected together through two-way district heating and cooling networks for even greater efficiencies.

What’s the demand-side management all about?

Buildings can store heating and cooling energy in their structures and systems, enabling them to operate without external supply of energy over short time periods without sacrificing their indoor conditions. When large enough heating or cooling masses are grouped and managed together, energy demand can be spread out more evenly, resulting in significantly lower peak demand. What this means for cities and energy companies is that once fully rolled out, demand side management can help to remove the need of some of the inefficient peak power plants. Ideally, this equals to lower emissions and lower cost throughout the value chain from the energy producer to the consumers.

Virtual power plants, today?

On electricity side there are already first commercial examples of large buildings operating in the electricity markets as virtual power plants. These types of examples are possible through a combination of integrated and automated building systems, combined with electricity storage and flexible loads inside the building. When such buildings are integrated to the grid, they can operate in the flexibility market. Likewise, aggregator business models, where geographically distributed smaller flexible loads combined together and connected to the flexibility market, are emerging.

What next?

Buildings can transform from consumers of resources, energy and services to active prosumers of all of these. This is where it all starts to make sense for the building owners, as there’s untapped revenue streams and savings that the new level of integration can bring. The examples are already many and the pace of change through roll-out of new business models is only accelerating.

If you want to read more about VTT’s vision regarding smart and sustainable cities, read our new white paper: Let’s turn your Smart City vision into reality.

Antti Ruuska VTT
Antti Ruuska
Business Development Manager, VTT
antti.ruuska(a)vtt.fi
Twitter: @antti_ruuska

 

Smart City development is inherently multi-technological and cross-disciplinary, and as an application-oriented research organisation VTT is an ideal partner. We work with the public sector and private companies as well as technology providers in research and innovation activities that expedites the development of smarter cities.  We can guide you from the early phases of vision-creation and concept development to practical implementations of smart outcomes.

How will we manage with artificial intelligence in the future?


What is machine learning? Why does artificial intelligence draw conclusions differently than humans do? How does artificial intelligence become superintelligence?

Early this year, I spent a night at a big hotel in Berlin. When I stepped into my room, it felt quite cool inside. There was a sticker by the door, telling that the hotel had introduced a ”Smart climate control” system and I could adjust the temperature to the desired level through my TV. I opened the TV and navigated to the climate control page through various turns. And there it was: the present temperature was 18 degrees and the target temperature set by the previous customer was 25. I set the target temperature to 22 degrees and went out to have dinner. When I returned to my room, the temperature had climbed to 19 degrees, probably due to my PC which I had left on in the room. It still felt quite cool, so I called the hotel reception for help. The help soon arrived. A janitor brought an old-style fan heater for my use. I could not keep the noisy fan on at night, so the temperature dropped back to around 18 degrees for the night. However, in the morning, I woke up well rested after a good night’s sleep. After all, you sleep better in a cool environment. This left me wondering that maybe the smart climate control was smart enough to understand better than I what was the ideal temperature for me. I would still have appreciated some kind of an explanation, because the “smart” system that does what it pleases without giving any say to a human left me feeling powerless. The hotel staff had also clearly resigned itself in front of the smart climate control and did not even try to fix the system in my room but resorted to using a good old fan heater. If the system really was smart, would it not also keep people up to date on the decisions it has made, telling what it is aiming at. If it does not function or cannot fulfil people’s wishes, would it not also give a reason for this?

From artificial intelligence to superintelligence

Artificial intelligence (AI) has been studied for decades, but now it is experiencing a strong renaissance. The earlier attempts to bring all expert knowledge on one subject into a single machine were defeated by their own impossibility. Today, the prevailing trend is the development of an AI based on machine learning, where the idea is that the machine learns little by little when being taught, but also on its own. Machine learning is well suited for the analysis of large masses of data and for supporting people in data-based decision-making. In medicine, for example, AI allows examination of different measurement data, and the machine can draw connections between data. Therefore, AI can be used for such a purpose as forecasting the development of a disease, when a patient’s data is compared to data on earlier patients. It is typical of machine learning that the result is not exact, but it is a probability-based forecast. That is why a machine cannot give similar detailed explanations for its conclusions as a human expert can.

A lot is expected of machine learning not only in medicine, but also in service business of companies, where AI can be used for analysing machine data collected from the field and forecasting, for example, occurrence of faults. In such applications, AI functions independently, analysing data and giving suggestions to people about the next necessary maintenance measures and even about their suitable timing, considering the financial factors.

In addition to these positive effects, futures researchers have also been painting some very gloomy scenarios about the “superintelligence” of the future that would be able to, for example, develop its own intelligence, draw its own conclusions and generate a will of its own, and could thus get out of the hands of both its designers and users.

What would be a potential path from the present machine learning-based AI systems to such superintelligence? AI is being introduced not only to services accessible via the internet, but also to mobile machines, such as autonomous cars and robots. Would this be the right time to consider making the future development paths such that the AI will remain under human control for sure?

A clever person solves the problems a wise person knows to avoid. This old wisdom should be applied to AI as well: if AI represents the cleverness and humans represent the wisdom, then humans must be secured a role in which they can prevent problems that AI might cause to itself or to humans. There must be an easy connection between AI and humans, and humans must have the final decision-making power. This prevents AI from getting out of human hands even as it learns new things.

In the next part of the blog series, I will focus more on the interaction between humans and AI.

Read more: VTT and Smart City

Kaasinen Eija
Eija Kaasinen
Senior Scientist, VTT
@eijakaasinen 
eija.kaasinen(a)vtt.fi

 

In the next part of the blog series, I will focus more on the interaction between humans and AI.

Pärjäämmekö tulevaisuuden tekoälyn kanssa?

Mitä on koneoppiminen? Miksi tekoäly päättelee eri tavoin kuin ihminen? Miten tekoälystä tulee superälyä?

Yövyin alkuvuodesta isossa hotellissa Berliinissä. Huoneeseen astuessani siellä tuntui olevan viileää. Ovensuusta löytyi tarra, jossa kerrottiin, että hotellissa oli otettu käyttöön ”Smart climate control” javoisin itse säätää haluamani lämpötilan TV:n kautta. Avasin TV:n ja navigoin muutaman mutkan kautta ilmastointisivulle. Sieltähän se löytyi: nykyinen lämpötila 18 astetta ja edellisen asiakkaan asettama tavoitelämpö 25. Säätelin tavoitelämmön 22 asteeseen ja lähdin illalliselle. Palattuani lämpö oli kivunnut 19 asteeseen, johtuen varmaankin huoneeseen päälle jääneestä PC:stäni. Aika viileältä tuntui vielä, joten soittelin apua hotellin vastaanotosta. Pian apua tulikin. Huoltomies toi käyttööni vanhan ajan lämpöpuhaltimen. Kovaäänistä puhallinta ei voinut pitää yöllä päällä, joten yöksi lämpö taas laski 18 asteen tuntumaan. Aamulla heräsin kuitenkin virkeänä oikein hyvin nukutun yön jälkeen, sillä onhan se niin, että viileässä nukkuu paremmin. Jäinkin miettimään, että ehkä se Smart climate control oli niin fiksu, että se tajusi minua paremmin minulle sopivan lämpötilan. Olisin kuitenkin arvostanut jonkinlaista selitystä, sillä nyt jäi voimaton olo ”älykkäästä” systeemistä, joka tekee mitä tahtoo, eikä ihmisellä ole siihen sanomista. Hotellin henkilöstökin oli selvästi alistunut älykkään ilmastoinnin edessä, eikä edes yrittänyt korjata huoneeni systeemiä vaan tukeutui vanhaan kunnon lämpöpuhaltimeen. Eikö oikeasti fiksu systeemi pitäisi myös ihmisen ajan tasalla päätöksistään – kertoisi, mihin se pyrkii. Jos se ei toimi tai ei pysty täyttämään ihmisen toivetta, niin myös kertoo syyn tälle?

Tekoälystä superälyyn

Tekoälyä on tutkittu jo vuosikymmeniä, mutta nyt se on kokemassa vahvan renessanssin. Aiemmat yritykset, joissa koneeseen koetettiin tuoda jonkun aiheenkaikki asiantuntijatietämys, kaatuivat omaan mahdottomuuteensa. Nykyään vallalla on koneoppimiseen perustuva tekoälyn kehittäminen, jossa ajatuksena on, että kone oppii pikkuhiljaa, kun sitä opetetaan, mutta myös itsekseen. Koneoppiminen soveltuu hyvin isojen datamäärien analysointiin ja tukemaan ihmistä datapohjaisessa päätöksenteossa. Esimerkiksi lääketieteessä tekoälyn avulla voidaan tutkia erilaisia mittauksia, ja kone pystyy muodostamaan yhteyksiä datan välille. Näin tekoälyn avulla voidaan muun muassa ennustaa taudin kehittymistä, kun verrataan potilasdataa aiempien potilaiden dataan. Koneoppimiselle on tyypillistä, että tulos ei ole eksakti vaan se on todennäköisyyksiin perustuva ennustus. Siksi kone ei pysty antamaan johtopäätöksilleen samanlaisia yksityiskohtaisia perusteluja kuin ihmisasiantuntija.

Koneoppimiselta odotetaan paljon paitsi lääketieteessä myös yritysten palveluliiketoiminnassa, jossa tekoälyn avulla voidaan analysoida kentältä kerättyä laitetietoa ja ennustaa esimerkiksi vikaantumista. Näissä sovelluksissa tekoäly toimii itsenäisesti, analysoi dataa ja antaa ihmisille ehdotuksia seuraavaksi tarvittavista huoltotoimista ja jopa niiden sopivasta ajankohdasta ottaen huomioon taloudelliset tekijät.

Näiden positiivisten vaikutusten lisäksi tulevaisuuden tutkijat ovat maalailleet synkkiäkin tulevaisuudenkuvia tulevaisuuden ”superälystä”, joka pystyisi esimerkiksi itse kehittämään omaa älykkyyttään, tekisi itse johtopäätöksiä, muodostaisi oman tahdon ja näin voisi karata niin suunnittelijoiden kuin käyttäjienkin käsistä.

Millainen olisi mahdollinen polku nykyisistä koneoppimiseen perustuvista tekoälysysteemeistä tuohon superälyyn? Tekoälyä on tulossa paitsi verkon kautta saataviin palveluihin myös liikkuviin koneisiin kuten autonomisiin autoihin ja robotteihin. Olisiko nyt jo syytä miettiä kehityspolkuja sellaisiksi, että tekoäly varmasti pysyy ihmisen hallinnassa?

Älykäs ihminen osaa ratkaista ongelmat, joihin viisas ihminen ei edes joudu. Tätä vanhaa viisautta kannattaa soveltaa myös tekoälyyn: jos tekoäly edustaa älykkyyttä ja ihminen viisautta, niin ihmiselle on taattava rooli, jossa hän pystyy estämään tekoälyn itselleen ja ihmisille aiheuttamat ongelmat. Tekoälyn ja ihmisen välillä on oltava sujuva yhteys ja päätösvallan pitää viime kädessä olla ihmisellä. Näin tekoäly ei oppiessaankaan karkaa ihmisten hallinnasta.

Lue lisää: VTT and Smart City

Kaasinen Eija
Eija Kaasinen
Senior Scientist, VTT
@eijakaasinen 
eija.kaasinen(a)vtt.fi

 

Blogisarjan seuraavassa osassa paneudutaan ihmisen ja tekoälyn vuorovaikutukseen.

Proper design reduces the risk of damage to swimming pool buildings

Structural air leaks can increase the risk of moisture damage in indoor swimming pool buildings. The height of the pool area affects the moisture load in the upper structures. Temperature differences can cause indoor over-pressure, which increases the risk of moisture accumulation in air leaky structures. Design, implementation and the use of technical systems can have an impact on moisture loads and the risks they pose to buildings. Air tightness of structures and indoor air relative humidity levels are the key issues for moisture safety.

Temperature differences between inside and outside air tends to cause overpressure in the upper parts of swimming pool buildings which have a high inside open air space. This allows warm and humid indoor air to flow out via the air leakage routes along the ceiling and upper walls, causing moisture from the inside air to condense in structures.

The long-term build-up of moisture causes various problems in structures, such as the growth of mould or, at worst, structural weakening. Air leakages into structures tend to be at their greatest during winter, when the indoor air humidity level is greatest compared to outside, in turn creating the greatest risk of moisture accumulating.

uimahallit

Figure 1. The indoor air pressure conditions in swimming pools, caused by indoor air conditions and the height of the premises, pose challenges to the moisture performance of structures.

 

‘Rain’ from the roof into the interior may appear in swimming halls with poor airtightness of the structure. During cold periods, indoor moisture condenses and freezes in structures via air leakage routes and when the weather turns milder, the melted water runs into the inside air space via air leaks. In cases where the airtightness is this poor, there is an elevated risk of structural damage and it is very likely that impurities within the structures can enter the indoor air as the direction of the air leakage flows changes.

The pressure conditions in a high and heated space cannot be fully controlled by ventilation. Even if a typical level of under-pressure is maintained at floor level, long periods of overpressure can occur in the upper areas. For example, if the ventilation maintains a constant under-pressure of 10 Pa at floor level in a 9-metre high hall, overpressure will occur in the upper areas for almost half of the year (Figure 2).

Figure 2 The duration of the pressure difference between indoor and outdoor air in the upper parts of indoor swimming pools of different heights, when the indoor and outdoor air pressure is the same at floor level. Evaluation performed for a one year period in Helsinki climate conditions (nominal heating year).

 

The study conducted by VTT presents the principles of design, implementation and use of technical system, which can have an effect on the moisture loads of structures and the risks they pose. The key issue is to have sufficient airtightness of structures. In addition, the relative humidity indoors is set to the lower limits of the comfort zone during winter, around 40% RH.

Due to the high humidity of the indoor air, the risk of indoor air leakages through the structures is elevated in indoor swimming pools. A similar risk can be associated with other high hall structures, even if they typically have lower indoor air humidity than swimming pools.

The results presented are part of the ’Uimahallien yläpohjarakenteiden kosteustekniikka ja paloturvalliset PU-lämmöneristeiset hallirakenteet’ (Moisture Performance of the Roof Structures of Indoor Swimming Pools and Fire Safe Hall Structures with PU thermal insulation) project, which was conducted from 21 September 2015 to 31 December 2017. The study involved examining research data, guidelines and regulations related to the indoor air and structural moisture load of indoor swimming pools, and compiling expert views on the subject. In addition, the duration of pressure differences in high halls in the Finnish climate were analysed.

You can read the publicly available customer report here http://www.vtt.fi/inf/julkaisut/muut/2017/VTT-CR-06833-17.pdf

 

Tuomo Ojanen VTT

Tuomo Ojanen
Senior Scientist, VTT
tuomo.ojanen(a)vtt.fi