Sensor-Based Moisture Prediction for Flat Roofs

Giacomo Da Col; Eva Eggeling; Marco Hudelist; Christoph Schinko; Christof Surtmann; Erich Teppan

doi:10.3233/FAIA220073

Sensor-Based Moisture Prediction for Flat Roofs

Giacomo Da Col^*, Eva Eggeling, Marco Hudelist, Christoph Schinko, Christof Surtmann, Erich Teppan

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Abstract

Flat roofs have become popular also in central and northern Europe during the last decades. One advantage when compared to pitched roofs is that flat roofs are typically significantly cheaper. Furthermore, the roof space can be used also as a garden, a terrace or simply to quite easily install photo-voltaic systems on it. However, flat roofs are known to be prone to drainage and leakage issues. Roof utilization as a garden or the shadowing of installed photo-voltaic systems magnify this problem. For these reasons, installing moisture sensors inside the roof in order to monitor the moisture levels is one possibility to detect roof damages early and keep repairing costs low. In this paper we report on first results of an industrial project that aims to go one step further. Based on past sensor values the goal is to predict how moisture levels will progress in the near future and thus be able to identify problems before they become critical.

Original language	English
Title of host publication	PAIS 2022 - 11th Conference on Prestigious Applications of Artificial Intelligence, co-located with IJCAI-ECAI 2022
Editors	Andrea Passerini, Thomas Schiex
Publisher	IOS Press BV
Pages	149 - 152
Number of pages	4
Volume	351
ISBN (Electronic)	978-164368294-5
DOIs	https://doi.org/10.3233/FAIA220073
Publication status	Published - 15 Jul 2022
Event	11th International Conference on Prestigious Applications of Intelligent Systems: PAIS 2022 - Vienna, Austria Duration: 25 Jul 2022 → 25 Jul 2022 Conference number: 11

Publication series

Name	Frontiers in Artificial Intelligence and Applications
Volume	351
ISSN (Print)	0922-6389

Conference

Conference	11th International Conference on Prestigious Applications of Intelligent Systems
Abbreviated title	PAIS 2022
Country/Territory	Austria
City	Vienna
Period	25/07/22 → 25/07/22
Other	co-located with the 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence, IJCAI-ECAI 2022

Keywords

Machine Learning
Internet of Things (IoT)
Predictive Maintenance
Experimental evaluation
Time series analysis
time series analysis
internet of things
real world problem
predictive maintenance
machine learning
experimental evaluation

ASJC Scopus subject areas

Artificial Intelligence

Fields of Expertise

Information, Communication & Computing

Treatment code (Nähere Zuordnung)

Application

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.3233/FAIA220073Licence: CC BY-NC 4.0

Cite this

Da Col, G., Eggeling, E., Hudelist, M., Schinko, C., Surtmann, C., & Teppan, E. (2022). Sensor-Based Moisture Prediction for Flat Roofs. In A. Passerini, & T. Schiex (Eds.), PAIS 2022 - 11th Conference on Prestigious Applications of Artificial Intelligence, co-located with IJCAI-ECAI 2022 (Vol. 351, pp. 149 - 152). (Frontiers in Artificial Intelligence and Applications; Vol. 351). IOS Press BV. https://doi.org/10.3233/FAIA220073

Sensor-Based Moisture Prediction for Flat Roofs. / Da Col, Giacomo; Eggeling, Eva; Hudelist, Marco et al.
PAIS 2022 - 11th Conference on Prestigious Applications of Artificial Intelligence, co-located with IJCAI-ECAI 2022. ed. / Andrea Passerini; Thomas Schiex. Vol. 351 IOS Press BV, 2022. p. 149 - 152 (Frontiers in Artificial Intelligence and Applications; Vol. 351).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Da Col, G, Eggeling, E, Hudelist, M, Schinko, C, Surtmann, C & Teppan, E 2022, Sensor-Based Moisture Prediction for Flat Roofs. in A Passerini & T Schiex (eds), PAIS 2022 - 11th Conference on Prestigious Applications of Artificial Intelligence, co-located with IJCAI-ECAI 2022. vol. 351, Frontiers in Artificial Intelligence and Applications, vol. 351, IOS Press BV, pp. 149 - 152, 11th International Conference on Prestigious Applications of Intelligent Systems, Vienna, Austria, 25/07/22. https://doi.org/10.3233/FAIA220073

Da Col G, Eggeling E, Hudelist M, Schinko C, Surtmann C, Teppan E. Sensor-Based Moisture Prediction for Flat Roofs. In Passerini A, Schiex T, editors, PAIS 2022 - 11th Conference on Prestigious Applications of Artificial Intelligence, co-located with IJCAI-ECAI 2022. Vol. 351. IOS Press BV. 2022. p. 149 - 152. (Frontiers in Artificial Intelligence and Applications). doi: 10.3233/FAIA220073

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abstract = "Flat roofs have become popular also in central and northern Europe during the last decades. One advantage when compared to pitched roofs is that flat roofs are typically significantly cheaper. Furthermore, the roof space can be used also as a garden, a terrace or simply to quite easily install photo-voltaic systems on it. However, flat roofs are known to be prone to drainage and leakage issues. Roof utilization as a garden or the shadowing of installed photo-voltaic systems magnify this problem. For these reasons, installing moisture sensors inside the roof in order to monitor the moisture levels is one possibility to detect roof damages early and keep repairing costs low. In this paper we report on first results of an industrial project that aims to go one step further. Based on past sensor values the goal is to predict how moisture levels will progress in the near future and thus be able to identify problems before they become critical.",

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AU - Teppan, Erich

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AB - Flat roofs have become popular also in central and northern Europe during the last decades. One advantage when compared to pitched roofs is that flat roofs are typically significantly cheaper. Furthermore, the roof space can be used also as a garden, a terrace or simply to quite easily install photo-voltaic systems on it. However, flat roofs are known to be prone to drainage and leakage issues. Roof utilization as a garden or the shadowing of installed photo-voltaic systems magnify this problem. For these reasons, installing moisture sensors inside the roof in order to monitor the moisture levels is one possibility to detect roof damages early and keep repairing costs low. In this paper we report on first results of an industrial project that aims to go one step further. Based on past sensor values the goal is to predict how moisture levels will progress in the near future and thus be able to identify problems before they become critical.

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KW - machine learning

KW - experimental evaluation

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Sensor-Based Moisture Prediction for Flat Roofs

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Fields of Expertise

Treatment code (Nähere Zuordnung)

UN SDGs

Access to Document

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Cite this