Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Natural climate reconstruction in the Norwegian stave churches through time series processing with variational autoencoders
https://orcid.org/0000-0003-4696-8626
https://orcid.org/0000-0001-6122-5252
https://orcid.org/0000-0002-5631-5584
https://orcid.org/0000-0001-6973-8634
https://orcid.org/0000-0002-6344-8051
In the field of heritage science, especially applied to buildings and artefacts made by organic hygroscopic materials, analyzing the microclimate has always been of extreme importance. In particular, in many cases, the knowledge of the outdoor/indoor microclimate may support the decision process in conservation and preservation matters of historic buildings. This knowledge is often gained by implementing long and time-consuming monitoring campaigns that allow collecting atmospheric and climatic data.
Sometimes the collected time series may be corrupted, incomplete and/or subjected to the sensors' errors because of the remoteness of the historic building location, the natural aging of the sensor or the lack of a continuous check of the data downloading process. For this reason, in this work, an innovative approach about reconstructing the indoor microclimate into heritage buildings, just knowing the outdoor one, is proposed. This methodology is based on using machine learning tools known as variational auto encoders (VAEs), that are able to reconstruct time series and/or to fill data gaps.
The proposed approach is implemented using data collected in Ringebu Stave Church, a Norwegian medieval wooden heritage building. Reconstructing a realistic time series, for the vast majority of the year period, of the natural internal climate of the Church has been successfully implemented.
The novelty of this work is discussed in the framework of the existing literature. The work explores the potentials of machine learning tools compared to traditional ones, providing a method that is able to reliably fill missing data in time series.
Natural climate reconstruction in the Norwegian stave churches through time series processing with variational autoencoders
https://orcid.org/0000-0003-4696-8626
https://orcid.org/0000-0001-6122-5252
https://orcid.org/0000-0002-5631-5584
https://orcid.org/0000-0001-6973-8634
https://orcid.org/0000-0002-6344-8051
In the field of heritage science, especially applied to buildings and artefacts made by organic hygroscopic materials, analyzing the microclimate has always been of extreme importance. In particular, in many cases, the knowledge of the outdoor/indoor microclimate may support the decision process in conservation and preservation matters of historic buildings. This knowledge is often gained by implementing long and time-consuming monitoring campaigns that allow collecting atmospheric and climatic data.
Sometimes the collected time series may be corrupted, incomplete and/or subjected to the sensors' errors because of the remoteness of the historic building location, the natural aging of the sensor or the lack of a continuous check of the data downloading process. For this reason, in this work, an innovative approach about reconstructing the indoor microclimate into heritage buildings, just knowing the outdoor one, is proposed. This methodology is based on using machine learning tools known as variational auto encoders (VAEs), that are able to reconstruct time series and/or to fill data gaps.
The proposed approach is implemented using data collected in Ringebu Stave Church, a Norwegian medieval wooden heritage building. Reconstructing a realistic time series, for the vast majority of the year period, of the natural internal climate of the Church has been successfully implemented.
The novelty of this work is discussed in the framework of the existing literature. The work explores the potentials of machine learning tools compared to traditional ones, providing a method that is able to reliably fill missing data in time series.
Natural climate reconstruction in the Norwegian stave churches through time series processing with variational autoencoders
https://orcid.org/0000-0003-4696-8626
https://orcid.org/0000-0001-6122-5252
https://orcid.org/0000-0002-5631-5584
https://orcid.org/0000-0001-6973-8634
https://orcid.org/0000-0002-6344-8051
In the field of heritage science, especially applied to buildings and artefacts made by organic hygroscopic materials, analyzing the microclimate has always been of extreme importance. In particular, in many cases, the knowledge of the outdoor/indoor microclimate may support the decision process in conservation and preservation matters of historic buildings. This knowledge is often gained by implementing long and time-consuming monitoring campaigns that allow collecting atmospheric and climatic data.
Sometimes the collected time series may be corrupted, incomplete and/or subjected to the sensors' errors because of the remoteness of the historic building location, the natural aging of the sensor or the lack of a continuous check of the data downloading process. For this reason, in this work, an innovative approach about reconstructing the indoor microclimate into heritage buildings, just knowing the outdoor one, is proposed. This methodology is based on using machine learning tools known as variational auto encoders (VAEs), that are able to reconstruct time series and/or to fill data gaps.
The proposed approach is implemented using data collected in Ringebu Stave Church, a Norwegian medieval wooden heritage building. Reconstructing a realistic time series, for the vast majority of the year period, of the natural internal climate of the Church has been successfully implemented.
The novelty of this work is discussed in the framework of the existing literature. The work explores the potentials of machine learning tools compared to traditional ones, providing a method that is able to reliably fill missing data in time series.
Natural climate reconstruction in the Norwegian stave churches through time series processing with variational autoencoders
Natural climate reconstruction in churches
Manara, Noemi (Autor:in) / Rosset, Lorenzo (Autor:in) / Zambelli, Francesco (Autor:in) / Zanola, Andrea (Autor:in) / Califano, America (Autor:in)
14.03.2024
17 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
| UB Braunschweig | 1953
Viking Spirit in Colorado A Whimsical Boathouse Reminiscent of Norwegian Stave Churches
| British Library Online Contents | 2007
| Emerald Group Publishing | 2024