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Open AccessArticle

Measurement of Moisture in Wood for Application in the Restoration of Old Buildings

Sensors and Actuators Group, Department of Tecnología de la Edificación, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Department of Materials, Universidad Carlos III de Madrid, 28903 Getafe, Spain
Author to whom correspondence should be addressed.
Academic Editor: Dirk Lehmhus
Sensors 2016, 16(5), 697;
Received: 16 March 2016 / Revised: 5 May 2016 / Accepted: 11 May 2016 / Published: 14 May 2016
There are many historic buildings whose construction is based on timber frame walls. Most buildings built during the nineteenth and early twentieth centuries were based on timber frame walls with vertical support elements. These timber frame elements are affected by their moisture content and by the passage of time. If the interaction of the timber frame walls with hygrothermal fluctuations were known, the maintenance of these buildings could be improved significantly. To determine the moisture content of wood there are two types of meters on the market: on the one hand, capacitance meters which consist of two side ends and where the moisture content is measured locally between two peaks. On the other hand, there are meters based on the variation of electromagnetic transmittance of timber, which depends on the moisture of timber. The second ones are very expensive and difficult to handle. This work presents a new non-intrusive capacitive sensor that measures the global moisture content in a section of the timber frame walls and therefore its accuracy is similar to the accuracy that can be obtained with electromagnetic transmittance meters. Additionally, as it is a capacitive sensor, it is low cost and easy to operate. View Full-Text
Keywords: capacitive sensor; moisture; wood; non-intrusive; restoration; building capacitive sensor; moisture; wood; non-intrusive; restoration; building
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Moron, C.; Garcia-Fuentevilla, L.; Garcia, A.; Moron, A. Measurement of Moisture in Wood for Application in the Restoration of Old Buildings. Sensors 2016, 16, 697.

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