Internal insulation applied in heritage multi-storey buildings with wooden beams embedded in solid masonry brick façades

Harrestrup, Maria; Svendsen, Svend

doi:10.1016/j.buildenv.2016.01.019

Cited by 64 publications

(34 citation statements)

References 22 publications

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“…Results from the VTT model suggests no wood decay in the beam end for neither the un-hydrophobized Walls 2 and 5 nor the hydrophobized Walls 3 and 6. The presented results are supported by the findings from other studies [3,23] investigating the performance of solid masonry walls fitted with the internal insulation in combination with deliberate thermal bridge near the floor partition. This indicates worse conditions in the beam ends due to the internal floor-to-ceiling insulation, while a deliberate thermal bridge above, or above and below the floor partition reduces the increase in RH caused by the internal insulation.…”

Section: The Embedded Wooden Beam Endssupporting

confidence: 89%

Hygrothermal assessment of internally insulated solid masonry walls fitted with exterior hydrophobization and deliberate thermal bridge

et al. 2018

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Relative humidity (RH) and temperature were measured in several solid masonry walls with embedded wooden beams, fitted with autoclaved aerated concrete (AAC) thermal insulation on the interior surface and exposed to a cool, temperate climate. The field study was based on the use of a 40-feet insulated reefer container reconfigured with eight 1 × 2 m holes containing the solid masonry walls. The study investigated the influence of AAC thermal insulation on the interior side with a combination of exterior hydrophobization and a deliberate thermal bridge in front of the embedded wooden wall plate using a material with higher thermal conductivity.Validated HAM simulations were used to investigate the effect of controlling the indoor humidity, and how this would affect the theoretical risk predictions from the damage models. Experimental findings indicate that hydrophobization of solid masonry walls with internal insulation have both positive and negative effects on the moisture balance of the wall, in relation to moisture-induced damage, and that a deliberate thermal bridge installed in front of the embedded wooden wall plate can reduce the moisture content in the wooden elements. Simulation findings indicate that a combination of exterior hydrophobization and decreased indoor moisture load can reduce the RH to acceptable levels in relation to moisture induced damage at the interface between existing wall and new insulation. No major changes were observed in relation to the risk of frost damage at the exterior surface. K E Y W O R D Sdamage models, field study, hydrophobization, internal insulation, mineral insulation board

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Section: The Embedded Wooden Beam Endssupporting

confidence: 89%

Hygrothermal assessment of internally insulated solid masonry walls fitted with exterior hydrophobization and deliberate thermal bridge

et al. 2018

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“…The basic characteristics of the used brick types can be found in Table 3 and Figure 3, which clearly show the variations in the bricks' moisture properties. When evaluating the hygrothermal performance of a massive masonry wall, one is typically interested in frost damage at the exterior surface, decay of embedded wooden floors and mould growth on the interior surface [3,[25][26][27]. The latter is mainly important in the case of thermal bridges and of less importance in 1D simulations.…”

Section: Hygrothermal Simulation Objectmentioning

confidence: 99%

Optimising Convolutional Neural Networks to Predict the Hygrothermal Performance of Building Components

2019

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Performing numerous simulations of a building component, for example to assess its hygrothermal performance with consideration of multiple uncertain input parameters, can easily become computationally inhibitive. To solve this issue, the hygrothermal model can be replaced by a metamodel, a much simpler mathematical model which mimics the original model with a strongly reduced calculation time. In this paper, convolutional neural networks predicting the hygrothermal time series (e.g., temperature, relative humidity, moisture content) are used to that aim. A strategy is presented to optimise the networks’ hyper-parameters, using the Grey-Wolf Optimiser algorithm. Based on this optimisation, some hyper-parameters were found to have a significant impact on the prediction performance, whereas others were less important. In this paper, this approach is applied to the hygrothermal response of a massive masonry wall, for which the prediction performance and the training time were evaluated. The outcomes show that, with well-tuned hyper-parameter settings, convolutional neural networks are able to capture the complex patterns of the hygrothermal response accurately and are thus well-suited to replace time-consuming standard hygrothermal models.

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“…Calcium silicate proved the best performance in regard to moisture performance, however calcium silicate also has the highest thermal conductivity. Harrestrup et al [22] monitored a case of internal insulation of 40mm aerowolle on a heritage brick building, and the effect of intentional thermal bridges above and below supportive wooden beams. They found that leaving a 200mm uninsulated gap above and below the beams yielded a lower risk of mould growth, however this was found to be very dependent on the orientation and the thickness of the existing wall.…”

Section: Accepted M M a N U mentioning

confidence: 99%

Long term in situ measurements of hygrothermal conditions at critical points in four cases of internally insulated historic solid masonry walls

Hansen

Bjarløv

Peuhkuri

et al. 2018

Energy and Buildings

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Internal insulation applied in heritage multi-storey buildings with wooden beams embedded in solid masonry brick façades

Cited by 64 publications

References 22 publications

Hygrothermal assessment of internally insulated solid masonry walls fitted with exterior hydrophobization and deliberate thermal bridge

Hygrothermal assessment of internally insulated solid masonry walls fitted with exterior hydrophobization and deliberate thermal bridge

Optimising Convolutional Neural Networks to Predict the Hygrothermal Performance of Building Components

Long term in situ measurements of hygrothermal conditions at critical points in four cases of internally insulated historic solid masonry walls

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