In-Situ Thermal Bridge Evaluation of a Building Using Bayesian Inference With Measured Infrared Thermography

Abstract

Abstract Useful thermal bridge performance indicators (ITBs) of existing buildings may differ from calculated thermal bridge performance derived theoretically due to actual construction conditions, such as irregular shapes and aging. To fill this gap practically, a more realistic quantitative evaluation of thermal bridge on-site needs to be considered to identify thermal behaviors throughout exterior walls and thus improve the overall insulation performance of buildings. In this study, a case study is conducted using an infrared thermal imaging method to evaluate the thermal bridge of an existing building practically. The study's main purpose is to review the thermal bridge performance indicators measured by the steady-state model under field conditions in terms of convergence and uncertainty. Bayesian Markov Chain Monte Carlo (MCMC) is used to examine the validity of the results by deriving evaluation results in the form of distribution, including uncertainty. After the measurement was completed, an analysis of the results was conducted. As a result of measurement for 3 days, it was found that the thermal bridge part had 1.221 times more heat loss than the non-thermal bridge part, which showed a 6.7% deviation from the numerical method. However, the uncertainty was 0.225 (18.4%, CI 95%), a large figure. This is due to the influence of field conditions and shows the limitations of the steady-state measurement model. Regarding the convergence of the results, the measurement results were found to converge continuously as the measurement time increased. This suggests that valid results can be obtained in the field if the measurement is performed for a sufficient time, even with a thermal bridge measurement method assuming a steady-state.