Abstract
Climate change has forced many sectors to establish measures to achieve decarbonisation. Building is amongst these sectors with the greatest challenge. To achieve decarbonisation, energy improvement measures should be established. These improvement measures depend on an appropriate characterization of the existing buildings. For this purpose, there are many experimental tests based on measuring envelope variables, such as surface temperature and heat flow. Thus, thermal parameters of envelopes could be accurately known. In view of this circumstance, the question arises as to whether it is possible to know other envelope parameters additionally, such as sound insulation. The previous studies have shown the feasibility of characterizing envelope variables through artificial intelligence predictive models. Thus, this study characterizes sound insulation by using these predictive models with the variables obtained from the thermal monitoring of an envelope through thermal transmittance tests.
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Berti, K., Tejedor, B., Durán, J., Bienvenido-Huertas, D. (2022). Combining Characterization Tests of Building Envelope Thermal Transmittance with the Acoustic Characterization Through Data Mining Approaches. In: Bienvenido-Huertas, D., Moyano-Campos, J. (eds) New Technologies in Building and Construction. Lecture Notes in Civil Engineering, vol 258. Springer, Singapore. https://doi.org/10.1007/978-981-19-1894-0_3
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