## 4 Conclusions  
This paper reveals the potential of a prediction model using an artificial neural networks approach to forecast airborne insulation curves based on 100 laboratory measurements of different lightweight facades. The developed model shows reliable results with root-mean-square error (\(RMSE\)) within 2.19-5.73 dB for estimating the sound insulation curves of various facades in the frequency bands 50 Hz-5 kHz. When the comparison considers the single-number quantity, the weighted sound reduction index \(R_{w}\), the prediction accuracy improved with errors of 0-3 dB.  
In general, the model demonstrates better estimations at middle frequencies (250 Hz-1 kHz). However, the forecast of \(R(f)\) around fundamental and critical frequencies in certain tested facades marked variations, underlying the resonance effects on the insulation behavior of facades and inefficiency of the model to predict around those frequency bands.  
A sensitivity analysis is implemented to estimate the effects of constructional input parameters on estimation of airborne insulation curves. Density of CLT panels is a significant variable in all frequency bands. Similar findings apply to the thickness of the insulation materials, especially at middle and high frequencies. Moreover, the total thickness and total density of interior, main and exterior parts of facades have significant effects in all frequencies with higher attribution to the total density of the exterior part. The coupling between resonant facade components, resulting from fundamental and critical frequencies of each component, has a significant influence on the prediction.  
The prediction of \(R_{w}\) with a maximum error of 3 dB encourages designers to consider the model from the perspective of practical engineering work on sound insulation in buildings. In particular, differences of 1 to 2 dB are less than the noticeable differences for the noise level. Additionally, the present model could provide a predictive tool with which the construction time and cost of acoustic measurements (for a certain type of partitions) could be saved.  
Further research would be expected on optimization of certain parameters to control and improve the prediction of sound insulation. This would pave the way to explore the appropriate structural parameters to achieve the desired isolation.  
Writing--original draft; methodology; validation; formal analysis; investigation, M.B.E.; writing--review and editing, M.B.E.; review; supervision; validation, N.-G.V., S.M., D.B.H. and J.-L.K.; project administration; funding acquisition, S.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Sciences and Engineering Research Council (NSERC) of Canada through its IRC and CRD programs (IRCPJ 461745-18 and RDCPJ 524504-18), the Region Nouvelle-Aquitaine (ref. 2017-1R10223) and the industrial partners of the NSERC industrial chair on eco-responsible wood construction (CIRCERB).  
Not applicable.  
Data Availability Statement: Data cannot be provided as they are confidential.  
Acknowledgments: The authors are grateful to Lund University and CNRC for providing the acoustic measurement data of sound insulation.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & \multicolumn{3}{c}{**Root-Mean-Square Errors in dB**} \\ \hline \multirow{2}{*}{**Frequency Bands**} & **Low** & **Middle** & **High** \\  & **50–200 Hz** & **250–1000 Hz** & **1250–5000 Hz** \\ \hline \(R\) (airborne sound) & 4.67 & 3.52 & 4.99 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Error distributions in the prediction of airborne sound reduction index \(R\) considering three frequency regions.