1. Locher, B., et al., Differences between outdoor and indoor sound levels for open, tilted, and closed windows. International journal of environmental research and public health, 2018. 15(1): p. 149. [DOI:10.3390/ijerph15010149] [
DOI:10.3390/ijerph15010149]
2. Garg, N., S. Dhruw, and L. Gandhi, Prediction of sound insulation of sandwich partition panels by means of artificial neural networks. Archives of Acoustics, 2017. 42. [DOI:10.1515/aoa-2017-0068] [
DOI:10.1515/aoa-2017-0068]
3. Recio, A., et al., Road traffic noise effects on cardiovascular, respiratory, and metabolic health: An integrative model of biological mechanisms. Environmental research, 2016. 146: p. 359-370. [DOI:10.1016/j.envres.2015.12.036] [
DOI:10.1016/j.envres.2015.12.036]
4. Pirrera, S., E. De Valck, and R. Cluydts, Field study on the impact of nocturnal road traffic noise on sleep: The importance of in-and outdoor noise assessment, the bedroom location and nighttime noise disturbances. Science of the Total Environment, 2014. 500: p. 84-90. [DOI:10.1016/j.scitotenv.2014.08.061] [
DOI:10.1016/j.scitotenv.2014.08.061]
5. Frei, P., E. Mohler, and M. Röösli, Effect of nocturnal road traffic noise exposure and annoyance on objective and subjective sleep quality. International journal of hygiene and environmental health, 2014. 217(2-3): p. 188-195. [DOI:10.1016/j.ijheh.2013.04.003] [
DOI:10.1016/j.ijheh.2013.04.003]
6. Basner, M., et al., Auditory and non-auditory effects of noise on health. The lancet, 2014. 383(9925): p. 1325-1332. [DOI:10.1016/S0140-6736(13)61613-X] [
DOI:10.1016/S0140-6736(13)61613-X]
7. Brink, M. A review of potential mechanisms in the genesis of long-term health effects due to noise-induced sleep disturbances. in INTER-NOISE and NOISE-CON Congress and Conference Proceedings. 2012. Institute of Noise Control Engineering.
8. Amundsen, A.H., R. Klæboe, and G.M. Aasvang, The Norwegian Façade Insulation Study: The efficacy of façade insulation in reducing noise annoyance due to road traffic. The Journal of the Acoustical Society of America, 2011. 129(3): p. 1381-1389. [DOI:10.1121/1.3533740] [
DOI:10.1121/1.3533740]
9. Schreckenberg, D. Exposure-response relationship for railway noise annoyance in the Middle Rhine Valley. in INTER-NOISE and NOISE-CON Congress and Conference Proceedings. 2013. Institute of Noise Control Engineering.
10. Öhrström, E., et al., Effects of road traffic noise and the benefit of access to quietness. Journal of sound and vibration, 2006. 295(1-2): p. 40-59. [DOI:10.1016/j.jsv.2005.11.034] [
DOI:10.1016/j.jsv.2005.11.034]
11. Granzotto, N., A. Di Bella, and E.A. Piana, Prediction of the sound reduction index of clay hollow brick walls. Building Acoustics, 2020. 27(2): p. 155-168. [DOI:10.1177/1351010X20903144] [
DOI:10.1177/1351010X20903144]
12. Hongisto, V., et al., Impact sound insulation of floating floors: A psychoacoustic experiment linking standard objective rating and subjective perception. Building and Environment, 2020. 184: p. 107225. [DOI:10.1016/j.buildenv.2020.107225] [
DOI:10.1016/j.buildenv.2020.107225]
13. Griefahn, B., et al., Physiological, subjective, and behavioural responses during sleep to noise from rail and road traffic. Noise and health, 2000. 3(9): p. 59.
14. Khawaja, H.A., Sound waves in fluidized bed using CFD-DEM simulations. Particuology, 2018. 38: p. 126-133. [DOI:10.1016/j.partic.2017.07.002] [
DOI:10.1016/j.partic.2017.07.002]
15. Taban, E., et al., Measurement, modeling, and optimization of sound absorption performance of Kenaf fibers for building applications. Building and Environment, 2020. 180: p. 107087. [DOI:10.1016/j.buildenv.2020.107087] [
DOI:10.1016/j.buildenv.2020.107087]
16. Ziegert, C., et al., Standardization of a Natural Resource, in Cultivated Building Materials. 2017, Birkhäuser. p. 40-45. [DOI:10.1515/9783035608922-005] [
DOI:10.1515/9783035608922-005]
17. Abbasi, E. and M. Hadji Hosseinlou, Predicting the Traffic Crashes of Taxi Drivers by Applying the Non-Linear Learning of ANFIS-PSO with M5 Model Tree. International Journal of Numerical Methods in Civil Engineering, 2019. 3(3): p. 50-57. [DOI:10.29252/nmce.3.3.50] [
DOI:10.29252/nmce.3.3.50]
18. fard, H., Conjugate gradient neural network in prediction of clay behavior and parameters sensitivities. Numerical Methods in Civil Engineering, 2016. 1: p. 9-20. [DOI:10.29252/nmce.1.2.9] [
DOI:10.29252/nmce.1.2.9]
19. Ahmadian, V., S. Beheshti Aval, and E. Darvishan, Real-time damage detection of bridges using adaptive time-frequency analysis and ANN. International Journal of Numerical Methods in Civil Engineering, 2019. 4(1): p. 49-61. [DOI:10.52547/nmce.4.1.49] [
DOI:10.52547/nmce.4.1.49]
20. Schiavi, A., A. Prato, and J.-C. Vallée. Building components and materials for low frequency airborne and structure-borne sound insulation. in 24th International Congress of Sound and Vibration (ICSV), London. 2017.
21. Cao, L., et al., Porous materials for sound absorption. Composites Communications, 2018. 10: p. 25-35. [DOI:10.1016/j.coco.2018.05.001] [
DOI:10.1016/j.coco.2018.05.001]
22. Sedighi, N., F. Jafari, and Y. Jafari, Using ANFIS to prediction the sound insulation of masonry materials and compare with linear regression, in 1st Conference on Architecture, Civil Engineering, Environment and Agriculture. 2021, https://civilica.com/doc/1170336.
23. Jafari, F., N. Sedighi, and Y. Jafari, prediction of sound insulation of walls with masonry materials using ANN and linear regression, in 1st International Conference on Architecture, Civil Engineering, Environment and Agriculture. 2021, https://civilica.com/doc/1170335.
24. Bienvenido-Huertas, D., et al., Applying an artificial neural network to assess thermal transmittance in walls by means of the thermometric method. Applied Energy, 2019. 233-234: p. 1-14. [DOI:10.1016/j.apenergy.2018.10.052] [
DOI:10.1016/j.apenergy.2018.10.052]
25. Ghiabaklou, Z., Fundamentals of Building Physics 1 (Acoustics), in Fundamentals of Building Physics 1 (Acoustics). 2011, Jihad Academic Publications, Amirkabir Industrial Branch: Tehran, Iran.
26. Sokhandan, Z., F. Nasrollahi, and A. Ghafari, Optimization of Acoustical Function of Sound Absorbers with Emphasis on Geometry and Height of Spaces. Hoviatshahr, 2019. 13(1): p. 8-18.
27. Ching, F.D. and C. Binggeli, Interior design illustrated. 2018: John Wiley & Sons.
29. Calleri, C., et al., Characterization of the sound insulation properties of a two-layers lightweight concrete innovative façade. Applied Acoustics, 2019. 145: p. 267-277. [DOI:10.1016/j.apacoust.2018.10.003] [
DOI:10.1016/j.apacoust.2018.10.003]
30. MarshallDay. Sound Insulation Prediction Program. 2017; Available from: http://www.insul.co.nz/media/30049/Insul-Manual-2017-word-version.pdf.
31. Keith, B. Sound Insulation Prediction Program. 2018; Available from: http://www.insul.co.nz/media/29388/Seminar-1.pdf.
32. Rezaie, M. and N. Sadighi, Prediction of slump and density of lightweight concretes using ANFIS and linear regression. International Journal of Civil Engineering and Technology, 2017. 8(10): p. 1635-1648.
33. Bystrov, D. and J. Westin, Practice. Neuro-Fuzzy Logic Systems Matlab Toolbox Gui. Cross-Cult. Manag. J, 2015. 17: p. 69-76.
34. Greshteyn, Y.a.P., L. . Matlab Fuzzy Toolbox. 2003 [cited 2003; Available from: http://www.mathworks.com/access/helpdesk/help/toolbox/fuzzy/fuzzy.shtml.
35. Rajasekaran, S. and G.V. Pai, Neural networks, fuzzy logic and genetic algorithm: synthesis and applications (with cd). 2003: PHI Learning Pvt. Ltd.
36. Chatterjee, S. and A.S. Hadi, Regression analysis by example. 2015: John Wiley & Sons.
37. Benjamin, J. and C. Cornell, Probability, Statistics, and Decision for Civil Engineers. New York, New York: McGraw-Hill". 1970.