Malzemelerin Ses Yutma Performansının Değerlendirilmesinde Yapay Zeka Yaklaşımı: Sistematik İnceleme

Abstract

Son yıllarda, yapay zekanın hızla artan kullanımı akademi ve piyasa dahil olmak üzere birçok alanda kendine yer bulmaya başlamıştır. Bu çalışma, malzemelerin ses yutma katsaysının belirlenmesinde yapay zeka kullanımının yerini araştırmaktadır. Bu amaçla, belirlenen üç anahtar kelime doğrultusunda son on yılda yapılan çalışmalar incelenmiştir. İnceleme sürecinde çeşitli kısıtlamalar uygulanmıştır. İlk olarak, çalışmaların başlıkları ve anahtar kelimeleri incelenerek bir ön eleme yapılmıştır. Daha sonra, yalnızca araştırma makaleleri seçilmiş ve diğer türdeki çalışmalar elenmiştir. Çalışmalarda kullanılan ikincil anahtar kelimeler belirlenmiş ve bir analiz programı kullanılarak alan değerlendirmesi yapılmıştır. Elde edilen sonuçlar farklı alt başlıklar altında gruplandırılarak değerlendirilmiştir. Değerlendirme, çalışmaların yapıldığı yılları, kullanılan yapay zeka yöntemlerini ve varsa ek çıkarımları içermektedir. Değerlendirme bölümünde, yapay zekanın ses yutucu malzemelerdeki kullanılabilirliği üzerine yorumlar yapılmış ve alandaki eksiklikler tartışılmıştır. Gelecek çalışmalara yönelik öneriler de sunulmuştur. Derleme çalışması, özellikle bu alanda yapılacak yeni araştırmalara yol gösterici olmayı amaçlamaktadır.

Keywords

• Yapay zekâ
• Sesin yutulması
• Ses yutucu malzemeler
• sistematik inceleme

An Artificial Intelligence Approach to Evaluating the Sound Absorption Performance of Materials: A Systematic Review

Abstract

In recent years, the rapidly increasing use of artificial intelligence has begun to be incorporated into many fields in academia and the market. This study investigates the extent to which artificial intelligence is used in determining the sound absorption performance of materials, which have practical implications in improving indoor acoustic conditions. To this end, studies conducted over the past ten years based on three specified keywords were examined. Various constraints were applied during the review process. First, titles and keywords were scrutinized to filter the studies. Then, research articles were selected, while other studies were eliminated. Secondary keywords used in the studies were identified, and a field assessment was conducted using an analysis program. The results were evaluated by grouping them under different subheadings. The evaluation included the year the studies were conducted, the artificial intelligence methods used, and any additional inferences, if available. In the evaluation section, comments were made on the usability of artificial intelligence in sound-absorbing materials, and the shortcomings in the field were addressed. Suggestions for future studies were also presented. The review study is intended to serve as a guide, particularly for new studies in this field.

Keywords

• Artificial intelligence
• Sound absorption
• Sound absorption material
• Systematic review

References

1. [1] URL 1: https://www.who.int/europe/news-room/fact-sheets/item/noise, received at (20.08.2024). [2] Yu, D.: Sound Advice: The development and use of early 20th-century acoustic wall and ceiling materials. Doctoral dissertation, Columbia University (2015).
2. [3] ISO 10534-2:2023: Acoustics – Determination of acoustic properties in impedance tubes. Part 2: Two-microphone technique for normal sound absorption coefficient and normal surface impedance (2023).
3. [4] ASTM E1050-12: Standard test method for impedance and absorption of acoustical materials using a tube, two microphones and a digital frequency analysis system. New York: American National Standards Institution (2012).
4. [5] ISO 354-2003: Acoustics —Measurement of sound absorption in a reverberation room. International Organisation for Standardisation, Geneva (2003).
5. [6] ASTM C423-17: Standard Test Method for Sound Absorption and Sound Absorption Coefficients by the Reverberation Room Method, ASTM International, West Conshohocken, PA (2017).
6. [7] Hasan, M., Hodgson, M.: Effectiveness of reverberation room design: Room size and shape and effect on measurement accuracy. In PROCEEDINGS of the 22nd International Congress on Acoustics, 5-9 (2016).
7. [8] Tang, Y., Chuang, X.J.: Tuning of estimated sound absorption coefficient of materials of reverberation room method. Shock and Vibration (2022).
8. [9] ASTM C384-04-2004: Standard test method for impedance and absorption of acoustical materials by impedance tube method, astm international, West Conshohocken, PA (2011).

9. [10] PK, F. A.: What is Artificial Intelligence?. Success is no accident. It is hard work, perseverance, learning, studying, sacrifice and most of all, love of what you are doing or learning to do, 65 (1984).
10. [11] McCarthy, J.: What is artificial intelligence (2007).
11. [12] Jha, D., Gupta, V., Liao, W. K., Choudhary, A., Agrawal, A.: Moving closer to experimental level materials property prediction using AI. Scientific reports, 12(1), 11953 (2022).
12. [13] Goswami, L., Deka, M., Roy, M.: Artificial Intelligence in Material Engineering: A Review on Applications of Artificial Intelligence in Material Engineering. Advanced Engineering Materials, 25 (2022).
13. [14] Agrawal, A., Choudhary, A.: Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science. Apl Materials, 4(5) (2016).
14. [15] Li, J., Lim, K., Yang, H., Ren, Z., Raghavan, S., Chen, P. Y., Wang, X.: AI applications through the whole life cycle of material discovery. Matter, 3(2), 393-432 (2020).
15. [16] To, W. M., Chung, A. W.: Exploring the roles of artificial intelligence and next-generation virtual technologies in soundscape studies. In INTER-NOISE and NOISE-CON Congress and Conference Proceedings Vol. 259, No. 4, 5321-5329 (2019).
16. [17] Jahani, A., Kalantary, S., Alitavoli, A.: An application of artificial intelligence techniques in prediction of birds soundscape impact on tourists’ mental restoration in natural urban areas. Urban Forestry & Urban Greening, 61, 127088 (2021).
17. [18] Hou, Y., Ren, Q., Zhang, H., Mitchell, A., Aletta, F., Kang, J., Botteldooren, D.: AI-based soundscape analysis: Jointly identifying sound sources and predicting annoyance. The Journal of the Acoustical Society of America, 154(5), 3145-3157 (2023).
18. [19] Wang, J., Li, C., Lin, Y., Weng, C., & Jiao, Y.: Smart soundscape sensing: A low-cost and integrated sensing system for urban soundscape ecology research. Environmental Technology & Innovation, 29, 102965 (2023).
19. [20] Lam, B., Ong, Z. T., Ooi, K., Ong, W. H., Wong, T., Watcharasupat, K. N., Gan, W. S.: Automating urban soundscape enhancements with AI: In-situ assessment of quality and restorativeness in traffic-exposed residential areas. arXiv preprint arXiv:2407.05744 (2024).
20. [21] Xu, J., Nannariello, J., Fricke, F.: Application of computational intelligence techniques to architectural and building acoustics. Artificial Intelligence in Energy and Renewable Energy Systems, 309 (2007).
21. [22] Wan, Y., Zhou, Y., Wen, J., Chen, Z., Zhao, J.: Research on prediction method of objective assessment of building acoustics based on machine learning. In Journal of Physics: Conference Series Vol. 2522, No. 1, p. 012010 (2023).
22. [23] Brown, A. G. P., Coenen, F. P., Shave, M. J., Knight, M. W.: An AI approach to noise prediction. Building Acoustics, 4(2), 137-150 (1997).
23. [24] Nourani, V., Gökçekuş, H., Umar, I. K.: Artificial intelligence based ensemble model for prediction of vehicular traffic noise. Environmental research, 180, 108852 (2020).
24. [25] Singh, D., Upadhyay, R., Pannu, H. S., Leray, D.: Development of an adaptive neuro fuzzy inference system based vehicular traffic noise prediction model. Journal of Ambient Intelligence and Humanized Computing, 12, 2685-2701 (2021).
25. [26] Tan, Y., Fang, Y., Zhou, T., Wang, Q., Cheng, J. C. P.: Improve indoor acoustics performance by using building information modeling. In ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction Vol. 34 (2017).
26. [27] Burfoot, M.: Integrating Acoustic Scene Classification with Variable Acoustics, to Intelligently Improve Acoustic Comfort in NZ Classrooms. In Proceedings–New Zealand Built Environment Research Symposium (2020).
27. [28] Drass, M., Kraus, M. A., Riedel, H., Stelzer, I.: SoundLab AI-Machine learning for sound insulation value predictions of various glass assemblies. Glass Structures & Engineering, 7(1), 101-118 (2022).
28. [29] Wang, F., Chen, Z., Wu, C., Yang, Y.: Prediction on sound insulation properties of ultrafine glass wool mats with artificial neural networks. Applied Acoustics, 146, 164-171 (2019).
29. [30] Luo, Z., Li, T., Yan, Y., Zhou, Z., Zha, G.: Prediction of sound insulation performance of aramid honeycomb sandwich panel based on artificial neural network. Applied Acoustics, 190, 108656 (2022).
30. [31] Paknejad, S. H., Vadood, M., Soltani, P., Ghane, M.: Modeling the sound absorption behavior of carpets using artificial intelligence. The Journal of the Textile Institute, 112(11), 1763-1771 (2021).
31. [32] Rother, E. T.: Systematic literature review X narrative review. Acta paulista de enfermagem, 20, v-vi (2007).
32. [33] Nightingale, A.: A guide to systematic literature reviews. Surgery (Oxford), 27(9), 381-384 (2009).
33. [34] Morton, S. C., Murad, M. H., O’Connor, E., Lee, C. S., Booth, M., Vandermeer, B. W., Steele, D. W.: Quantitative synthesis—an update. Methods Guide for Effectiveness and Comparative Effectiveness Reviews (2018).
34. [35] Higgins, S.: Meta‐synthesis and comparative meta‐analysis of education research findings: some risks and benefits. Review of Education, 4(1), 31-53 (2016).
35. [36] Aliabadi, M., Golmohammadi, R., Khotanlou, H., Mansoorizadeh, M., Salarpour, A.: Artificial neural networks and advanced fuzzy techniques for predicting noise level in the industrial embroidery workrooms. Applied Artificial Intelligence, 29(8), 766-785 (2015).
36. [37] Iannace, G., Ciaburro, G., Trematerra, A.: Modelling sound absorption properties of broom fibers using artificial neural networks. Applied Acoustics, 163, 107239 (2020).
37. [38] Ciaburro, G., Iannace, G.: Numerical simulation for the sound absorption properties of ceramic resonators. Fibers, 8(12), 77 (2020).
38. [39] Ciaburro, G., Iannace, G., Puyana-Romero, V., Trematerra, A.: A comparison between numerical simulation models for the prediction of acoustic behavior of giant reeds shredded. Applied Sciences, 10(19), 6881 (2020).
39. [40] Kuschmitz, S., Ring, T. P., Watschke, H., Langer, S. C., Vietor, T.: Design and additive manufacturing of porous sound absorbers—A machine-learning approach. Materials, 14(7), 1747 (2021).
40. [41] Ciaburro, G., Iannace, G., Ali, M., Alabdulkarem, A., Nuhait, A.: An artificial neural network approach to modelling absorbent asphalts acoustic properties. Journal of King Saud University-Engineering Sciences, 33(4), 213-220 (2021).
41. [42] Ring, T. P., Langer, S. C.: On the Relationship of the Acoustic Properties and the Microscale Geometry of Generic Porous Absorbers. Applied Sciences, 12(21), 11066 (2022).
42. [43] Ciaburro, G., Parente, R., Iannace, G., Puyana-Romero, V.: Design optimization of three-layered metamaterial acoustic absorbers based on PVC reused membrane and metal washers. Sustainability, 14(7), 4218 (2022).
43. [44] Otaru, A. J.: Research of the numerical simulation and machine learning backpropagation networks analysis of the sound absorption properties of cellular soundproofing materials. Results in Engineering, 20, 101588 (2023).
44. [45] Puyana-Romero, V., Chuquín, J. S. A., Chicaiza, S. I. M., Ciaburro, G.: Characterization and Simulation of Acoustic Properties of Sugarcane Bagasse-Based Composite Using Artificial Neural Network Model. Fibers, 11(2), 18 (2023).
45. [46] Farahani, M. D., Jeddi, A. A. A., Hasanzadeh, M.: Predicting the sound absorption performance of warp-knitted spacer fabrics via an artificial neural network system. Fibers and Polymers, 24(4), 1491-1501 (2023).
46. [47] Busra, S., Giuseppe, C., Gino, I., Mustafa, O.: Preparation of PPA based composite reinforcing with glass beads and clays: Investigation of sound absorbing. Building Acoustics, 1351010X241254956 (2024).
47. [48] Cheng, B., Wang, M., Gao, N., Hou, H.: Machine learning inversion design and application verification of a broadband acoustic filtering structure. Applied Acoustics, 187, 108522 (2022).
48. [49] Dogra, S., Singh, L., Nigam, A., Gupta, A.: A deep learning-based approach for the inverse design of the Helmholtz resonators. Materials Today Communications, 37, 107439 (2023).
49. [50] Mahesh, K., Ranjith, S. K., Mini, R. S.: A deep autoencoder based approach for the inverse design of an acoustic-absorber. Engineering with Computers, 40(1), 279-300 . (2024).
50. [51] Gao, N., Wang, M., Cheng, B., Hou, H.: Inverse design and experimental verification of an acoustic sink based on machine learning. Applied Acoustics, 180, 108153 (2021).
51. [52] Pan, B., Song, X., Xu, J., Sui, D., Xiao, H., Zhou, J., Gu, J.: Accelerated inverse design of customizable acoustic metaporous structures using a CNN-GA-based hybrid optimization framework. Applied Acoustics, 210, 109445 (2023).
52. [53] Lee, S. Y., Lee, J., Lee, J. S., Lee, S.: Deep learning-based prediction and interpretability of physical phenomena for metaporous materials. Materials Today Physics, 30, 100946 (2023).
53. [54] Pan, B., Song, X., Xu, J., Zhou, J., Sui, D., Shui, Y., Zhang, Z.: Bottom-up approaches for rapid on-demand design of modular metaporous structures with tailored absorption. International Journal of Mechanical Sciences, 263, 108784 (2024).
54. [55] Gao, N., Wang, M., Cheng, B.: Deep auto-encoder network in predictive design of Helmholtz resonator: on-demand prediction of sound absorption peak. Applied Acoustics, 191, 108680 (2022).
55. [56] Mi, H., Guo, W., Liang, L., Ma, H., Zhang, Z., Gao, Y., Li, L.: Prediction of the Sound Absorption Coefficient of Three-Layer Aluminum Foam by Hybrid Neural Network Optimization Algorithm. Materials, 15(23), 8608 (2022).
56. [57] Wang, R., Yao, D., Zhang, J., Xiao, X., Jin, X. Sound-insulation prediction model and multi-parameter optimisation design of the composite floor of a high-speed train based on machine learning. Mechanical Systems and Signal Processing, 200, 110631 (2023).
57. [58] Gao, N., Liu, J., Deng, J., Chen, D., Huang, Q., Pan, G.: Design and performance of ultra-broadband composite meta-absorber in the 200Hz-20kHz range. Journal of Sound and Vibration, 574, 118229 (2024).
58. [59] Ghizdavet, Z., Ștefan, B. M., Nastac, D., Vasile, O., Bratu, M.: Sound absorbing materials made by embedding crumb rubber waste in a concrete matrix. Construction and Building Materials, 124, 755-763 (2016).
59. [60] Jin, Y., Yang, Y., Wen, Z., He, L., Cang, Y., Yang, B., ... & Li, Y.: Lightweight sound-absorbing metastructures with perforated fish-belly panels. International Journal of Mechanical Sciences, 226, 107396 (2022).