Complaint-guided robust optimization of highway noise barriers with hourly traffic variability

Yogeesh Nijalingappa
Department of Electrical and Electronics Engineering, SR University, Warangal 506371, India; Department of Mathematics, Government First Grade College, Tumkur 572102, India
Markala Karthik
Department of Electrical and Electronics Engineering, SR University, Warangal 506371, India
Asokan Vasudevan
Faculty of Business and Communications, INTI International University, Nilai 71800, Malaysia
Mohammed Almakki
School of Engineering, Architecture, and Interior Design, Amity University Dubai, Dubai 345019, United Arab Emirates
Zetty Pakir Mastan
Faculty of Engineering and Quantity Surveying, INTI International University, Nilai 71800, Malaysia
Mayibongwe Tafara Mudzengi
International Relations and Collaborations Centre (IRCC), INTI International University, Nilai 71800, Malaysia

Article ID: 4013

Keywords: road-traffic noise; noise barrier optimization; α-cut uncertainty; robust multi-objective optimization; type-2 fuzzy modelling; hourly LAeq; mixed traffic; evolutionary algorithm (NSGA-II)

Abstract

Road-traffic noise is a daily problem in many fast-growing tier-2 cities. On busy corridors, mixed traffic and stop-go movement raise both exposure and public annoyance. This study presents an uncertainty-aware framework for optimizing roadside noise barriers by combining hourly traffic variability with community complaint signals. The NH-48 urban approach corridor in Tumakuru, Karnataka, was examined using a 7-day dataset at hourly resolution. A calibrated baseline model related hourly A-weighted equivalent sound levels to log-scaled traffic flow, mean speed, and heavy-vehicle fraction, with good agreement with measurements (overall MAE 2.3 dB(A), RMSE 3.1 dB(A)). Input uncertainty was represented through nested α-cut interval bands, and the measured hourly levels were increasingly captured as the bands widened (coverage from 0.66 at α = 0.8 to 0.92 at α = 0.2). Barrier design was posed as a multi-objective robust optimization problem that minimized conservative exceedance, complaint-weighted nuisance, and a normalized cost index. The evolutionary search produced a Pareto set with a clear cost-performance trade-off. The preferred solutions lowered robust exceedance and complaint-weighted objective values by up to 35% and 42%, respectively, relative to baseline candidates. Receptor-level exceedance hours fell by about 39–45%, and mean upper-bound levels dropped by as much as 3.9 dB(A) at near-road receptors. Overall, the results show that complaint signals can help identify perceptual hotspots, while the uncertainty-aware model maintains robust exposure reduction under day-to-day traffic variation.

Published

2026-07-02

How to Cite

Nijalingappa, Y., Karthik, M., Vasudevan, A., Almakki, M., Mastan, Z. P., & Mudzengi, M. T. (2026). Complaint-guided robust optimization of highway noise barriers with hourly traffic variability. Sound & Vibration, 60(4). https://doi.org/10.59400/sv4013

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Vol. 60 No. 4 (2026): In Progress

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Article

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

[1]World Health Organization. Environmental Noise Guidelines for the European Region. WHO Regional Office for Europe; 2018. Available online: https://www.who.int/publications/i/item/9789289053563

[2]World Health Organization. Night Noise Guidelines for Europe. WHO Regional Office for Europe; 2009. Available online: https://www.who.int/europe/publications/i/item/9789289041737

[3]Basner M, Babisch W, Davis A, et al. Auditory and non-auditory effects of noise on health. The Lancet. 2014; 383(9925): 1325–1332. doi: 10.1016/S0140-6736(13)61613-X

[4]Zadeh LA. Fuzzy sets. Information and Control. 1965; 8(3): 338–353. doi: 10.1016/S0019-9958(65)90241-X

[5]Government of Karnataka. Integrated Mobility & Service Plan, Tumakuru. Government of Karnataka; 2020.

[6]Sakagami K, Nakamori T, Morimoto M, et al. Double-leaf microperforated panel space absorbers: A revised theory and detailed analysis. Applied Acoustics. 2009; 70(5): 703–709. doi: 10.1016/j.apacoust.2008.09.004

[7]Watts GR, Crombie DH, Hothersall DC. Acoustic Performance Of New Designs Of Traffic Noise Barriers: Full Scale Tests. Journal of Sound and Vibration. 1994; 177(3): 289–305. doi: 10.1006/jsvi.1994.1435

[8]Ishizuka T, Fujiwara K. Performance of noise barriers with various edge shapes and acoustical conditions. Applied Acoustics. 2004; 65(2): 125–141. doi: 10.1016/j.apacoust.2003.08.006

[9]Pohl A, Stephenson UM. A Combination of the Sound Particle Simulation Method and the Radiosity Method. Building Acoustics. 2011; 18(1–2): 97–122. doi: 10.1260/1351-010X.18.1-2.97

[10]Maekawa Z. Noise reduction by screens. Applied Acoustics. 1968; 1(3): 157–173. doi: 10.1016/0003-682X(68)90020-0

[11]Khan J, Ketzel M, Jensen SS, et al. Comparison of Road Traffic Noise prediction models: CNOSSOS-EU, Nord2000 and TRANEX. Environmental Pollution. 2021; 270: 116240. doi: 10.1016/j.envpol.2020.116240

[12]Umar IK, Adamu M, Mostafa N, et al. The state-of-the-art in the application of artificial intelligence-based models for traffic noise prediction: a bibliographic overview. Cogent Engineering. 2024; 11(1): 2297508. doi: 10.1080/23311916.2023.2297508

[13]Acosta Ó, Montenegro C, Crespo RG. Road traffic noise prediction model based on artificial neural networks. Heliyon. 2024; 10(17): e36484. doi: 10.1016/j.heliyon.2024.e36484

[14]Ben-Tal A, Nemirovski A. Robust solutions of uncertain linear programs. Operations Research Letters. 2000; 25(1): 1–13. Available online: https://www2.isye.gatech.edu/~nemirovs/stablpn.pdf

[15]Bertsimas D, Sim M. The Price of Robustness. Operations Research. 2004; 52(1): 35–53. doi: 10.1287/opre.1030.0065

[16]Mendel JM, John RIB. Type-2 fuzzy sets made simple. IEEE Transactions on Fuzzy Systems. 2002; 10(2): 117–127. doi: 10.1109/91.995115

[17]Wagner C, Hagras H. Toward General Type-2 Fuzzy Logic Systems Based on zSlices. IEEE Transactions on Fuzzy Systems. 2010; 18(4): 637–660. doi: 10.1109/TFUZZ.2010.2045386

[18]Yogeesh N. Fuzzy Logic Modelling of Nonlinear Metamaterials. In: Advances in Wireless Technologies and Telecommunication. IGI Global; 2023. pp. 230–269. doi: 10.4018/978-1-6684-8287-2.ch010

[19]Gasco L, Clavel C, Asensio C, et al. Beyond sound level monitoring: Exploitation of social media to gather citizens subjective response to noise. Science of The Total Environment. 2019; 658: 69–79. doi: 10.1016/j.scitotenv.2018.12.071

[20]Gasco L, Schifanella R, Aiello LM, et al. Social Media and Open Data to Quantify the Effects of Noise on Health. Frontiers in Sustainable Cities. 2020; 2: 41. doi: 10.3389/frsc.2020.00041

[21]Peplow A, Thomas J, AlShehhi A. Noise Annoyance in the UAE: A Twitter Case Study via a Data-Mining Approach. International Journal of Environmental Research and Public Health. 2021; 18(4): 2198. doi: 10.3390/ijerph18042198

[22]Kollia B, Basch CH, Park E, et al. Social Media Depictions of the Impact of Noise Pollution on Communication and Mental and Physical Health. Journal of Community Health. 2025; 50(4): 694–699. doi: 10.1007/s10900-025-01457-7

[23]Tong H, Kang J. Characteristics of noise complaints and the associations with urban morphology: A comparison across densities. Environmental Research. 2021; 197: 111045. doi: 10.1016/j.envres.2021.111045

[24]Duhamel D. Shape optimization of noise barriers using genetic algorithms. Journal of Sound and Vibration. 2006; 297(1–2): 432–443. doi: 10.1016/j.jsv.2006.04.004

[25]Han N, Mak CM. Prediction of flow-generated noise produced by acoustic and aerodynamic interactions of multiple in-duct elements. Applied Acoustics. 2008; 69(6): 566–573. doi: 10.1016/j.apacoust.2007.01.001

[26]Sohrabi S, Pàmies Gómez T, Romeu Garbí J. Using genetic algorithms to optimize the location of transducers for an active noise barrier. Journal of Low Frequency Noise, Vibration and Active Control. 2024; 43(1): 494–509. doi: 10.1177/14613484231184701

[27]Rahmani S, Mousavi SM, Kamali MJ. Modeling of road-traffic noise with the use of genetic algorithm. Applied Soft Computing. 2011; 11(1): 1008–1013. doi: 10.1016/j.asoc.2010.01.022

[28]Yogeesh N. Fuzzy Graph Dominance for Networked Communication Optimization. In: Wireless Communication Technologies. CRC Press; 2024. pp. 36–65. doi: 10.1201/9781003389231-3

[29]Ranpise RB, Tandel BN. Urban road traffic noise monitoring, mapping, modelling, and mitigation: A thematic review. Noise Mapping. 2022; 9(1): 48–66. doi: 10.1515/noise-2022-0004

[30]Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation. 2002; 6(2): 182–197. doi: 10.1109/4235.996017

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