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September 10, 2025Machines3 citationsOpen Access

An Integrated SEA–Deep Learning Approach for the Optimal Geometry Performance of Noise Barrier

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HWHao WuLHLingshan HeZTZiyu Tao

Key Points

  • Optimized noise barriers led to significant sound pressure level reductions of 4–8 dB at sensitive sites.
  • Field measurements identified peak near-track noise amplification at 1 m from barriers, with maximum increases of 4.13 dB for fully enclosed barriers.
  • Neural network surrogate model achieved high precision (R2 = 0.9094), facilitating optimal geometric designs for noise barriers.
  • Maa's theory of micro-perforated plates enhanced noise barrier performance, providing efficient solutions for urban noise pollution.

Abstract

The escalating environmental noise pollution along urban rail transit corridors, exacerbated by rapid urbanization, necessitates innovative and efficient noise control measures. A comprehensive investigation was conducted that utilized field measurements of train passing-by noise to establish a statistical energy analysis model for evaluating the acoustic performance of both vertical (VB) and fully enclosed (FB) barrier configurations. The study incorporated Maa’s theory of micro-perforated plate (MPP) parameter optimization and developed a neural network surrogate model focused on insertion loss maximization for barrier geometric design. Key findings revealed significant barrier-induced near-track noise amplification, with peak effects observed at the point located 1 m from the barrier and 2 m above the rail. Frequency-dependent analysis demonstrated a characteristic rise-and-fall reflection pattern, showing maximum amplifications of 1.47 dB for VB and 4.13 dB for FB within the 400–2000 Hz range. The implementation of optimized MPPs was found to effectively eliminate the near-field noise amplification effects, achieving sound pressure level reductions of 4–8 dB at acoustically sensitive locations. Furthermore, the high-precision surrogate model (R2 = 0.9094, MSE = 0.8711) facilitated optimal geometric design solutions. The synergistic combination of MPP absorption characteristics and geometric optimization resulted in substantially enhanced barrier performance, offering practical solutions for urban rail noise mitigation strategies.

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Cite This Study

Wu et al. (2025) studied this question.

synapsesocial.com/papers/68c1b34d54b1d3bfb60e9b39https://doi.org/10.3390/machines13080670
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