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Sound insulation prediction and optimization of wooden support structure for high-speed train floor based on machine learning.

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Title: Sound insulation prediction and optimization of wooden support structure for high-speed train floor based on machine learning.
Authors: Haiyang Ding1, Ruiqian Wang1,2 ruiquanwang@163.com, Xuefei Zhang1, Ziyan Xu1, Ancong Zhang1, Lei Xu3
Source: Sound & Vibration. 2025, Vol. 59 Issue 1, p1-23. 23p.
Subjects: Statistical energy analysis, Wood floors, High speed trains, Support vector machines, Value engineering, Feature selection, Soundproofing
Abstract: In order to improve the sound insulation performance of high-speed train floors, this study first obtained the necessary data for model training based on the reverberation test method, and then conducted data sorting and feature selection. Next, the maximum mutual information minimum redundancy (mRMR) feature selection algorithm was used to calculate the selected features and screen out a subset of significant features. Subsequently, the decision tree, BP neural network, and support vector machine regression (SVR) methods were applied in sequence, and the standardized feature data were used for the high-speed train floor under the same evaluation criteria of the mean square error (MSE) and coefficient of determination (R2). We conducted training and validation of the sound insulation prediction models for timber-framed support structures. The prediction accuracy of the trained model was compared and evaluated with the finite element statistical energy analysis (FE-SEA) prediction model. Finally, the SVR model was used to optimize the design under constraint conditions. The research results show that based on the research object, sample library, and model training in this article, compared with the FE-SEA model, the prediction error of the SVR model is only 0.3 dB, showing better performance. In engineering practice, the SVR model can effectively optimize the wooden support structure in the floor under certain constraints, and it predicts that the weighted sound insulation of the entire floor is 50.45 dB, which has important engineering application value. [ABSTRACT FROM AUTHOR]
Copyright of Sound & Vibration is the property of Academic Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 184360254
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  Data: Sound insulation prediction and optimization of wooden support structure for high-speed train floor based on machine learning.
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  Data: <searchLink fieldCode="AR" term="%22Haiyang+Ding%22">Haiyang Ding</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Ruiqian+Wang%22">Ruiqian Wang</searchLink><relatesTo>1,2</relatesTo><i> ruiquanwang@163.com</i><br /><searchLink fieldCode="AR" term="%22Xuefei+Zhang%22">Xuefei Zhang</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Ziyan+Xu%22">Ziyan Xu</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Ancong+Zhang%22">Ancong Zhang</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Lei+Xu%22">Lei Xu</searchLink><relatesTo>3</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Sound+%26+Vibration%22">Sound & Vibration</searchLink>. 2025, Vol. 59 Issue 1, p1-23. 23p.
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  Data: <searchLink fieldCode="DE" term="%22Statistical+energy+analysis%22">Statistical energy analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Wood+floors%22">Wood floors</searchLink><br /><searchLink fieldCode="DE" term="%22High+speed+trains%22">High speed trains</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Value+engineering%22">Value engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Soundproofing%22">Soundproofing</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In order to improve the sound insulation performance of high-speed train floors, this study first obtained the necessary data for model training based on the reverberation test method, and then conducted data sorting and feature selection. Next, the maximum mutual information minimum redundancy (mRMR) feature selection algorithm was used to calculate the selected features and screen out a subset of significant features. Subsequently, the decision tree, BP neural network, and support vector machine regression (SVR) methods were applied in sequence, and the standardized feature data were used for the high-speed train floor under the same evaluation criteria of the mean square error (MSE) and coefficient of determination (R2). We conducted training and validation of the sound insulation prediction models for timber-framed support structures. The prediction accuracy of the trained model was compared and evaluated with the finite element statistical energy analysis (FE-SEA) prediction model. Finally, the SVR model was used to optimize the design under constraint conditions. The research results show that based on the research object, sample library, and model training in this article, compared with the FE-SEA model, the prediction error of the SVR model is only 0.3 dB, showing better performance. In engineering practice, the SVR model can effectively optimize the wooden support structure in the floor under certain constraints, and it predicts that the weighted sound insulation of the entire floor is 50.45 dB, which has important engineering application value. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Sound & Vibration is the property of Academic Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.59400/sv2073
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      – Code: eng
        Text: English
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        PageCount: 23
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        Type: general
      – SubjectFull: Wood floors
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      – SubjectFull: High speed trains
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      – SubjectFull: Value engineering
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      – SubjectFull: Feature selection
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      – SubjectFull: Soundproofing
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      – TitleFull: Sound insulation prediction and optimization of wooden support structure for high-speed train floor based on machine learning.
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            NameFull: Haiyang Ding
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            NameFull: Ruiqian Wang
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              M: 01
              Text: 2025
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