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Analysis of Structural Performance Evaluation of Asphalt Concrete Pavement Based on Unsupervised Learning.

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Title: Analysis of Structural Performance Evaluation of Asphalt Concrete Pavement Based on Unsupervised Learning.
Authors: Yang, Linqing1 (AUTHOR), Li, Shikun2 (AUTHOR), Pan, Yichen3 (AUTHOR), Han, Zejun3 (AUTHOR) zjhangdgy@gdut.edu.cn, Yang, Tangjun2 (AUTHOR), An, Zhiyuan2 (AUTHOR), Hu, Biao (AUTHOR) biaohu3-c@szu.edu.cn
Source: Advances in Civil Engineering. 5/7/2026, Vol. 2026, p1-18. 18p.
Subjects: Asphalt concrete pavements, Autoencoders, Spectral element method, Structural health monitoring, Machine learning, Nondestructive testing, Asphalt pavements
Abstract: The structural performance evaluation of asphalt concrete pavements is critical for ensuring traffic safety and optimizing maintenance strategies. Traditional nondestructive testing methods often rely on complex models and extensive manual intervention, limiting their efficiency and real‐time applicability. This study proposes an unsupervised learning‐based approach to enhance the accuracy and automation of pavement performance assessment. A forward model of pavement dynamics under falling weight deflectometer (FWD) loading was established using the spectral element method (SEM) to generate time‐history deflection curves. A convolutional autoencoder (CAE) was then employed as an unsupervised learning model, trained exclusively on normal pavement data. Performance anomalies were detected by evaluating reconstruction error (ReError) and structural similarity (SSIM) between input and reconstructed deflection signals. The results demonstrated that the proposed method effectively identified abnormal pavement conditions, with ReError increasing and SSIM decreasing significantly as the dynamic modulus or thickness of pavement layers degraded. Linear relationships between dynamic modulus and deflection were observed for one‐ and two‐layer pavements, while three‐layer pavements exhibited nonlinear behavior due to asynchronous parameter variations. The unsupervised learning framework provides a robust, data‐driven tool for qualitative pavement performance evaluation. [ABSTRACT FROM AUTHOR]
Copyright of Advances in Civil Engineering is the property of Wiley-Blackwell 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.)
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  Data: Analysis of Structural Performance Evaluation of Asphalt Concrete Pavement Based on Unsupervised Learning.
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  Data: <searchLink fieldCode="AR" term="%22Yang%2C+Linqing%22">Yang, Linqing</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Shikun%22">Li, Shikun</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pan%2C+Yichen%22">Pan, Yichen</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Han%2C+Zejun%22">Han, Zejun</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> zjhangdgy@gdut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Yang%2C+Tangjun%22">Yang, Tangjun</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22An%2C+Zhiyuan%22">An, Zhiyuan</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hu%2C+Biao%22">Hu, Biao</searchLink> (AUTHOR)<i> biaohu3-c@szu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Advances+in+Civil+Engineering%22">Advances in Civil Engineering</searchLink>. 5/7/2026, Vol. 2026, p1-18. 18p.
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  Data: <searchLink fieldCode="DE" term="%22Asphalt+concrete+pavements%22">Asphalt concrete pavements</searchLink><br /><searchLink fieldCode="DE" term="%22Autoencoders%22">Autoencoders</searchLink><br /><searchLink fieldCode="DE" term="%22Spectral+element+method%22">Spectral element method</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+health+monitoring%22">Structural health monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Nondestructive+testing%22">Nondestructive testing</searchLink><br /><searchLink fieldCode="DE" term="%22Asphalt+pavements%22">Asphalt pavements</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The structural performance evaluation of asphalt concrete pavements is critical for ensuring traffic safety and optimizing maintenance strategies. Traditional nondestructive testing methods often rely on complex models and extensive manual intervention, limiting their efficiency and real‐time applicability. This study proposes an unsupervised learning‐based approach to enhance the accuracy and automation of pavement performance assessment. A forward model of pavement dynamics under falling weight deflectometer (FWD) loading was established using the spectral element method (SEM) to generate time‐history deflection curves. A convolutional autoencoder (CAE) was then employed as an unsupervised learning model, trained exclusively on normal pavement data. Performance anomalies were detected by evaluating reconstruction error (ReError) and structural similarity (SSIM) between input and reconstructed deflection signals. The results demonstrated that the proposed method effectively identified abnormal pavement conditions, with ReError increasing and SSIM decreasing significantly as the dynamic modulus or thickness of pavement layers degraded. Linear relationships between dynamic modulus and deflection were observed for one‐ and two‐layer pavements, while three‐layer pavements exhibited nonlinear behavior due to asynchronous parameter variations. The unsupervised learning framework provides a robust, data‐driven tool for qualitative pavement performance evaluation. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Advances in Civil Engineering is the property of Wiley-Blackwell 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: 10.1155/adce/2770162
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        Text: English
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        PageCount: 18
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      – SubjectFull: Asphalt concrete pavements
        Type: general
      – SubjectFull: Autoencoders
        Type: general
      – SubjectFull: Spectral element method
        Type: general
      – SubjectFull: Structural health monitoring
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Nondestructive testing
        Type: general
      – SubjectFull: Asphalt pavements
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      – TitleFull: Analysis of Structural Performance Evaluation of Asphalt Concrete Pavement Based on Unsupervised Learning.
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            NameFull: Yang, Linqing
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            – D: 07
              M: 05
              Text: 5/7/2026
              Type: published
              Y: 2026
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