Prediction of Multi-layered Pavement Moduli Based on Falling Weight Deflectometer Test Using Soft Computing Approaches

The application of supervised machine learning algorithms to provide solutions for various civil engineering problems is an emerging trend. This paper presents the utilization of artificial neural network (ANN) and random forest regression (RFR) for the prediction of the elastic moduli of multi-layered pavement based on the falling weight deflectometer (FWD) test. The establishment of ML models includes data preprocessing, hyperparameter optimization, and performance evaluations. The ML models are created from both ANN and RFR techniques using 122,500 datasets from a theoretical model of the FWD test, generated by employing an exact stiffness matrix method for the analysis of multi-layered flexible pavement. The performance measures of both ML models, developed from the synthetic dataset, indicate that the output variables (the predicted pavement moduli) are precisely explained by the input parameters (the measured surface displacements). Both ML solutions are then compared with the FWD test results performed on the road infrastructures in Thailand, showing good agreement with the predicted moduli from the FWD tests. Between the two ML solutions, RFR displays better accuracy in predicting the pavement moduli from the FWD tests with the R² values of the predicted elastic moduli exceeding 90%. Besides, a sensitivity analysis is carried out to illustrate the impact of surface deflections recorded at each geophone on the predicted pavement moduli. The present study demonstrates the efficacy of ML techniques in assessing road infrastructures and highlights the significance of sensitivity analysis in enhancing the accuracy of pavement performance prediction.

• Record URL:
  • https://doi.org/10.1007/s40515-024-00370-1
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/21967202
• Supplemental Notes:
  • © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
• Authors:
  • Phulsawat, Barami
  • Senjuntichai, Angsumalin
  • Senjuntichai, Teerapong
  • 0000-0001-5806-0610
• Publication Date: 2024-8

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 2348-2381
• Serial:
  • Transportation Infrastructure Geotechnology
  • Volume: 11
  • Issue Number: 4
  • Publisher: Springer Publishing
  • ISSN: 2196-7202
  • EISSN: 2196-7210
  • Serial URL: http://link.springer.com/journal/40515

Subject/Index Terms

• TRT Terms: Falling weight deflectometers; Machine learning; Modulus of elasticity; Pavement performance; Predictive models; Sensitivity analysis
• Subject Areas: Data and Information Technology; Pavements;

Filing Info

• Accession Number: 01932631
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 3 2024 9:37AM