Prediction of Resilient Modulus of Asphalt Pavement Material Using Support Vector Machine

The purpose of this paper is to simulate the resilient modulus of asphalt pavement material using support vector machines (SVM). First, selecting 15 training datasets randomly and remaining 9 testing datasets to create model, then radial basis function kernel and polynomial kernel based on support vector machines are used to simulate the resilient modulus. The correlation coefficients values were achieved by radial basis function kernel and polynomial kernel based on support vector machines. The result of sensitivity analysis among input parameters such as P4.75, P2.36, P0.075, Va, Pb shows that the parameter Pb has maximum influence on resilient modulus. The predicting results indicate that the proposed SVM model can gain higher precision than artificial neural network approach and multiple regression, which provides a new way for predicting resilient modulus and other mechanical behaviour index of asphalt pavement material.

• Record URL:
  https://doi.org/10.1061/47624(403)3
• Record URL:
  http://ascelibrary.org/doi/abs/10.1061/47624%28403%293
• Availability:
  • Find a library where document is available. Order URL: http://ascelibrary.org/doi/book/10.1061/9780784476246
• Supplemental Notes:
  • Copyright © 2011 ASCE
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Kezhen, Yan
  • Yin, Honghui
  • Liao, Huarong
  • Huang, Likui
• Conference:
  • GeoHunan International Conference 2011
  • Location: Hunan , China
  • Date: 2011-6-9 to 2011-6-11
• Publication Date: 2011

Language

• English

Media Info

• Media Type: Digital/other
• Features: References;
• Pagination: pp 16-23
• Monograph Title: Road Pavement and Material Characterization, Modeling, and Maintenance

Subject/Index Terms

• TRT Terms: Asphalt pavements; Machine learning; Mathematical prediction; Mechanical properties; Modulus of resilience; Sensitivity analysis
• Uncontrolled Terms: Kernel estimation; Support vector machines
• Subject Areas: Highways; Materials; Pavements; I22: Design of Pavements, Railways and Guideways; I31: Bituminous Binders and Materials;

Filing Info

• Accession Number: 01347774
• Record Type: Publication
• ISBN: 9780784476246
• Files: TRIS, ASCE
• Created Date: Aug 8 2011 2:20PM