A predictive model for the permittivity of asphalt pavements using support vector regression

Chan Ho Song; Yun Jeong Kim; Joon Kyu Lee

doi:10.22702/jkai.2024.14.2.24

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2024 Vol.14, Issue 2 Preview Page Next Page

Scientific Paper

A predictive model for the permittivity of asphalt pavements using support vector regression Support Vector Regression을 이용한 아스팔트포장의 유전율 예측모델

31 December 2024. pp. 260-268

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Abstract

In order to effectively apply ground penetrating radar technology for the quality control of asphalt pavements, a comprehensive understanding of the permittivity of the pavement is essential. This study aims to predict the permittivity of asphalt pavements based on material properties using support vector regression, a type of machine learning model. The predictive performance of the proposed model indicates that the Radial Basis Function kernel exhibits superior outcomes compared to the other three kernels. The results underscore the potential of the support vector regression technique as a promising tool for estimating the permittivity of asphalt pavements.

Keywords

Asphalt pavement

Permittivity

Support vector regression

Kernel function

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Information

Publisher :KOREAN ASPHALT INSTITUTE
Publisher(Ko) :한국아스팔트학회
Journal Title :Journal of the Korean Asphalt Institute
Journal Title(Ko) :한국아스팔트학회지
Volume : 14
No :2
Pages :260-268
Received Date : 2024-12-19
Revised Date : 2024-12-24
Accepted Date : 2024-12-31
DOI :https://doi.org/10.22702/jkai.2024.14.2.24

Journal of the Korean Asphalt Institute ISSN:2234-0785(Print) 2635-9553(Online) 한국아스팔트학회지

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