Using multivariate adaptive regression splines (MARS) in pavement roughness prediction

被引：0
|
作者：
Attoh-Okine, NO [1 ]
Mensah, S
Nawaiseh, M
机构：
[1] Univ Delaware, Dept Civil & Environm Engn, Newark, DE 19711 USA
[2] Florida Int Univ, Dept Civil & Environm Engn, Miami, FL 33199 USA
来源：
PROCEEDINGS OF THE INSTITUTION OF CIVIL ENGINEERS-TRANSPORT | 2003年 / 156卷 / 01期
关键词：
pavement design; roads & highways; statistical analysis;
D O I：
10.1680/tran.2003.156.1.51
中图分类号：
TU [建筑科学];
学科分类号：
0813 ;
摘要：
The paper presents the application of a new statistical technique, multivariate adaptive regression splines (MARS), to a flexible pavement roughness prediction model. MARS is a non-parametric function estimation technique that shows great promise for fitting non-linear multivariate functions. The MARS approach was used to develop a roughness equation, based on available input, and was able to identify the threshold values of each input and the most important variables contributing to the roughness equation. The MARS technique allows easy interpretation of the relative importance of pavement condition variables, environmental factors and traffic for the overall fit.
引用
收藏
页码：51 / 55
页数：5
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