TRB Pubsindex
Text Size:
x

« Prev Next »

Return to Results | New Search

Bookmark and Share

Title:

Using Regression Analysis and Distribution Fitting to Analyze Pavement Sensing Patterns for Condition Assessments

Accession Number:

01764954

Record Type:

Component

Abstract:

Pavement condition assessment is essential for pavement asset management and it involves data collecting, pattern detecting, and condition monitoring processes. The paper presents an approach using regression analysis and probability distribution fitting to analyze pavement sensing patterns and signals collected on the I-10 corridors in Phoenix, Arizona. A vehicle is equipped with four sensors placed on the top of the control arms of the vehicle and one sensor is inside of the vehicle to gather the data for analysis. The result of the multiple regression analysis shows that the mean of sensors differ in a logarithmic scale at significance level 0.05, which suggests that all sensors should be included for pavement condition assessments. The distribution models are fitted using the acceleration vibration and can be used to determine the threshold values by computing a specified percentile, for example 99th percentile. The determination of thresholds varies based on the statistical analysis and the data falls in the remaining percent would indicate the pavement deterioration, which is called significant points in the paper. The ANOVA results show that there is an association between two variables (pavement temperatures and the number of significant points) at the significance level 0.05 which indicates the pavement temperature does play an important role in controlling pavement condition. Based on the Time-Series analysis and prediction, the pavements will be deteriorated if the maintenance and rehabilitation will not be scheduled. The paper concludes that using multiple regression analysis and distribution fitting method provides a promoting approach that can be used to help determine the level of different pavement conditions as well as predicting future performance.

Supplemental Notes:

This paper was sponsored by TRB committee AED60 Standing Committee on Statistical Methods.

Report/Paper Numbers:

TRBAM-21-04008

Language:

English

Corporate Authors:

Transportation Research Board

Authors:

Zhang, Dada
Ho, Chun-Hsing
Zhang, Fangfang

Pagination:

20p

Publication Date:

2021

Conference:

Transportation Research Board 100th Annual Meeting

Location: Washington DC, United States
Date: 2021-1-5 to 2021-1-29
Sponsors: Transportation Research Board; Transportation Research Board

Media Type:

Digital/other

Features:

Figures; Maps; References; Tables

Geographic Terms:

Phoenix (Arizona)

Subject Areas:

Highways; Maintenance and Preservation; Pavements

Source Data:

Transportation Research Board Annual Meeting 2021 Paper #TRBAM-21-04008

Files:

TRIS, TRB, ATRI

Created Date:

Dec 23 2020 11:25AM