A DATA-DRIVEN APPROACH TO THE EVALUATION OF ASPHALT PAVEMENT STRUCTURES USING FALLING WEIGHT DEFLECTOMETER

被引：8

作者：

Liu, Hanjie ^{[1
]}

Cao, Jinde ^{[1
,2
]}

Huang, Wei ^{[3
]}

Shi, Xinli ^{[4
]}

Zhou, Xingye ^{[5
]}

机构：

[1] Southeast Univ, Jiangsu Prov Key Lab Networked Collect Intelligen, Sch Math, Nanjing 210096, Peoples R China

[2] Yonsei Univ, Yonsei Frontier Lab, Seoul 03722, South Korea

[3] Southeast Univ, Intelligent Transportat Syst Res Ctr, Nanjing 210096, Peoples R China

[4] Southeast Univ, Sch Cyber Sci & Engn, Nanjing 210096, Peoples R China

[5] Minist Transport, Res Inst Highway, Beijing 100088, Peoples R China

来源：

DISCRETE AND CONTINUOUS DYNAMICAL SYSTEMS-SERIES S | 2022年 / 15卷 / 11期

基金：

中国国家自然科学基金;

关键词：

Data-driven; unsupervised machine learning; pavement design; RIOHTrack; FWD; COMMUNITY STRUCTURE; TEMPERATURE; CONNECTIVITY; PERFORMANCE;

D O I：

10.3934/dcdss.2022139

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

The evaluation of asphalt pavement structures has been a critical challenge in the field due to the practical limitations in methodology. In this paper, we propose a data-driven framework to evaluate structural performance of nineteen widely used asphalt structures in the Research Institute of Highway Ministry of Transport track (RIOHTrack). Specifically, we utilize the unsupervised machine learning method to delineate the similar and disparate performance among tested structures based on four years of falling weight deflectometer (FWD) experiments. Next, the structural performance is investigated on the temporal scale and the dynamic performance variations are captured over the course of the testing. Finally, experimental results are discussed and we provide essential evidence to aid future asphalt pavement design and construction.

引用

页码：3223 / 3241

页数：19

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