Using graphical models to interpret pavement condition data

被引：5
|
作者：
Attoh-Okine, NO [1 ]
Paris, D
机构：
[1] Univ Delaware, Dept Civil & Environm Engn, Newark, DE 19716 USA
[2] Tuskegee Univ, Dept Elect Engn, Tuskegee, AL 36088 USA
来源：
PROCEEDINGS OF THE INSTITUTION OF CIVIL ENGINEERS-TRANSPORT | 2005年 / 158卷 / 04期
关键词：
maintenance & inspection; pavement design; statistical analysis;
D O I：
10.1680/tran.2005.158.4.213
中图分类号：
TU [建筑科学];
学科分类号：
0813 ;
摘要：
Most of the multiple linear regressions developed in pavement performance studies have one response variable and one block of several explanatory variables. In this paper, graphical chain models are used to develop blocks of several possibly interacting responses with each block containing several interacting variables. The graphical chain model provides easy interpretation of conditional independence structure within the blocks. This ultimately provides an idea about association and dependence of different pavement condition variables. An example was presented using data from the Highway maintenance and Design Model (HDM).
引用
收藏
页码：213 / 218
页数：6
相关论文
共 50 条
  • [41] A generalised approach to outlier identification in pavement condition data
    Byrne, Matthew
    Isola, Riccardo
    Parry, Tony
    INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING, 2013, 14 (01) : 60 - 70
  • [42] Pavement Condition Monitoring with Crowdsourced Connected Vehicle Data
    Dennis, Eric Paul
    Hong, Qiang
    Wallace, Richard
    Tansil, William
    Smith, Matt
    TRANSPORTATION RESEARCH RECORD, 2014, (2460) : 31 - 38
  • [43] Data augmentation algorithm for graphical models with missing data
    Kuroda, M
    NEURAL NETWORK WORLD, 2005, 15 (04) : 343 - 350
  • [44] Differentially Private Learning of Undirected Graphical Models Using Collective Graphical Models
    Bernstein, Garrett
    McKenna, Ryan
    Sun, Tao
    Sheldon, Daniel
    Hay, Michael
    Miklau, Gerome
    INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 70, 2017, 70
  • [45] Investigation of the Data Variability of Network-Level Pavement Condition Data
    Shekharan, Raja
    Baranikumar, Aishwarya
    Asam, Kalyan
    Wang, Zhaohua
    Frith, Douglas
    TRANSPORTATION RESEARCH RECORD, 2024, 2678 (11) : 393 - 404
  • [46] Pavement condition prediction using clusterwise regression
    Luo, Zairen
    Chou, Eddie Y. J.
    PAVE MANAGEMENT; MONITORING, EVALUATION, AND DATA STORAGE; AND ACCELERATED TESTING 2006, 2006, (1974): : 70 - 77
  • [47] Pavement condition prediction using Markov process
    Pulugurta, Haricharan
    Shao, Q.
    Chou, Y. J.
    JOURNAL OF STATISTICS & MANAGEMENT SYSTEMS, 2009, 12 (05): : 853 - 871
  • [48] Case Study: Effect of the Condition Data of Automated Pavement Surveys on Pavement Performance Indicators
    Chowdhury, Faisal Quibria
    Yoon, Yoojung
    CONSTRUCTION RESEARCH CONGRESS 2024: SUSTAINABILITY, RESILIENCE, INFRASTRUCTURE SYSTEMS, AND MATERIALS DESIGN IN CONSTRUCTION, 2024, : 720 - 730
  • [49] Pavement Condition Data from Traffic Speed Deflectometer for Network Level Pavement Management
    Shrestha, S.
    Katicha, S. W.
    Flintsch, G. W.
    AIRFIELD AND HIGHWAY PAVEMENTS 2019: DESIGN, CONSTRUCTION, CONDITION EVALUATION, AND MANAGEMENT OF PAVEMENTS, 2019, : 392 - 403
  • [50] Ensemble Machine Learning Classification Models for Predicting Pavement Condition
    Chung, Frederick
    Doyle, Andy
    Robinson, Ernay
    Paik, Yejee
    Li, Mingshu
    Baek, Minsoo
    Moore, Brian
    Ashuri, Baabak
    TRANSPORTATION RESEARCH RECORD, 2024,
12345