A probabilistic approach to detect structural problems in flexible pavement sections at network level assessment

被引:18
|
作者
Fuentes, Luis [1 ]
Taborda, Katherine [1 ,6 ]
Hu, Xiaodi [2 ]
Horak, Emile [3 ,4 ]
Bai, Tao [2 ]
Walubita, Lubinda F. [1 ,5 ]
机构
[1] Univ Norte UniNorte, Dept Civil & Environm Engn, Barranquilla, Colombia
[2] Wuhan Inst Technol WIT, Sch Civil Engn & Architecture, Wuhan, Peoples R China
[3] Kubu Consultancy Pty Ltd, Pretoria, South Africa
[4] Univ Pretoria, Pretoria, South Africa
[5] Texas A&M Univ Syst, Texas A&M Transportat Inst TTI, College Stn, TX USA
[6] Univ Costa, Dept Civil & Environm Engn, Barranquilla, Colombia
关键词
Falling weight deflectometer; deflection bowl parameters; logistic model regression; pavement rehabilitation; non destructive testing; DEFLECTION BASIN;
D O I
10.1080/10298436.2020.1828586
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Presently, most of the road agencies use Non-Destructive (NDT) tools to help them prioritise pavement maintenance and rehabilitation (M&R) activities at the network level, thus optimising the limited budgetary resources. One of the most widely used NDT techniques for pavement structural evaluations, at the network level assessment, is the Falling Weight Deflectometer (FWD). Using a database comprising of a wide array of typical layer moduli and thicknesses of traditional flexible pavements, that were generated based on multiple Monte Carlo numerical simulations, as a reference datum, this study successfully developed probabilistic models that allow for analysing the condition of a flexible pavement, at the network level, from FWD surface deflection data, namely the Deflection Bowl Parameters (DBPs), to identify which layers of the pavement structure present a probability of structural failure or damage.
引用
收藏
页码:1867 / 1880
页数:14
相关论文
共 50 条
  • [31] A bilevel program for solving project scheduling problems in network level pavement management system
    Peng H.
    Chen Z.
    Sun L.
    Tongji Daxue Xuebao/Journal of Tongji University, 2010, 38 (03): : 380 - 385
  • [32] A neural network approach to detect local structural damage for wooden houses
    Ju, D.
    Hayashi, Y.
    Suzuki, Y.
    STRUCTURAL HEALTH MONITORING AND INTELLIGENT INFRASTRUCTURE, VOLS 1 AND 2, 2006, : 831 - 837
  • [33] Model to Estimate Pavement Structural Number at Network Level with Rolling Wheel Deflectometer Data
    Abdel-Khalek, Ahmed M.
    Elseifi, Mostafa A.
    Gaspard, Kevin
    Zhang, Zhongjie
    Dasari, Karthik
    TRANSPORTATION RESEARCH RECORD, 2012, (2304) : 142 - 149
  • [34] Development of structural condition index to support pavement maintenance and rehabilitation decisions at network level
    Zhang, ZM
    Claros, G
    Manuel, L
    Damnjanovic, I
    HIGHWAY PAVEMENTS AND STRUCTURES MAINENANCE AND SECURITY: MAINTENANCE, 2003, (1827): : 10 - 17
  • [35] Assessment of Use of Automated Distress Survey Methods for Network-Level Pavement Management
    Underwood, B. S.
    Kim, Y. R.
    Corley-Lay, J.
    JOURNAL OF PERFORMANCE OF CONSTRUCTED FACILITIES, 2011, 25 (03) : 250 - 258
  • [36] Advancing Pavement Safety Assessment Through the Implementation of Network Level Continuous Friction Testing
    Tetley, Simon
    Daleiden, Jerome
    Taylor, Claire
    PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON MAINTENANCE AND REHABILITATION OF PAVEMENTS, MAIREPAV-10, VOL 2, 2024, 523 : 49 - 56
  • [37] Gray and fuzzy clustering method-based on network level pavement performance assessment
    Zhang L.
    Ling J.
    Zhu Y.
    Tongji Daxue Xuebao/Journal of Tongji University, 2010, 38 (02): : 252 - 256
  • [38] Assessment of prediction ability for reduced probabilistic neural network in data classification problems
    Maciej Kusy
    Jacek Kluska
    Soft Computing, 2017, 21 : 199 - 212
  • [39] Assessment of prediction ability for reduced probabilistic neural network in data classification problems
    Kusy, Maciej
    Kluska, Jacek
    SOFT COMPUTING, 2017, 21 (01) : 199 - 212
  • [40] Intelligent Assessment of Pavement Structural Conditions: A Novel FeMViT Classification Network for GPR Images
    Liu, Zhen
    Wang, Siqi
    Gu, Xingyu
    Wang, Danyu
    Dong, Qiao
    Cui, Bingyan
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2024, : 1 - 13