Pavement roughness index estimation and anomaly detection using smartphones

被引:28
|
作者
Yu, Qiqin [1 ]
Fang, Yihai [1 ]
Wix, Richard [2 ]
机构
[1] Monash Univ, Dept Civil Engn, Melbourne, Australia
[2] Australian Rd Res Board, Melbourne, Australia
基金
澳大利亚研究理事会;
关键词
Pavement roughness; Roughness index; Surface distress; Smartphone; Algorithm; VEHICLE RESPONSES; FREQUENCY-DOMAIN; SYSTEM; RECONSTRUCTION; ACCELEROMETER; QUALITY; ROADS;
D O I
10.1016/j.autcon.2022.104409
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The prevalence of smartphones among vehicle drivers presents exciting opportunities in assessing pavement roughness in a more efficient and cost-effective manner, compared with using conventional instruments. This paper describes the body of knowledge in smartphone-based roughness assessment, reports knowledge gaps and casts light on future research directions. First, a systematic literature search found 192 academic publications in relevant fields. These works were critically reviewed with regard to sensor selection, pre-processing methods, and assessment algorithms. Special attention was given to practical factors that are expected to affect the accuracy and robustness of smartphone-based methods, including data collection speed, vehicle type, smartphone specifications and mounting configuration. Findings from this research are expected to provide a thorough understanding of the potentials and limitations of smartphone-based roughness assessment methods and inform future research and practices in this domain.
引用
收藏
页数:20
相关论文
共 50 条
  • [31] Pavement roughness index impact for specific wavebands and causative factors
    Bae, Abraham
    Stoffels, Shelley M.
    KSCE JOURNAL OF CIVIL ENGINEERING, 2017, 21 (05) : 1764 - 1773
  • [32] Applicability of the international roughness index as a predictor of asphalt pavement condition
    Park, Kyungwon
    Thomas, Natacha E.
    Lee, K. Wayne
    JOURNAL OF TRANSPORTATION ENGINEERING, 2007, 133 (12) : 706 - 709
  • [33] Gray system model for estimating the pavement international roughness index
    Jiang, Y
    Li, S
    JOURNAL OF PERFORMANCE OF CONSTRUCTED FACILITIES, 2005, 19 (01) : 62 - 68
  • [34] International standards organization-compatible index for pavement roughness
    Papagiannakis, AT
    Raveendran, B
    PAVEMENT MANAGEMENT AND MONITORING OF TRAFFIC AND PAVEMENTS, 1998, (1643): : 110 - 115
  • [35] Decision Model of Pavement Maintenance Based on International Roughness Index
    Zhi S.
    Hu W.
    Guo Y.
    Lan H.
    Jing H.
    Journal of Engineering Science and Technology Review, 2023, 16 (05) : 45 - 51
  • [36] The predicted model of international roughness index for drainage asphalt pavement
    Chen, Chien-Ta
    Hung, Ching-Tsung
    Chou, Chien-Cheng
    Chiang, Ziping
    Lin, Jyh-Dong
    ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS, PROCEEDINGS: WITH ASPECTS OF THEORETICAL AND METHODOLOGICAL ISSUES, 2008, 5226 : 937 - +
  • [37] Pavement roughness index impact for specific wavebands and causative factors
    Abraham Bae
    Shelley M. Stoffels
    KSCE Journal of Civil Engineering, 2017, 21 : 1764 - 1773
  • [38] Estimation of Road Roughness Condition from Smartphones under Realistic Settings
    Douangphachanh, Viengnam
    Oneyama, Hiroyuki
    2013 13TH INTERNATIONAL CONFERENCE ON ITS TELECOMMUNICATIONS (ITST), 2013, : 433 - 439
  • [39] Multi-index probabilistic anomaly detection for large span bridges using Bayesian estimation and evidential reasoning
    Xu, Xiang
    Forde, Michael C.
    Ren, Yuan
    Huang, Qiao
    Liu, Bin
    STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL, 2023, 22 (02): : 948 - 965
  • [40] Intelligent Detection of Asphalt Pavement Roughness with kNN Method
    Zeng, Jingxiang
    Zhang, Jinxi
    Cao, Dandan
    Wu, Yang
    Chen, Guanghua
    Huanan Ligong Daxue Xuebao/Journal of South China University of Technology (Natural Science), 2022, 50 (03): : 50 - 56