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
  • [41] Experimental Verification for Cable Force Estimation Using Handheld Shooting of Smartphones
    Zhao, Xuefeng
    Ri, Kwang
    Wang, Niannian
    JOURNAL OF SENSORS, 2017, 2017
  • [42] Transportation Mode Detection by Using Smartphones and Smartwatches with Machine Learning
    Hasan, Raed Abdullah
    Irshaid, Hafez
    Alhomaidat, Fadi
    Lee, Sangwoo
    Oh, Jun-Seok
    KSCE JOURNAL OF CIVIL ENGINEERING, 2022, 26 (08) : 3578 - 3589
  • [43] Real-Time Estimation of Distance Traveled by Cart Using Smartphones
    Phuc Huu Truong
    Kim, Sung-Il
    Jeong, Gu-Min
    IEEE SENSORS JOURNAL, 2016, 16 (11) : 4149 - 4150
  • [44] MMLung: Moving Closer to Practical Lung Health Estimation using Smartphones
    Mosuily, Mohammed
    Welch, Lindsay
    Chauhan, Jagmohan
    INTERSPEECH 2023, 2023, : 2333 - 2337
  • [45] Hyperspectral Anomaly Detection via a Sparsity Score Estimation Framework
    Zhao, Rui
    Du, Bo
    Zhang, Liangpei
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2017, 55 (06): : 3208 - 3222
  • [46] IRT-SD-SLE: An Improved Real-Time Step Detection and Step Length Estimation Using Smartphone Accelerometer
    Sadhukhan, Pampa
    Mazumder, Saptadipa
    Chowdhury, Chandreyee
    Paiva, Sara
    Das, Pradip K.
    Dahal, Keshav
    Wang, Xinheng
    IEEE SENSORS JOURNAL, 2023, 23 (24) : 30858 - 30868
  • [47] Evaluation framework for smartphone-based road roughness index estimation systems
    Yu, Qiqin
    Fang, Yihai
    Wix, Richard
    INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING, 2023, 24 (01)
  • [48] Fetal brain age estimation and anomaly detection using attention-based deep ensembles with uncertainty
    Shi, Wen
    Yan, Guohui
    Li, Yamin
    Li, Haotian
    Liu, Tingting
    Sun, Cong
    Wang, Guangbin
    Zhang, Yi
    Zou, Yu
    Wu, Dan
    NEUROIMAGE, 2020, 223
  • [49] Unsupervised anomaly detection for network traffic using artificial immune network
    Shi, Yuanquan
    Shen, Hong
    NEURAL COMPUTING & APPLICATIONS, 2022, 34 (15) : 13007 - 13027
  • [50] Estimating the International Roughness Index of asphalt concrete pavement by response-based testing equipment and intelligent algorithms
    Xu, Shuzhan
    Liu, Quansheng
    Bo, Yin
    Chen, Zitao
    Wang, Changbai
    CONSTRUCTION AND BUILDING MATERIALS, 2024, 433