Assessment and prediction of high speed railway bridge long-term deformation based on track geometry inspection big data

Wang，Yuan1; Wang，Ping2; Tang，Huiyue3,4; Liu，Xiang5; Xu，Jinhui6; Xiao，Jieling2; Wu，Jingshen1

2021-09-01

10.1016/j.ymssp.2021.107749

MECHANICAL SYSTEMS AND SIGNAL PROCESSING   影响因子和分区

0888-3270

1096-1216

This paper proposes a low-cost, data-driven approach to assess and predict bridge deformation using track inspection big data, which is primarily used for assessing track conditions. Firstly, a Bridge Deformation Assessment model with a sophisticated signal processing process is introduced to manipulate track geometry inspection data for extracting bridge-related components. Secondly, a Bridge Dynamical Deformation index (BDD index) is defined to quantify bridge deformation based on track geometry inspection data. Thirdly, the Temperature-Time-Deformation model (TTD model) is established to describe bridge deformation with respect to ambient temperature and length of service time of the bridge. Three types of TTD equations are proposed, including exponential-, hyperbolic- and linear-TTD equations. Fourthly, a track geometry inspection dataset over 2.6 years involving 563 bridge spans is applied as a case study. It is found that the BDD index changes with ambient temperature by 0.02 mm/°C on average, and increases with time by 0.2 mm/year during the 2.6-year period. Furthermore, a prediction on the amount of increase of the BDD index over the following 3 years is given with a 95% confidence level. It is expected that BDD index will increase by 0.5 mm in 2 years and 0.7 mm in 3 years according to the TTD model. Finally, the model uncertainty is discussed from data aspect and model aspect. The methods in this paper are of reference value for research topics on bridge condition evolution, rail geometry degradation and prediction-based infrastructure maintenance.

Bridge deformation assessment High speed railway Prediction Signal processing Track geometry inspection

SCI ; EI

英语

第一

WOS:000636421400007

20211010021424

Big data ; Boolean functions ; Deformation ; Forecasting ; Geometry ; Inspection ; Railroad plant and structures ; Railroad transportation ; Signal processing ; Temperature ; Uncertainty analysis

Bridges:401.1 ; Railroad Transportation, General:433.1 ; Thermodynamics:641.1 ; Railway Plant and Structures, General:681.1 ; Information Theory and Signal Processing:716.1 ; Data Processing and Image Processing:723.2 ; Mathematics:921 ; Algebra:921.1 ; Probability Theory:922.1

ENGINEERING

2-s2.0-85101849389

Scopus

1.School of System Design and Intelligent Manufacturing,Southern University of Science and Technology,Shenzhen,China
2.Key Laboratory of High-speed Railway Engineering,Ministry of Education,Chengdu,China
3.Institute of Robotics and Intelligent Manufacturing,The Chinese University of Hong Kong,Shenzhen,China
4.Shenzhen Institute of Artificial Intelligence and Robotics for Society,Shenzhen,China
5.Department of Civil and Environmental Engineering,Rutgers,The State University of New Jersey,United States
6.Department of Civil and Architecture Engineering,East China Jiaotong University,Nanchang,China

Wang，Yuan,Wang，Ping,Tang，Huiyue,et al. Assessment and prediction of high speed railway bridge long-term deformation based on track geometry inspection big data[J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING,2021,158.

Wang，Yuan.,Wang，Ping.,Tang，Huiyue.,Liu，Xiang.,Xu，Jinhui.,...&Wu，Jingshen.(2021).Assessment and prediction of high speed railway bridge long-term deformation based on track geometry inspection big data.MECHANICAL SYSTEMS AND SIGNAL PROCESSING,158.

Wang，Yuan,et al."Assessment and prediction of high speed railway bridge long-term deformation based on track geometry inspection big data".MECHANICAL SYSTEMS AND SIGNAL PROCESSING 158(2021).