Data driven condition assessment of railway infrastructure

2021 ◽  
pp. 693-693
Author(s):  
C. Hoelzl ◽  
V. Dertimanis ◽  
E. Chatzi ◽  
D. Winklehner ◽  
S. Züger ◽  
...  
Keyword(s):  
Condition Assessment ◽  
Data Driven ◽  
Railway Infrastructure

scholarly journals Artificial Intelligence Based Structural Assessment for Regional Short- and Medium-Span Concrete Beam Bridges with Inspection Information

2021 ◽  
Vol 13 (18) ◽  
pp. 3687
Author(s):  
Ye Xia ◽  
Xiaoming Lei ◽  
Peng Wang ◽  
Limin Sun
Keyword(s):  
Artificial Intelligence ◽  
Structural Characteristics ◽  
Highway Bridges ◽  
Condition Assessment ◽  
Transport Infrastructure ◽  
Data Driven ◽  
Bridge Inspection ◽  
Structural Deterioration ◽  
Data Driven Approach ◽  
The Impact

The functional and structural characteristics of civil engineering works, in particular bridges, influence the performance of transport infrastructure. Remote sensing technology and other advanced technologies could help bridge managers review structural conditions and deteriorations through bridge inspection. This paper proposes an artificial intelligence-based methodology to solve the condition assessment of regional bridges and optimize their maintenance schemes. It includes data integration, condition assessment, and maintenance optimization. Data from bridge inspection reports is the main source of this data-driven approach, which could provide a substantial amount og condition-related information to reveal the time-variant bridge condition deterioration and effect of maintenance behaviors. The regional bridge condition deterioration model is established by neural networks, and the impact of the maintenance scheme on the future condition of bridges is quantified. Given the need to manage limited resources and ensure safety and functionality, adequate maintenance schemes for regional bridges are optimized with genetic algorithms. The proposed data-driven methodology is applied to real regional highway bridges. The regional inspection information is obtained with the help of emerging technologies. The established structural deterioration models achieve up to 85% prediction accuracy. The obtained optimal maintenance schemes could be chosen according to actual structural conditions, maintenance requirements, and total budget. Data-driven decision support can substantially aid in smart and efficient maintenance planning of road bridges.

A hybrid predictive methodology for head checks in railway infrastructure

2021 ◽  
pp. 095440972199361
Author(s):  
Annemieke Meghoe ◽  
Ali Jamshidi ◽  
Richard Loendersloot ◽  
Tiedo Tinga
Keyword(s):  
Hybrid Method ◽  
Data Driven ◽  
Evolution Analysis ◽  
Railway Infrastructure ◽  
Model Defect ◽  
The Us ◽  
Defect Evolution ◽  
Different Characteristics ◽  
Future Condition ◽  
Rail Surface Defect

This paper presents a hybrid method to assess the rail health with the focus on a specific type of rail surface defect called head check. The proposed method uses physics-based and data-driven models in order to model defect initiation and defect evolution on a rail for a given rail traffic tonnage. Ultrasonic (US) and Eddy Current (EC) defect detection measurements are used to provide Infrastructure Managers (IMs) with insight in the current rail condition. The defect initiation results obtained from the first part of the hybrid method which consists of the physics-based model is successfully validated with the EC measurements. Furthermore, the US and EC measurements are utilized to derive a data-driven model for defect evolution. Finally, a set of robust and predictive Key Performance Indicators (KPIs) are proposed to quantify the future condition of the rail based on different characteristics of rail health resulting from the defect initiation and defect evolution analysis.

Data-driven estimation of deterioration curves: a railway supporting structures case study

2021 ◽  
pp. 1-15
Author(s):  
Saviz Moghtadernejad ◽  
Gérald Huber ◽  
Jürgen Hackl ◽  
Bryan T Adey
Keyword(s):  
Markov Models ◽  
Time History ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Railway Network ◽  
Condition Rating ◽  
Use Of Data ◽  
Railway Infrastructure ◽  
Changes Over Time ◽  
Supporting Structures

A significant portion of railway network income is spent on the maintenance and restoration of the railway infrastructure to ensure that the networks continue to provide the expected level of service. The execution of the interventions – that is, when and where to perform maintenance or restoration activities, depends on how the state of the infrastructure assets changes over time. Such information helps ensure that appropriate interventions are selected to reduce the deterioration speed and to maximise the effect of the expenditure on monitoring, maintenance, repair and renewal of the assets. Presently, there is an explosion of effort in the investigation and use of data-driven methods to estimate deterioration curves. However, real-world time history data normally includes measurement of errors and discrepancies that should not be neglected. These errors include missing information, discrepancies in input data and changes in the condition rating scheme. This paper provides solutions for addressing these issues using machine learning algorithms, estimates the deterioration curves for railway supporting structures using Markov models and discusses the results.

scholarly journals Detecting Embankment Instability Using Measurable Track Geometry Data

2020 ◽  
Vol 5 (3) ◽  
pp. 29
Author(s):  
David Kite ◽  
Giulia Siino ◽  
Matthew Audley
Keyword(s):  
Asset Management ◽  
System Performance ◽  
Data Driven ◽  
Track Geometry ◽  
Track Maintenance ◽  
Management Scenario ◽  
Railway Infrastructure ◽  
The World ◽  
Railway System ◽  
Data Driven Decisions

The British railway system is the oldest in the world. Most railway embankments are aged around 150 years old and the percentage of disruption reports that feature them is frequently higher than other types of railway infrastructure. Remarkable works have been done to understand embankment deterioration and develop asset modelling. Nevertheless, they do not represent a sufficient way of managing assets in detail. As a result, reactive approaches combined with proactive ones would improve the whole asset management scenario. To guarantee good system performance, geotechnical asset management (GAM) aims to reduce uncertainty through informed, data driven decisions and optimisation of resources. GAM approaches are cost sensitive. Thus, data driven approaches that utilize existing resources are highly prized. Track geometry data has been routinely collected by Network Rail, over many years, to identify track defects and subsequently plan track maintenance interventions. Additionally, in 2018 Network Rail commissioned AECOM to undertake a study, described in this paper, to investigate the use of track geometry data in the detection of embankment instabilities. In this study, track geometry data for over 1800 embankments were processed and parameters offering the best correlation with embankment movements were identified and used by an algorithm to generate an embankment instability metric. The study successfully demonstrated that the instability of railway embankments is clearly visible in track geometry data and the metric gives an indication of the worsening of track geometry, that is likely due to embankment instability.

scholarly journals A Hybrid Multi-Criteria Decision Making Model for Defect-Based Condition Assessment of Railway Infrastructure

2021 ◽  
Vol 13 (13) ◽  
pp. 7186
Author(s):  
Laith El-khateeb ◽  
Eslam Mohammed Abdelkader ◽  
Abobakr Al-Sakkaf ◽  
Tarek Zayed
Keyword(s):  
Case Studies ◽  
Relative Weight ◽  
Analytic Network Process ◽  
Structural Condition ◽  
Condition Assessment ◽  
Geometrical Condition ◽  
Track Geometry ◽  
Railway Infrastructure ◽  
Network Process ◽  
Order Preference

The condition of railway infrastructure, such as rails, ballasts and sleepers, should always be monitored and analyzed to ensure ride safety and quality for both passengers and freight. It is hard to assess the condition of railway infrastructure due to the existence of various components. The existing condition assessment models are mostly limited to only assess track geometry conditions and structural condition of the railway infrastructure. Therefore, the present research develops a defect-based structural and geometrical condition model of railway infrastructure. The defects of each component are identified and examined through literature and experts in the field. Two main inputs are used to develop the model: (1) the relative weight of importance for components, defects and their categories and (2) defects severities. To obtain the relative weights, the analytic network process (ANP) technique is adopted. Fuzzy logic is used to unify all the different defect criteria and to interpret the linguistic condition assessment grading scale to a numerical score. Hence, the technique for order preference by similarity to ideal Solution (TOPSIS) is used to integrate both weights and severities to determine the railway infrastructure condition. The developed model gives a detailed condition of the railway infrastructure by representing a three-level condition state, for defect categories, components and an overall railway infrastructure. The developed model is implemented to five case studies from Ontario, Canada. The developed model is validated by comparing its results with the real case studies results, which shows similar results, indicating the robustness of the developed model. This model helps in minimizing the inaccuracy of railway condition assessment through the application of severity, uncertainty mitigation and robust aggregation

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