Case Study of Railway Infrastructure Modelling Oz Assignments

Case Study of Railway Infrastructure Modelling Oz Assignments

Case Study of Railway Infrastructure Modelling Oz Assignments

Overview:

Railways is one of the oldest developed system of transportation. Over the centuries, the railway infrastructure sector has had immense evolution in terms of freight sizes and capacity, technology, track networks, distances, modes of energy and functionalities. In Australia, the railways were first built by private companies of New South Wales and South Australia in the mid-19th century. Since 1920, the federal government of Australia has substantially funded the Commonwealth rail networks which in 1997 was privatized and set up as the Australian national railway corporation (ANRC). The Australian rail track corporation (ARTC) was then established in 1997 which was responsible for developing business and maintaining the railway tracks all over the country.

As there has been a tremendous increase in the development of rail infrastructure for transportation of freight and people, maintaining the infrastructure has become one of fundamental works. For proper functionality, safe transportation and safeguarding the economy it is essential to understand the maintenance and operational procedures [ CITATION Krz15 \l 1033 \m Kie10]. This research is based on the key factors associated with the rail infrastructure; deterioration modelling and condition assessment. As learnt from history, a well consolidated infrastructure system is one of the key contributors for economic growth and development of civilizations.

Problem Statement:

Rail infrastructure, accounts for more than one half of the freight activities in Australia, which is a 36% increase since the onset of the 21st century. Australian railways carried over 1 billion tonnes of freight in 2012-2013 which is an increase of 57% since 2007-2008, and in 2013 Australian heavy and light rail operators provided 850 million passenger trips, which is equivalent to 2.3 million passenger trips per day (Australian rail association). Maintaining the adequate infrastructure for the ever-increasing passengers and freight rail transportations has been a challenge in creating a complex and competitive rail industry. Billions of dollars have been spent on rail infrastructure for works such as upgradations and maintenance to the existing system of rail. The rail infrastructure in a composite system made up of numerous individual components of variable materials which have often been associated with defects and deterioration mechanisms. However, the condition assessment models for avoiding damages are limited which creates a challenge for performing the maintenance and upgradation works. Difference variables such as axle loads, train speeds, soil conditions, etc. are responsible towards affecting the integrity of the rail infrastructure. However well interpreted or expensive the assessments are to interpret the condition, it is a difficult and time consuming process which also varies due to different conditions and types of materials used in the rail network.

Research Objectives:

This research project’s primary focus is on developing and publishing a comprehensive railway assessment model that responses to the following:

1. Analyzing and documenting the factors affecting the rail infrastructure deterioration.
2. Assisting appropriate strategies based on the data collected by deterioration modelling of rail infrastructure.
3. Identification of the factors that govern the condition assessment of rail infrastructure and documenting the suitable inferences.
4. Developing defect-based models for condition assessment of rail infrastructure.
5. Building a condition deteriorating grading scale for individual railway components based on data collected from condition assessment and deterioration modelling.

1.4 Research Methodology:

The aim of this research is to undergo the process of compiling a set of analysis and inferences that are of publishable quality. In order to develop this research, literature of previously developed models, condition assessment reports published by railway experts, deterioration mechanisms for rail infrastructure, tools/techniques available for condition assessment and deterioration modelling are needed to be reviewed thoroughly.

In order to set course for developing and publishing this research, there is need to identify a path according to which this research will be carried forward. Having recognized the study materials and reference reports that are to be evaluated, the following methodology has been formulated.

Literature of articles, reports, thesis and journals on railway infrastructure is reviewed0 while setting the objectives and aims as milestones for this research.

Hierarchy of defects and deterioration mechanisms for rail components are determined.

Condition assessment of rail infrastructure and its individual components are described.

The severity levels of the deterioration mechanisms, defects and condition assessment grading scales are detailed on the basis of data gathered.

Examples of deterioration modelling and condition assessment of rail infrastructure are explained as case studies.

The inferences from case studies is combined with the research in order to detail defect-based model for condition assessment of rail infrastructure

Chapter 2: Review Of Previous Research Work

There are a number of research works that have been carried out in the investigation and modelling of the deterioration of railway infrastructure. The research in this area of condition assessment allows for the evaluation of the degradation process of the rail networks. Two research works stand out among the research works.

The first is the research into the deterioration of the rail network in Montreal [ CITATION Lai17 \l 1033 ]. This research applied the weighted sum technique in order to model the deterioration. The Analytic Network Process (APN) produces efficient weights for the weighted sum model by considering the relationship between the infrastructure aspects [ CITATION Lai17 \l 1033 ].

The second is the research on the development of a deterioration probabilistic model [ CITATION Zak12 \l 1033 ]. The research cites the case of the Lorestan railway. The study develops a model that applies two purely statistical techniques to produce a model with a binary response. The applied techniques are Markov’s Model which uses the Transition Probabilities Matrix [ CITATION Sha11 \l 1033 \m ONe13] and Hazard Rate Function which is associated with frequency of failure [ CITATION Nat12 \l 1033 \m How08].

The research in this paper builds on the techniques in the above works. Emphasis is however given to the factors considered for the analysis process. This research allows for a broader inclusion of factors in the model development process. This thereby enables the creation of a more complete picture of the deterioration of railway infrastructure in Australia.

Chapter 3: methodology

3.1. Overview of data collection

This research will rely on three sources of data; two secondary sources and one primary source. The first secondary source will be previously done work on deterioration and condition assessment of railway infrastructure. These research works will provide a comparative view for the eventual model developed in this paper.

The information from the previously done work will also give an idea on how to develop the most reliable model for predicting the deterioration of railway infrastructure as well as assessing its condition.

The research works will work as gauges for the accuracy of the data and analysis done in this research paper. The error in the analysis can be evaluated by considering analysis and conclusions arrived at in previous works.

The data collected in previous works also form a reliable pool of data for consideration in present research.

The second secondary source will be the data on the railway infrastructure in Australia. This will be data on the metrics of the railway infrastructure. These railway infrastructure parameters are; Track Geometry, Ballast, Sleepers, Rails, Speeds, Load Capacity and Weather Conditions.

These parameters are going to enable an understanding of the critical factors of the railways infrastructures that are both subject to deterioration and causes of deterioration. The data on these parameters is going to be collected from records kept by the relevant agencies, authorities and organizations.

The primary source of the data in this research will be an online survey that will involve a short questionnaire. The online survey will help in getting a sense of both the degradation and maintenance of the railway infrastructure in Australia. The focus of the online survey will be on the consumer end rather than on the experts end. This will be a new approach to analysis of the deterioration of railway infrastructure.

3.2. Resources used for the researce

Resources will be mainly used in the collection of data from two out of the three data sources. The collection of data from the work previously done on deterioration of railway infrastructure will be the exception.

The primary data source will require the development of a questionnaire. The questionnaire will be made available for online filling and submission. The aim will be to target the users of the Australian railway network to fill the questionnaires.

The secondary data source that will require resources to obtain data will be the railway infrastructure parameters. The resources required for the parameters will be as follows:

1. Track Geometry: In order to collect information on the track geometry, Track Geometry Cars will be used. These are vehicles that move on the rails and collect information on the track geometry [ CITATION Dep08 \l 1033 ]. The cars are high speed moving and operate in such a way that they don’t interfere with the operations in the rail network [ CITATION Tra09 \l 1033 ]. These cars will provide this research with information on the condition of the track geometry of the railways.

2. Ballast: The information on the ballast will be collected using three methods: visual inspection, digital inspection and the use of the LIDAR technology. The visual and digital inspection will work on an almost similar way. Observations will be made on the condition of the ballast either physically and in person or from recorded videos and images of the rails. The LIDAR (in full: Laser Image Detection and Ranging) Technology on the other hand operates by shooting laser light into the ballast and taking measurements of the light that reflects back [ CITATION Her09 \l 1033 \m Sha08 \m Voo10]. This enables the LIDAR to get information on the quality and condition of the ballast.

3. Sleepers: The information on the Sleepers is also collected using three methods: visual inspection, digital inspection and the use of the ultrasonic energy technology. The visual and digital inspection will work on an almost similar way. Observations will be made on the condition of the sleeper either physically and in person or from recorded videos and images of the rails. The ultrasonic technology operates by shooting ultrasonic energy onto the sleepers and taking measurements of the energy reflected back [ CITATION Mid07 \l 1033 ]. These measurements allow for information on the type and condition of the sleepers to be collected.

4. Rails: Similar to the sleepers, information on the rails is collected using three methods: visual inspection, digital inspection and the use of the ultrasonic energy technology. The visual and digital inspection will work on an almost similar way. Observations will be made on the condition of the rails either physically and in person or from recorded videos and images of the rails. The ultrasonic technology operates by shooting ultrasonic energy onto the rails and taking measurements of the energy reflected back [ CITATION Mid07 \l 1033 ]. These measurements allow for information on the type and condition of the rails to be collected.

5. Speeds: The average speeds of the trains is going to be considered for this parameter. Information will be collected from the agencies operating the railway systems on the average speeds of the train that use a particular rail network. This will provide information on the speeds that the rail is regularly subjected to.

6. Load Capacity: The average load capacity of the trains is going to be considered for this parameter. Information will be collected from the agencies operating the railway systems on the average load capacity of the train that use a particular rail network. Both the passenger and freight trains will be considered in computing the average load capacity. This will provide information on the load capacity that the rail is regularly subjected to.

7. Weather Condition: The dominant weather condition along a rail network will be considered for this parameter. This information will be collected from the relevant weather agencies throughout Australia. The information will be an indicator on the type of weather and climatic conditions that a rail network is exposed to.

3.3. development of models

3.3.1. classification of parameters

The diagram below shows the parameters that are going to be considered for the development of the deterioration of the railway infrastructure model in this research.

PARAMETERS(for the deterioration of railway infrastructure model)

extrinsic parameters

intrinsic parameters

Figure 1: Broad Classification of Railway Infrastructure Parameters

The parameters for the railway infrastructure can be grouped into two main groups as shown in the diagram in Figure 1 above. The intrinsic parameters can be described as the parameters that are in born to the rail network itself. They can be termed as the internal factors of the rail system. These parameters are also static and hence are not expected to be varying for the analysis of developing the deterioration model. However, these parameters can vary due to maintenance activities.

The other group of the railway infrastructure parameters is the extrinsic parameters. These parameters are the external factors that influence the deterioration of the rail network. These are factors can be either controllable or uncontrollable. The controllable can be said to be adjustable and therefore manageable for the purpose of rail network maintenance. The uncontrollable are however neither adjustable nor manageable.

Figure 2: Breakdown of Intrinsic Parameters of Railway Infrastructure

EXTRINSIC PARAMETERS

UNCONTROLLABLE EXTRINSIC PARAMETERS

CONTROLLABLE EXTRINSIC PARAMETERS

WEATHER CONDITIONS

Figure 3: Breakdown of Extrinsic Parameters of Railway Infrastructure

Figure 2 and Figure 3 above show the breakdown of each of the categories of the parameters of railway infrastructure.

online survey

This research will pose a simple to fill three question consumer questionnaire as shown below:

consumer questionnaire

How long have you been using the railway network in Australia?

Which railway network do you use most frequently?

How would you describe the state of the railway network on a scale of 1 to 5? (where 5 is perfect)

The data from the online survey will be categorized according to the railway network and then sample collected on the responses that had the highest number years of railway network usage.

This will enable data to be collected for the different railway networks separately, and at the same time have reliable data from individuals that have used the rail network system over the longest period of time.

Liker Scale

This research is going to make use of the Likert Scale in order to grade the various parameters of the railway. The Likert Scale is a grading technique for questions designed to evaluate the strength of an attribute [ CITATION Nao11 \l 1033 \m Nor10]. The Likert Scale presents an ordinal measure of an attribute [ CITATION Rei08 \l 1033 ].

This research will apply the Likert Scale by posing the question, and then use the information available to provide an answer on the strength of an attribute.

The Likert Scale will first be applied in determining the weights of the various data sources. The question posed will be on the level of importance of the data source to development of the deterioration model for the railway infrastructure. This will need the use of a second questionnaire for a focus group. The focus group may be made up of five peers or five experts in the field of deterioration modelling and preferably railway infrastructure deterioration modelling.

The second questionnaire will be of the format below:

focus group questionnaire (tick where applicable)

How would you describe the importance of consumer opinion on the development of a deterioration model for the Australian railway infrastructure?

Extremely Important _

Important _

Averagely Important _

Relatively Important _

Not Important _

How would you describe the importance of rail design on the development of a deterioration model for the Australian railway infrastructure?

Extremely Important _

Important _

Averagely Important _

Relatively Important _

Not Important _

How would you describe the importance of external factors on the development of a deterioration model for the Australian railway infrastructure?

Extremely Important _

Important _

Averagely Important _

Relatively Important _

Not Important _

The importance of the online survey, intrinsic parameters and extrinsic parameters are evaluated in questions one, two and three respectively in the Focus Group Questionnaire.

The values assigned for the 5-point Likert Scale used for the Focus Group Questionnaire above are as follows:

RESPONSE

VALUE

Not Important

0

Relatively Important

1

Averagely Important

2

Important

3

Extremely Important

4

Table 1: Likert Scale Values for Focus Group Questionnaire

The average for the responses to the Focus Group Questionnaire will then be used as the weights for the data sources.

We will then apply the Likert Scale in determining the state or condition of the rail by considering the data collected on the intrinsic parameters. All the four parameters will be subject to the same Likert Scale. The aim will be to answer the question below:

How would you describe the condition of “parameter x” on “the given” Australian railway network?

Perfect _

Good _

Average _

Poor _

Deplorable _

The “parameter x” would represent the various intrinsic parameters while “the given” represents the specific Australian rail network being observed.

The values assigned for the 5-point Likert Scale used for the intrinsic parameters above are as follows:

RESPONSE

VALUE

Deplorable

0

Poor

1

Average

2

Good

3

Perfect

4

Table 2: Likert Scale Values for Intrinsic Parameters

Each of the three parameter will have separate evaluation and Likert Scale as follows:

For the Speed, the aim will be to answer the question below:

How would you describe the average speed on “the given” Australian rail network?

Very Fast _

Fast _

Average _

Poor _

Very Slow _

The “the given” represents the specific Australian rail network design being observed. The range for the speed will be divided into five parts to accommodate the Likert Scale. The values assigned for the 5-point Likert Scale used for the speed parameter above are as follows:

RESPONSE

VALUE

Very Fast

0

Fast

1

Average

2

Slow

3

Very Slow

4

Table 3: Likert Scale Values for Speed Parameter

For the Load Capacity, the aim will be to answer the question below:

How would you describe the average load capacity on “the given” Australian rail network?

Very High _

High _

Average _

Low_

Very Low _

The “the given” represents the specific Australian rail network being observed. The range for the load capacity will be divided into five parts to accommodate the Likert Scale. The values assigned for the 5-point Likert Scale used for the load capacity parameter above are as follows:

RESPONSE

VALUE

Very High

0

High

1

Average

2

Low

3

Very Low

4

Table 4: Likert Scale Values for Load Capacity Parameter

For the Weather Condition, the aim will be to answer the question below:

How would you describe the general weather condition on “the given” Australian rail network?

Balanced _

Cold and Dry _

Hot and Dry _

Cold and Wet _

Hot and Wet _

The “the given” represents the specific Australian rail network being observed. The values assigned for the 5-point Likert Scale used for the weather condition parameter above are as follows:

RESPONSE

VALUE

Hot and Wet

0

Cold and Wet

1

Hot and Dry

2

Cold and Dry

3

Balanced

4

Table 5: Likert Scale Values for Weather Condition Parameter

Grading scale and critic

Weighted sum model using likert scale

We can assign the weights for the various data sources as follows:

DATA SOURCE

WEIGHT

Online Survey

WO

Intrinsic Parameters

WI

Extrinsic Parameters

WE

Table 6: Weights for Data Sources

The responses from the Consumer Questionnaire on the opinion on the state of the rail network will be averaged for the specific rail network being observed. This will give a single value on the Likert Scale for the consumer opinion of that specific rail network say RS.

Thus,

The consumer opinion will be computed as:

……….. Equation 1

For the intrinsic parameters, say the results are given as follows;

IR for the rail condition.

IS for the sleepers’ condition.

IB for the ballast quality and condition.

IT for the track geometry condition.

Thus,

The intrinsic parameters will be computed as:

……….. Equation 2

For the extrinsic parameters, say the results are given as follows;

ES for the average speed.

EL for the average load capacity.

EW for the weather condition.

Thus,

The extrinsic parameters will be computed as:

Hence the aggregate score for the condition of a specific rail network would be computed summing the three equations above as follows:

The lowest possible score for the model above would occur for when the weights and parameters register the lowest scores on the 5-point Likert Scale which is 0. Thus, the lowest possible score in the model would be:

This value would represent the highest level of deterioration of a rail network.

The highest possible score for the model above would occur for when the weights and parameters register the highest scores on the 5-point Likert Scale which is 4. Thus, the highest possible score in the model would be:

This value would represent the lowest level of deterioration of a rail network.

Therefore the resultant grading scale for the model above will be:

SCORE

CONDITION

0-25.6

Highly Risky

25.7-51.2

Risky

51.3-76.8

Relatively Safe

76.9-102.4

Safe

102.5-128

Very Safe

Table 7: Grading Scale for Model

Chapter 4: Defect Based Models For Condition Assessement

CASE STUDY

RESEARCH WORK

MODEL USED

DESCRIPTION

Case Study of Montreal Rail Network

[ CITATION Lai17 \l 1033 ]

Weighted Sum Model (By use of Analytic Network Process Technique)

The research in this case study applies the use of the ANP (Analytic Network Process) Technique.

This Techniques is applied in order to generate the weights of the various components of the railway infrastructure. The purpose for using the ANP Technique, other than to generate the weights, was to account for the inter-relationship that may exist between the components [ CITATION Saa09 \l 1033 ].

The generated weights were then applied to develop the eventual model for the deterioration of railway infrastructure in the case of the Montreal Rail Network.

Case Study of Lorestan Railway

[ CITATION Zak12 \l 1033 ]

Deterioration Probabilistic Model

For the case study for the Lorestan Railway, the research applies a statistical approach. This approach applied the Markov and Semi-Markov model together with the hazard function to develop the final Deterioration Probabilistic model. The aim of the research was to produce a model with a binary output, that is, 0 for good condition railway infrastructure and 1 for poor condition infrastructure.

Table 8: Summary of Defect Based Condition Assessment Models

MODEL

GRADING

DRAWBACKS

Weighted Sum Model (By use of Analytic Network Process Technique)

E1 and E2 for emergency.

P1, P2, P3 for Priority.

N for Normal

A lot of emphasis is placed on the internal factors, that is, the components of the rail itself.

Little to no attention is given to the external factors that contribute to the deterioration of rail networks.

No data is collected on the consumer end.

Deterioration Probabilistic Model

0 for poor condition.

1 for good condition.

The model is too statistical and lacks a component of logical approach.

The model output or response is far too narrow. The output is either good or bad. This does not give room for other conclusions such as the extent to which a rail network is good or bad.

No data is collected on the consumer end.

Table 9: Grading and Drawbacks of Models

MODEL

CHANGES PROPOSED

IMPLEMENTATION

Weighted Sum Model (By use of Analytic Network Process Technique)

More attention to be given to external factors that contribute to deterioration of rail networks.

Consumer input also considered in the model development.

Collection of data on the external (non-intrinsic) factors contributing to deterioration of rail networks.

Collection of data from commuters that use the rail network system.

Deterioration Probabilistic Model

Provision of more options from the response variable to enable a non-binary evaluation.

Consumer input also considered in the model development.

Re-development of the model to ensure that the response is a categorical output as opposed to a binary output. This will give more options for evaluation of the rail network.

Collection of data from commuters that use the rail network system.

Table 10: Changes Proposed and Implementation

Pros

Cons

Takes into account the external factors that contribute to the deterioration of rail networks.

Collects and factors in data from the consumers, that is, the commuters that use the rail network systems.

Applies a mixed logical and statistical approach by using the Weighted Sum and Likert Scale together.

All the data collected is transformed into ordinal data entries, this may make other analysis, especially parametric statistics analysis, impossible.

Table 11: Pros and Cons of Weighted Sum Model Using Likert Scale

Chapter 5: Improvements To Existing Condition Assessment Approaches

There are two main improvements that can be made to the existing condition assessment approaches. These improvements are:

Inclusion of a broader range of factors that are to be assessed. This give the model reliability and efficiency. It ensures that all aspects of the process have been covered by the model that has been developed. Leaving other factors out of the modelling process leaves a gap in the prediction, which in turn leads to an inaccurate model.

Inclusion of the consumer aspect into the model. Incorporating the consumer aspect of the process also increases the accuracy of the model. The consumer aspect acts as a control especially for instances when the model is being used to compare several processes.

Chapter 6: Recommendations And Conclusions

From the research in this paper we can conclude that:

Many of the existing models for the deterioration of railways infrastructure do not put into consideration the consumer opinion.

Most of the existing models place emphasis on the intrinsic factors rather than the entire range of factors.

Therefore the recommendations would be giving consideration to the consumer opinion during the modelling process, as well as focusing on both the extrinsic and intrinsic factors that contribute to railway infrastructure deterioration.

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