Railway Track Quality Assessment and Related Decision Making

Stasha Jovanovic

Delft University of Technology,

Faculty of Civil Engineering & Geosciences,

Road and Railway Engineering Group,

The Netherlands

ABSTRACT:

Maintenance and renewal (M&R) of railway networks require significant investments. For example the

annual expenditure for the Dutch Infra Provider “ProRail” permanent way (3,500km of tracks) amounts to

about EUR (€€ ) 180 million (price level 2000) (1). It is completely clear that even marginal improvements in

the efficiency of the Maintenance Management can yield large absolute savings.

In order to keep the Railway Infrastructure (RI) in the satisfactory condition, the manner in which this

condition of every single RI element changes, must be properly understood. Understanding this change in

condition in fact means understanding the behavior of RI objects, which paves the way towards predicting

it. In order to relate the observed (captured) behavior in the past with the predicted behavior in the future,

so called “deterioration models” are needed.

The research presented in this paper describes a generic/universal deterioration model that was

developed specifically so that it would be flexible enough to be applicable to any parameter’s

deterioration, yet powerful and flexible enough to accurately represent/fit various condition behaviors.

Developing such a generic deterioration model and empowering it with LCC and numerical optimization

techniques, and incorporating them all in a suitable, powerful yet flexible, Track Maintenance

Management System (TMMS) or full-scale Asset Management System (AMS), allows Railways to perform

true long-term simulations of the track behavior, balancing effectively achieved quality with the costs of

M&R works, inspections and other consequences like traffic disruptions, availability, etc., enabling

significant cost-savings.

BASIC DATA NEEDED FOR PREDICTION AND PLANNING

Track Maintenance Management Systems (TMMS) logically represent constitutive part of Railway Asset

Management Systems (AMS). However, until Railway AMS become sufficiently developed and fully

accepted as a concept, TMMS will exist more as stand-alone systems. Even as stand-alone, if designed

properly, they represent invaluable tools for any track and/or infrastructure manager. In order to properly

manage track maintenance a vast amount of data is needed. Types of data to be collected for computer-

aided TMMS are summarized in Figure 1.

However, with such a big diversity of information frequently changing along thousands of kilometers of

railway networks, a question arises as to how effectively to utilize all this information in order to

understand and model the behavior of railway infrastructure assets. The answer to this question lies in the

Segmentation process, by which the linear infrastructure is divided into segments, and all above-

mentioned information is aggregated and associated to them. Segments’ lengths typically differ between

different infrastructure objects (e.g. track and overhead line wire), in order to better conform both to the

respective deterioration patterns as well as to the established maintenance practices. For example, for

track, in most cases these lengths historically corresponded to the segment lengths for which (now

already old-fashioned) track-recording cars produced statistically processed data (e.g. standard deviations

and quality indices), which were historically 100 or 200 m, or alternatively on a kilometer basis. For

overhead line wire however, these lengths were usually kept at the kilometric level, due to the fact that the

wires were usually installed in those lengths. But, these kinds of “fixed segmentations” were much too

rigid to conform to the actual change of behavior as seen along the track, for which reason, more

sophisticated segmentation concepts were needed.

TRACK SEGMENTATION

It is has been long established that track segments with nominally identical conditions (identical

components, e.g. rails, sleepers, ballast, substructure, installed at the same time, and subjected to the

same traffic) could exhibit markedly different behavior. This difference in behavior, in the lack of better

data and knowledge, was attributed to the so-called “inherent behavior”. This inherent behavior, on the

other hand, was usually explained by the unknown conditions of substructure layers, possible presence of

various elements, the exact location of which and their precise influence on certain condition parameters

could not have been established at that time.

Nowadays, we have at our disposal much better data, both inventory and condition, as well as the

location, but still there are many unknowns in the behavior equation, mostly attributed to the inability in

efficiently monitoring the substructure condition, i.e. without causing serious traffic disruptions, especially

in quick, continuous and above all reliable manner. This situation, both in the past and today, necessitated

splitting of the track into segments. This provided effectively the only way of allowing different track

locations to behave differently, and thus to be modeled the same way.

The basic idea is quite straightforward – every single track segment is treated as a separate “organism”,

i.e. assumed and allowed to have its own (distinct) behavior characteristics. Following this fundamental

assumption, logically, the behavior of each and every such segment is also modeled independently. Of

course, globally speaking, as for every single condition parameter is concerned, every track segment

would have the same deterioration concept (i.e. the type of curves and the types of activities that would

influence the condition parameter values), but still, eventually, every single segment would have its own

curve (i.e. the equation/curve-formula coefficients).

In other words, it could be said that, generally speaking, aging functions for every single condition

parameter do not change their global/overall shape along the network, but instead they are scaled,

(stretched and/or shifted), i.e. the curve “formula type” does not vary, but the curve-defining parameters

(formula coefficients) do.

Simplistically speaking, the essence of the segmentation process is to chop up the linear assets into

segments in such a way as to allow for those segments to have as uniform and as unique behavior as

possible. In order to achieve that, the system that does it (TMMS/AMS) must be informed (by the User)

which are the factors that generally influence the behavior. These factors are usually the component types

and their distribution, as well as layout and operational characteristics, presence of “condition-interfering”

objects, e.g. turnouts, bridges, level-crossings, culverts, etc.

Once this is announced to the system, it simply queries those influencing factors in the database and

every time some influencing factor changes, “current” segment ends and a new segment starts (Figure

xxx). After the track has been divided into segments in such a manner, all other (user-desired)

information, which are not of consequential influence on the segments’ behavior, but are still of interest,

are assigned/aggregated to the so-created segments.

After that, segments are allowed to (and indeed, most often they will) behave differently – i.e. they are

treated as different “organisms”, which suffer from different “illnesses” (defects/deteriorations) and recover

from them by being treated by different “medicine” (M&R Works) in a different way. Knowing that (from the

past data, marking past behavior) and having ability to capture and model it in a suitable system, they are

being projected into the future to predict future behavior. Thus, the behavior of every segment, with

respect to every (user-defined) aspect (e.g. condition parameter) is modeled allowing the system to define

the moment when certain (user-defined) limit values (Thresholds) will be reached, signifying the moment

when certain corresponding (again user-defined) M&R (or other) activities should be performed.

Grouping all segments together we obtain the Activity Plan for the entire Network, or any part of it, which

may have been selected by the User as designated for analysis.

MASTERING THE RAILWAY INFRASTRUCTURE ELEMENTS’ BEHAVIOR

In order to keep the Railway Infrastructure (RI) in the satisfactory condition, the manner in which the

condition of every single RI element changes, must be properly understood. Understanding this change of

condition in fact means understanding the behavior of RI objects, which paves the way towards predicting

it. In order to relate the observed (captured) behavior in the past with the predicted behavior in the future,

so called “deterioration models” are needed. However, not every exhibited behavior can be successfully

captured and used for predictive purposes. This depends on the actual Failure Mode of each and every

deterioration pattern – behavior. These modes, in turn, fall within the domain of Failure Mode, Effect and

Criticality Analysis (FMECA).

A Basis for Estimation of Reliability Parameters

Reliability parameters are important input for strategic maintenance planning. The following parameters

are found to be the most important:

Parameters for "non-observable" failure progression

• Mean time to failure (inverse of the failure rate)

• Ageing parameter (α)

Other parameters

• Mean time to repair

• Spare part consumption

• Mean down time when a failure has occurred.

Parameters for "observable" failure progression

• Parameters in measurable quantity, e.g. failure progression – “deterioration”, D(t)

• Parameters describing the "failure limit"

Failure cause analysis

In order to identify relevant preventive maintenance actions it is crucial to have a good understanding of

failure causes and mechanisms. Failure causes and mechanisms are revealed in FMECA. To support this

task four categories of failure progression are defined, as shown in Figure 3:

1. The component is subject to gradual degradation, which might be observed (by suitable equipment).

2. The component is subject to gradual degradation, which cannot be observed.

3. The component is subject to a rather "sudden” degradation, which can be observed by suitable

equipment.

4. The component is subject to a sudden ("shock”) degradation, immediately leading to a failure.

This classification serves as a tool for communication between the analyst and the track experts, and has

proven to be very helpful in order to arrive at a common understanding between the analyst

("theoretician”) and the expert ("practitioner”). It is particularly useful when reliability parameters are

assessed, as these parameters have different interpretations for the four categories of failure progression

illustrated in Figure 3.

Track geometry and its deterioration

A good example for observable gradual failure progression is the track geometry. Since track geometry

represents one of the crucial track condition parameters, closely related to many other degradation

phenomena, and is often used for triggering the whole range of track M&R activities, it will be used for the

discussion here.

Track geometry deteriorates primarily due to the influence of dynamic loads exerted by vehicles. The

mechanism governing this phenomenon is rather complex. If a track is freshly tamped it is well known that

directly afterwards relatively large settlements occur. If every point of the track were to settle equally no

irregularities would develop. However, these settlements are often far from uniform due to in-

homogeneities in support conditions, track structure and load distribution. This results in differential

settlements, which lead to the development of irregularities in the wavebands experienced by the rolling

stock.

Many investigations have been carried out, mostly by various individual Railways, as well as universities

and other research institutions, on the fundamentals of the deterioration mechanism and the possibilities

of controlling this phenomenon via existing or improved maintenance methods. However, due to the fact

that most of these researches were performed in the times (e.g. in the 80’s and 90’s of the last century)

when only few condition data were available (especially in the digital format suitable for thorough

analysis), and when computer-power was rather limited, no wonder they often resulted in very simplistic

representations of the track behavior and rather rough approximations used for building deterioration

models, in order to enable feeble computers of that time to cope with them.

Most often these models ended up in fact being simplified down to a mere linear representation (Figure 4),

concentrating only on the “deterioration” part and completely neglecting the “restoration” part, i.e. the

effectiveness of M&R works (in this case primarily Tamping), which prevented any consideration of the

increase of M&R works (Tamping) frequency in time, which all made them usable only for a very limited

range of condition parameters and only for short time-span forecasts (up to 2-3 years), completely

unsuitable for middle and long-term simulations ranging up to 30-50 years or more. Knowing that due to a

relatively long service lives of track components (typically 20-60 years) only long-term strategic

optimizations could yield real benefits, it was not difficult to conclude that better, more flexible models

were needed, especially taking into account today’s volume of available condition data being perhaps

100-fold in comparison to the “old days” and computer power having grown enormously.

Basics of the Prediction of geometry deterioration

In order to know what the limiting quality is and to decide when M&R is required, it is necessary to be able

to predict geometry deterioration. Also, similar track sections may have very different rates of

deterioration, as they may have very different rates of improvements (restoration) as a consequence of

certain M&R activities. Therefore, it is necessary to aggregate geometry data into short segments for the

purpose of optimizing M&R and identifying the influencing factors.

As explained before, these segments may be as short as 100-200 m, or indeed much shorter (e.g. 10–

50m). The types of data that are required are as follows:

• track geometry

• maintenance history;

• short-wave rail geometry;

• infra data.

Track geometry is normally measured by track recording cars. They enable the standard deviations (as

well as other types of Quality Indices - QI) to be calculated, which have proved to be useful for predictive

purposes. In some cases, also vehicle reactions calculated from the recorded geometry are used to

assess track quality. When measurements have been made over two or more tamping and lining cycles,

average values for both the "deterioration rate" and the “restoration rate” (improvement from maintenance)

can be found for each segment.

The deterioration rates of quality indices are usually calculated either as a function of traffic in mm/MGT or

of time in mm/year. Without including the quick settlement and rapid deterioration of track immediately

after tamping, the deterioration rate often displays a linear trend between two maintenance operations.

Normally, the deterioration line exhibits the so-called “saw-tooth” line, where the quality deteriorates

between the two subsequent activities (in this case Tamping), which is normally seen as the increase in

the measured values (or processed values like standard deviations or QI), after which the tamping is

performed causing the sudden increase of quality (i.e. drop/decrease in the measured values or QI).

However, over the time, as the track grows older, several other things change. The first thing that changes

is the efficiency of tamping, e.g. the intensity of the “vertical drop”. Other thing that changes is the

“deterioration rate”, i.e. the slope of the line defined by measured points. Finally, both of these two events

have their impact on the required tamping frequency, which becomes higher and higher, i.e. the time

period between two tamping works (tamping cycle) becomes shorter and shorter. Eventually, tamping

frequency becomes so high, that tamping becomes inefficient, but rather there is something else needed

to be done, i.e. some other M&R activity, like for example ballast cleaning or renewal. This kind of logic is

also normally adopted in the TMMS/AMS systems to search for the optimal work to be done on a certain

track section, i.e. for its decision-making process (4).

The global idea is to analyze the track elements’ condition from as many aspects as possible. This is why

a track condition database should be quite extensive. The goal is to enable the track manager to see a

“big picture”, i.e. to display simultaneously all kinds of information that could influence the condition of the

track, in order to be able to search for the real cause of certain track problems and reach decisions about

the best possible remedial actions. This decision-making can be performed either manually, displaying

and overlaying all sorts information, or automatically using the pre-defined decision rules.

THE GENERIC/UNIVERSAL DETERIORATION MODEL

Following the above-explained shortcomings of the available models, as well as the basic analysis

principles, the research partially presented in this paper has been undertaken, with an ultimate goal to

develop a generic/universal deterioration model that would be flexible enough to be applicable to any

parameter’s deterioration, yet powerful and flexible enough to accurately represent/fit various condition

behaviors.

Having the above as the final goal, the starting position had to be seen according to following basic

statements:

• There is a condition-parameter representing an aspect of a condition of an object of a system

• There is a certain number of activities that influence the behavior of this parameter over time. Some

activities must be considered as “essential”, and some as “temporary”.

o Essential activities - profoundly influencing the behavior of a certain condition-parameter – by

in fact “re-setting” the entire model (e.g. ballast renewal for the track geometry behavior)

o Temporary activities - e.g. maintenance activities - performed several times between two (or

more) essential activities, changing temporarily the value of a condition parameter (e.g.

improving it), and the efficiency of which is (allowed to be) decreasing in time, as an object

grows older.

Taking some imaginary condition-parameter and e.g. 3 activities (A, B, C), the long-term behavior may

look as on Figure 5. Activity A represents an “essential” activity, while the activities B and C represent

“temporary” activities. B is the temporary activity of the Level 1, and C of the Level 2 (with the number of

“Levels” being unlimited – although, in practice situations with more than 3 levels seldom occur).

According to the Figure 5 we can also distinguish several “deterioration patterns” or models. The Full-line

curve (of any general form, e.g. polynomial, exponential, etc.) (i.e. the “Level 1” curve) representing the

(“basic”) the deterioration of the basic deterioration curve over several temporary activities of the type A;

the Dashed-curve (the “Level 2” curve) representing the change of deterioration of the basic deterioration

curve (the Full-line Level1 curve) (i.e. “the change of the change”) between the two temporary activities of

the type C; and finally, the Dotted-curve (the “Level 3” curve), representing the change of the Level 2

(Dashed-curve), i.e. “the change of the change of the change”, etc.

Particular solutions considering the types of the curves and Mathematical Formulations

The basic structure of the model is shown on the Figure 6. Obviously, depending on the actual types the

curves D, R and Alpha (α) (of all levels) would assume, the modeling approach and the solution will be

different. More particularly, depending on the parametric shape of the curve (i.e. the actual number of the

unknown parameters), different number of known points will be required.

With the number of levels growing, the complexity of the model builds up rapidly, with the number of

“governing curves” increasing by the factor of 3 with every additional Level. The full Model of course can

handle any number of Levels of diagnosis, and any types of curves at each of the Levels, and it is

currently being incorporated in the system called RAMSYS (Railway Asset Management SYStem)

developed by the Italian company MERMEC S.p.A. (4).

According to the basic structure of the model, Figure 6, we effectively got maximum four conditions to help

us define the D(t) curve:

D(tS) = R(tS) (1)

D(tE) = L (2)

D’(tS) = α(tS) (3)

D’(tE) = β(tE) (4)

Important to note about the above 3 conditions is that besides the unknown parameters of the curve D(t)

we effectively got one additional unknown variable tE.

Also, we said “maximum” four condition, cause in some cases, when D(t) will be having only 2 unknown

parameters, we might not utilize all 4 conditions, cause that would result in problem being over-defined.

The fact that we have maximum 4 conditions also tells us that the curve D(t) can also have maximum 3

unknown parameters, cause as we said, even if we wanted to utilize all 4 conditions, we would still have

one additional unknown variable tE. This effectively dismisses the possibility of the curve D(t) assuming

shape of a polynomial of the order higher than 2.

In practice various curve types can be found, and within this research, as well as in RAMSYS, all the

major ones (linear, quadratic, exponential, logarithmic and power) have been mathematically resolved in

order to serve the Generic Deterioration Model. Since, however in the railway practice the most often used

combination of curve types is Linear for the Level 1 curves, and Quadratic (Square) for the upper level

curves (Level 2 in particular), this case will be elaborated in greater detail in this paper hereafter.

Case D(t) polynomial of the 1st order (linear)

If we took that: ● D(t) curve is linear,

• R(t) and α(t)= β(t) are square (polynomial, 2nd order)

• There are only two “Levels of diagnosis”, i.e. there are only two types of works – one

“temporary” (Tamping) and one “essential” (Ballast renewal).

we would get much simpler situation than in the case of general D(t) curve. With D(t) being linear we got a

significant simplification in it that D’(tS) = D’(tE), and that in fact we got only two unknown coefficients, so in

other words, we do not really need the conditions (2) & (4).

According to the basic structure of the model, Figure 6, with D(t) = bt + c, we effectively got the following 3

conditions for the Model to satisfy in order to unequivocally define the D(t) curve:

b = α(tS), (5)

c = R(tS) - α(tS)tS, (6)

tE = tS + [L - R(tS)] / α(tS) (7)

So finally our D(t) takes up the following form:

D(t) = R + (t – tS)α(tS) (8)

THE CASE STUDY

For the purposes of this paper, a Case Study for Linear D(t) curve will be presented. The following data

are used: Measurements (Alignment Standard Deviation, values for 200 m track segments), as measured

by Track Geometry Vehicles:

Date Measured Value Date Measured Value

3/3/98 1.20 3/3/01 1.50 9/9/98 1.40 9/9/01 1.90 3/3/99 1.60 3/3/02 2.30 9/9/99 1.80 9/9/02 1.80 3/3/00 2.30 3/3/03 2.10 9/9/00 1.40

Work History (Tamping):

Date Activity

1/1/98 Tamping 6/6/00 Tamping 6/6/02 Tamping

Since Activities (Tamping works) interfere with the values of the condition parameter, we can distinguish 3

“Analysis periods”:

Analysis Period Starting Moment Ending Moment

1 Start of the Model–First Activity (Tamping) (1/1/98)

The date of Second Activity (Tamping) (6/6/00)

2 The date of Second Activity (Tamping) (6/6/00)

The date of Third Activity (Tamping) (6/6/02)

3 The date of Third Activity (Tamping) (6/6/02)

Today (or start of the Planning Period – e.g. the “Refer. Date”)

Therefore, we can write our measurements as follows:

Date Value Date Value Date Value

1st Analysis

Period

2nd Analysis

Period

3rd Analysis

Period

3/3/98 1.20 9/9/00 1.40 9/9/02 1.80 9/9/98 1.40 3/3/01 1.50 3/3/03 2.20 3/3/99 1.60 9/9/01 1.90 9/9/99 1.80 3/3/02 2.30 3/3/00 2.30

Applying the Model we obtain the following resulting situation in terms of modeling the past behavior

(Figure 7a):

Or if we apply the Model to get the future progression of the forecasted behavior (Figure 7b):

This again would allow us to count both the Level 1 and Level 2 activities during the pre-specified

Planning Period, which would have to be long enough (e.g. 50 years) and, knowing the unit costs of these

works, to calculate the total costs of the Work Plan, perhaps also including the costs of the track

possession, etc. With this being fully definable, thus automated, we could start testing different

scenarios/strategies, e.g. by applying different Rules, different (assumed Deterioration curves of the Level

1&2), and calculating consequential costs, as well as quality (the value of the condition parameter in

question) at any given time in future.

One of the approaches to defining various Decision-making strategies would be to determine when the

Level 2 Work (in this case Ballast Renewal) should be applied, as opposed to the Level 1 (maintenance)

Work (in this case Tamping). Our strategy could for example be directed towards minimizing the time

between two consecutive Maintenance (Tamping) works, following the situation in practice where traffic

closures are necessary for the performance of those works, causing unpleasant and costly traffic

disturbances, thus clearly calling for minimization. On the Figure 7b the minimal time between two

consecutive maintenance (tamping) works was set to 6 months (182 days), and the resulting annual costs

were 2590 units, and the resulting quality was 1.68. If we for example set it to 30 days, which is

sometimes applied at some Railways and metros in some specific conditions characterized typically by old

and contaminated ballast, often with poor substructure conditions, yet with inability to perform major

remedial works (e.g. ballast cleaning/renewal), we obtain quite different situation, as on Figure 7c, with the

resulting annual costs of 3364 units (more expensive), and quality 1.71 (i.e. worse – higher value in case

of this particular condition parameter, i.e. track geometry, signifies worse condition). It is true though, that

by allowing shorter Tamping cycle (and thus higher Tamping frequency) we managed to postpone the

Ballast Cleaning/Renewal by roughly 3 years, but at the end, the overall costs were much higher.

As the final exercise, we could set the minimal tamping cycle to 2 years, 730 days, in which case we

obtain the situation as on Figure 7d, with the resulting costs of 2368 units (lowest!), and quality 1.61

(best!).

Another prudent strategy (fully supported by this Model) could be to specify the minimal quality

improvement expected to be achieved by a maintenance activity (Tamping), i.e. should the quality

improvement after a certain maintenance activity become too small, it should call for performance of a

renewal activity rather than repeating of maintenance activity which has clearly become ineffective.

However, for the reasons of available space here, we did not describe this inherent capability of the Model

to govern and use the efficiency of the works in question (e.g. Level 1 - tamping) to decide on the moment

of need of the Level 2 works (ballast renewal), although it is in fact one of its most significant features, and

as is proved in RAMSYS, extremely useful for determining the optimal level between the costs of the M&R

plan and the resulting quality.

TRACK MAINTENANCE MANAGEMENT SYSTEMS

Obviously, complex analyses like these, utilizing such a vast amount of data, could never be performed

manually. For successful performance of such analyses suitable Track Maintenance Management

Systems (TMMS) are needed, or indeed full-scale/complete Asset Management Systems (AMS), with

sufficient power and flexibility to handle multitude of cases and their variations. This was the reason why

already in the fist chapter, the notion of Track Maintenance Systems was brought in, cause indeed,

eventually, all these analyses inevitably lead to them.

In reality, all the condition analyses, beside the ultimate/extreme purpose of guarding traffic safety, have

but one purpose, and that is to support (or indeed guide) the decision-making process concerning the

planning of activities (e.g. Maintenance & Renewal, Inspections, etc.). In that sense, TMMS/AMS usually

provide the condition-analysis capability in two levels – Low Level (manual) and High Level (automatic).

This will be explained further on in the text on the example of the RAMSYS system.

Two Levels of Analysis in RAMSYS

Condition Support that RAMSYS provides is catered through the various data-analysis functionalities,

typically divided into two Levels – Low Level Analyses and High Level Analyses. The session (typically

Planning or Viewing) starts by selecting the piece of railway network to be analyzed. The selection is

extremely simple – from the Tree View Structure on the left-hand side of the screen, the Lines/Tracks to

be analyzed are simply drag-dropped into the Work Space area, starting and ending stationages defined

and the Work Space saved (if necessary). Immediately upon that any kind of analysis (low or high level)

can be initiated.

Low level Analysis – i.e. the deep analysis of any of the condition parameters, independently or in cross-

examination with any number of other given parameters, is again performed typically by Planner and

Viewer types of Users, simply by drag-dropping the condition parameters, or assets and their

characteristics, or Work-history, or Plans, or Segmentation, etc., into the Work Space area (Figure 8).

The analysis of the data is extremely easy and intuitive. Data can be arranged in any User-preferred way,

overlaid and combined in any order allowing, in the first step, excellent overview of the situation and

noticing of anomalies, e.g. threshold exceedences, local clustering of defects, etc.

In addition to this, User can simply select by mouse-dragging a piece of track (any stretch), and easily get

the progression of any of the condition parameters in time, as seen through several consecutive

measurements (Figure 9).

After the stretch of data and the (Condition) Parameter in question are defined as well as the function to

be performed on the data (e.g. several consecutive measurements), the system automatically calculates

and displays the time progression (Figure 9):

User can quickly obtain the Trend, interrupted Trend (the saw-tooth line interrupted by Maintenance &

Renewal works), specify which works are to be included (those works that influence the displayed

parameter) or specify a Threshold and calculate the time when it will be reached.

However, the above represents only a “quick”, first hand, analysis. Deeper analysis of the deterioration

trends of various parameters is provided within a specialized Deterioration View. In this view, depending

on the Parameter settings (e.g. types of curves – linear, non-linear, polynomial, logarithmic, etc.) the time

progression of the parameter is shown, as well as projected into the future based on the sophisticated

Deterioration Models (as explained before), Figure 10.

High Level Analysis – i.e. the automatic analysis of any part of a network (or the entire network) on a

short, middle and/or long term basis, based on the User-defined set of Decision Rules and Threshold,

powered by the Deterioration Models.

High-level analysis is performed on (infrastructure/track) Segments. Segments in turn are the product of

the Segmentation process, as explained before. Segmentation process represents a process where the

(linear) infrastructure (track, but also overhead-line (OHL), or any other structure that is feely definable in

the system) is divided into Segments based on User pre-defined Criteria. The segmentation process is

completely flexible and free of any constraints – segments can be within any User-defined length-span

(e.g. between 10 and 200 m); the actual starts and ends of the segments are determined by the change of

any of the User-specified set of characteristics. These characteristics can again be any of the data-items

specified (declared) in the system through the Object Model, typically defined by the Administrators in the

initial implementation/Set-up Phase of the use of the system. These characteristics are typically

representing the Asset Characteristics (e.g. type of rails, installation date of rails, type of ballast, etc.)

and/or other important data (e.g. Operational data (speeds, curves, grades)), as well as condition data, if

desired by the User, etc. There can also be unlimited number of parallel existing official (on which official

Plans and Work Schedules would be made) and non-official (for testing purposes – e.g. simulations,

testing of decision-rules, thresholds, deterioration models, etc.) segmentations; e.g. for detailed

determination of the suitable works to be performed in the, typically short-term, a “finer” segmentation

could be made by Local Engineers, e.g. drilling down to 10-20 m, on the other hand, for the long-term

(e.g. 30-50 years, or more), often Budgeting purposes, often run on the “Network-scale” a coarser

segmentation could be used (e.g. 500 – 1000 m), allowing quicker analyses.

Following this concept “semi-uniform” segments are created. The word “semi” is used, cause the

Segments will be fully uniform with regards to those characteristics that the User has selected as

“important”, whereas all others would be assigned to the created segments with a certain level of

“compromise”, following several User-definable “strategies”.

Based on such segments the automatic analyses are performed, based on User-defined set of Decision

Rules and Thresholds, powered by Deterioration Models. Namely, within this analysis, for each and every

segment, the Decision Rules (differentiated by their relative importance) are run. Normally, the structure of

a Decision Rule is such that it first checks if the segment satisfies certain conditions (e.g. if it holds certain

type of rail, if it is on a main line or secondary, if the ballast age is within certain limits, etc.) and then

checks certain condition parameters against their respective thresholds and/or calculates when those

thresholds would be reached. In order to calculate when these thresholds would be reached the

Deterioration Models are used. Namely, in short, for every given condition parameter a “Deterioration

Model” is assigned – typically, that would man what type of curves (linear, non-linear, polynomial,

exponential, logarithmic, etc.) and which are the “influencing works” (cause not all works are influencing all

parameters, and even between those works which are influencing a certain parameter, not all of them are

influencing it in the same way, etc.). Once this is described (pre-defined by the User), the system

calculates, for every single segment, based on the Actual data (e.g. measurements and Work History) the

Actual Curves that fit the behavior of this particular Segment. Based on this “captured behavior”, the

system calculates the future behavior (e.g. deterioration, response to various works – i.e. their efficiency,

etc.), and based on this the system forecasts and proposes the Maintenance & Renewal works (or indeed

Inspections and/or other activities) to be performed.

These Rules, after being defined as well as the corresponding Thresholds, are saved in the Database

(differentiation can also be made between “Official Rules” to be used for the production of “Official Work

Plans” and “un-official Rules” to be used for testing purposes) and can be activated and applied at any

time and on any User-defined selection (portion of the Railway Network), yielding Work Plans and

associated Costs (Figure 11). Moreover, the achieved “average quality”, i.e. as seen as the average value

of the specified Condition Parameters over the entire Planning Span (e.g. 20-50 years) can be produced,

allowing balancing between the Costs and the achieved Quality, as well as between Maintenance and

Renewal.

Obviously, both Rules and Thresholds are freely definable by the User at any time, and simply drag-

dropped into the Work Space area to be activated for a given Work Space/Project.

CONCLUSION

Developing generic deterioration model allows Railways to perform long-term simulations of the track

behavior, balancing effectively achieved quality with the costs of M&R works, inspections and other

consequences like traffic disruptions, availability, etc. Empowering this model with Life Cycle Costing and

numerical optimization techniques (with proper formulation of the global objective) as it has been done in

RAMSYS (5), truly optimal long-term balance can be reached, bringing significant cost-savings for the

railways organizations.

REFERENCES

1. Esveld C., “Modern Railway Track”, MRT-Productions, Zaltbommel, The Netherlands, 2001

2. ERRI D187/DT299, Decision Support System for Track Maintenance & Renewal, Utrecht, April 1994

3. Jovanovic S., Condition-based decision-making minimizes track costs, Railway Gazette International,

May 2003 issue, pp. 277 – 282.

4. www.mermec.it

FIGURES WITH TITLES

FIGURE 1: Types of data to be collected for computer-aided Track Maintenance Management Systems

CONDITION MEASUREMENTS

PLANNING

INFRASTRUCTURE INVENTORY

INSPECTIONS

ACTIVITY (WORK) HISTORY

COSTS

TMMS

FIGURE 2: Segmentation Concept

Sleeper Type

Rail Type

Subgrade Type

Curvature

S&C

Bridges

Speed

Grade/Slope

Result

Sleeper Type

Rail Type

Subgrade Type

Curvature

S&C

Bridges

Speed

Grade/Slope

Result

FIGURE 3: Types of failure progression

1. Observable gradual failure progression

Fai

lure

Pro

gres

sion

Time To Action Remaining Time To Failure

Action Limit

Failure

Last Action

Time

1. Observable gradual failure progression

Fai

lure

Pro

gres

sion

Time To Action Remaining Time To Failure

Action Limit

Failure

Last Action

Time

2. Non-Observable failure progression

Fai

lure

Pro

gre

ssio

n

Non-Observable failure progression

Failure

Last Action

Time

2. Non-Observable failure progression

Fai

lure

Pro

gre

ssio

n

Non-Observable failure progression

Failure

Last Action

Time

3. Observable “sudden” failure progression

Fai

lure

Pro

gres

sion

Mean Time To Action PF-interval

Failure

Last Action

Time

P F

3. Observable “sudden” failure progression

Fai

lure

Pro

gres

sion

Mean Time To Action PF-interval

Failure

Last Action

Time

P F

4. Shock

Fai

lure

Pro

gres

sion

PF-interval

Failure

Last Action

Time

P F

4. Shock

Fai

lure

Pro

gres

sion

PF-interval

Failure

Last Action

Time

P F

FIGURE 4: Track deterioration and restoration in terms of the Track Quality Index (QI) (2)

FIGURE 5: Mono-parametric long-term generic deterioration model

A A C C C C C C B B

Time

Parameter value

FIGURE 6: Generic Starting Shape of the Deterioration Curve

L

D(t) R(t)

α (t)

β (t)

Time [t]

Time [t]

t S t E

α (t S )

β (t E )

Parameter value

FIGURE 7: Modeled past and future behavior – various scenarios

a) Modeled past behavior

b) Modeled future behavior, 182 days (6 months) minimal time difference (Tamping cycle)

c) Modeled Future behavior, 30 days (1 month) minimal time difference (Tamping Cycle)

d) Future behavior, 730 days (2 years) minimal time difference (Tamping Cycle)

FIGURE 8: Integrated Visualization Functionality

Work History

Dynamic Forces

Track Quality

Corrugation

Track Chart

Asset Inventory System

Management

Rail Wear

Images

FIGURE 9: Selecting User-defined stretch of data and applying User-defined function(s)

Drag Selection & choose a function

Select Threshold, influencing Works, calculate Trends

FIGURE 10: Deterioration View of a Segment – integrated simultaneous deterioration representation of various Parameters, with given Influencing Works (from Work History) and Deterioration Trend line-characteristics (linear, non-linear, etc.), calculating short, middle and long-term progressions of parameters

User-definable Threshold

Degradation Trend

Work History Measured Values

Planned Work

System Management

List of Parameters shown in the View and

their characteristics

Work History Work History

FIGURE 11: Graphical and Printout representation of the Work Plans and associated Costs

Full print-out of all Planned Activities, with

Location, Dates and Costs

Graphical Representation

of the Work Plan