897

1 INTRODUCTION

Extensive research conducted over previous decades

was meant to detect different ship accidents [7], [6].

Accident investigation is a prerequisite for the

identification of the causes that led to dramatic events,

and caused partial or total loss of ships or human

lives as well as environmental pollution.

The research published by Knapp et al. confirmed

that general and dry cargo vessels exhibit the highest

likelihood of accidents such as are collision, fire,

explosion, flooding, loss of control, hull failure,

A Multistate Approach to Reliability Analyses of a Ship

Hull Structure

J. Soszyńska-Budny

& S. Ivošević

Gdynia Maritime University, Gdynia, Poland

The University of Montenegro, Kotor, Montenegro

ABSTRACT: Structural damage in the form of corrosion, fatigue, damage, cracks and fouling can significantly

reduce the structural integrity of ships in navigation and decrease the navigation safety. Therefore, numerous

studies aim to improve international rules and regulations, and ensure proper maintenance of ships and timely

inspections. Classification societies, flag states and port states strive to conduct appropriate inspections of ships

with the aim of preventive detection of defects. Through the application of International Safety Management

system, companies strive to improve maintenance systems, monitor the condition of ships and conduct risk

assessments to reduce potential accidents and negative consequences for people, material goods and the

environment. By means of advanced Structural Health Monitoring, the observation and analysis of the physical,

chemical or electrical characteristics of components or systems are conducted over time. Such examinations tend

to identify the variations that lead to degradation of the current or future performance of the inspected systems

and their components. The condition of hull structures is monitored by mandatory requirements which are

prescribed by classification societies e.g. the number of thickness measurements of hull structure areas and

elements. The measurements create extensive databases that are further used to monitor the condition of

existing ships and predict their future conditions. This study relies on a database of 25 bulk carriers aged

between 5 and 25 years. The measurements were performed during regular special surveys on the total of 110

fuel tanks located in double bottom area of ships, which provided 3070 measured data in total. The values of the

thickness diminution of steel plates due to corrosion were obtained through the measurements of the thickness

of the steel plates by means of ultrasound thickness gauging equipment, in accordance with the rules of

classification societies. Based on those rules and allowable substance and extensive corrosion, the paper

considers the excessive corrosion values (above 20%) that were identified as failures and required the

replacement of corroded surfaces. The multistate approach to the reliability analysis of the steel plates of inner

bottom plating and the improvement of reliability after critical conditions showed that the usability of the

analyzed ships significantly dropped after 15 years of exploitation.

http://www.transnav.eu

the International Journal

on Marine Navigation

and Safety of Sea Transportation

Volume 18

Number 4

December 2024

DOI: 10.12716/1001.18.04.16

898

contact, damage to ship equipment, grounding, etc.

[16], [9]. Furthermore, the analysis of over 4700 sea

accidents that occurred between 2004 and 2021

revealed that the collisions and contacts are the most

frequent causes of sea accidents [2].

However, the most significant research was

focused on tankers and bulk carriers as ships that are

often associated with catastrophic accidents and

consequences [5], [28]. The research conducted

between 2011 and 2020 on 34 accidents that resulted

in total losses of bulk carriers showed that the

accidents were related to cargo shift and liquefaction,

collision, structural failure, grounding, and fire

explosion [19]. The research of accidents significantly

affects the improvement of international regulations.

For instance, Hermann [10] investigated the impact of

bulk carrier disasters on the amendments to the

SOLAS Convention.

Structural failures such as coating breakdown,

cracks, deformation, corrosion, fouling or fatigue, can

significantly decrease the safety of vessels and their

projected lifespan. The influence of different types of

environment, operation, maintenance procedures,

ship route etc., accelerate different types of corrosion

(general corrosion, pitting, stress-corrosion, corrosion

fatigue, fretting, erosion, cavitation etc.). Previous

research examined the corrosive degradation of

structural areas (inner bottom plating [12], cargo hold

transverse bulkheads [31], cargo hold mainframes

[18], or the main deck [8]), the chemical or mechanical

properties of materials and their behaviour in

different types of environment [4] as well as the

simulations of cavitation flows around propellers and

rudders [20] etc. Furthermore, the examination of

different structural problems required several

reliability approaches such as: time-variant reliability

formulation, failure probability and reliability,

inspection and maintenance planning, reliability

centred maintenance, etc. [29].

Previous studies contributed to the revision of

international regulations and their improvements

through national and international standards, rules

and regulations. International Maritime Organization

has significantly contributed to the changes in

maritime conventions in terms of enhanced maritime

safety and security. These conventions were an

obligation for all participants in maritime industry

and required an active participation of flag states, port

states, classification societies, ship owners, ship

management companies and other independent

institutions.

In terms of ship maintenance and operation

management, the most significant improvements were

certainly the changes in the SOLAS convention and

the introduction of the International Safety

Management Code. Additionally, Common Structure

Rules improved the design of ships in construction

phases, which led to the stronger and heavier ships

that resist corrosive damage over projected lifespans

[11]. The Performance Standard for Protective Coating

implemented in April 2006 enabled the application

and maintenance of surface protection for a longer

period, which delayed the onset of corrosion. Besides

the postponed beginning of corrosion, the new

standards also postponed the time for intensive

replacement of the damaged and corroded structural

surfaces that are a consequence of intensive corrosion

processes. Furthermore, the impressed current

cathodic protection (ICCP) [23] or sacrificial anodes

and their influence on hull protection [24]

significantly reduce corrosion.

Along with the development of international

regulations, the progress of technology, innovation,

and digitalization simplified the monitoring of ship

conditions, as well as the application of the new

technologies that offered an insight into the stages of

construction and systems in real conditions. Likewise,

the application of remote technical solutions enabled

easier and more effective inspections of ships in

navigation and ports. Traditional inspections (visual

inspection, acoustic-based testing, electromagnetic

testing, and imaging-based testing) and modern

techniques utilizing robotics (underwater vehicles,

unmanned aerial vehicles, or climbing robots)

improved the effectiveness of inspections and reduced

the number of accidents [22], [3].

Structural Health Monitoring integrates several

engineering fields such as sensor technology,

materials science, artificial intelligence and machine

learning, data science and structural engineering, all

of which enabled the optimization of design,

operation and/or maintenance. The use of sensors

prevents premature failures and ensures a satisfactory

performance of structures by collecting various types

of data on ship conditions e.g. vibrations,

deformations, temperatures, pressure, and other

parameters that indicate the health and stability of the

SHM structure [ 21], [26].

A new artificial, neural, network-based data fusion

model has recently been developed to provide

tailored corrosion predictions [30]. The application of

virtual reality for remote ship inspections and surveys

and the application of virtual reality in related

activities/procedures are yet to be researched [25].

This paper is organized into five paragraphs. The

second paragraph presents the database of the steel

plates damaged by corrosion and explains relevant

classification criteria for the assessment of steel

thickness. The third paragraph focuses on the

theoretical aspects of the multistate approach to the

reliability analysis of the steel plates and the

improvement of reliability. The research results are

presented in the fourth paragraph while the fifth

paragraph contains concluding remarks.

2 DATABASE

The research relies on the database of 25 bulk carriers,

whose exploitation varied between 5 and 25 years.

The bulk carriers were subjected to 3070

measurements which inspected corrosive damage to

the plates during the time of exposure. The

considered thickness values were measured during

regular special surveys by approved ultrasound

measurement devices, operators and a company. All

measured data which were connected to intermediate

survey were included in special surveys, as well. The

research takes into consideration five-year cycles of

ship exploitation.

899

The measures focused on the inner bottom plating

of cargo holds only in the areas of fuel tanks in double

bottoms [12]-[15]. Each surveyed vessel has between 2

and 4 fuel oil tanks. Inner bottom plating near fuel

oil tanks were analyzed, whereby each tank was

divided in five parallel sections. Two sections at the

end of tanks, one in the middle of tanks and two

between the ends and the middle of tank lengths. The

gauging was performed on the cross section of the

steel plates of inner bottom plating to express steel

damage caused by corrosion. The corrosive damage

represents the diminution of steel thickness on the

upper side of tanks (near cargo holds) and on the

bottom side (near fuel oil tanks). Table 1 shows the

main information about the database and ships.

Table 1. The database used for the research on inner bottom

plating

________________________________________________

The age The ship The number The number Average of

of ships surveys of tanks of measured corrosion

(years) data wastage

(%)

________________________________________________

0-5 4 9 230 0,5 %

5-10 4 10 296 2,2 %

10-15 7 19 530 8,9 %

15-20 13 43 998 11,7 %

20-25 10 29 1016 18,2 %

________________________________________________

Total: 38 110 3070

________________________________________________

According to the rules, the measurement of the

thickness of steel elements is expressed in millimetres

or percentage of diminution, depending on the

internal rules of classification societies. Each

classification society in its internal rules clearly

explains the criteria for the acceptance of steel damage

for each element of a hull structure, and strictly

defines the measured values that represent substantial

and excessive corrosion. In that regard, the paper

considers three specific categories of the obtained

values expressed in percentage of damage to plate

thickness compared to the original value, namely:

− Acceptable diminution – the measured steel plate

thickness is acceptable (the values below 0.15% of

the original thickness),

− Substantial corrosion – the measured steel plate

thickness is not for replacement but should be

annually monitored in the future (the values

between 15% and 20% of the original thickness),

− Extensive corrosion – the measured steel plate

thickness is not acceptable and should be renewed

(the values exceed 20% of the original thickness).

The database created in this way clearly defined

the condition of structures at the time of survey and

inspection and identified the number and location of

thickness measurements as well as the surface of

structural plates damaged by corrosion (Figure 1).

Figure 1. Substantial and renewal area of inner bottom

plating

Each mandatory inspection of the ships identified

the surfaces affected by excessive corrosion that had

to be replaced before further use of the ships. Based

on the procedure described, the database was used to

define the values that are considered as extensive

corrosion and are therefore unacceptable values for

the research.

3 METHODOLOGY

3.1 Multistate Approach to Reliability Analysis of Steel

Plates of Inner Bottom Plating

The study is based on an adapted multistate approach

[17], [27] to the analysis of the reliability of the steel

plates of inner bottom plating. Accordingly, there are

several distinguishable reliability states e.g. s = 0,1, …,

n. For example, reliability state 1 or higher indicates

that a steel plate is able to perform its tasks. Reliability

state s = 0 is interpreted as a failure. For the purposes

of this analysis, the steel plates of an inner bottom of

one ship are considered as a system. The reliability

function of the steel plates is thus defined as a vector

[17], [27]

( ) ( ) ( ) ( ) ( )

R , R ,0 ,R ,1 , ,R , , ,R , , 0,



 =   



t t t t s t n t

(1)

whereby the coordinate R(t,s) of this vector is

interpreted as the probability that a steel plate is in the

reliability state subset {s,s+1,..., n}, s = 0, 1,..., n, at the

moment t, whereas it was in the best reliability state n

at the moment t = 0.

The above definition means that

( ) ( )

R , P(T ), 0, 0,1,...,=   =t s s t t s n

(2)

whereby T(s) is a random variable representing the

lifespan of a steel plate in reliability state subset {s,

s+1,..., n}, s = 0, 1,..., n.

Based on the expression (2), it follows that R(t,0)=1

for t ≥ 0, and the variable R will further be used in

vector (1).

Another important reliability characteristic that

can be determined is risk function. Risk function is

defined as the probability that steel plates in a subset

of reliability states are below critical reliability state

(r), whereby the plates were in the best reliability state

(n) at the moment t = 0, which can be represented by

formula:

( ) ( ) ( )

( )

r 1 R , P T , 0,= − =  t t r r t t

(3)

In the formula

 

1,2,...,rn

and is an adapted

(on the basis of expert opinions) critical reliability

state, while R(t,r) is a coordinate of the reliability

function of steel plates.

If an inverse function r

-1

(t) of the risk function (3)

exists, then τ can be determined as a moment when a

steel plate risk exceeds a permitted threshold ().

( )



−

(4)

900

3.2 Improving Reliability After a Critical State is

Exceeded

The improvement of the reliability of the steel plates

of inner bottom plating is very important for the

maintenance of system availability and safety in

general. Multistate reliability approach assumes that a

system (steel plate) needs repair after exceeding a

critical reliability state, in accordance with certain

reliability levels. In this study, critical state is defined

as the state when the acceptable reliability level of

90% is exceeded. The replacement of a failed/corroded

steel plate improves the reliability of a system, but

does not necessarily provide the best reliability. The

system will not work like a new one after the

replacement because other components of the system,

which were not replaced, may be in lower subsets of

reliability state.

If the reliability of steel plates improves after

exceeding a permitted threshold risk level (), after

time (), as determined by formula (4), and if the time

for the improvement of reliability (renovation time) is

µ, then the coordinate of the function of reliability

improvement in reliability state subsets {r, r+1,..., n}, r

= 1, 2,..., n, are exemplified by the following formulas:

[1]

( ) ( )

( )



= −  +R t r R t k µ r

for

( ) ( )

  

 +    + +k µ t k µ

(5)

( ) ( )



=R t r R r

for

( ) ( ) ( )

  

 + +   +  +k µ t k µ

(6)

In the formulas above k=0,1,…,N, r=1,2,…,n, t0

and N is the number of subsequent reliability

improvements. Figure 2. illustrates the coordinate of

an exemplary function of reliability improvement.

Figure 2. The coordinate of exemplary function of reliability

improvement

Based on previously presented methodology, the

subsequent steps for improving reliability are shown

in the diagram in Figure 3.

Determine the number of

system reliability state

0,1…,n and define them.

The state 0 is a failure.

Determine the coordinates

of reliability function R(t,s)

for s=1,2,…,n.

Determine reliability

characteristic e.g. risk

function r(t)

Determine the moment of

exceeding the risk level τ

Improve reliability after

exceeding critical state r

and determine improving

reliability function R(t,r)

• Information on degradation of

steel plates

• Information on steel plates

reliability parameters

• set a critical state r

• set a permitted risk

threshold

• set a repair time µ

Figure 3. The diagram of procedure for reliability

improvement after the critical state was exceeded

4 CASE STUDY OF RELIABILITY ANALYSIS OF

THE STEEL PLATES OF INNER BOTTOM

PLATING

4.1 Reliability and Risk Evaluation

Reliability analysis of the study is based on the

allowable wear of 20% of the original thickness.

Namely, the values that were below 80% of the

original thickness were considered unacceptable i.e.,

interpreted as a failure. Although the remaining

thickness ensures the impermeability of steel plates,

classification societies still require the replacement of

corroded surfaces in those cases.

According to experts, there are three distinct three

reliability states:

− Reliability state 2 – the system performs its tasks

and is completely safe, while the wear values are

below 0,15% of the original thickness.

− Reliability state 1 – the system performs its tasks,

but its operation is less safe because of the

possibility for environmental pollution, cargo

damage, ship’s safety etc. The wear values are

below 15% - 20% of the original thickness.

− Reliability state 0 – the system does not work (and

does not fulfil the prescribed requirements). The

wear values exceed 20% of the original thickness.

Critical state (r) adopted the reliability state 1, due

to the fact that the limit of accepted reliability is 90% .

Reliability characteristics were estimated on the

basis of the data from regular special surveys after 5,

10, 15, 20, 25 years, in accordance with a precisely

defined methodology. The estimation detected the

values of the reduction in the thickness of the steel

plates due to corrosion over time.

Table 2 shows the number of the measurements of

the steel plates of inner bottom plating in particular

reliability states s, s = 0,1,2.

901

Table 2. The number of the measurements of the steel plates

of inner bottom plating in particular reliability states s, s =

0,1,2.

________________________________________________

Time Number of Number of Number of

(years) measurements measurements measurements

that the values that the values that exceeded

<0,15%) of the are <15%,20%) 20% of the original

original thickness of the original thickness.

thickness

________________________________________________

5 230 0 0

10 296 0 0

15 427 78 25

20 673 129 196

25 456 108 452

________________________________________________

Table 3 shows the number of measurements in

reliability state subsets {s,s+1,…2}, s = 0,1,2.

Table 3. The numbers of the measurements of the steel

plates of inner bottom plating in reliability state subsets

{0,1,2}, {1,2}, {2}.

________________________________________________

(1) Time (years)

(2) Number of measurements

(3) Number of measurements in the reliability state subset {0,1,2})

(4) Number of measurements in the reliability state subset {1,2})

(5) Number of measurements in the reliability state subset {2})

________________________________________________

(1) (2) (3) (4) (5)

________________________________________________

5 230 230 230 230

10 296 296 296 296

15 530 530 505 427

20 998 998 802 637

25 1016 1016 564 456

________________________________________________

Based on the data from Table 3, further analysis

determined the values of the reliability function

coordinates R(t,s), s = 0,1,2, after 5, 10, 15, 20 and 25

years. These values were calculated as the probability

that the steel plates of inner bottom plating are in

reliability state subset {0,1,2}, {1,2} or {2} and are

presented in Table 4.

Table 4. The reliability characteristics of the steel plates of

inner bottom plating in reliability state subsets {0,1,2}, {1,2},

{2}.

________________________________________________

Time (years) R(t,0) R(t,1) R(t,2)

________________________________________________

5 1 1 1

10 1 1 1

15 1 0,952 0,806

20 1 0,804 0,638

25 1 0,555 0,449

________________________________________________

Figure 4. A graphical presentation of reliability function

coordinates R(t,s), s = 0,1,2

The data presented in Table 4 show that, after 20

years of operation, the component of the reliability

function R(t,1) is 0.804. This means that the reliability

function in the subset of reliability states {1, 2} fell

below the acceptable level of 90%. Moreover, the

acceptable level of reliability will be exceeded after

between 15 and 20 years of exploitation. The costs of

the replacement of corroded surfaces are very high

and require extensive preparation and a long

retention of ships in yards. Therefore, it should be

defined whether the acceptable level of reliability is

exceeded after 15 or after 20 years of operation. Since

the acceptance threshold is as high as 90%,

postponing the replacement of corroded steel plates

until the fourth special inspection (i.e. after 20 years of

operation) is more cost-efficient. For this reason, it is

important to determine the moment when the risk

function exceeds the acceptable level of 0.1.

Since state 1 was considered as critical reliability

state, the moment when a steel plate risk exceeds the

permitted threshold is  = 0.1. This finding answers

the question whether reliability should be improved

after 15 or after 20 years of ship exploitation.

This study does not observe the distributions of

the components of reliability functions e.g. R(t,s), s =

1,2, because the system lifespans in reliability states

subsets {1,2} and {2} were not available. The available

values are related to the reliability functions of

components at particular points (Table 4), which was

estimated on the basis of thickness diminution

measurements conducted during the surveys. From a

mathematical point of view, if the values of a function

at a finite number of points are known, then, for any

distribution, such function can be interpolated by

means of Lagrange polynomial interpolation. Thus,

the component of reliability function can be described

by the Lagrange interpolating polynomial in the

following way:

( ) ( ) ( ) ( )

R ,1 R ,1 R ,1

=



t t t l t

(7)

whereby Lagrange’s basis polynomials are expressed

as:

( )



−



(8)

whereby:

i = 0 using the data from Table 4 and from expression

(8) it follows that:

( )

( )( )

( )

( )( )( )( )

1 2 3 4

0 0 1 0 2 0 3 0 4

10 15 20 25

15000



−

− − − −

= = =

− − − − −



t t t t t t t t

t t t t

t t t t t t t t t t

(9)

i = 1 using the data from Table 4 and from expression

(8) it follows that:

( )

( )( )( )( )

5 15 20 25

3750

0 2 3 4

1 0 1 2 1 3 1 4

−

− − − −

= = = −

−

− − − −





t t t t t t t t

t t t t

t t t t t t t t

(10)

i = 2 using the data from Table 4 and expression (8) it

follows that:

902

( )

( )( )( )( )

5 10 20 25

2500

0 1 3 4

2 0 2 1 2 3 2 4

−

− − − −

= = =

−

− − − −





t t t t t t t t

t t t t

t t t t t t t t

(11)

i = 3 using the data from Table 4 and from expression

(8) it follows that:

( )

( )( )( )

( )( )( )( )

5 10 15 25

3750

0 1 2 4

3 0 3 1 3 2 3 4

−

− − − −

= = = −

−

− − − −





t t t t t t t t

t t t t

t t t t t t t t

(12)

i = 4 using the data from Table 4 and from expression

(8) it follows that:

( )

( )( )

( )

( )( )

( )

( )( )( )( )

5 10 15 20

15000

0 1 2 3

4 0 4 1 4 2 4 3

−

− − − −

= = =

−

− − − −





t t t t t t t t

t t t t

t t t t t t t t

(13)

According to the considerations above and based

on formula (7), the reliability function in reliability

state subset {1,2}, between 5 and 25 years, is given by

the following approximate formula:

( ) ( )

( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )

( ) ( ) ( ) ( ) ( )

0 0 1 1 2 2 3 3 4 4

0 1 2 3 4

R ,1 R ,1

R ,1 R ,1 R ,1 R ,1 R ,1

0.952 0,804 0,555

=

= + + + =

= + + + +

t l t t l t t l t t l t t l t

l t l t l t l t l t

(14)

whereby l0(t), l1(t), l2(t), l3(t), l4(t) are expressed in (9)-

(13).

Furthermore, the study identifies the moment

when the risk function of the steel plates exceeds the

acceptable level of 0.1.

( ) ( )

1 ,1 =−r t R t

(15)

Figure 5. Approximate reliability and risk functions

estimated by Lagrange interpolation

Table 5. Values for the approximate function

( )

R ,1t

and

r(t) between 15 and 25 years

________________________________________________

( )

R ,1t

r(t)

________________________________________________

15 0,952 0,048

16 0,931166 0,068834

17 0,905998 0,094002

17,1 0,903239 0,096761

17,2 0,900435 0,099565

17,3 0,897587 0,102413

18 0,876406 0,123594

19 0,842382 0,157618

20 0,804 0,196

21 0,761414 0,238586

22 0,714862 0,285138

23 0,664662 0,335338

24 0,611214 0,388786

25 0,555 0,445

________________________________________________

Using formulas (14), (15) and (4), the moment ()

when a steel plate risk exceeds the permitted

threshold ( =0.1) equals  =17.2 years. The analysis

above indicates that the risk function would exceed

the acceptable level of 0.1 after only 17.2 years. This

means that the reliability of steel plates should be

improved by the replacement of corroded surfaces

after the third special survey/inspection, i.e. after 15

years of operation. The replacement would reduce

hazardous effects of corrosion such as the possibility

for environmental pollution, structural damage or

cargo contamination.

4.2 The Improvement of Reliability

The time needed for the replacement of corroded

surfaces on a ship depends on many factors.

However, according to previous studies, the average

period that a ship must spend in a yard (renovation

time µ) is one month. Based on (5)-(6), formulas (16)

and (17) show the coordinate of the function of

improved reliability of steel plates in reliability state

subsets {1, 2}, assuming that the reliability is

improved upon the third special survey, i.e. after 15

years.

( ) ( )

( )

( ) ( )

,1 15 0,083 ,1

15 0,083 15 0,083 15,

= −  +

 +    + +

R t R t k for

k t k

(16)

( ) ( )

( ) ( ) ( )

,1 15,1

15 0,083 15 1 15 0,083 ,

 + +   +  +

R t R for

k t k

(17)

whereby k=0,1,…,N, t0.

As the operation time of a ship is around 25 years,

the repair happens once in a ship’s lifespan, in most

cases. Therefore, the function of improved reliability

takes the following form:

( ) ( )

( )

( ) ( )

,1 15 0,083 ,1

15 0,083 15 0,083 15,

0,1,

= −  +

 +    + +

R t R t k for

k t k

(18)

( ) ( ) ( )

,1 15,1 15 15 0,083 ,=   +R t R for t

(19)

903

Figure 6. A graphical presentation of the function of

improved reliability, coordinate R(t,1), after 15 years of

operation

The analysis indicated that ship management and

maintenance companies should carefully inspect the

steel plates of inner bottom plating after 15 years of

operation. Monitoring and appropriate preventive

maintenance would improve reliability, extend the life

expectancy of ships, ensure safety, and greatly reduce

the costs of potential subsequent major repairs.

Considering the cost of the replacement of

corroded surfaces and the retention of a ship out of

service, many owners choose not to improve

reliability of inner bottom plating after 15 years.

Perhaps the owners consider it more cost-effective to

operate their ship for additional few years and then to

scrap the ship. For that reason, the problem of cost

optimization for the replacement of corroded surfaces

and the maintenance of ships seems a highly

interesting research problem that requires further

investigation.

5 CONCLUSION

This study applies reliability theory and multistate

analysis with special improvements. The application

was realized based on an empirical database

regarding the structural damage to the inner bottom

plating of fuel oil tanks inside bulk carriers during

their lifespan.

Based on the theory of reliability, the research

results showed that reliability drops significantly after

15 years of ship exploitation. The research further

determined the error criterion at the moment when

corrosion exceeds the wear of 20% of the original

thickness prescribed by classification societies. The

decrease in efficiency after 15 years is a result of

maintenance interventions and replacement of

damaged surfaces.

Future research should analyse the impact of new

rules on the condition of ship structures and focus on

a larger database and other structural elements.

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