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Mathematical Problems in Engineering

Volume 2008 (2008), Article ID 512343, 26 pages

http://dx.doi.org/10.1155/2008/512343

## Probabilistic and Fuzzy Arithmetic Approaches for the Treatment of Uncertainties in the Installation of Torpedo Piles

^{1}Laboratory of Computer Methods and Offshore Systems (LAMCSO), Civil Engineering Department, COPPE/UFRJ-Postgraduate Institute of the Federal University of Rio de Janeiro, 21945-970 Rio de Janeiro, RJ, Brazil^{2}COPPE/UFRJ, Civil Engineering Department, Centro de Tecnologia Bloco B sala B-101, Cidade Universitária, Ilha do Fundão, Caixa Postal 68.506, 21945-970 Rio de Janeiro, RJ, Brazil

Received 2 December 2007; Accepted 27 March 2008

Academic Editor: Paulo Gonçalves

Copyright © 2008 Denise Margareth Kazue Nishimura Kunitaki et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

The “torpedo” pile is a foundation system that has been recently considered to anchor mooring lines and risers of floating production systems for offshore oil exploitation. The pile is installed in a free fall operation from a vessel. However, the soil parameters involved in the penetration model of the torpedo pile contain uncertainties that can affect the precision of analysis methods to evaluate its final penetration depth. Therefore, this paper deals with methodologies for the assessment of the sensitivity of the response to the variation of the uncertain parameters and mainly to incorporate into the analysis method techniques for the formal treatment of the uncertainties. Probabilistic and “possibilistic” approaches are considered, involving, respectively, the Monte Carlo method (MC) and concepts of fuzzy arithmetic (FA). The results and performance of both approaches are compared, stressing the ability of the latter approach to efficiently deal with the uncertainties of the model, with outstanding computational efficiency, and therefore, to comprise an effective design tool.

#### 1. Introduction

##### 1.1. Context: Offshore Platforms, Mooring Systems, and Anchors

Petroleum
companies around the world have been faced with the challenge of developing
offshore oil production activities in deep and ultradeep waters. In
shallow water, the traditional solution consists in employing platforms
supported by fixed framed structures, such as the *jackets* where the foundation system consists of driven piles [1]. Presently, as oil fields have been
identified in deeper water such as in the Campos Basin (southeastern Brazil),
offshore platforms have included several types of floating units, such as the *semisubmersible* platforms, the *tension*-*leg platforms* (TLPs), and *floating production, storage, and offloading* (FPSOs) units based on ships.

Floating platforms can be maintained in position by
different types of mooring systems, which in turn may employ anchors based on
different types of foundation elements. Semisubmersible platforms and FPSO
units, for instance, may be kept in position by mooring lines in *catenary* or *taut-leg* configurations. Mooring lines in a free-hanging catenary
configuration transmit essentially horizontal loads to the foundation system.
This fact introduces a greater flexibility in the selection of the appropriate
anchor type. However, the mooring radius (the horizontal distance, measured at
the sea bottom, from the center of the platform) is relatively large; typically,
about two to three times the water depth. Therefore, the application of
catenary configurations may not be feasible in deep or ultradeep waters, due to
the increased weight of the mooring lines, and also due to installation
problems that may arise in congested scenarios with several platforms close
together (as is the case of some oil fields in the Campos Basin).

The taut-leg configuration has been proposed to tackle these constraints. This configuration, where the lines are not slack, allows the use of smaller line lengths. When associated with the use of new materials (such as polyester fiber ropes) [2], this leads to considerable reduction in the weight of the mooring system. Moreover, since at the anchor point the lines are not in contact with the seabed, and may reach inclinations around 45°, the mooring radius is typically equal to the water depth, therefore, considerably shorter than in catenary configurations.

However, taut-leg mooring systems transmit vertical loads to the foundation system. This is also the case with the tension leg platforms, which are moored by vertical tendons. Therefore, care should be taken in the selection of anchor types that can withstand vertical loads.

Amongst the foundation elements that have been applied
in deep water systems, two types of anchors can be mentioned: the *suction anchor* and the *vertically loaded anchor* (VLA) [3]. However, some installation
difficulties have been reported for suction anchors, due to added mass effects
and the resonant period for the lifting system at the installation depth that
may approach the dominant wave period at the site [4].
Vertically loaded anchors are easier to install, but require drag procedures
that may hinder their correct positioning, mainly in congested areas with many others
nearby platforms.

##### 1.2. The Torpedo Pile

The *torpedo pile* (illustrated in Figure 1) was proposed [5] as a solution to withstand
vertical loads while circumventing the problems associated with other types of
anchors. It consists simply in a metallic pipe, with closed tip, filled with
scrap chain, and concrete [6].

The installation does not require drag procedures such as employed in VLAs; the procedure is quite simple, and is illustrated in Figure 2. First, the installation vessel hangs the pile (connected to the mooring line) at a specified drop height, above the target point on the seabed. The design embedment is then reached by simply releasing the pile, letting it accelerate, fall freely, and then penetrate into the soil.

More than one hanging
configuration has been conceived, for instance, one of the alternatives (considered
for the installation of torpedo piles to anchor flexible risers or *mobile drilling
units* (MODUs)) has a chain loop at the top of the installation line, as
shown in Figure 3. As the pile falls, this loop
is pulled and unfolded. Therefore, the torpedo pile presents not only low cost
of manufacture, but also low cost of installation, since the same vessel can
transport and install the pile.

There is another configuration, for permanent mooring of production units, which does not present the chain loop, but requires two vessels to hang, respectively, the installation line and the mooring line (to which the torpedo pile is connected). In this configuration, the bottom end of the installation line is connected to an intermediate point of the mooring line, therefore, maintaining the pile suspended at the desired drop height. Above this connection point, there is a trigger that allows the mooring line and the pile to be released, causing the pile to fall (dragging with it the mooring line), and penetrate the soil.

Another advantage of the torpedo pile concept is that, since it can withstand horizontal and vertical loads, it can be used with mooring lines in a taut-leg configuration that, as mentioned before, is the preferred alternative for semisubmersible platforms and FPSO units in deeper waters and congested scenarios.

##### 1.3. Objective of the Paper

The design of a torpedo pile should employ theoretical models to predict pile penetration depth, such as the dynamic penetration model proposed by True [7]. This model relies on soil parameters whose values are assumed as known, fixed, and deterministic.

However, it is well known that the soil properties present a significant degree of variability that, associated to imprecisions in the determination of their design values, can affect the accuracy of the response given by the simulation method. The objective of this paper, therefore, is to study techniques to deal with the uncertainty of the soil parameters, and to associate these techniques to an analytical/numerical penetration model for the torpedo pile.

Two different approaches are considered for the
treatment of uncertainties of the penetration model. The first is a
probabilistic approach, based on the classical Monte Carlo method. The second is a “possibilistic” approach, derived using concepts from *fuzzy arithmetic* and fuzzy sets.

The following sections of the paper begin by describing the theoretical model and solution procedure considered for the simulation of the pile penetration. Firstly, the analytical formulation originally presented by True [7] is described; then a numerical solution procedure in the time domain is described, followed by an application where a pile dropped from a height of 200 m above the seabed is analyzed for deterministic, fixed values of the soil parameters.

The paper then proceeds by describing the soil parameters that are considered uncertain. Methodologies to assess the sensitivity of the response to the variation of these uncertain parameters are then presented, based on the Monte Carlo method (MC) and fuzzy arithmetic (FA). More important, such methodologies allow the designer to incorporate, into the analysis method, techniques for the formal treatment of these uncertainties.

Finally, results of applications of these concepts for the treatment of uncertainties are presented for an actual case study, beginning with results of deterministic parametric studies in order to assess the sensitivity of the response to the variation of the uncertain parameters. Results for the “probabilistic” MC approach are then presented, followed by the novel implementation and application of the approach based on FA. The results and performance of both approaches are compared, stressing the ability of the latter approach to efficiently deal with the uncertainties of the model, with outstanding computational efficiency, and therefore, to comprise an effective design tool.

#### 2. The Penetration Model

##### 2.1. Original Formulation: Penetration of Projectiles

Studies on the behavior of penetration of projectiles were initially intended for military applications [8] and were followed by studies on the prediction of final embedment depth of projectiles into soils [9, 10], and estimation of undrained shear strength [11, 12].

The development of a dynamic penetration model by the US navy was required to represent the penetration of propellant-embedded plate anchors into seafloor soils [13]. This kind of anchor is directly positioned on the mud line and an explosion, caused by the propeller system, pushes the anchor fluke down. In order to fulfill this objective, True [7] took into account recommendations given by authors of empirical models (such as Young [9]) and modified traditional bearing capacity formulations (for deep foundations in cohesive soils) to consider variations in penetration resistance with velocity and penetrator shape.

The analytical model developed in [7] to simulate the dynamic penetration of plate anchors is
based on Newton's
second law. Considering that the penetrator velocity *v* can be expressed as (where *z* stands for the soil depth), and
therefore, its acceleration can be expressed as ,
the governing equation can be written as follows: where is the effective mass of the
penetrator, given by

In this latter
equation, *M* and *V* are, respectively, the structural mass and the volume of the
penetrator, and is the mass density of the soil. It
can be seen that the term is similar to the “added mass” term of
the Morison equation [14], which has
been traditionally employed to calculate hydrodynamic drag and inertia loads on
cylinders immersed in fluid. In the present case, when multiplied by the
acceleration at the left-hand side of (2.1), the term introduces an additional inertia
force that corresponds to the contribution of the soil in which the penetrator
is immersed.

The forces in the right-hand side of (2.1) are *W _{s}* (the submerged weight of the penetrator);

*F*,

_{D}*F*, and

_{T}*F*(which are, resp., the drag force, the tip resistance, and the side resistance); and

_{S}*F*(the external driving force applied by the propeller system).

_{E}The submerged weight *W _{s}* is defined in terms of the weight in air

*W*, volume

*V*, and the unit weight of soil by the following expression:

The drag force *F _{D}* is similar to the longitudinal drag component given by Morison’s equation
[14], which is expressed as: where

*A*is the frontal or cross-sectional area of the penetrator and

_{f}*C*is the empirical drag coefficient, that can have the value of 0.7 as proposed by True according to [13].

_{D}The classic formulation for *static* bearing
capacity of deep pile foundation states that, for undrained conditions, the tip
resistance *Q _{T}* and side
resistance

*Q*[15] are defined by where Su is the undrained soil shear strength;

_{S}*N*is the bearing capacity factor (assumed equals to 9 for homogeneous clay);

_{c}*α*is the dimensionless side adhesion factor; and

*A*and

_{f}*A*are, respectively, the frontal and lateral areas of the pile.

_{s}The dynamic tip and side resistance *F _{T}* and

*F*are now considered by the inclusion, in this classic static formulation, of a

_{S}*side adhesion reduction factor*, a

*soil strain rate factor*, and the

*soil sensitivity value*Sti. The latter represents the loss of shear strength that clays suffer when remolded, and is defined as the ratio of undisturbed and remolded strengths [16]. Thus, the tip resistance

*F*and side resistance

_{T}*F*are defined by the following expressions:

_{S} Values for the side adhesion reduction factor were
determined in [17] based on results of model tests. An
expression for the strain rate factor was
also defined in [17], as a function of the velocity *v* and the diameter (or thickness) of the
penetrator *d*, the undrained soil
shear strength Su, and other
empirical parameters. This expression can be written as

Values for the empirical parameters (maximum soil strain rate at high velocity values), *C _{e}* (strain
rate velocity factor), and

*C*(strain rate constant) were also determined in [17] based on results of model tests.

_{0}In the penetration model considered for offshore
applications in the Campos Basin [5], the undrained shear strength Su of the soil is assumed to vary linearly with depth *z*,
according to the following expression: where Su_{0} is the undrained shear strength at the mudline;
and Su_{k} is the rate of
increase with depth.

*Original Solution Procedure*

To solve (2.1), True [7] developed an incremental finite-difference algorithm
and considered that the penetrator is a point object at the *i*th depth increment, thus some
simplifications could be made:
Substituting the expressions for , and *W _{s}* in (2.9), ignoring the external
driving force

*F*, and assuming (according to [7]), the following expression is obtained, which can be applied repeatedly to obtain the velocities of the penetrator at each depth increment :

_{E}The final penetration depth can then be seen as the product of the depth increment by the number of the increment for which the penetrator velocity drops to zero. It should be recalled that, as in any numerical solution procedure, the accuracy of the results also depends on the careful selection of the depth increment. This will depend on the particular example that is analyzed, and may also involve the use of different increment values to assess the convergence to an accurate solution.

##### 2.2. Formulation for Free-Falling Pile

A free-falling cylindrical penetrometer, dropped
from a given height above the mudline, is studied in [12] for the prediction of penetration depth and undrained
shear strength. Equation (2.1),
with some modifications, is also applied to describe the movement of this
penetrometer. Firstly, since it is free falling and there is no external driving
force, the term *F _{E}* is
omitted. Also, the added mass in (2.2)
is considered negligible for slender penetrometers moving along their long
axis, thus, the effective mass is equal to the structural mass ().
Therefore, also replacing the velocity

*v*by and considering that the acceleration

*a*is equal to , (2.1) becomes

Obviously, torpedo piles behave similarly to the free-falling penetrometers, therefore, their motions can also be described by the True formulation [7], with the same considerations as employed in [12], resulting in (2.11). Moreover, the traditional Morison hydrodynamic formulation can also be applied to describe the forces acting in the pile while it is still in the water, before reaching the seabed, and the same submerged weight can be assumed for both media.

It should be emphasized that such semiempirical formulations incorporate some assumptions, which in turn leads to uncertainties in the model. However, as mentioned in the introduction, this work is focused on the influence of the uncertainties in selected soil parameters. Studies regarding uncertainties associated with the penetration model itself will be dealt with in future works.

*Solution Procedure in the Time Domain*

It can be observed that the procedure originally
proposed by True [7], as described in (2.9) and (2.10), involved the spatial integration of (2.1) to obtain velocities as functions of depth *z*. This indeed would be the more natural
solution procedure if one is concerned only with the representation of the
isolated pile and the natural random variability of the soil parameters with
depth. However, as will be commented later, there are other sources of
uncertainty to be concerned as well.

Moreover, as will be commented in the final section of
this paper, the final goal of the developments presented here is to incorporate
the penetration model (including the techniques that will be described later,
based on fuzzy arithmetic, for the formal treatment of uncertainties) in a finite
element (FE) spatial discretization scheme, associated to a time domain nonlinear
dynamic solver. The idea is to model not only the isolated pile, but also all
the other components of the system being installed (i.e., the mooring line
itself and the other lines and chains involved in the installation procedure),
in a complete 3D model submitted to other loadings such as marine current.

With this scenario in mind, it is more convenient to
integrate (2.11) in the time domain. At the current stage, where the focus is in
modeling an isolated pile and evaluating the uncertainties in the soil
parameters, the added mass can still be disregarded as assumed in [12]
and in (2.11). In a posterior implementation of
the penetration model in a time domain solver associated to the full FE model, the dynamic equations will also
incorporate the added mass effects of the complete configuration of the pile
with the installation and mooring lines.

The solution in the time domain, in terms of the acceleration , velocity , and displacement , at a given time , can be accomplished by applying a time-integration algorithm such as the Chung and Lee explicit method [18] that can be stated as: where and are algorithmic parameters defined as ; and is the time step, which should not exceed a critical time step () in order to maintain the stability of the numerical solution [18]. The full time domain solution procedure is shown in Algorithm 1. It should also be recalled that the displacements, velocities, and accelerations are positive in the downward direction.

*Application*

The application of the penetration model described
above is now illustrated for a problem also studied in [19], corresponding to a pile dropped from a
height of 200 m above the seabed. The pile and soil data are presented in Tables 1 and 2. It should be emphasized that the
soil parameters and the sensitivity value of 3 are related to a specific
deposit and may not necessarily be representative of general applications.

This application is not intended to represent an actual
installation procedure for a torpedo pile (such as the depicted in Figure 2), since, as mentioned before, the current implementation
of the penetration model represents only the pile. Therefore, in order to take
into account the increase on drag effects due to the mooring line and chain
loop that are not explicitly represented, the model employs a value for the
drag coefficient *C _{D}* equal to 2.7, larger than the
value of 0.7 as proposed by True according to [13]. In future
works, which will consider the implementation of a coupled finite element-based,
time domain simulation program, there will be no need to perform this “fudging”
of the drag coefficient

*C*, since the coupled 3D model will explicitly include the complete installation configuration (e.g., the mooring line and chain loop for the application in MODUs described earlier).

_{D}The time domain solution considered a total time of 15 seconds (enough, as will be seen, for the pile to fully penetrate the soil). The analysis is performed with a time increment of 0.002 seconds. The value considered for the algorithmic parameter of the time-integration algorithm is .

The results are presented in Figure 4, in terms of a graph relating the vertical position of the pile to its velocity, and in Figure 5 in terms of time histories of depth and velocity. The origin of the graph of Figure 4 corresponds to the pile in its initial position, before being dropped (therefore, with velocity and displacement equal to zero). It is seen that, as the pile drops in the water, its velocity increases until it nearly reaches the so-called “terminal velocity” due to the water drag (of course, this requires the pile to be released from an appropriate height). As the pile reaches the seabed and begins to penetrate in the soil, the velocity is reduced; when it returns to zero the penetration is completed and the final depth of the pile tip is reached.

#### 3. Uncertainties of the Soil Parameters

*Selected Parameters*

As stated before in (2.8), for cohesive soils in offshore applications in Campos Basin [5], the undrained soil shear strength Su is assumed to vary linearly with
depth *z*, in terms of Su_{0} (undrained shear
strength at the mudline) and of Su_{k} (the rate of increase with depth). For the normally consolidated clay
encountered offshore in the Campos Basin [5], typical values that may be considered for Su_{0} and Su_{k} are, respectively, kPa
and kPa/m.
Therefore, (2.8) could be written as
Actual values for these
parameters that affect the undrained soil shear strength Su for offshore sites may be obtained
fromin situ tests (such as CPT—cone
penetration tests [20], or vane tests based on a torsion procedure),
or from laboratory tests with undisturbed samples, such as triaxial and
minivane tests. It should also be recalled that the soil sensitivity Sti represents the loss of shear strength that clays suffer when remolded, and is
defined as the ratio of undisturbed and remolded strengths [16]. Remolded strength values can be
obtained from vane, triaxial, or minivane tests of disturbed soils.

Thus, it can be seen that values for Su (undrained shear strength) and Sti (sensitivity) are obtained from testing. Traditionally, a deterministic procedure is employed to obtain design values for these parameters, by calculating the average of values obtained from several tests. However, it is known that the results of both in-situ or laboratory tests may be influenced by several factors. The latter tests can be affected by factors such as mechanical disturbance in the soil samples, in the process of extraction and remolding; by changes in the samples during storage, and so forth. In-situ tests can also be affected by mechanical interferences, inadequate execution, and so on.

Therefore, it can be intuitively understood that there is a high degree of local soil variability, and imprecisions in the determination of the design values of these soil parameters. Large variations in the response of the torpedo pile, mainly in terms of the final penetration depth reached by the pile, may be expected due to these uncertainties. The main objective of this paper, then, is to present a methodology to take into account uncertainties and imprecision in the values of input parameters that define the physical and numerical models involved in the design and analysis of torpedo piles.

This work focuses on Su (specifically, the rate of increase with depth Su_{k}) and Sti. Of course, other parameters (not
necessarily related only to the soil) could be considered; however, those can be
dealt with in future works.

*Sources of Uncertainty*

Before proceeding further, it is
important to recall some basic concepts regarding sources of uncertainty. In soil
profile modeling, they may be grouped in two types [21–23]: (1) noncognitive, random natural variability, usually referred
as *aleatory* uncertainty; and (2) cognitive or *epistemic* uncertainties, that involve
abstraction or subjectivity.

The first group
comprises the inherent uncertainty type, due to natural heterogeneity or
in-situ variability of the soil, such as varying depths of strata during soil
formation, variation in mineral composition, and stress history [24]. This corresponds for instance
to the natural variability of the soil strength from point to point vertically
at the position where the pile is to be installed.

The second
group includes epistemic uncertainties due to lack of knowledge; in this case,
information about subsurface conditions is few, because soil profile
characteristics must be inferred from field or laboratory investigation of a
limited number of samples. It includes also uncertainties generated from sample
disturbance, test imperfections, human factors, and also, when engineering
properties are obtained through correlation with index properties, as in the case
of CPT tests where empirical models are used to calculate the undrained shear
resistance by applying correlation factors to the cone tip resistance [24].

According to
this classification, two major approaches, respectively, probabilistic or
“possibilistic” can be employed to deal with uncertainties [25, 26]. Therefore, the
remainder of this paper will deal with methodologies based on these approaches, to
assess the sensitivity of the response to the variation of the selected
parameters and mainly to incorporate, into the analysis method, techniques for
the formal treatment of uncertainties. Section 4 will describe a probabilistic
approach based on the Monte Carlo method and an approach based on fuzzy arithmetic (FA).

Before
proceeding, some additional comments should be presented regarding these
sources of uncertainty. Inherent or natural variability are random by nature and
cannot be reduced by increasing the number of tests [27]. The cognitive,
epistemic uncertainties are reducible; however, for offshore sites, they will
usually be present since the cost of performing in-situ tests at offshore sites
is very expensive. Such tests may not be performed for every installation site
and sometimes the values of the parameters are even estimated or extrapolated
from previous tests made at other locations. Moreover, disturbances in these
few samples are very common.

As strange as
it may seem to experienced geotechnical engineers, not involved in deepwater offshore
activities, this is precisely what has happened in soil investigations in the Campos Basin.
Those are the reasons why *epistemic uncertainties are always added to the
natural variability*: the use of limited data, of data arising from disturbed
soil samples, and data from locations other than the one at which the torpedo
pile is to be installed. In summary, the fact that there may be no knowledge of
the exact site local soil variability is the very reason why (as presented in
the next section) probabilistic approaches may fail, and is the motivation of
the use of the approach based on FA.

#### 4. Approaches for Treatment of Uncertainties

##### 4.1. Probabilistic Approach: The Monte Carlo Method

As mentioned before, noncognitive sources of uncertainty involve parameters that can be treated as random variables, and to which a probabilistic distribution can be associated, based on statistical data. In such cases, the probabilistic approach is traditionally recommended.

Probabilistic approaches for treatment of uncertainties can be divided in two main categories. The first one comprises statistical methods that involve simulation, such as the classical Monte Carlo simulation method and its variants. The second category comprises nonstatistical methods such as those based on perturbation techniques. For instance, the stochastic finite element method [26] falls in this latter category; it is based on expanding the random parameters around their mean values via Taylor series, in order to formulate linear relationships between some random characteristics of the response and the random input parameters.

In the implementation of the classical
Monte Carlo simulation, *N* samples of
the uncertain parameters are randomly generated using a given joint probability
density function. The deterministic analysis procedure is employed for each
sample of the simulation process [28], obtaining
then *N* responses that are statistically
treated to get the first two statistical moments of the response (mean and
standard deviation values).

The MC method is completely
general, for linear or nonlinear analyses. However, in general, the accuracy of
the statistical response is only adequate when the number of sample data *N* is sufficiently large; therefore, it
is usually considered too expensive in terms of computing time. This fact has
motivated studies on variants of the classical method, involving for instance
variance reduction techniques and the Neumann expansion [29].

Due to its robustness and ability to effectively treat the noncognitive, random uncertainties, the classical MC simulation method has been used to calibrate and validate all other probabilistic techniques. The studies presented in this paper will also employ this method as a benchmark to compare the performance of the approach based on fuzzy arithmetic, which will be described in Section 4.2, in the representation of the random uncertainties.

##### 4.2. Fuzzy Arithmetic (FA) Approach

It is important to recall that the cognitive sources of
uncertainty are related not to chance, but rather to imprecise or vague
information, involving subjectivity and/or dependent on expert judgment.
Moreover, the axioms of probability and statistics are not adequate to deal with
such types of uncertainties, which can be more effectively treated by
“possibilistic” approaches employing for instance the theory of *fuzzy sets*.

The theory of fuzzy sets was introduced by Zadeh in [30] to define classes of objects with continuous
membership graduations or associations in the interval [0, 1]. A fuzzy set has
vague limits, allowing graded changes from one class to another, instead of
exact limits characteristic of ordinary or *crisp* sets. In classical
Boolean algebra, the notion of false and true values is limited to 1 or zero.
In fuzzy logic, values that are “more or less” true or false can be treated,
defined by real numbers that vary continuously from 0 to 1.

The treatment of uncertainties that derive from
imprecise information is then possible, avoiding the use of random information. Therefore, complex systems, that would be hard to
model with the theory of conventional sets, can be easily modeled by fuzzy
sets. The fuzzy set theory allows the representation of imprecise and uncertain
measures as fuzzy numbers, defined as: where is the numeric support of the fuzzy number *A*,
and is the membership function (MF).

Fuzzy numbers are completely characterized by their MFs, that are built based on knowledge of an expert, who can assign “low,” “probable,” or “high” values for the desired parameters. Based on this subjective information, MFs can be constructed presenting either linear or nonlinear shapes. The more usually employed shapes for engineering problems are triangular, trapezoidal, and sinusoidal; the choice will depend on the type of application, and will also follow the assessment of the expert. In this work, triangular fuzzy MFs are used, defined by estimating three values [31]:

(i)a more
reliable value, *m*, to which is
attributed a membership degree equals to1;(ii)an
inferior value, *a*, that most
certainly will be exceeded by another value, and to which is attributed a
membership degree equals to 0;(iii)a superior
value, *b*, that most certainly will
not be exceeded by another value, and to which is also attributed a membership
degree equals to 0.

The membership function can then be defined as zero
outside the interval of
possible values; taken as linear into this range, increasing from *a* to *m*,
and decreasing from *m* to *b*. This function is triangular, not
necessarily symmetric, and can be defined as parameterized piecewise linear
functions as: where *a* and *b* are, respectively, the lower and upper bounds, and *m* is the dominant value, as illustrated
in Figure 6.

Fuzzy numbers can also be defined by *L* (left) and *R* (right) MFs, resulting into the so-called *L*-*R* fuzzy numbers. In this context, a two-parameter modification of
an *L*-type MF applies to all ,
whereas the *R*-MF defines *A* for ,
thus yielding

Therefore, the fuzzy number can also be identified by the notation , where and are the spreads of the number, which represents its uncertainty [32].

*Fuzzy arithmetic (FA)* operations, involving fuzzy numbers, can be used to propagate fuzziness from
inputs to outputs. General operations can be deduced from the extension
principle, which is used to transform fuzzy sets via functions [32]
, and plays a fundamental role in translating
set-based concepts into their fuzzy set counterparts. However, simplified formulae can be
obtained considering the *L*-*R* formulation of fuzzy numbers and .
The standard arithmetic operations are computed as follows.

The addition of triangular fuzzy numbers results in another triangular fuzzy number. Both addition and subtraction conserves the linearity of the numbers. These operations are expressed as, respectively,

The multiplication of two
fuzzy numbers produces a quadratic number. However, a linear approximation can
be assumed when the spreads * α* and are small in comparison to the modal or
dominant values

*m*. Therefore, this operation can be approximated to

The
multiplication of a fuzzy number by a scalar *a* is defined as:

The division between two fuzzy numbers is computed as

To apply the FA in a given engineering problem, the uncertainty on each variable is modeled as a triangular fuzzy number; moreover, all the operations related to them have their expressions replaced by the corresponding FA expressions, as shown above on operations (4.4) to (4.7) .

#### 5. Implementation and Case Studies

Before proceeding with the study of the approaches described above, this section will begin with deterministic studies to assess the sensitivity of the response of the penetration model to the variation of the selected soil parameters. Later, in order to reach the goal of incorporating the formal treatment of uncertainties in the analysis of the penetration of torpedo piles, this section will proceed by presenting the application of the probabilistic approach based on the Monte Carlo method, followed by the implementation and application of the approach based on FA.

Recalling that uncertainties related to the penetration model involve a combination of both noncognitive (random, natural variability of soil parameters) and cognitive (epistemic, due to incomplete or imprecise information), it will be seen that, while the MC method can effectively deal only with the random uncertainties, the implementation of the fuzzy approach presented here can represent all sources of uncertainty.

##### 5.1. Deterministic Sensitivity Studies

In order to perform an assessment of the sensitivity of
the torpedo pile penetration to the uncertainty of the selected soil
parameters, a parametric study is performed by considering the same problem
described in Tables 1 and 2. The penetration model is applied to deterministic
and arbitrary variations on both uncertain parameters: the rate of increase with
depth of the undrained shear strength (Su_{k})
and the soil sensitivity (Sti).

Recalling that according to Table 2, the fixed, “deterministic” values are kPa/m
and . Initially, Su_{k} and Sti are individually increased by 10, 20, and 30%. Then, their values are reduced, also by 10, 20
and 30%. The results of the analyses for the different values of the parameters
are presented in the graphs of Figure 7,
corresponding to analyses where they are increased and reduced, respectively.

It should be noted that, since the drop height and the characteristics of the pile have not been changed, the behavior of the pile from the drop point until it reaches the seabed is the same as observed in Figure 4. Therefore, the graphs of Figure 7 represent only the behavior of the pile as it penetrates the soil, beginning from the depth of 200 m (that corresponds to the seabed) until it completes the penetration.

A summary of the results of Figure 7 is presented in Table 3 and Figure 8, in terms of penetration values (displacement
minus the drop height) for each variation of the parameters Su_{k} and Sti. It can be verified that, as expected, reducing the undrained
shear strength (and therefore, the soil resistance) leads to the increase on
the final depth values. On the other hand, decreasing the sensitivity values
increases the soil resistance and, consequently, reduces the penetration of the
pile.

##### 5.2. Probabilistic Analysis Using the Monte Carlo Method

*Statistical Treatment of the Soil Input Parameters*

In
the probabilistic analysis using the MC method, both uncertain parameters (the
undrained shear strength increase rate Su_{k} and soil sensitivity Sti) are varied
simultaneously. Their values are randomly simulated, following a statistical
distribution and its associated values of mean and standard deviation, derived
from a given set of soil data from laboratory and/or in situ tests (in this case, the data were acquired from many
tests performed at different sites in a certain cluster of Campos Basin). This is
accomplished by performing a statistical treatment on the available data, representative
of offshore fields in Campos Basin. As a result, for
each uncertain parameter, the mean (which
in this case is the sample average) and standard deviation values were
estimated. These values are presented
in Table 4.

In order to determine an appropriate probability
distribution function for the data, a
normality verification is performed for each parameter. Figure 9 present the results, respectively, for Su_{k} and Sti.
It can be observed that, despite Sti data fits better than Su_{k},
the normal pdf is not the ideal approximation for them. Hence, other functions
are fitted and verified, as presented in Figure 10. Observing this figure, it can be
verified that the lognormal pdf provides a better fit for both sets of
available data. Another advantage of this distribution is that it does not
generate negative values for the soil parameters, which does not have physical
meaning and can generate erroneous results.

The number of simulations in an MC strategy is dictated
by the convergence of the mean value of the considered parameter to the
deterministic design value. In the present case, 1000 generations were needed
to obtain satisfactory convergence. Figure 11 depicts the distribution of the
1000 randomly generated values of Su_{k} and Sti, following the
lognormal distribution.

*Results*

The probabilistic study then comprises a total number
of 1000 analyses with the penetration model, each taking a randomly generated
pair of values for the soil parameters Su_{k} and Sti, following the lognormal
probability distribution with expected values and standard deviations given in
Table 4.

The results of the 1000 analyses are then gathered to
proceed to a statistical treatment, which will represent the penetration value
in terms of mean and standard deviation.
These results are presented in Table 5.

These values will be compared
with the results obtained with the approach using FA, which will be presented in
Section 5.3. We recall that the mean value of 39.8 m for the penetration cannot
be directly compared to the “deterministic” value of 35.6 m obtained in the
previous section, since the fixed, “deterministic” values for the soil
parameters were kPa/m and , and the mean
probabilistic values gathered from the set of soil data considered are kPa/m and . Anyway, the
results are consistent since, as could be observed in the results of the
deterministic sensitivity studies summarized in Figure 8, lower values of Su_{k} and
higher values of Sti lead to
higher penetration values.

##### 5.3. Fuzzy Arithmetic: Implementation and Application

*Implementation*

In the computational implementation of the approach using
FA, the uncertain variables Su_{k} and Sti are represented as triangular fuzzy numbers. Therefore, the
computational code is altered, and all operations performed with those
parameters in the solution procedure (as described in Algorithm 1 and (2.2)–(2.6)) have the traditional
arithmetic operators replaced by the fuzzy operators presented in (4.4) to (4.7).

As mentioned in Section
4.2, the fuzzy operations of multiplication and division generate quadratic
numbers; however, when their spreads are small, they can be approximated by
linear ones. Therefore, although (4.5) to (4.7) are approximations for small
spreads, they are feasible for this specific work, since the values of final
penetration (dominant value, and the lower and upper bounds) are more important
than the shape of the membership function.

Once these fuzzy
operators are implemented in the computational code, it remains to determine the
values that define the triangular membership functions, which represent Su_{k} and Sti as fuzzy numbers. As illustrated in Figure 6
, these values are the lower and upper bounds *a* and *b*, and the dominant value *m*;
they can be derived by investigating the statistical distribution of the soil
parameters, taking the lognormal distribution generated as described in the
previous item.

The lower and
upper bounds *a* and *b* can be assumed as defining an interval
of confidence of 75% corresponding to one standard deviation below and above
the mean. This criterion provides samples that have consistent values for the
uncertain parameters (positive values, Sti greater than 1.0, etc.), and is
illustrated in Figures 12 and 13, for
Su_{k} and Sti distributions, respectively.

Regarding the
“dominant” value *m*, the first choice
could be to take the mean value; however, since the most representative value
of a sample with large dispersion is the median, its value was chosen as *m* for each parameter. The values thus
obtained for *a*, *b*, and *m* that define the membership functions for Su_{k} and Sti are presented in Table 6 and graphically represented in Figure 14.

*Results*

Finally, the evaluation of the uncertain response using
this FA approach consists simply in performing one analysis with the
penetration model. The uncertainties embedded in the fuzzy numbers that
represent the parameters Su_{k} and Sti are incorporated in the
calculation of the terms *F _{T}* and

*F*defined in (2.6), at the right-hand side of step b.1 of Algorithm 1, and therefore are updated and propagated at each time step of the solution procedure presented in that table. This fact points to the remarkable computational advantage of this approach, compared to the probabilistic MC method with conventional arithmetic that required a total number of 1000 analyses.

_{S}The results of the fuzzy analyses, in terms of lower, dominant, and upper bound for the final penetration of the torpedo pile are presented in Table 7.

##### 5.4. Comparison of Results

This section compares the results of analyses of the
torpedo pile with the penetration model, considering both MC and FA approaches.
Before comparing the final pile penetration, Figure 15 presents the full behavior of the
pile as it penetrates the soil, in terms of penetration *x* velocity curves, beginning from
the depth of 200 m (that corresponds to the seabed) until it completes the
penetration.

Three curves are presented for each approach, corresponding to the “dominant” or “most probable” result, and a lower and upper “bounds” of the response. For the MC simulation, the “most probable” curve is represented by taking the median values of penetration and velocities at each time step of the response; the lower and upper bounds are determined, respectively, by taking the mean value and subtracting or adding one standard deviation (similarly to the procedure applied to determine the bounds of the fuzzy input parameters).

For the FA approach, the “dominant” curve is represented by taking the “crisp” result, that is, the values corresponding to a degree of membership equal to one. The lower and upper bounds are determined by the support of the fuzzy set defined by the values corresponding to a membership degree greater than zero.

Table 8 summarizes and compares the results presented in Tables 5 and 7 for the MC and fuzzy approaches. Observing this table and also Figure 15, it can be observed that the “dominant” or “most probable” results for the final penetration are practically the same; the difference between the median value of the MC analyses and the dominant value of FA analyses is insignificant.

Regarding dispersion of results, it should be recalled that MC “lower” and “upper” results (defined by subtracting and adding one standard deviation to the mean value) cannot be directly compared to the spreads of the fuzzy results, where lower and upper bounds define the interval where a value can possibly represent the calculated penetration. Anyway, it can be noted that the uncertainties of the soil parameters are quite significant and can have a decisive influence in the design of the torpedo pile.

A better comparison for the final penetration can be
graphically assessed in terms of the probability distribution of the MC
simulation, and the membership function that characterizes the fuzzy number for
the FA approach. Therefore, Figure 16 compares the
results obtained from the MC and FA approaches, in terms of the probability
distribution and membership function. For this example, while the assumed supports
of the fuzzy input parameters Su_{k} and Sti corresponded to a certainty
interval of 75% of their lognormal distribution, the support of the fuzzy
number that represents the final penetration corresponds to a certainty
interval of around 80% of the MC distribution.

Finally, the most remarkable comparison in the performance of both methods can be stated in terms of the total CPU time required. While the MC probabilistic approach required 1000 analyses with the penetration model using the solution procedure of Algorithm 1, only one analysis was required for the approach employing FA.

#### 6. Final Remarks and Conclusions

The torpedo pile has been acknowledged as a very promising alternative to anchor mooring systems. It has recently been considered for use not only in mooring lines of floating production systems, but also for mobile offshore drilling units (MODUs) operating in deep and ultradeep waters. Therefore, oil exploitation companies are devoting intense research and design activities in order to deliver efficient mooring solutions using this concept.

One of the main aspects concerning the design of foundation systems are the uncertainties involved in the determination of values for the soil parameters. For conventional onshore systems, this aspect has been tackled by performing a large number of tests with soil samples. However, on deepwater offshore sites the cost of performing such tests may be very expensive, if not prohibitive; therefore, tests may not be performed for every installation site, and sometimes results of tests made at other locations are used to estimate or extrapolate the values of the soil parameters.

This fact can severely affect the effectiveness of the design and analysis of torpedo piles, leading to large discrepancies in the response of the torpedo pile, mainly in terms of the final depth reached by the pile. Therefore, it is very important to develop and employ methodologies to properly assess the sensitivity of the response to the variation of these parameters, and to incorporate, into the analysis method, techniques for the formal treatment of the uncertainties.

The classical probabilistic Monte Carlo simulation could be considered for this purpose, since it is a sound methodology to estimate the effect of random uncertainties. Nevertheless, in the problem described in this work, there are a great amount of epistemic uncertainties in the model equations and parameters, and therefore, MC simulation results provide only a rough estimation of the uncertainty. In addition, the application of MC simulation requires excessive computational costs, as has been confirmed in the case study considered in this work. More than 1000 simulations were needed to obtain the results.

On the other hand, the computational efficiency of the fuzzy arithmetic approach is outstanding—around three orders of magnitude less. Therefore, the results of the application of the FA approach demonstrated its ability to provide low-cost approximations of the bounds of the uncertainties, and therefore, to comprise an effective design tool for the practitioner.

*Future Developments*

In this study, only two soil parameters, the undrained
shear strength and soil sensitivity, were considered as uncertain. Extensions
of the fuzzy methodology presented in this work could consider the treatment of
other uncertain parameters, such as for instance, the empirical maximum soil
strain rate factor (*S _{e}*),
the empirical soil strain rate factor (

*C*), and the drag coefficient

_{e}*C*considered for the calculation of the soil drag force as the pile penetrates; this latter parameter can vary for different anchor or pile shapes. Also, this work did not consider the uncertainties associated with the penetration model itself. These could also be considered, since it is a mainly empirical model and involves imprecision in its formulation.

_{D}Finally, a promising approach for the design of offshore systems would be to incorporate the pile penetration model, associated with the fuzzy methodology, in the implementation of a coupled finite element-based, time domain simulation program. In such implementation, not only the isolated torpedo pile is considered, but also a full finite-element model of all components involved in the installation of the pile (i.e., the mooring line itself and the other lines and chains, illustrated in Figures 2 and 3). The result is a complete 3D model, also submitted to environmental loadings other than dead weight (such as marine current). In such coupled model, there will be no need to “fudge” the drag coefficient

*C*, to account for the presence of the mooring line and chain loop.

_{D}Such computational tool would therefore comprise an efficient tool for the design of mooring systems based on torpedo piles, and for the simulation of the procedures needed for the installation of such complex offshore system.

#### Acknowledgments

The authors would like to acknowledge the help of Mr. Cláudio dos Santos Amaral and Dr. Álvaro Maia da Costa, experts in geotechnical engineering from CENPES-Petrobras (Research and Development Center of the Brazilian state oil company), for the invaluable collaboration regarding information and data essential for the completion of this work.

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