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Research article

Extension of TOPSIS method under intuitionistic fuzzy hypersoft environment based on correlation coefficient and aggregation operators to solve decision making problem

  • Received: 30 October 2020 Accepted: 20 December 2020 Published: 05 January 2021
  • MSC : 03E72, 62A86, 68T35, 90B50

  • Intuitionistic fuzzy hypersoft set is an extension of the intuitionistic fuzzy soft set used to express insufficient evaluation, uncertainty, and anxiety in decision-making. It is a new technique to realize computational intelligence and decision-making under uncertain conditions. The intuitionistic fuzzy hypersoft set can deal with uncertain and fuzzy information more effectively. The concepts and properties of the correlation coefficient and the weighted correlation coefficient of the intuitionistic fuzzy hypersoft sets are proposed in the following research. A prioritization technique for order preference by similarity to ideal solution (TOPSIS) based on correlation coefficients and weighted correlation coefficients is introduced under the intuitionistic fuzzy hypersoft sets. We also introduced aggregation operators, such as intuitionistic fuzzy hypersoft weighted average and intuitionistic fuzzy hypersoft weighted geometric operators. Based on the established TOPSIS method and aggregation operators, the decision-making algorithm is proposed under an intuitionistic fuzzy hypersoft environment to resolve uncertain and confusing information. A case study on decision-making difficulties proves the application of the proposed algorithm. Finally, a comparative analysis with the advantages, effectiveness, flexibility, and numerous existing studies demonstrates this method's effectiveness.

    Citation: Rana Muhammad Zulqarnain, Xiao Long Xin, Muhammad Saeed. Extension of TOPSIS method under intuitionistic fuzzy hypersoft environment based on correlation coefficient and aggregation operators to solve decision making problem[J]. AIMS Mathematics, 2021, 6(3): 2732-2755. doi: 10.3934/math.2021167

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  • Intuitionistic fuzzy hypersoft set is an extension of the intuitionistic fuzzy soft set used to express insufficient evaluation, uncertainty, and anxiety in decision-making. It is a new technique to realize computational intelligence and decision-making under uncertain conditions. The intuitionistic fuzzy hypersoft set can deal with uncertain and fuzzy information more effectively. The concepts and properties of the correlation coefficient and the weighted correlation coefficient of the intuitionistic fuzzy hypersoft sets are proposed in the following research. A prioritization technique for order preference by similarity to ideal solution (TOPSIS) based on correlation coefficients and weighted correlation coefficients is introduced under the intuitionistic fuzzy hypersoft sets. We also introduced aggregation operators, such as intuitionistic fuzzy hypersoft weighted average and intuitionistic fuzzy hypersoft weighted geometric operators. Based on the established TOPSIS method and aggregation operators, the decision-making algorithm is proposed under an intuitionistic fuzzy hypersoft environment to resolve uncertain and confusing information. A case study on decision-making difficulties proves the application of the proposed algorithm. Finally, a comparative analysis with the advantages, effectiveness, flexibility, and numerous existing studies demonstrates this method's effectiveness.



    1. Introduction

    Fluid flow through deformable porous media is relevant for many applications in biology, medicine and bioengineering. Some important examples include blood flow through tissues in the human body [9, 11] and fluid flow inside cartilages, bones and engineered tissue scaffolds [10, 13, 21, 31, 37]. The mechanics of biological tissues typically exhibits both elastic and viscoelastic behaviors resulting from the combined action of various components, including elastin, collagen and extracellular matrix [22, 23, 26, 30]. Thus, from the mathematical viewpoint, the study of fluid flows through deformable porous biological structures requires the coupling of poro-elasticity with structural viscoelasticity, leading to poro-visco-elastic models.

    The theoretical study of fluid flow through deformable porous media has attracted a lot of attention since the beginning of the last century, initially motivated by applications in geophysics and petroleum engineering. The development of the field started with the work of Terzaghi in 1925 [38], which focused on finding an analytic solution for a one-dimensional (1D) model. However, it was Biot's work in 1941 [5] that set up the framework and ignited the mathematical development for fluid flow through poro-elastic media. To date, several books and articles have been devoted to the mathematical analysis and numerical investigation of poro-elastic models, such as [8, 12, 14, 15, 16, 25, 27, 28, 29, 34, 35, 36, 37, 40], with applications ranging from engineering and geophysics to medicine and biology. Recently, our team has developed a theoretical and numerical framework to study both poro-elastic and poro-visco-elastic models, as motivated by biological applications [6]. The study showed that structural viscoelasticity plays a crucial role in determining the regularity requirements for volumetric and boundary forcing terms, as well as for the corresponding solutions. Moreover, in [2] it has been shown that the solution of the fluid-solid mixture (elastic displacement, fluid pressure, and Darcy velocity) is more sensitive to the boundary traction in the elastic case than in the visco-elastic scenario. These theoretical findings are also supported by experimental and clinical evidences showing that changes in tissue viscoelasticity are associated with various pathological conditions, including atherosclerosis [24], osteoporosis [1], renal disease [20] and glaucoma [17].

    Interestingly, the study in [6] provided numerical clues that sudden changes in body forces and/or stress boundary conditions may lead to uncontrolled fluid-dynamical responses within the medium in the absence of structural viscoelasticity. This finding led us to formulate a novel hypothesis concerning the causes of damage in biological tissues, namely that abrupt time variations in stress conditions combined with lack of structural viscoelasticity could lead to microstructural damage due to local fluid-dynamical alterations, as illustrated in Fig. 1.

    Figure 1. Illustration of a novel hypothesis on potential causes of microstructure damage in deformable porous media. In the case that (ⅰ) the medium is subjected to a time-discontinuous mechanical load and (ⅱ) structural viscoelasticity is reduced or absent, then the fluid velocity within the porous medium will experience a blow-up, possibly leading to microstructural damage.

    The importance of the coupling between structural mechanics and fluid dynamics in the damage of deformable porous media has been investigated by several authors [39, 23, 33]. In the present study, we focus on a particular aspect of this coupling and we aim at characterizing and quantifying the influence of structural viscoelasticity on the biomechanical and fluid-dynamical responses to sudden changes in stress conditions. Biomechanical applications are characterized by the fact that tissues have a mass density that is similar to that of water. For this reason, we consider in these pages the case of deformable porous media constituted by incompressible solid and fluid components. To mathematically define this concept, we introduce the following relation between fluid pressure p, variation of fluid content ζ and volumetric dilation u, u denoting the solid displacement

    ζ=c0p+αu, (1a)

    where c0 is the storage coefficient and α is the Biot coefficient (see [16]). In the case of incompressible solid and fluid components, we have c0=0 and α=1 (see [16]) so that (1a) becomes

    ζ=u. (1b)

    Notice that unlike in standard elasticity theory, incompressibility of each component of a deformable porous medium does not mean that both solid displacement and fluid velocity are divergence-free, rather, that the volumetric deformation of the solid constituent corresponds to the variation of fluid volume per unit volume of porous material, with the convention that u is positive for extension while ζ is positive for a "gain" of fluid by the porous solid.

    Building upon our theoretical and numerical results presented in [6], we devise a 1D problem for which we exhibit an explicit solution where the discharge velocity goes to infinity if the stress boundary condition is not sufficiently smooth in time and the solid component is not viscoelastic. Interestingly, this blow-up in the velocity occurs even in the simple case where the permeability of the medium is assumed to be constant, in comparison to the general case of nonlinear permeability depending on dilation [6, 8] or pressure [36]. In addition, we perform a dimensional analysis that allows us to identify the parameters influencing the solution blow-up, thereby opening the path to sensitivity analysis on the system, and providing practical directions on how to control the biomechanical and fluid-dynamical response of the fluid-solid mixture, prevent microstructural damage and, in perspective, aid the experimental design of bioengineered tissues [18].

    The article is organized as follows. Poro-visco-elastic models are described in Section 2, along with a summary of the related theoretical results. Section 3 focuses on a special 1D case, for which an explicit solution is derived and its well-posedness is studied in the presence or absence of viscoelasticity. The dimensional analysis of the 1D problem is carried out in Section 4. Solution properties are explored in detail for the particular case of boundary traction with a discontinuity in time, see Section 5, and with a trapezoidal time profile, see Section 6. The application of this analysis to the case of confined compression of biological tissues is discussed in Section 7. Conclusions and perspectives are summarized in Section 8.


    2. Poro-visco-elastic models: Description and theoretical results

    Following the same notation as in [6], let ΩRd, with d=1,2 or 3, be the region of space occupied by a deformable porous medium and let (0,T) be the observational time interval. In the assumptions of small deformations, full saturation of the mixture, incompressibility of the mixture constituents and negligible inertia, we can write the balance of linear momentum for the mixture and the balance of mass for the fluid component as

    σ+F=0inΩ×(0,T) (2a)
    ζt+v=SinΩ×(0,T) (2b)

    respectively, where σ is the total stress tensor, ζ is the fluid content, v is the discharge velocity and F and S are volumetric sources of linear momentum and fluid mass, respectively. The system must be completed by constitutive equations for σ, ζ and v. In line with the literature for poro-visco-elastic models in biological applications, we will adopt the following constitutive equations:

    σ=σe+σvpI (2c)
    σe=λe(u) I+2μeϵ(u) (2d)
    σv=λv(ut) I+2μvϵ(ut) (2e)
    ζ=u (2f)
    v=Kp (2g)

    where u is the displacement of the solid component, p is the Darcy pressure, I is the identity tensor and ϵ(w)=(w+(w)T)/2 is the symmetric part of the gradient of the vector field w. The elastic parameters λe and μe and the viscoelastic parameters λv and μv are assumed to be given positive constants. We remark that structural viscoelasticity is embodied in the term σv defined in (2e). If structural viscoelasticity is not present, then σv=0 and the set of equations (2a)-(2g) defines a poro-elastic medium. In addition, the permeability K may be a positive constant, as in [5], or may depend nonlinearly on the solution, for example on the structural dilation, namely K=K(u), as in [6, 8]. Finally, notice that (2f) corresponds to (1b) consistently with the assumption of incompressibility of mixture constituents.

    Most of the theoretical studies focused on the poro-elastic case without accounting for structural viscoelasticity. In the case of constant permeability, the coupling between the elastic and fluid subproblems is linear and the well-posedness and regularity of solutions have been studied by several authors [25, 35, 40]. In the case of non-constant permeability, the coupling between the two subproblems becomes nonlinear and only few theoretical results have been obtained. In [36], Showalter utilized monotone operator theory techniques in order to provide well-posedness of solutions in the case where the permeability is a nonlinear function of pressure.

    To the best of our knowledge, Cao et al in [8] were the first to consider the permeability as a nonlinear function of dilation and provide existence of weak solutions for this nonlinear poro-elastic case. However, the analysis in [8] is performed upon assuming homogeneous boundary conditions for both pressure and elastic displacement, which is often not the case from the viewpoint of applications.

    Our recent paper in [6] extends the works mentioned above by considering poro-elastic and poro-visco-elastic models with dilation-dependent permeability, non-zero volumetric sources of mass and momentum and non-homogeneous, mixed Dirichlet-Neumann boundary conditions. More precisely, in [6] we assumed that the boundary of Ω can be decomposed as Ω=ΓNΓD, with ΓD=ΓD,pΓD,v, where the following boundary conditions are imposed:

    σn=g,vn=0onΓN×(0,T) (3)
    u=0,p=0onΓDp×(0,T) (4)
    u=0,vn=ψonΓDv×(0,T). (5)

    Our analysis showed that the data time regularity requirements and the smoothness of solutions significantly differ depending on whether the model is poro-elastic or poro-visco-elastic. In particular, in the visco-elastic case, if the source of linear momentum F is L2 in time and space, namely FL2(0,T;(L2(Ω))d), and the source of boundary traction g is L2 in time and H1/2 on the boundary, namely gL2(0,T;(H1/2(ΓN))d), then there exists a weak solution to the system, with elastic displacement uH1 in time and space, namely uH1(0,T;(H1(Ω))d), and pressure pL2(0,T;H1(Ω)). In comparison, in the poro-elastic case, one needs both F and g to be H1 in time in order to obtain a solution where both u and p are L2 in time and H1 in space. This proves that the system dynamics fundamentally changes as visco-elastic effects fade away. Moreover, the numerical simulations performed in [6] hinted at blow-up of fluid energy functional in the poro-elastic case with non-smooth sources F and g. In the following sections, we are going to further elaborate these concepts by means of a 1D poro-visco-elastic model for which we can obtain an explicit solution and assess the effect of structural viscoelasticity on the response of the deformable porous medium to sudden changes in mechanical load. The 1D model is described next.


    3. 1D poro-visco-elastic model: Description and formulation

    Let the space domain Ω be given by the interval (0,L) and the volumetric sources of linear momentum and fluid mass be zero. In this case, the balance of linear momentum for the mixture and the balance of mass for the fluid component simplify to

    σx=0in(0,L)×(0,T) (6a)
    2utx+vx=0in(0,L)×(0,T). (6b)

    The associated constitutive equations are given by

    σ0=μux+η2utxin(0,L)×(0,T) (7a)
    σ=σ0pin(0,L)×(0,T) (7b)
    v=Kpxin(0,L)×(0,T) (7c)

    where we have set μ=λe+2μe, η=λv+2μv and we have introduced the symbol σ0 to denote the contribution of the solid component to the total stress tensor σ. We emphasize that the permeability may be a function of space and dilation, namely

    K=K(x,ux).

    We complete the system with the following boundary and initial conditions:

    u(0,t)=v(0,t)=0for0<t<T (8a)
    p(L,t)=0 ; σ(L,t)=P(t)for0<t<T (8b)
    u(x,0)=0for0<x<L (8c)

    where g(t)=P(t) is the boundary traction. For the ease of reference, the boundary conditions are schematized in Fig. 2.

    Figure 2. Schematic representation of the one-dimensional problem considered in this article. The deformable porous medium may be either poro-elastic or poro-visco-elastic. The forcing term P(t) may have discontinuities in time.

    If η=0 (i.e., no viscoelasticity is present) and under the assumption that the permeability K and the boundary forcing term P are given positive constants, system (6) degenerates into the poro-elastic model studied experimentally and analytically in [37]. Specifically, the boundary and initial conditions (8a)-(8c) correspond to the creep experimental test performed in confined compression, where the boundary condition (8a) represents an impermeable interface at the bottom of the specimen whereas the boundary condition (8b) represents a free-draining permeable piston at the top of the specimen.

    System (6a)-(8c) can be rewritten solely in terms of the displacement. Indeed, integration of (6b ) with respect to x yields

    ut(x,t)+v(x,t)=A(t)

    where A(t) is arbitrary. Thus, taking (8a) into account, we obtain that A(t)=0. From (7c), we then have that

    utKpx=0 in (0,L)×(0,T) .

    On the other hand, using (7b) and (6a), we derive that

    px=σ0x .

    Therefore the system (6a)-(8c) reduces to the following initial boundary value problem in terms of the sole elastic displacement u:

    utKμ2ux2Kη3utx2=0in (0,L)×(0,T) (9a)
    μux(L,t)+η2utx(L,t)=P(t)for0<t<T (9b)
    u(0,t)=0for0<t<T (9c)
    u(x,0)=0for0<x<L . (9d)

    Subsequently, we can recover the solid part of the stress tensor, discharge velocity, pressure and total stress tensor on (0,L)×(0,T), respectively, as

    σ0=μux+η2utx (10a)
    v=ut=Kσ0x (10b)
    p=σ0+P(t) (10c)
    σ=P(t) . (10d)

    Remark 1.  For later reference, we write below the explicit form of the purely elastic problem which corresponds to setting η=0 in (9):

    utKμ2ux2=0in (0,L)×(0,T) (11a)
    μux(L,t)=P(t)for0<t<T (11b)
    u(0,t)=0for0<t<T (11c)
    u(x,0)=0for0<x<L . (11d)

    Remark 2.  An important quantity associated with the fluid-solid mixture is the fluid power density P(t), defined as

    P(t)=L01K|v(x,t)|2dx. (12)

    From its definition, it follows that P(t) depends on both discharge velocity and permeability.


    3.1. Formal derivation of the solution

    Let us further assume that the permeability is constant, i.e.

    K=K(x,ux)K0=constant>0.

    In this case, system (9) is a linear initial boundary value problem, whose solution can be obtained by Fourier series expansion as described below.

    Case 1. (η>0). First of all, we homogenize the boundary condition by setting

    w(x,t)=u(x,t)+U(t)μx

    having introduced the auxiliary function

    U(t)=μηt0exp(μη(ts))P(s)ds=μηexp(μηt)P(t)

    where the star symbol denotes convolution. Thus, w(x,t) satisfies the problem

    wtK0μ2wx2K0η3wtx2=xμU(t)in (0,L)×(0,T)μwx(L,t)+η2wtx(L,t)=0for0<t<Tw(0,t)=0for0<t<Tw(x,0)=0for0<x<L (13)

    where the prime symbol denotes differentiation for functions of a single variable. The associated eigenvalue problem is

    Find y=y(x), 0<x< L, such that
    y+λy=0,y(0)=y(L)=0 .

    The eigenvalues and the corresponding eigenfunctions are given by

    λn=(2n+1)2π24L2andyn(x)=sin(2n+1)πx2Lforn=0,1, (14)

    We seek a solution of the form

    w(x,t)=n=0cn(t)yn(x)

    where the coefficients cn(t) of the above series are determined by the differential equation and the initial condition in (13 ). Using the Fourier Sine series expansion

    x=2Ln=0(1)nλnyn(x),0xL

    the uniqueness of the Fourier expansion leads to the family of ordinary differential equations

    (1+K0ηλn)cn(t)+K0μλncn(t)=2(1)nμLλnU(t),cn(0)=0

    whose solution is given by

    cn(t)=2(1)nμLλn(1+K0ηλn)exp(K0μλn1+K0ηλnt)U(t) .

    Therefore, we get

    u(x,t)=U(t)μx+2μLn=0(1)nyn(x)λn(1+K0ηλn)exp(K0μλn1+K0ηλnt)U(t).

    In conclusion, after performing integration by parts in the convolution term and using the identity ηU(t)+μU(t)=μP(t), the formal solution to problem (9) is given by

    u(x,t)=2K0Ln=0(1)nyn(x)1+K0ηλnexp(K0μλn1+K0ηλnt)P(t) . (15)

    Case 2. (η=0). A direct substitution of expression (15) (with η=0) into problem (11) shows that

    u(x,t)=2K0Ln=0(1)nyn(x)exp(K0μλnt)P(t) (16)

    is the formal solution of the purely elastic problem. In particular, in the case P(t)=constant, expression (16) coincides with Eq. (8) of [37].


    3.2. Weak solutions and well-posedness

    In this section we prove that the formal solutions (15) and (16) indeed solve the visco-elastic problem (9) and the purely elastic problem (11), respectively, in well-defined functional spaces. Let us begin by introducing the functional framework. We consider the real Hilbert space H=L2(0,L) equipped with the scalar product

    (f,g)H=L0f(x)g(x) dxf,gH,

    and endowed with the induced norm

    fH=(f,f)H.

    The orthonormal sequence of eigenfunctions {2Lyn(x)} forms a Hilbert space basis for H. A distribution v in (0,L) belongs to H if and only if it has a series expansion (converging in D(0,L))

    v(x)=n=0cnyn(x), (17)

    with coefficients cn satisfying

    n=0|cn|2<.

    If this is the case, the series expansion of v converges in the mean. Next, let

    V={vH:vH,v(0)=0}

    be the real Hilbert space equipped with the scalar product

    (v,w)V=(v,w)Hv,wV,

    and endowed with the induced norm (due to Poincaré's inequality)

    vV=vH.

    Sobolev's Embedding Theorem ensures that VC0([0,L]), so that the boundary value v(0)=0 for vV is assumed in a strong sense. The sequence of functions {2Lλnyn(x)} forms a Hilbert space basis for V. A function vH belongs to V if and only if the Fourier coefficients of the series expansion (17) satisfy

    n=0λn|cn|2<.

    More generally, the eigenfunction expansion (17) enables us to define a one-parameter family Vs (sR) of spaces: the distribution (17) belongs to Vs if and only if its coefficients cn satisfy

    n=0λsn|cn|2< .

    In particular, we have that V1V (the dual of V).

    Case 1. (η>0). Now we introduce the definition of weak solution for the poro-visco-elastic case.

    Definition 3.1.  A function u:[0,T]V is a weak solution to problem (9) if:

    (ⅰ) uH1(0,T;V), implying that u,uL2(0,T;V);

    (ⅱ) for every vV and for t pointwise a.e. in [0,T],

    (u(t),v)H+K0(μu(t)+ηu(t),v)V=K0P(t)v(L); (18)

    (ⅲ) u(0)=0,

    where we used the notation u(t)=u(,t) and u(t)=ut(,t).

    Remark 3.  The initial condition (9d) is satisfied by u(t)V in the pointwise sense of condition (ⅲ), since it is well known that condition (ⅰ) implies that uC0([0,T];V). The Dirichlet boundary condition (9c) at x=0 is included in the requirement that u(t)V, whereas the boundary condition (9b) at x=L is taken into account in the linear form on the right hand side of (18), where v(L) is the pointwise value of the (continuous) test function vV.

    Theorem 3.2.  Suppose P(t)L2(0,T). Then there is a unique weak solution u to problem (9), in the sense of Definition 3.1. Moreover, u is represented by the series expansion (15).

    Proof. For sake of exposition, we rewrite u(x,t), given by (15), as

    u(x,t)=n=0un(t)yn(x) (19)

    where

    un(t)=2K0L(1)n1+K0ηλnexp(K0μλn1+K0ηλnt)P(t) (20)

    so that

    ut(x,t)=n=0un(t)yn(x) (21)

    where

    un(t)=2K0L(1)n1+K0ηλnP(t)K0μλn1+K0ηλnun(t). (22)

    [Regularity]. Firstly, we show that u(x,t) has the desired regularity, namely uH1(0,T;V) and u(0)=0 . With this goal in mind, we study the convergence of the series expansions in (19) and (21). For any a>0 Schwarz inequality implies that

    |eatP(t)|12aPL2(0,T).

    Upon applying this estimate to (20) and (22) we see that there are positive constants, generically denoted by C, that are independent on n and t and for which it holds that

    |un(t)|CλnPL2(0,T) (23)

    and

    |un(t)|Cλn(|P(t)|+PL2(0,T)). (24)

    Thus, u,uL2(0,T;Vs) for every s<3/2, thereby implying that conditions (ⅰ) and (ⅲ) of Def. 3.1 are satisfied.

    [Existence]. In order to show that u satisfies condition (ⅱ), it suffices to consider v=yn as test function, since {2Lλnyn(x)} are a basis of V. Each term on the left hand side of (18) can be computed as

    (u(t),yn)H=L2un(t)
    (u(t),yn)V=(ux(,t),dyndx())H=L2λnun(t)
    (u(t),yn)V=(2utx(,t),dyndx())H=L2λnun(t) .

    Adding the above three terms, we obtain that

    (u(t),yn)H+K0(μu(t)+ηu(t),yn)V=L2[(1+K0ηλn)un(t)+K0μλnun(t)] .

    Thus, from (22) and the fact that yn(L)=(1)n, it follows that the right hand side is equal to K0P(t)yn(L), and this completes the proof of existence.

    [Uniqueness] From the linearity of the equation, it suffices to show that P=0 implies u=0. Let P=0 and let uH1(0,T;V) be a solution of

    (u(t),v)H+K0(μu(t)+ηu(t),v)V=0,vV .

    If we choose v=u(t) as a test function, then we obtain that

    (u(t),u(t))H+K0μu(t)2V+K0η(u(t),u(t))V=0

    implying that

    12ddt(u(t)2H+K0ηu(t)2V)=K0μu(t)2V0 .

    Integrating with respect to time and using the initial condition u(0)=0, we get

    u(t)2H+K0ηu(t)2V0

    Hence u(t)=0 for every t in [0,T].

    Remark 4.  From (23), the fact that |yn(x)|1, and the standard Weierstrass Test, it follows that the series (19) converges absolutely and uniformly in the space-time rectangle Q=[0,L]×[0,T] and its sum is continuous, i.e. u(x,t)C0(Q). In a similar way, under the stronger assumption P(t)L(0,T), estimate (24) implies |un(t)|Cλ1nPL(0,T) so that ut(x,t)C0(Q) also holds. Thus, in the physically realistic case of bounded boundary traction P(t), discharge velocity and fluid power density are continuous and bounded both in space and time, and this is true even though the traction has jump discontinuities (e.g., it is a step pulse).

    Case 2. (η=0). In the purely elastic problem, the formal solution (16) is again written as given in (19) where now

    un(t)=2K0L(1)nexp(K0μλnt)P(t).

    Then, estimates (23) and (24) become, respectively,

    |un(t)|CλnPL2(0,T)

    and

    |un(t)|Cλn(|P(t)|+PL2(0,T)).

    As a consequence, it can only be asserted that uL2(0,T;Vs) for every s<1/2 and uL2(0,T;Vs) for every s<3/2. On the other hand, if one assumes P(t)L(0,T), we find that

    |un(t)|CλnPL(0,T)

    and

    |un(t)|CPL(0,T).

    Hence, in this case, uL2(0,T;Vs) for every s<3/2 and uL2(0,T;Vs) for every s<1/2. In particular, when the boundary traction is bounded, we have u(x,t)C0(Q) whereas the discharge velocity is a distribution. In conclusion, we state the following definition and theorem (without proof):

    Definition 3.3.  A function u:[0,T]V is a weak solution to problem (11) if:

    (ⅰ) uL2(0,T;V), uL2(0,T;V);

    (ⅱ) for every vV and for t pointwise a.e. in [0,T],

    u(t),v+K0μ(u(t),v)V=K0P(t)v(L) (25)

    where the brackets , denote pairing between V and V;

    (ⅲ) u(0)=0.

    Theorem 3.4.  Suppose P(t)L(0,T). Then there exists a unique weak solution u to problem (11), in the sense of Definition 3.3. Moreover, u is represented by the series expansion (16).


    4. Dimensionless problem

    The goal of this section is to rewrite problem (9) in dimensionless form so that we can identify combinations of geometrical and physical parameters that most influence the solution properties. Dimensional analysis relies on the choice of a set of characteristic values that can be used to scale all the problem variables. Let us use the hat symbol to indicate dimensionless (or scaled) variables and the square brackets to indicate the characteristic value of that quantity. Then, for the problem at hand we would write:

    ˆx=x[x],ˆt=t[t],ˆη=η[η],ˆλn=λn[λn],ˆP=P[P],ˆu=u[u],ˆv=v[v],ˆP=P[P] . (26)

    It is important to emphasize that there is no trivial choice for the characteristic values and, in general, this choice is not unique. In this particular case, though, we will leverage our knowledge of the forcing terms and the explicit formulas we obtained for the solution to guide us in the choice of some of these values. Since the problem is driven by the boundary condition on the traction with the given function P(t), we set

    [P]=Pref (27)

    where Pref is a reference value, for example the mean value, of the given function P(t). The expression for λn in (14) suggests to choose

    [λn]=1L2and[x]=L (28)

    and, consequently, the expression for u(x,t) in (15) suggests to choose

    [η]=1K0[λn]=L2K0,[t]=1K0μ[λn]=L2K0μ,[u]=K0L[P][t]=PrefLμ , (29)

    whereas the expressions for v(x,t) in (10b) and P(t) in (12) suggest to choose

    [v]=[u][t]=K0LPref (30)

    and

    [P]=LK0[v]2=K0LP2ref (31)

    respectively. Using the above scalings, we obtain the following dimensionless problem:

    ˆuˆt2ˆuˆx2ˆη3ˆuˆtˆx2=0in (0,1)×(0,ˆT) (32a)
    ˆuˆx(1,ˆt)+ˆη2ˆuˆtˆx(1,ˆt)=ˆP(ˆt)for0<ˆt<ˆT (32b)
    ˆu(0,ˆt)=0for0<ˆt<ˆT (32c)
    ˆu(ˆx,0)=0for0<ˆx<1 (32d)

    where ˆT=T/[t] and ˆη0. Recalling again the expressions (15), (10b) and (12), we can now write solid displacement, discharge velocity and power density in dimensionless form as

    ˆu(ˆx,ˆt)=2n=0(1)nyn(ˆx)1+ˆηˆλnexp(ˆλn1+ˆηˆλnˆt)ˆP(ˆt) (33)
    ˆv(ˆx,ˆt )=2n=0(1)nyn(ˆx)1+ˆηˆλn{ˆP(ˆt )ˆλn1+ˆηˆλnexp(ˆλn1+ˆηˆλnˆt)ˆP(ˆt)} (34)
    ˆP(ˆt)=2n=01(1+ˆηˆλn)2{ˆP(ˆt )ˆλn1+ˆηˆλnexp(ˆλn1+ˆηˆλnˆt)ˆP(ˆt)}2 . (35)

    Remark 5.  As already mentioned above, the choice for the characteristic values is not unique. In this regard, it is worth noticing that our choice for [v], namely K0Pref/L, differs from that utilized in [37], which was K0μ/L instead. This difference actually follows from a different choice for the pressure scaling, namely Pref in our case, as opposed to μ in [37]. Indeed, the two scalings are equivalent if Pref and μ are of the same order of magnitude. In general, though, the scaling K0μ/L represents the characteristic velocity of wave propagation within the medium, whereas the scaling K0Pref/L represents the characteristic velocity induced by an external load of magnitude Pref. Since our analysis aims at assessing the influence of the external load on the biomechanical response of the medium, we chose [P]=Pref. However, the analysis could be easily adapted to other choices, should the scope of the investigation differ from that of this paper.


    5. The case ˆP(ˆt )=step pulse

    Let the boundary traction ˆP(ˆt ) be the dimensionless unit step starting at ˆt=0 defined as

    ˆP(ˆt )=H(ˆt )={0ifˆt<01ifˆt0 . (36)
    Figure 3. Schematic representation of the dimensionless step pulse ˆP(ˆt ) defined in (36). Here, the signal is discontinuous at the switch on time.

    In this case, the dimensionless solid displacement (33), discharge velocity (34) and power density (35) (hereon denoted with the subscript ˆη for later reference) become, respectively,

    ˆuˆη(ˆx,ˆt )=2n=0(1)nˆλn{1exp(ˆλnˆt1+ˆηˆλn)}yn(ˆx)=ˆx+2n=0(1)nˆλnexp(ˆλnˆt1+ˆηˆλn)yn(ˆx) (37a)
    ˆvˆη(ˆx,ˆt)=2n=0(1)n1+ˆηˆλnexp(ˆλnˆt1+ˆηˆλn)yn(ˆx) (37b)
    ˆPˆη(ˆt)=2n=01(1+ˆηˆλn)2exp(2ˆλnˆt1+ˆηˆλn) . (37c)

    The solution in the purely elastic case can be obtained by setting ˆη=0 in (37).

    Remark 6.  If the unit step is shifted at ˆt=α>0, i.e. ˆP(ˆt)=H(ˆtα), then the solution (33) is

    ˆu(ˆx,ˆt)=ˆuˆη(ˆx,ˆtα)H(ˆtα)={0if0ˆt<αˆuˆη(ˆx,ˆtα)ifˆtα.

    The space-time behavior of ˆuˆη, ˆvˆη and ˆPˆη is reported in Figures 4, 5 and 6, respectively, in the case of ˆη=0 and ˆη=1. We remark that ˆvˆη and ˆPˆη are reported in logarithmic scale to highlight the presence of a blow-up at ˆx=1 and ˆt=0 in the purely elastic case ˆη=0. Indeed, by setting ˆη=ˆt=0 in (37c), we can verify that the power density satisfies

    Figure 4. Dimensionless displacement ˆuˆη as a function of ˆx and ˆt. Left panel: ˆη=0. Right panel: ˆη=1.
    Figure 5. Dimensionless discharge velocity ˆvˆη as a function of ˆx and ˆt. Left panel: ˆη=0. Right panel: ˆη=1. In order to highlight the velocity blow-up at ˆx=1, ˆt=0, the log10 plot of |ˆvˆη| is plotted in both panels.
    Figure 6. Dimensionless power density ˆPˆη as a function of ˆt. Left panel: ˆη=0. Right panel: ˆη=1. In order to highlight the power density blow-up at ˆt=0, the log10 plot of ˆPˆη is plotted in both panels.
    ˆP0(0)=2n=01=+.

    Proceeding analogously in the case of the discharge velocity, the Fourier expansion in the purely elastic case is given by

    ˆv0(ˆx,0)=2n=0(1)nyn(ˆx)

    which clearly lacks pointwise convergence for any ˆx0.

    From the physical viewpoint, this means that, at the switch on time of the driving term, here set at ˆt=0, the discharge velocity contains high-frequency components whose superposition exhibits the following behaviors depending on ˆx: (ⅰ) it relaxes towards zero as ˆx0; (ⅱ) it tends to infinity as ˆx1, thereby exhibiting a blow-up; (ⅲ) it looks like an amorphous blob for 0<ˆx<1. On the other hand, if viscoelastic effects are present, then the dimensionless discharge velocity at ˆt=0 is given by

    ˆvˆη(ˆx,0)=2n=0(1)n1+ˆηˆλnyn(ˆx) .

    Here, the nth coefficient is decreasing as 1/n2 and the discharge velocity is continuous at ˆt=0. These concepts are illustrated in Fig. 7, which shows the spatial distribution of ˆvˆη at ˆt=0 for several values of ˆη in the interval [0,1]. The log10 plot of ˆvˆη is reported in the figure to highlight the blow-up of the discharge velocity in the purely elastic case at ˆx=1.

    Figure 7. Dimensionless discharge velocity ˆvˆη(ˆx,0) as a function of ˆx. In order to highlight the velocity blow-up at ˆx=1, ˆt=0, in the case ˆη=0, the log10 plot of |ˆvη| is plotted.

    In order to further investigate this blow-up and its dependence on the structural viscoelasticity, we observe that the maximum value of ˆvˆη is attained at ˆx=1 and ˆt=0 and can be written as a function of ˆη as follows:

    ˆvmax(ˆη)=max0ˆx1ˆt0|ˆvˆη(ˆx,ˆt)|=ˆvˆη(1,0)=2n=011+ˆηˆλn .

    The above series may be summed ([19], formula no. 1.4212) and the final result is

    ˆvmax(ˆη)=1ˆηtanh(1ˆη) . (38)

    Similarly, the dimensionless power density (37c) is decreasing in time and its maximum is attained at ˆt=0:

    ˆPmax(ˆη)=maxˆt0ˆPˆη(ˆt)=ˆPˆη(0)=2n=01(1+ˆηˆλn)2=12ˆη(tanh21ˆη+ˆηtanh1ˆη1) .

    The behaviors of ˆvmax(ˆη) and ˆPmax(ˆη) with respect to ˆη are visualized in Fig. 8.

    Figure 8. Left panel: maximal discharge velocity as a function of ˆη. Right panel: power density as a function of ˆη. Log10-scale is used on the y-axis to better highlight blow-up of both quantities as ˆη0.

    Clearly, the dimensionless parameter ˆη is the sole determinant of the blow-up in ˆvmax(ˆη) and ˆPmax(ˆη), with smaller values of ˆη leading to higher values of ˆvmax(ˆη) and ˆPmax(ˆη). However, it is important to recall that ˆη does not depend solely on the viscoelasticity coefficient. The definition ˆη=ηK0/L2 implies that also small values of the permeability K0 and/or large values of the domain dimension L would reduce the value of ˆη. Precisely, going back to dimensional units, we can use (30) and (31) to obtain the following expressions:

    vmax(ˆη)=PrefK0Lˆvmax(ˆη) (39)
    Pmax(ˆη)=P2refK0LˆPmax(ˆη). (40)

    We can see that the magnitude Pref of the driving term appears as a multiplying constant in the expressions above, meaning that larger values of Pref will lead to larger values of vmax and Pmax. However, the factor that controls the blow-up still remains the dimensionless parameter ˆη=ηK0/L2. These concepts are illustrated in Fig. 9, where we set K0/L=1 m2s Kg1 and we consider increasing values of Pref in the range [103,103] Nm2.

    Figure 9. Left panel: dimensional maximal discharge velocity as a function of ˆη. Right panel: dimensional power density as a function of ˆη. Log10-scale is used on the y-axis to better highlight blow-up of both quantities as ˆη0. We set K0/L=1 m2sKg1. The black arrows indicate increasing values of Pref in the range [103,103] Nm2.

    6. The case ˆP(ˆt) = trapezoidal pulse

    Let us now consider the case of a driving term given by a trapezoidal pulse, where the signal switch on and switch off are characterized by linear ramps. Thus, let ˆP(ˆt) be the dimensionless trapezoidal pulse of unit amplitude and let ˆε, ˆτ>0. Here we consider the following expression for ˆP(ˆt):

    ˆP(ˆt)={0ifˆt<0ˆtˆεif0ˆt<ˆε1ifˆεˆt<ˆε+ˆτˆτˆtˆε+2ifˆε+ˆτˆt<2ˆε+ˆτ0ifˆt2ˆε+ˆτ (41)

    where ˆε is the pulse rise/fall time and ˆτ is the pulse duration, as depicted in Fig. 10. Thus, the trapezoidal pulse defined in (41) reduces to a rectangular step pulse as ˆε0.

    Figure 10. Schematic representation of the dimensionless trapezoidal pulse ˆP(ˆt) defined in (41). Here, the signal switch on and switch off are characterized by linear ramps.

    Let us now compute the dimensionless discharge velocity resulting from the application of the trapezoidal pulse at the boundary, henceforth denoted by ˆVˆη(ˆx,ˆt) to distinguish it from the discharge velocity ˆvˆη(ˆx,ˆt) obtained for the step pulse. To this end, we observe that the trapezoidal pulse is actually the linear combination of four ramp pulses of unit slope starting at times ˆt=0, ˆε , ˆε+ˆτ, 2ˆε+ˆτ:

    ˆP(ˆt)=1ˆε{ˆtH(ˆt)(ˆtˆε)H(ˆtˆε) (42)
    (ˆtˆτˆε)H(ˆtˆτˆε)+(ˆtˆτ2ˆε)H(ˆtˆτ2ˆε)}

    where the function H(ˆt) was defined in (36). In addition, we can see that

    ˆu(ˆx,ˆt)=ˆt0ˆuˆη(ˆx,s)ds

    solves problem (32) with ˆP(ˆt)=ˆtH(ˆt) (the ramp pulse of unit slope starting at time ˆt=0), where ˆuˆη(ˆx,ˆt) is given by (37a). Thus, using (10b), we have that the discharge velocity is given by ˆuˆη(ˆx,ˆt), so that, applying the linear superposition principle, ˆVˆη(ˆx,ˆt) is the linear combination of the velocities corresponding to the various components of the pulse, namely:

    ˆVˆη(ˆx,ˆt)=1ˆε{ˆuˆη(ˆx,ˆt)H(ˆt)+ˆuˆη(ˆx,ˆtˆε)H(ˆtˆε)+ˆuˆη(ˆx,ˆtˆτˆε)H(ˆtˆτˆε)ˆuˆη(ˆx,ˆtˆτ2ˆε)H(ˆtˆτ2ˆε)} . (43)

    An illustration of the typical form of ˆVˆη(ˆx,ˆt) for ˆη0 and ˆε>0 is reported in Fig. 11.

    Figure 11. Dimensionless discharge velocity ˆVˆη(ˆx,ˆt) for ˆη=0.1, ˆε=0.2, ˆτ=1.

    The maximum possible discharge velocity occurs at ˆx=1, ˆt=ˆε and can be written as

    ˆVmax(ˆη,ˆε)=max0ˆx1ˆt0|ˆVˆη(ˆx,ˆt)|=ˆVˆη(1,ˆε)=2ˆεn=01ˆλn{1exp(ˆλnˆε1+ˆηˆλn)}. (44)

    The behavior of ˆVmax with respect to ˆη and ˆε is reported in Fig. 12. Interestingly, for all ˆη>0 and ˆε0 we have

    Figure 12. Dimensionless maximal discharge velocity ˆVmax(ˆη,ˆε) as a function of ˆη and ˆε.
    ˆVmax(ˆη,ˆε)ˆVmax(ˆη,0)

    and

    ˆVmax(ˆη,0)=ˆvmax(ˆη)

    since the trapezoidal pulse reduces to a rectangular pulse as ˆε0. Thus, no blow-up takes place in the viscoelastic case when the pulse is rectangular. Similarly, for all ˆη0 and ˆε>0 we have

    ˆVmax(ˆη,ˆε)ˆVmax(0,ˆε)=2ˆεn=01exp(ˆλnˆε)ˆλn,

    hence no blow-up takes place even in the purely elastic case when the pulse is trapezoidal.


    7. Application in biomechanics: Role of structural viscoelasticity in confined compression tests for biological tissues

    In this section, we utilize the mathematical analysis developed above to study some interesting features of confined compression tests, which are often utilized in biomechanics to characterize the properties of biological tissues. A schematic of the confined compression experimental setting is depicted in Fig. 13, where a compressive load is applied at the chamber top surface while the bottom surface is maintained fixed. Due to confinement, deformation occurs only in the x direction. The analogy with the 1D setting depicted in Fig. 2 is straightforward, with a note of caution that 1D solutions are most representative of the biomechanical status of the material along vertical lines towards the center of the chamber.

    Figure 13. Schematic representation of a confined compression chamber.

    The 1D model described in Section 3 allows us to generalize the mathematical analysis carried out in [37] by quantifying the effect of structural viscoelasticity on the tissue response to sudden changes in external pressure during confined compression experiments. In this perspective, L represents the thickness of the undeformed tissue sample and Pref represents the magnitude of the total compressive stress applied at x=L. It is important to notice that the confined compression creep experiment described in [37] is characterized by the same initial and boundary conditions as those given in (8). However, the mathematical description adopted in [37] (based on the theory developed in [22]), does not account for structural viscoelasticity, which amounts to setting η=0 in (7a). Table 1 summarizes the parameter values corresponding to the experiments on articular cartilage reported in [37].

    Table 1. Numerical values of model parameters in the confined compression experiment for articular cartilage reported in [37].
    symbol value units
    L 0.81103 m
    μ 0.97106 Nm2
    η 0 N s m2
    K0 2.91016 m4N1s1
    Pref 6104 Nm2
     | Show Table
    DownLoad: CSV

    Characteristic velocities for the confined compression experiments reported in [37] are of the order of 0.35 μm/s. However, our analysis showed that velocities in the sample may attain much higher values, particularly near the permeable piston, should viscoelasticity be too low and/or the time profile of the load protocol be too sharp. In order to make these concepts more specific, let us assume that: (ⅰ) the load protocol for the confined compression experiment corresponds to the trapezoidal pulse described in Section 6; and that (ⅱ) we should ensure that discharge velocities always remain below the threshold value Vth=0.35 μm/s in order to preserve the sample from microstructural damage. This means that ˆη and ˆε should be chosen as to guarantee that

    ˆVmax(ˆη,ˆε)<ˆVth=LPrefK0 Vth=16.3 , (45)

    where ˆVmax(ˆη,ˆε) can be computed using the expression (44). Fig. 14 compares ˆVth with the values of ˆVmax obtained for different values of ˆη and ˆε. Indeed, we can identify a whole region in the (ˆη,ˆε)plane for which ˆVmax(ˆη,ˆε)<ˆVth.

    Figure 14. Comparison between the dimensionless maximum velocity ˆVmax(ˆη,ˆε) obtained in the case of trapezoidal pulse using expression (44) and the threshold velocity ˆVth=16.3 typical of confined compression experiments [37].

    To better visualize the various regions of interest in the (ˆη,ˆϵ)plane, we reported the colormap of the difference ˆVthˆVmax as a function of ˆη and ˆε in Fig. 15, where we clearly marked the curve in the parameter space for which ˆVth=ˆVmax. These results show that, for example, choosing ˆε5103 would lead to fluid velocities below the recommended threshold regardless of the presence of structural viscoelasticity. On the other hand, should the particular experimental conditions constrain us to enforce a rectangular pulse corresponding to ˆε=0, we can still maintain the maximum velocity below the threshold by modifying the structural properties of the sample to have, for example, ˆη4103.

    Figure 15. Colormap of the difference ˆVthˆVmax as a function of ˆη and ˆε. As in Fig. 14, ˆVmax(ˆη,ˆε) is obtained using expression (44) and ˆVth=16.3 is the typical velocity of confined compression experiments [37]. The curve in the parameter space for which ˆVth=ˆVmax is reported in a thick black mark.

    Interestingly, expression (29) defines ˆη=(K0/L2)η, meaning that ˆη can be increased by increasing the viscoelastic parameter η, increasing the permeability parameter K0, or decreasing the sample thickness L. In the same spirit as [7], a typical value of the viscoelastic parameter η for biological tissues can be estimated by setting

    η=μτe (46a)

    where

    τe=Lρμ (46b)

    is the characteristic elastic time constant of the porous material under compression and ρ is the mass density of the fluid component of the mixture. Since the densities of most of the biological fluids are very similar to that of water, let us set ρ=1000 Kg m3. Substituting (46a) and (46b) in (29) we obtain

    ˆη=τe[t]=K0Lρμ. (46c)

    Replacing the parameter values of Table 1 into (46c) we obtain ˆη=1.15108, which is 5 orders of magnitude smaller than what is needed to attain fluid velocities below the recommended threshold in the case of a rectangular pulse in the pressure load. Thus, in order to control the maximum fluid velocity within the tissue sample, one may decide to: (ⅰ) vary the properties of the biological sample by manipulating ρ, μ, η, K0 or L; and/or (ⅱ) vary the time profile of the load experimental protocol. The choice will depend on the particular conditions and constraints of the experiment at hand.


    8. Conclusions and future perspectives

    The exact solutions obtained for the 1D models considered in this article allowed us to clearly identify a blow-up in the solution of certain poroelastic problems. Compared to the analysis carried out in [6], in which singularities in the solution were demonstrated by numerical evidences when the hypotheses of the existence theorem were not satisfied, the 1D study conducted here allowed us, via analytical solution, to prove that singularities may arise even in the simple case of constant permeability. The analysis allowed us to identify the main factors that give rise to the blow-up, namely the absence of structural viscoelasticity and the time-discontinuity of the boundary source of traction. It is very important to emphasize that even a small viscoelastic contribution, namely ˆη1, will prevent the blow-up. Similarly, a continuous time profile of the applied boundary load will prevent blow-up even if the structure is purely elastic.

    These findings actually provide an evidence of a blow-up that was hypothesized by the theoretical work presented in [6]. Interestingly, the a priori estimates derived in [6] were not sufficient to bound the power density if the data were not regular enough. The 1D examples considered in this article show that indeed it is not possible to bound the power density if the structure is purely elastic and the boundary forcing term is discontinuous in time. Moreover, we have shown that this blow-up occurs even in the simple case of constant permeability. We expect that similar results can be obtained for the 3D fluid-solid mixture described in (2a)-(2g) with boundary conditions (3)-(5). In particular, if one considers the coupling driven by a source of linear momentum and a source of boundary traction that are not time differentiable, then one can only obtain local in time existence of weak solutions for the system (in comparison to [6]), and prove blow-up of solutions in finite time (in the norm of the fluid energy). We plan to provide this analysis in a subsequent paper.

    In real situations, we will never see the fluid velocity spiking to infinity as predicted by the mathematical blow-up, since something will break first! For example, if the poroelastic model represents a biological tissue perfused by blood flowing in capillaries, as the maximum velocity becomes too high, capillaries will break letting blood out. Thus, from a practical perspective, it is crucial to identify parameters that can control the maximum value of the fluid velocity within a deformable porous medium in order to avoid microstructure damage. Our analysis showed that the maximum fluid velocity can be limited by: (ⅰ) decreasing Pref, thereby reducing the load on the medium; (ⅱ) increasing ˆε, thereby smoothing the load action on the medium; (ⅲ) increasing η, thereby changing the structural properties of the solid component of the medium; (ⅳ) decreasing K0, thereby changing the pore size, geometry and/or fluid viscosity; (ⅴ) decreasing L, thereby changing the domain geometry. We have explored these concepts more concretely by applying them to the confined compression tests for biological tissues.

    It is worth noting that the extreme sensitivity of biological tissues to the active role of viscoelasticity predicted by our theoretical analysis seems to agree with the conclusions of [32] where experimental data based on magnetic resonance elastography show that viscoelasticity of the brain is a result of structural alteration occurring in the course of physiological aging, this suggesting that cerebral viscoelasticity may provide a sensitive marker for a variety of neurological diseases such as normal pressure hydrocephalus, Alzheimer's disease, or Multiple Sclerosis.

    Further extensions of the current work may include: (1) the study of porous deformable media with compressible components. This case mathematically corresponds to considering (1a) instead of (1b), and is of interest in the wider context of applications of the poro-visco-elastic model to problems in geomechanics (cf. the original contribution by Biot in [5] and the more recent works [27, 28, 29] and [3, 4], devoted to computational analysis and theoretical investigations, respectively); (2) the investigation of time discontinuities in the volumetric sources of linear momentum, which were identified by the analysis in [6] as additional possible causes of blow-up. This case is actually extremely relevant in situations where the gravitational acceleration varies abruptly, such as during space flight take off and landing.

    Finally, it is worth emphasizing that the 1D analysis presented in this article has made available a series of testable conditions leading to blow-up, and consequent microstructural damage, that could indeed be verified in laboratory experiments. We see the design and implementation of such experiments as the most interesting development of the present work.


    Acknowledgments

    Dr. Bociu has been partially supported by NSF CAREER DMS-1555062. Dr. Guidoboni has been partially supported by the award NSF DMS-1224195, the Chair Gutenberg funds of the Cercle Gutenberg (France) and the LabEx IRMIA (University of Strasbourg, France). Dr. Sacco has been partially supported by Micron Semiconductor Italia S.r.l., SOW nr. 4505462139.




    [1] L. A. Zadeh, Fuzzy Sets, Information and Control, 8 (1965), 338–353.
    [2] K. Atanassov, Intuitionistic Fuzzy Sets, Fuzzy Set. Syst., 20 (1986), 87–96.
    [3] H. Garg, G. Kaur, Cubic intuitionistic fuzzy sets and its fundamental properties, J. Mult-valued Log. S., 33 (2019), 507–537.
    [4] K. Atanassov, G. Gargov, Interval valued intuitionistic fuzzy sets, Fuzzy Set. Syst., 31 (1989), 343–349. doi: 10.1016/0165-0114(89)90205-4
    [5] H. Garg, K. Kumar, Linguistic interval-valued Atanassov intuitionistic fuzzy sets and their applications to group decision-making problems, IEEE T. Fuzzy Syst., 27 (2019), 2302–2311. doi: 10.1109/TFUZZ.2019.2897961
    [6] J. C. R. Alcantud, A. Z. Khameneh, A. Kilicman, Aggregation of infinite chains of intuitionistic fuzzy sets and their application to choices with temporal intuitionistic fuzzy information, Information Sciences, 514 (2020), 106–117. doi: 10.1016/j.ins.2019.12.008
    [7] D. Molodtsov, Soft set theory-First results, Comput. Math. Appl., 37 (1999), 19–31.
    [8] P. K. Maji, R. Biswas, A. R. Roy, Fuzzy soft sets, Journal of Fuzzy Mathematics, 9 (2001), 589–602.
    [9] G. Ali, M. Akram, A. N. A. Koam, J. C. R. Alcantud, Parameter Reductions of Bipolar Fuzzy Soft Sets with Their Decision-Making Algorithms, Symmetry, 11 (2019), 1–25.
    [10] P. Maji, R. Biswas, A. Roy, Intuitionistic fuzzy soft sets, Journal of Fuzzy Mathematics, 9 (2001), 677–692.
    [11] X. Yang, T. Y. Lin, J. Yang, Y. Li, D. Yu, Combination of interval-valued fuzzy set and soft set, Comput. Math. Appl., 58 (2009), 521–527. doi: 10.1016/j.camwa.2009.04.019
    [12] M. Akram, G. Ali, J. C. R. Alcantud, New decision-making hybrid model: intuitionistic fuzzy N-soft rough sets, Soft Comput., 23 (2019), 9853–9868. doi: 10.1007/s00500-019-03903-w
    [13] T. Gerstenkorn, J. Mafiko, Correlation of intuitionistic fuzzy sets, Fuzzy Set. Syst., 44 (1991), 39–43. doi: 10.1016/0165-0114(91)90031-K
    [14] C. Yu, Correlation of fuzzy numbers, Fuzzy Set. Syst., 55 (1993), 303–307.
    [15] D. A. Chiang, N. P. Lin, Correlation of fuzzy sets, Fuzzy Set. Syst., 102 (1999), 221–226.
    [16] H. Garg, An improved cosine similarity measure for intuitionistic fuzzy sets and their applications to decision-making process, Hacet. J. Math. Stat., 47 (2018), 1578–1594.
    [17] H. Garg, K. Kumar, An advanced study on the similarity measures of intuitionistic fuzzy sets based on the set pair analysis theory and their application in decision making, Soft Comput., 22 (2018), 4959–4970. doi: 10.1007/s00500-018-3202-1
    [18] H. Garg, D. Rani, A robust correlation coefficient measure of complex intuitionistic fuzzy sets and their applications in decision-making, Appl. Intell., 49 (2018), 496–512.
    [19] W. L. Hung, J. W. Wu, Correlation of intuitionistic fuzzy sets by centroid method, Information Sciences., 144 (2002), 219–225. doi: 10.1016/S0020-0255(02)00181-0
    [20] H. Bustince, P. Burillo, Correlation of interval-valued intuitionistic fuzzy sets, Fuzzy Set. Syst., 74 (1995), 237–244. doi: 10.1016/0165-0114(94)00343-6
    [21] D. H. Hong, A note on correlation of interval-valued intuitionistic fuzzy sets, Fuzzy Set. Syst., 95 (1998), 113–117. doi: 10.1016/S0165-0114(96)00311-9
    [22] H. B. Mitchell, A correlation coefficient for intuitionistic fuzzy sets, Int. J. Intell. Syst., 19 (2004), 483–490. doi: 10.1002/int.20004
    [23] H. Garg, R. Arora, TOPSIS method based on correlation coefficient for solving decision-making problems with intuitionistic fuzzy soft set information, AIMS Mathematics, 5 (2020), 2944–2966. doi: 10.3934/math.2020190
    [24] H. L. Huang, Y. Guo, An Improved Correlation Coefficient of Intuitionistic Fuzzy Sets, J. Intell. Syst., 28 (2019), 231–243. doi: 10.1515/jisys-2017-0094
    [25] S. Singh, S. Sharma, S. Lalotra, Generalized Correlation Coefficients of Intuitionistic Fuzzy Sets with Application to MAGDM and Clustering Analysis, Int. J. Fuzzy Syst., 22 (2020), 1582–1595. doi: 10.1007/s40815-020-00866-1
    [26] C. L. Hwang, K. Yoon, Multiple Attribute Decision Making Methods and Applications, Lecture Notes in Economics and Mathematical Systems, Springer-Verlag Berlin Heidelberg, 1981.
    [27] M. Zulqarnain, F. Dayan, M. Saeed, TOPSIS Analysis for The Prediction of Diabetes Based on General Characteristics of Humans, International Journal of Pharmaceutical Sciences and Research, 9 (2018), 2932–2939.
    [28] A. Sarkar, A TOPSIS method to evaluate the technologies, International Journal of Quality & Reliability Management, 31 (2013), 2–13.
    [29] R. M. Zulqarnain, S. Abdal, B. Ali, L. Ali, F. Dayan, M. I. Ahamad, et al. Selection of Medical Clinic for Disease Diagnosis by Using TOPSIS Method, International Journal of Pharmaceutical Sciences Review and Research, 61 (2020), 22–27.
    [30] C. T. Chen, Extensions of the TOPSIS for group decision-making under fuzzy environment, Fuzzy Set. Syst., 114 (2000), 1–9. doi: 10.1016/S0165-0114(97)00377-1
    [31] L. Dymova, P. Sevastjanov, A. Tikhonenko, An approach to generalization of fuzzy TOPSIS method, Information Sciences, 238 (2013), 149–162. doi: 10.1016/j.ins.2013.02.049
    [32] M. Zulqarnain, F. Dayan, Choose Best Criteria for Decision Making Via Fuzzy Topsis Method, Mathematics and Computer Science, 2 (2017), 113–119. doi: 10.11648/j.mcs.20170206.14
    [33] A. Y. Yayla, A. Özbek, A. Yildiz, Fuzzy TOPSIS method in supplier selection and application in the garment industry, Fibres Text. East.Eur., 20 (2012), 20–23.
    [34] M. Zulqarnain, F. Dayan, Selection Of Best Alternative For An Automotive Company By Intuitionistic Fuzzy TOPSIS Method, International journal of scientific & technology research, 6 (2017), 126–132.
    [35] M. Akram, A. Adeel, J. C. R. Alcantud, Multi-Criteria Group Decision-Making Using an m-Polar Hesitant Fuzzy TOPSIS Approach, Symmetry, 11 (2019), 1–23.
    [36] H. Garg, R. Arora, Generalized and group-based generalized intuitionistic fuzzy soft sets with applications in decision-making, Appl. Intell., 48 (2018), 343–356. doi: 10.1007/s10489-017-0981-5
    [37] K. Zhang, J. Zhan, X. Wang, TOPSIS-WAA method based on a covering-based fuzzy rough set: an application to rating problem, Information Sciences, 539 (2020), 397–421. doi: 10.1016/j.ins.2020.06.009
    [38] H. Jiang, J. Zhan, B. Sun, J. C. R. Alcantud, An MADM approach to covering-based variable precision fuzzy rough sets: an application to medical diagnosis, Int. J. Mach. Learn. Cyb., 11 (2020), 2181–2207. doi: 10.1007/s13042-020-01109-3
    [39] T. Mahmood, Z. Ali, Entropy measure and TOPSIS method based on correlation coefficient using complex q-rung orthopair fuzzy information and its application to multi-attribute decision making, Soft Comput., 11 (2020), 1–27. doi: 10.5121/ijsc.2020.11401
    [40] J. Zhan, H. Jiang, Y. Yao, Covering-based variable precision fuzzy rough sets with PROMETHEE-EDAS methods, Information Sciences, 538 (2020), 314–336. doi: 10.1016/j.ins.2020.06.006
    [41] J. Ye, J. Zhan, Z. Xu, A novel decision-making approach based on three-way decisions in fuzzy information systems, Information Sciences, 541 (2020), 362–390. doi: 10.1016/j.ins.2020.06.050
    [42] J. Zhan, H. Jiang, Y. Yao, Three-way multi-attribute decision-making based on outranking relations, IEEE T. Fuzzy Syst., 2020, DOI: 10.1109/TFUZZ.2020.3007423.
    [43] L. Zhang, J. Zhan, Y. Yao, Intuitionistic fuzzy TOPSIS method based on CVPIFRS models: an application to biomedical problems, Information Sciences, 517 (2020), 315–339. doi: 10.1016/j.ins.2020.01.003
    [44] J. Zhan, B. Sun, Covering-based intuitionistic fuzzy rough sets and applications in multi-attribute decision-making, Artif. Intell. Re., 53 (2020), 671–701. doi: 10.1007/s10462-018-9674-7
    [45] F. Smarandache, Extension of Soft Set to Hypersoft Set, and then to Plithogenic Hypersoft Set, Neutrosophic Sets and Systems, 22 (2018), 168–170.
    [46] S. Rana, M. Qayyum, M. Saeed, F. Smarandache, Plithogenic Fuzzy Whole Hypersoft Set: Construction of Operators and their Application in Frequency Matrix Multi Attribute Decision Making Technique, Neutrosophic Sets and Systems, 28 (2019), 34–50.
    [47] R. M. Zulqarnain, X. L. Xin, M. Saqlain, F. Smarandache, Generalized Aggregate Operators on Neutrosophic Hypersoft Set, Neutrosophic Sets and Systems, 36 (2020), 271–281.
    [48] S. Alkhazaleh, Plithogenic Soft Set, Neutrosophic Sets and Systems, 33 (2020), 256–274.
    [49] M. Abdel-Basset, W. Ding, R. Mohamed, N. Metawa, An integrated plithogenic MCDM approach for financial performance evaluation of manufacturing industries, Risk Manag., 22 (2020), 192–218. doi: 10.1057/s41283-020-00061-4
    [50] M. Abdel-Basset, R. Mohamed, A. E. N. H. Zaied, F. Smarandache, A Hybrid Plithogenic Decision-Making Approach with Quality Function Deployment for Selecting Supply Chain Sustainability Metrics, Symmetry, 11 (2019), 1–21.
    [51] R. Arora, H. Garg, Robust aggregation operators for multi-criteria decision-making with intuitionistic fuzzy soft set environment, Sci. Iran., 25 (2018), 931–942.
    [52] Z. Xu, J. Chen, J. Wu, Clustering algorithm for intuitionistic fuzzy sets, Information Sciences, 178 (2008), 3775–3790. doi: 10.1016/j.ins.2008.06.008
    [53] H. M. Zhang, Z. S. Xu, Q. Chen, On clustering approach to intuitionistic fuzzy sets, Control and Decision, 22 (2007), 882–888.
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