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In numerical analysis, the interval finite element method (interval FEM) is a finite element method that uses interval parameters. Interval FEM can be applied in situations where it is not possible to get reliable probabilistic characteristics of the structure. This is important in concrete structures, wood structures, geomechanics, composite structures, biomechanics and in many other areas. The goal of the Interval Finite Element is to find upper and lower bounds of different characteristics of the model (e.g. stress, displacements, yield surface etc.) and use these results in the design process. This is so called worst case design, which is closely related to the limit state design. Worst case design requires less information than probabilistic design however the results are more conservative öylüoglu and Elishakoff 1998">Elishakoff.html" ;"title="öylüoglu and Elishakoff">öylüoglu and Elishakoff 1998


Applications of the interval parameters to the modeling of uncertainty

Consider the following equation: ax=b where ''a'' and ''b'' are real numbers, and x = \frac . Very often, the exact values of the parameters ''a'' and ''b'' are unknown. Let's assume that a\in[1,2]=\mathbf and b\in[1,4]=\mathbf . In this case, it is necessary to solve the following equation ,2= ,4 There are several definitions of the solution set of this equation with interval parameters.


United solution set

In this approach the solution is the following set \mathbf = \left\ = \frac = \frac = .5, 4 This is the most popular solution set of the interval equation and this solution set will be applied in this article. In the multidimensional case the united solutions set is much more complicated. The solution set of the following system of linear interval equations \begin & \\ & \end \begin x_1 \\ x_2 \end = \begin \\ \end is shown on the following picture 600px \sum(\mathbf,\mathbf)=\ The exact solution set is very complicated, thus it is necessary to find the smallest interval which contains the exact solution set 600px \diamondsuit\left(\sum(\mathbf,\mathbf)\right)=\diamondsuit\ or simply \diamondsuit\left(\sum(\mathbf,\mathbf)\right)= underline x_1,\overline x_1\times underline x_2,\overline x_2\times \dots \times underline x_n,\overline x_n where \underline x_i=\min\, \ \ \overline x_i = \max\ x_i\in\= underline x_i,\overline x_i See als


Parametric solution set of interval linear system

The Interval Finite Element Method requires the solution of a parameter-dependent system of equations (usually with a symmetric positive definite matrix.) An example of the solution set of general parameter dependent system of equations \begin p_1 & p_2 \\ p_2 + 1 & p_1 \end \begin u_1 \\ u_2 \end = \begin \frac \\ 2p_1-6 \end, \ \ \text \ \ p_1\in ,4 p_2\in
2,1 This list contains selected positive numbers in increasing order, including counts of things, dimensionless quantity, dimensionless quantities and probability, probabilities. Each number is given a name in the Long and short scales, short scale ...
is shown on the picture below.E. Popova, Parametric Solution Set of Interval Linear System


Algebraic solution

In this approach x is an interval number for which the equation ,2= ,4 is satisfied. In other words, the left side of the equation is equal to the right side of the equation. In this particular case the solution is x = ,2 because ax = ,21,2]= ,4 If the uncertainty is larger, i.e. a= ,4, then x= ,1/math> because ax = ,41,1]= ,4 If the uncertainty is even larger, i.e. a= ,8, then the solution doesn't exist. It is very complex to find a physical interpretation of the algebraic interval solution set. Thus, in applications, the united solution set is usually applied.


The method

Consider the PDE with the interval parameters where p = (p_1,\dots,p_m) \in is a vector of parameters which belong to given intervals p_i\in underline p_i,\overline p_i_i, = _1\times _2 \times \cdots \times _m. For example, the heat transfer equation k_x \frac+ k_y\frac +q =0 \text x \in \Omega u(x)=u^*(x) \text x \in \partial\Omega where k_x, k_y are the interval parameters (i.e. k_x\in_x, \ k_y\in_y ). Solution of the equation () can be defined in the following way \tilde(x):= \ For example, in the case of the heat transfer equation \tilde(x) = \left\ Solution \tilde is very complicated because of that in practice it is more interesting to find the smallest possible interval which contain the exact solution set \tilde . (x)=\lozenge \tilde(x) = \lozenge \ For example, in the case of the heat transfer equation (x) = \lozenge \left\ Finite element method lead to the following parameter dependent system of algebraic equations K(p) u = Q(p), \ \ \ p \in where is a stiffness matrix and is a right hand side. Interval solution can be defined as a multivalued function = \lozenge \ In the simplest case above system can be treat as a system of linear interval equations. It is also possible to define the interval solution as a solution of the following optimization problem \underline u_i = \min \ \overline u_i = \max \ In multidimensional case the interval solution can be written as \mathbf = \mathbf_1 \times \cdots \times \mathbf_n = underline u_1,\overline u_1\times \cdots\times underline u_n,\overline u_n


Interval solution versus probabilistic solution

It is important to know that the interval parameters generate different results than uniformly distributed random variables. Interval parameter \mathbf= underline p,\overline p take into account all possible probability distributions (for p\in underline p,\overline p). In order to define the interval parameter it is necessary to know only upper \overline p and lower bound \underline p . Calculations of probabilistic characteristics require the knowledge of a lot of experimental results. It is possible to show that the sum of n interval numbers is \sqrt times wider than the sum of appropriate normally distributed random variables. Sum of ''n'' interval number \mathbf= underline p,\overline p is equal to n\mathbf = \underline p,n\overline p Width of that interval is equal to n\overline p - n\underline p = n(\overline p - \underline p) = n\Delta p Consider normally distributed random variable ''X'' such that m_X=E \frac, \sigma_X=\sqrt=\frac Sum of ''n'' normally distributed random variable is a normally distributed random variable with the following characteristics (see Six Sigma) E Xn\frac, \sigma_=\sqrt=\sqrt\sigma=\sqrt\frac We can assume that the width of the probabilistic result is equal to 6 sigma (compare Six Sigma). 6\sigma_=6\sqrt\frac=\sqrt\Delta p Now we can compare the width of the interval result and the probabilistic result \frac = \frac = \sqrt Because of that the results of the interval finite element (or in general worst-case analysis) may be overestimated in comparison to the stochastic fem analysis (see also propagation of uncertainty). However, in the case of nonprobabilistic uncertainty it is not possible to apply pure probabilistic methods. Because probabilistic characteristic in that case are not known exactly (
Elishakoff Isaac Elishakoff is a Distinguished Research Professor in the Ocean and Mechanical Engineering Department in the Florida Atlantic University, Boca Raton, Florida. He is an authoritative figure in the broad area of mechanics. He has made several ...
2000). It is possible to consider random (and fuzzy random variables) with the interval parameters (e.g. with the interval mean, variance etc.). Some researchers use interval (fuzzy) measurements in statistical calculations (e.g

. As a results of such calculations we will get so called imprecise probability. Imprecise probability is understood in a very wide sense. It is used as a generic term to cover all mathematical models which measure chance or uncertainty without sharp numerical probabilities. It includes both qualitative (comparative probability, partial preference orderings, ...) and quantitative modes (interval probabilities, belief functions, upper and lower previsions, ...). Imprecise probability models are needed in inference problems where the relevant information is scarce, vague or conflicting, and in decision problems where preferences may also be incomplet


Simple example: modeling tension, compression, strain, and stress)

image:TensionCompression.JPG, 400px


1-dimension example

In the tension- compression problem, the following
equation In mathematics, an equation is a formula that expresses the equality of two expressions, by connecting them with the equals sign . The word ''equation'' and its cognates in other languages may have subtly different meanings; for example, in ...
shows the relationship between displacement and
force In physics, a force is an influence that can change the motion of an object. A force can cause an object with mass to change its velocity (e.g. moving from a state of rest), i.e., to accelerate. Force can also be described intuitively as a p ...
: \fracu = P where is length, is the area of a cross-section, and is Young's modulus. If the Young's modulus and force are uncertain, then E\in underline E,\overline E P\in underline P,\overline P To find upper and lower bounds of the displacement , calculate the following
partial derivative In mathematics, a partial derivative of a function of several variables is its derivative with respect to one of those variables, with the others held constant (as opposed to the total derivative, in which all variables are allowed to vary). Part ...
s: \frac = \frac < 0 \frac = \frac > 0 Calculate extreme values of the displacement as follows: \underline u = u(\overline E,\underline P) = \frac \overline u = u(\underline E,\overline P) = \frac Calculate strain using following formula: \varepsilon = \frac u Calculate derivative of the strain using derivative from the displacements: \frac = \frac \frac = \frac < 0 \frac = \frac \frac = \frac > 0 Calculate extreme values of the displacement as follows: \underline \varepsilon = \varepsilon(\overline E,\underline P) = \frac \overline \varepsilon = \varepsilon(\underline E,\overline P) = \frac It is also possible to calculate extreme values of strain using the displacements \frac = \frac > 0 then \underline \varepsilon = \varepsilon(\underline u) = \frac \overline \varepsilon = \varepsilon(\overline u) = \frac The same methodology can be applied to the stress \sigma = E \varepsilon then \frac = \varepsilon + E\frac =\varepsilon + E\frac \frac = \frac - \frac= 0 \frac = E\frac = E\frac \frac = \frac >0 and \underline \sigma = \sigma (\underline P) = \frac \overline \sigma = \sigma (\overline P) = \frac If we treat stress as a function of strain then \frac=\frac(E\varepsilon)=E> 0 then \underline \sigma = \sigma (\underline \varepsilon) =E\underline \varepsilon = \frac \overline \sigma = \sigma (\overline \varepsilon) = E\overline \varepsilon = \frac Structure is safe if stress \sigma is smaller than a given value \sigma_0 i.e., \sigma < \sigma_0 this condition is true if \overline \sigma < \sigma_0 After calculation we know that this relation is satisfied if \frac < \sigma_0 The example is very simple but it shows the applications of the interval parameters in mechanics. Interval FEM use very similar methodology in multidimensional cases ownuk 2004 However, in the multidimensional cases relation between the uncertain parameters and the solution is not always monotone. In that cases more complicated optimization methods have to be applied.


Multidimensional example

In the case of tension- compression problem the equilibrium equation has the following form \frac\left( EA\frac \right)+n=0 where is displacement, is Young's modulus, is an area of cross-section, and is a distributed load. In order to get unique solution it is necessary to add appropriate boundary conditions e.g. u(0)=0 \left.\frac\_ EA = P If Young's modulus and are uncertain then the interval solution can be defined in the following way (x)=\left\ For each FEM element it is possible to multiply the equation by the test function \int_^ \left( \frac\left( EA\frac \right)+n \right)v=0 where x \in ,L^ After integration by parts we will get the equation in the weak form \int_^ EA\frac \frac dx = \int_^ nv \, dx where x\in ,L^ Let's introduce a set of grid points x_0, x_1, \dots, x_ , where Ne is a number of elements, and linear shape functions for each FEM element N_1^(x)=1-\frac, \ \ N_2^(x) = \frac. where x\in _^, x_^ x_^ left endpoint of the element, x_^ left endpoint of the element number "e". Approximate solution in the "e"-th element is a linear combination of the shape functions u^_(x) = u^_1 N_1^(x)+u^_2 N_2^(x), \ \ v^_(x) = u^_1 N_1^(x)+u^_2 N_2^(x) After substitution to the weak form of the equation we will get the following system of equations \begin \frac & -\frac \\ -\frac & \frac \\ \end \begin u^_1 \\ u^_2 \end = \begin \int_^ n N_1^(x)dx \\ \int_^ n N_2^(x)dx \end or in the matrix form K^ u^ = Q^ In order to assemble the global stiffness matrix it is necessary to consider an equilibrium equations in each node. After that the equation has the following matrix form K u = Q where K= \begin K_^ & K_^ & 0 & \cdots & 0 \\ K_^ & K_^+K_^ & K_^ & \cdots & 0 \\ 0 & K_^ & K_^+K_^ & \cdots & 0 \\ \vdots & \vdots & \ddots & \ddots & \vdots \\ 0 & 0 & \cdots & K_^ + K_^ & K_^ \\ 0 & 0 & \cdots & K_^ & K_^ \end is the global stiffness matrix, u = \begin u_0 \\ u_1 \\ \vdots \\ u_ \\ \end is the solution vector, Q=\begin Q_0 \\ Q_1 \\ \vdots \\ Q_ \\ \end is the right hand side. In the case of tension-compression problem K= \begin \frac & -\frac & 0 & \cdots & 0 \\ -\frac & \frac + \frac & -\frac & \cdots & 0 \\ 0 & -\frac & \frac+ \frac & \cdots & 0 \\ \vdots & \vdots & \ddots & \ddots & \vdots \\ 0 & 0 & \cdots & \frac + \frac & -\frac \\ 0 & 0 & \cdots & -\frac & \frac \end If we neglect the distributed load Q= \begin R \\ 0 \\ \vdots \\ 0 \\ P \\ \end After taking into account the boundary conditions the stiffness matrix has the following form K= \begin 1 & 0 & 0 & \cdots & 0 \\ 0 & \frac + \frac & -\frac & \cdots & 0 \\ 0 & -\frac & \frac + \frac & \cdots & 0 \\ \vdots & \vdots & \ddots & \ddots & \vdots \\ 0 & 0 & \cdots & \frac + \frac & -\frac \\ 0 & 0 & \cdots & -\frac & \frac \end = K(E,A) = K Right-hand side has the following form Q= \begin 0 \\ 0 \\ \vdots \\ 0 \\ P \\ \end = Q(P) Let's assume that Young's modulus , area of cross-section and the load are uncertain and belong to some intervals E^ \in underline E^,\overline E^ A^ \in underline A^,\overline A^ P \in underline P,\overline P The interval solution can be defined calculating the following way \mathbf u = \lozenge \left\ Calculation of the interval vector is in general
NP-hard In computational complexity theory, NP-hardness ( non-deterministic polynomial-time hardness) is the defining property of a class of problems that are informally "at least as hard as the hardest problems in NP". A simple example of an NP-hard pr ...
, however in specific cases it is possible to calculate the solution which can be used in many engineering applications. The results of the calculations are the interval displacements u_i \in underline u_i, \overline u_i Let's assume that the displacements in the column have to be smaller than some given value (due to safety). u_i< u^_i The uncertain system is safe if the interval solution satisfy all safety conditions. In this particular case u_i< u^_i, \ \ \ u_i\in underline u_i, \overline u_i or simple \overline u_i< u^_i In postprocessing it is possible to calculate the interval stress, the interval strain and the interval limit state functions and use these values in the design process. The interval finite element method can be applied to the solution of problems in which there is not enough information to create reliable probabilistic characteristic of the structures (
Elishakoff Isaac Elishakoff is a Distinguished Research Professor in the Ocean and Mechanical Engineering Department in the Florida Atlantic University, Boca Raton, Florida. He is an authoritative figure in the broad area of mechanics. He has made several ...
2000). Interval finite element method can be also applied in the theory of imprecise probability.


Endpoints combination method

It is possible to solve the equation K(p)u(p)=Q(p) for all possible combinations of endpoints of the interval \hat p .
The list of all vertices of the interval \hat p can be written as L=\ .
Upper and lower bound of the solution can be calculated in the following way \underline u_i = \min\ \overline u_i = \max\ Endpoints combination method gives solution which is usually exact; unfortunately the method has exponential computational complexity and cannot be applied to the problems with many interval parameters.A. Neumaier, Interval methods for systems of equations, Cambridge University Press, New York, 1990


Taylor expansion method

The function u=u(p) can be expanded by using Taylor series. In the simplest case the Taylor series use only linear approximation u_i(p) \approx u_i(p_0)+\sum_j\frac\Delta p_j Upper and lower bound of the solution can be calculated by using the following formula \underline u_i \approx u_i(p_0)-\left, \sum_j\frac\\Delta p_j \overline u_i \approx u_i(p_0)+\left, \sum_j\frac\\Delta p_j The method is very efficient however it is not very accurate.
In order to improve accuracy it is possible to apply higher order Taylor expansion ownuk 2004
This approach can be also applied in the interval finite difference method and the
interval boundary element method Interval boundary element method is classical boundary element method with the interval parameters. Boundary element method is based on the following integral equation c\cdot u=\int\limits_\left(G\frac - \fracu\right)dS The exact interval sol ...
.


Gradient method

If the sign of the derivatives \frac is constant then the functions u_i= u_i(p) is monotone and the exact solution can be calculated very fast. :if \frac \ge 0 then p_i^ = \underline p_i, \ p_i^ = \overline p_i :if \frac < 0 then p_i^ = \overline p_i, \ p_i^ = \underline p_i Extreme values of the solution can be calculated in the following way \underline u_i=u_i(p^), \ \overline u_i=u_i(p^{\max}) In many structural engineering applications the method gives exact solution.
If the solution is not monotone the solution is usually reasonable. In order to improve accuracy of the method it is possible to apply monotonicity tests and higher order sensitivity analysis. The method can be applied to the solution of linear and nonlinear problems of computational mechanics ownuk 2004 Applications of sensitivity analysis method to the solution of civil engineering problems can be found in the following paper .V. Rama Rao, A. Pownuk and I. Skalna 2008
This approach can be also applied in the interval finite difference method and the
interval boundary element method Interval boundary element method is classical boundary element method with the interval parameters. Boundary element method is based on the following integral equation c\cdot u=\int\limits_\left(G\frac - \fracu\right)dS The exact interval sol ...
.


Element by element method

Muhanna and Mullen applied element by element formulation to the solution of finite element equation with the interval parameters.R.L. Muhanna, R.L. Mullen, Uncertainty in Mechanics Problems - Interval - Based Approach. Journal of Engineering Mechanics, Vol.127, No.6, 2001, 557-556 Using that method it is possible to get the solution with guaranteed accuracy in the case of truss and frame structures.


Perturbation methods

The solution u = u(p) stiffness matrix K = K(p) and the load vector Q = Q(p) can be expanded by using perturbation theory. Perturbation theory lead to the approximate value of the interval solution.Z. Qiu and
I. Elishakoff Isaac Elishakoff is a Distinguished Research Professor in the Ocean and Mechanical Engineering Department in the Florida Atlantic University, Boca Raton, Florida. He is an authoritative figure in the broad area of mechanics. He has made several ...
, Antioptimization of structures with large uncertain but non-random parameters via interval analysis Computer Methods in Applied Mechanics and Engineering, Volume 152, Issues 3-4, 24 January 1998, Pages 361-372
The method is very efficient and can be applied to large problems of computational mechanics.


Response surface method

It is possible to approximate the solution u = u(p) by using response surface. Then it is possible to use the response surface to the get the interval solution.U.O. Akpan, T.S. Koko, I.R. Orisamolu, B.K. Gallant, Practical fuzzy finite element analysis of structures, Finite Elements in Analysis and Design, 38, pp. 93–111, 2000. Using response surface method it is possible to solve very complex problem of computational mechanics.M. Beer, Evaluation of Inconsistent Engineering data, The Third workshop on Reliable Engineering Computing (REC08) Georgia Institute of Technology, February 20–22, 2008, Savannah, Georgia, USA.


Pure interval methods

Several authors tried to apply pure interval methods to the solution of finite element problems with the interval parameters. In some cases it is possible to get very interesting results e.g. opova, Iankov, Bonev 2008 However, in general the method generates very overestimated results. Kulpa Z., Pownuk A., Skalna I., Analysis of linear mechanical structures with uncertainties by means of interval methods. Computer Assisted Mechanics and Engineering Sciences, vol. 5, 1998, pp. 443–477


Parametric interval systems

PopovaE. Popova, On the Solution of Parametrised Linear Systems. W. Kraemer, J. Wolff von Gudenberg (Eds.): Scientific Computing,Validated Numerics, Interval Methods. Kluwer Acad. Publishers, 2001, pp. 127–138. and SkalnaI. Skalna, A Method for Outer Interval Solution of Systems of Linear Equations Depending Linearly on Interval Parameters, Reliable Computing, Volume 12, Number 2, April, 2006, pp. 107–120 introduced the methods for the solution of the system of linear equations in which the coefficients are linear combinations of interval parameters. In this case it is possible to get very accurate solution of the interval equations with guaranteed accuracy.


See also

*
Interval boundary element method Interval boundary element method is classical boundary element method with the interval parameters. Boundary element method is based on the following integral equation c\cdot u=\int\limits_\left(G\frac - \fracu\right)dS The exact interval sol ...
*
Interval (mathematics) In mathematics, a (real) interval is a set of real numbers that contains all real numbers lying between any two numbers of the set. For example, the set of numbers satisfying is an interval which contains , , and all numbers in between. Othe ...
* Interval arithmetic * Imprecise probability * Multivalued function * Differential inclusion * Observational error * Random compact set * Reliability (statistics) *
Confidence interval In frequentist statistics, a confidence interval (CI) is a range of estimates for an unknown parameter. A confidence interval is computed at a designated ''confidence level''; the 95% confidence level is most common, but other levels, such as 9 ...
* Best, worst and average case * Probabilistic design * Propagation of uncertainty * Experimental uncertainty analysis * Sensitivity analysis * Perturbation theory *
Continuum mechanics Continuum mechanics is a branch of mechanics that deals with the mechanical behavior of materials modeled as a continuous mass rather than as discrete particles. The French mathematician Augustin-Louis Cauchy was the first to formulate such m ...
* Solid mechanics * Truss *
Space frame In architecture and structural engineering, a space frame or space structure ( 3D truss) is a rigid, lightweight, truss-like structure constructed from interlocking struts in a geometric pattern. Space frames can be used to span large areas with ...
* Linear elasticity * Strength of materials


References

* Dempster, A. P. (1967). "Upper and lower probabilities induced by a multivalued mapping". The Annals of Mathematical Statistics 38 (2): 325–339

Retrieved 2009-09-23 * Analyzing Uncertainty in Civil Engineering, by W. Fellin, H. Lessmann, M. Oberguggenberger, and R. Vieider (eds.), Springer-Verlag, Berlin, 2005 *
I. Elishakoff Isaac Elishakoff is a Distinguished Research Professor in the Ocean and Mechanical Engineering Department in the Florida Atlantic University, Boca Raton, Florida. He is an authoritative figure in the broad area of mechanics. He has made several ...
, Possible limitations of probabilistic methods in engineering. Applied Mechanics Reviews, Vol.53, No.2, pp. 19–25, 2000. * Hlavácek, I., Chleboun, J., BabuÅ¡ka, I.: Uncertain Input Data Problems and the Worst Scenario Method. Elsevier, Amsterdam (2004) * Köylüoglu, U.,
Isaac Elishakoff Isaac Elishakoff is a Distinguished Research Professor in the Ocean and Mechanical Engineering Department in the Florida Atlantic University, Boca Raton, Florida. He is an authoritative figure in the broad area of mechanics. He has made several ...
; A comparison of stochastic and interval finite elements applied to shear frames with uncertain stiffness properties, Computers & Structures Volume: 67, Issue: 1–3, April 1, 1998, pp. 91–98 * D. Moens and D. Vandepitte, Interval sensitivity theory and its application to frequency response envelope analysis of uncertain structures. Computer Methods in Applied Mechanics and Engineering Vol. 196, No. 21-24,1 April 2007, pp. 2486–2496. * Möller, B., Beer, M., Fuzzy Randomness - Uncertainty in Civil Engineering and Computational Mechanics, Springer, Berlin, 2004. * E. Popova, R. Iankov, Z. Bonev: Bounding the Response of Mechanical Structures with Uncertainties in all the Parameters. In R.L.Muhannah, R.L.Mullen (Eds): Proceedings of the NSF Workshop on Reliable Engineering Computing (REC), Svannah, Georgia USA, Feb. 22–24, 2006, 245-265 * A. Pownuk, Numerical solutions of fuzzy partial differential equation and its application in computational mechanics, Fuzzy Partial Differential Equations and Relational Equations: Reservoir Characterization and Modeling (M. Nikravesh, L. Zadeh and V. Korotkikh, eds.), Studies in Fuzziness and Soft Computing, Physica-Verlag, 2004, pp. 308–347 * A. Pownuk, Efficient Method of Solution of Large Scale Engineering Problems with Interval Parameters Based on Sensitivity Analysis, Proceeding of NSF workshop on Reliable Engineering Computing, September 15–17, 2004, Savannah, Georgia, USA, pp. 305–316 *M.V. Rama Rao, A. Pownuk and I. Skalna, Stress Analysis of a Singly Reinforced Concrete Beam with Uncertain Structural Parameters, NSF workshop on Reliable Engineering Computing, February 20–22, 2008, Savannah, Georgia, USA, pp. 459–478 * Bernardini, Alberto, Tonon, Fulvio, Bounding Uncertainty in Civil Engineering, Springer 2010 * Ben-Haim Y., Elishakoff I., 1990, Convex Models of Uncertainty in Applied Mechanics. Elsevier Science Publishers, New York * Valliappan S., Pham T.D., 1993, Fuzzy Finite Element Analysis of A Foundation on Elastic Soil Medium. International Journal for Numerical and Analytical Methods in Geomechanics, Vol.17, pp. 771–789 * Elishakoff I., Li Y.W., Starnes J.H., 1994, A deterministic method to predict the effect of unknown-but-bounded elastic moduli on the buckling of composite structures. Computer methods in applied mechanics and engineering, Vol.111, pp. 155–167 * Valliappan S. Pham T.D., 1995, Elasto-Plastic Finite Element Analysis with Fuzzy Parameters. International Journal for Numerical Methods in Engineering, 38, pp. 531–548 * Rao S.S., Sawyer J.P., 1995, Fuzzy Finite Element Approach for the Analysis of Imprecisly Defined Systems. AIAA Journal, Vol.33, No.12, pp. 2364–2370 * Köylüoglu H.U., Cakmak A., Nielsen S.R.K., 1995, Interval mapping in structural mechanics. In: Spanos, ed. Computational Stochastic Mechanics. 125–133. Balkema, Rotterdam * Muhanna, R. L. and R. L. Mullen (1995). "Development of Interval Based Methods for Fuzziness in Continuum Mechanics" in Proceedings of the 3rd International Symposium on Uncertainty Modeling and Analysis and Annual Conference of the North American Fuzzy Information Processing Society (ISUMA–NAFIPS'95),IEEE, 705–710


External links


Reliable Engineering Computing, Georgia Institute of Technology, Savannah, USA

Interval Computations

Reliable Computing (Journal)





E. Popova, Parametric Solution Set of Interval Linear System

The Society for Imprecise Probability: Theories and Applications
Finite element method