Computational Stochastic Mechanics โ Proc. of the 7th International Conference (CSM-7) G. Deodatis and P.D. Spanos (eds.) Santorini, Greece, June 15-18, 2014
RELIABILITY ASSESSMENT OF NONLINEAR MDOF SYSTEMS SUBJECT TO EVOLUTIONARY STOCHASTIC EXCITATION IOANNIS P. MITSEAS1*, IOANNIS A. KOUGIOUMTZOGLOU2, POL D. SPANOS3, M. BEER1+ 1
Institute for Risk and Uncertainty, University of Liverpool, Brodie Tower, Brownlow Street, L69 3GQ, Liverpool, United Kingdom. E-mail: *
[email protected],
[email protected] 2 Department of Civil Engineering and Engineering Mechanics, Columbia University, 500 West 120th Street, New York, NY 10027,USA. E-mail:
[email protected] 3 L.B. Ryon Chair in Engineering, Rice University MS 321, P.O. Box 1892, Houston, TX 77251, USA. E-mail:
[email protected] An approximate analytical technique for determining the survival probability and first-passage probability density function (PDF) of nonlinear multi-degree-of-freedom (MDOF) structural systems subject to a non-stationary stochastic excitation vector is developed. The proposed technique can be construed as a two-stage approach. First, relying on statistical linearization and utilizing a dimension reduction approach the nonlinear n-degree-of-freedom system is cast/decoupled into (n) effective single-degree-of-freedom (SDOF) linear time-variant (LTV) systems corresponding to each and every degree of freedom of the original MDOF system. Second, an approximate technique based on stochastic averaging is employed in conjunction with the time-varying stiffness and damping elements of the effective SDOF LTV oscillator for determining the survival probability and first-passage PDF of the nonlinear MDOF system. Overall, the technique appears to be efficient and versatile since it can handle readily, at a low computational cost, a wide range of nonlinear/hysteretic behaviors as well as various stochastic excitation forms, even of the fully non-stationary in time and frequency kind. A 3-DOF structural system exhibiting hysteresis following the Bouc-Wen model is included as a numerical example. The reliability of the technique is demonstrated by pertinent Monte Carlo simulations. Keywords: first-passage problem, evolutionary stochastic processes, hysteretic system, statistical linearization, stochastic averaging, reliability assessment
1
Introduction
Structural systems are subject to stochastic excitations that exhibit strong variability in both intensity and frequency content. This fact necessitates the representation of this class of structural loads by non-stationary stochastic
1
processes. Further, structural systems under severe dynamic excitations, such as those due to earthquakes, can exhibit significant nonlinear behavior of the hysteretic kind. Thus, there is considerable interest within the engineering community for determining the response and assessing the reliability of
Reliability Assessment of Nonlinear MDOF Systems Subject to Evolutionary Stochastic Excitation I.P. Mitseas, I.A. Kougioumtzoglou, P. D. Spanos, and M. Beer
nonlinear/hysteretic systems subject to evolutionary stochastic excitations (Roberts and Spanos 2003). In engineering dynamics, the evaluation of the probability that the system response stays within prescribed limits for a specified time interval is advantageous for reliability based system design applications. The determination of the above time-variant probability, known as survival probability, has been coined in the literature as the firstpassage problem and has been a persistent challenge in the field of stochastic dynamics. In this regard, several researchers have focused on developing Monte Carlo simulation (MCS) based techniques (e.g. Schueller et al. 2004) for structural system reliability assessment. Note, however, that there are cases where the computational cost can be prohibitive; thus, rendering the development of alternative efficient approximate analytical/numerical techniques necessary (e.g. Kougioumtzoglou and Spanos 2013). In this paper, an approximate analytical technique for determining the survival probability and first-passage probability density function (PDF) of nonlinear multidegree-of-freedom (MDOF) structural systems subject to a non-stationary stochastic excitation vector is developed. Specifically, first relying on a statistical linearization based dimension reduction approach the original MDOF system is cast into effective singledegree-of-freedom (SDOF) linear time-variant (LTV) systems corresponding to every degree of freedom of the original MDOF system. Second, a stochastic averaging based approximate technique is utilized to derive the nonlinear MDOF system survival probability and first-passage PDF at a low computational cost. A Bouc-Wen 3-DOF structural system subject to an evolutionary stochastic earthquake excitation is included as a numerical example. Pertinent MCS data demonstrate the reliability of the technique.
2
2 2.1
MDOF System Dimension Reduction Statistical linearization treatment
Consider an n-degree-of-freedom nonlinear structural system governed by the equation ๐ด๐ + ๐ช๐ + ๐ฒ๐ + ๐ ๐, ๐ = ๐ญ ๐ก
(1)
where q denotes the response acceleration vector, q is the response velocity vector, q is the response displacement vector; M, C and K denote the n ร n mass, damping and stiffness matrices, respectively. g q, q is an arbitrary nonlinear n ร 1 vector function of the variables q and q. F t T = (f1 t , f2 t , โฆ , fn t ) is a n ร 1 zero mean, non-stationary stochastic excitation vector process defined as F t = ฮณa t , where ฮณT = (ฮณ1 , ฮณ2 , โฆ , ฮณn ) is an arbitrary constant vector of weighting coefficients, and a t is a non-stationary process with an evolutionary power spectrum (EPS) Sa ฯ, t . In this regard, F t possesses the EPS matrix ๐บ๐ญ ๐, ๐ = ๐พ1 2 ๐๐ ๐, ๐ก 0 โฏ 0 2 0 ๐พ2 ๐๐ ๐, ๐ก โฏ 0 โฎ โฑ โฏ โฎ 0 0 โฏ ๐พ๐ 2 ๐๐ ๐, ๐ก
(2)
Further, the non-stationary stochastic excitation process is regarded to be a filtered stationary stochastic process according to the concept proposed by Priestley (Priestley 1965); see also (Dalhaus 1997, Spanos and Kougioumtzoglou 2012, Kougioumtzoglou 2013). Thus, the excitation EPS matrix Eq.(2) can be given by ๐บ๐ญ ๐, ๐ก = ๐จ ๐, ๐ก ๐บ๐ญ ๐ ๐(๐, ๐ก)๐ปโ
(3)
where the superscripts (T) and (*) denote matrix transposition and complex conjugation, respectively; A ฯ, t is the modulating matrix which serves as a time-variant filter; and
Computational Stochastic Mechanics โ Proc. of the 7th International Conference (CSM-7) G. Deodatis and P.D. Spanos (eds.) Santorini, Greece, June 15-18, 2014
SF ฯ is the power spectrum matrix corresponding to the stationary stochastic vector process F(t). Note that both separable and non-separable EPS can be defined considering Eq.(3). In this manner, realistic excitations exhibiting variability in both intensity and frequency content can be considered. Focusing next on the frequency domain, the response determination problem is defined as seeking the corresponding system response EPS matrix Sq ฯ, t . In the following a statistical linearization approach (Roberts and Spanos 2003) is employed for determining the response EPS matrix Sq ฯ, t . In this regard, a linearized version of Eq.(1) is given in the form
๐ก
๐ ๐ก โ ๐ ๐จ ๐, ๐ ๐ โ๐๐ (๐กโ๐) ๐๐
(8)
0
In Eq.(8) h t denotes the impulse response function matrix. Furthermore, the time dependent cross โ variance of the response can be evaluated by the expression โ
๐ธ ๐๐ ๐๐ = โโ
๐๐ ๐ ๐ ๐ ๐, ๐ก ๐๐
(9)
Next, omitting the convolution of the impulse response function matrix with the modulating matrix can lead to substantial reduction of the computational effort, especially for the case of MDOF systems (Kougioumtzoglou and Spanos 2013, Mitseas et al. 2014). In this manner, Eq.(8) takes the form
๐ด๐ + ๐ช + ๐ช๐๐ ๐ + ๐ฒ + ๐ฒ๐๐ ๐ = ๐ญ ๐ก (4)
๐ฏ๐๐๐ ๐, ๐ก = ๐ฏ ๐ ๐จ ๐, ๐ก
Following (Roberts and Spanos 2003) and adopting the standard assumption that the response processes are Gaussian, the timedependent elements of the equivalent linear matrices Ceq and K eq are given by the expressions
where H ฯ is the frequency response function (FRF) matrix defined as
๐๐
๐๐,๐ = ๐ธ
๐๐๐ ๐๐๐
(5)
๐๐ ๐๐,๐
๐ฏ ๐ = (โ๐2 ๐ด + ๐๐ ๐ช + ๐ช๐๐ + โฏ โฆ + (๐ฒ + ๐ฒ๐๐ ))โ๐
๐๐๐ =๐ธ ๐๐๐
(6)
Further, for a linear MDOF system subject to evolutionary stochastic excitation a matrix input-output spectral relationship of the form โ
๐บ๐ ๐, ๐ก = ๐ฏ๐๐๐ ๐, ๐ก ๐บ๐ญ ๐ ๐ฏ๐ป
๐๐๐
๐, ๐ก (7)
can be derived (Kougioumtzoglou and Spanos 2009), where ๐ฏ๐๐๐ ๐, ๐ก =
3
(11)
Consequently, taking into account Eq.(3), Eq. (7) becomes ๐บ๐ ๐, ๐ก = ๐ฏ ๐ ๐บ๐ญ ๐, ๐ก ๐ฏ๐ปโ ๐
and
(10)
(12)
Note that Eq.(12) constitutes a straightforward generalization of the celebrated spectral relationship based on stationarity and on the Wiener-Khinchin theorem. In this regard, the abovementioned approximation can be construed as a quasi-stationary approach which, in general, yields satisfactory accuracy in cases of relatively stiff systems (Hammond 1973, Jangid and Datta 1999). Note in passing that the spectral input-output relationship of Eq.(12) is exact for the case of stationary processes (Roberts and Spanos 2003). Further, adopting the aforementioned quasi-stationary approach, it can be readily seen that for the i-
Reliability Assessment of Nonlinear MDOF Systems Subject to Evolutionary Stochastic Excitation I.P. Mitseas, I.A. Kougioumtzoglou, P. D. Spanos, and M. Beer
th degree of freedom, using Eq.(2), Eq.(9) and Eq.(12) yields โ 2
๐ธ ๐๐ ๐ก
=
( ๐ป๐1 ๐
2
๐พ1 + โฏ
๐2
โโ
โโ
(13)
and โ
=
+ ๐ป๐๐ (๐)
โโ 2 2 ๐พ๐ )๐๐
๐2 ( ๐ป๐1 ๐
2
๐พ1 2 + โฏ
๐, ๐ก ๐๐
(14)
Eqs.(13) and (14) hold true in the approximate quasi-stationary sense delineated earlier. Clearly, Eq.(12) constitutes an approximate formula for determining the MDOF system response EPS matrix at a low computational cost; thus, circumventing computationally intensive Monte Carlo simulations. 2.2 Effective SDOF linear time-variant system Adopting next the system dimension reduction approach developed in (Kougioumtzoglou and Spanos 2013), an auxiliary effective SDOF LTV system corresponding to the i-th degree of freedom can be defined as 2 ๐๐ + ๐ฝ๐๐ ,๐ ๐ก ๐๐ + ๐๐๐ ,๐ ๐ก ๐๐ = ๐๐ ๐ก
(15)
where the time-varying stiffness and damping elements of the effective LTV system can be determined by equating the variances of the response displacement and velocity expressed utilizing Eq.(15) with the corresponding ones (Eqs.(13-14)) evaluated via the statistical linearization approach delineated in the previous section; that is, E ๐๐ 2 ๐ก โ
โโ
=
1 2 2 )2 + (๐ฝ 2 (๐๐๐ ๐ก โ ๐ ๐๐ ,๐ ๐ก ๐) ,๐
ร ๐พ๐ 2 ๐๐ ๐, ๐ก ๐๐
E ๐๐ 2 ๐ก โ
2
+ ๐ป๐๐ (๐) 2 ๐พ๐ 2 )๐๐ ๐, ๐ก ๐๐
๐ธ ๐๐ 2 ๐ก
and
(16)
4
= 1 2 2 )2 + (๐ฝ 2 (๐๐๐ ๐ก โ ๐ ๐๐ ,๐ ๐ก ๐) ,๐
ร ๐พ๐ 2 ๐๐ ๐, ๐ก ๐๐
(17)
Clearly, Eqs.(16) and (17) in conjunction with Eqs.(13) and (14) constitute a nonlinear system of two algebraic equations for the evaluation of the LTV system time-varying stiffness and damping elements. Further, considering the case where the LTV system of Eq.(15) is a lightly damped system, and relying on a combination of deterministic and stochastic averaging (e.g. Spanos 1978), it was shown in (Spanos and Lutes 1980, Kougioumtzoglou and Spanos 2009) that the non-stationary LTV system response amplitude PDF is given by the time-dependent Rayleigh distribution of the form ๐ ๐๐ , ๐ก =
๐๐ ๐๐ 2 exp โ ๐๐ (๐ก) 2๐๐ (๐ก)
(18)
In Eq.(18) ๐๐ represents the LTV oscillator response amplitude defined as ๐๐ 2 ๐ก = ๐๐2 ๐ก +
๐๐ ๐ก ๐๐๐ ,๐ (๐ก)
2
(19)
where ci (t) accounts for the non-stationary variance of the LTV system of Eq.(15). As it was shown in (Spanos and Lutes 1980, Kougioumtzoglou and Spanos 2009) the nonstationary LTV system response is given by ๐๐ ๐ก = โ๐ฝ๐๐ ,๐ ๐ก ๐๐ ๐ก +
๐๐๐ ๐๐๐ ,๐ ๐ก , ๐ก (20) 2 ๐๐๐ ,๐ ๐ก
Eq.(20) constitutes a first-order nonlinear ordinary differential equation (ODE) which can be solved utilizing standard numerical schemes such as the Runge-Kutta; see references (Spanos and Lutes 1980, Roberts and Spanos 2003, Kougioumtzoglou and
Computational Stochastic Mechanics โ Proc. of the 7th International Conference (CSM-7) G. Deodatis and P.D. Spanos (eds.) Santorini, Greece, June 15-18, 2014
Spanos 2013) for a more detailed presentation of the topic.
๐
๐๐๐ต ๐ =
๐ต 1 โ ๐น๐,๐
(24)
๐ =1
3
MDOF Nonlinear System Reliability Assessment
The survival probability ๐๐๐ต is defined as the probability that the system response amplitude ๐๐ stays below a prescribed barrier ๐ต over the time interval [0, ๐], given that ๐๐ ๐ก = 0 < ๐ต. In the ensuing analysis, a recently developed approximate technique (Spanos and Kougioumtzoglou 2014) is employed for determining the survival probability and the first-passage PDF of the LTV system of Eq.(15). The first-passage PDF ๐๐๐ต ๐ and the survival probability ๐๐๐ต are related according to the expression ๐๐๐ต ๐ = โ
๐๐๐๐ต ๐ ๐๐
(21)
Next, adopting the discretization scheme employed in (Spanos and Solomos 1983) yields intervals of the form
๐๐๐ ,๐ (๐ก ๐,๐ โ1 ) 2
(22)
where Teq ,i represents the LTV system equivalent natural period given by ๐๐๐ ,๐ ๐ก =
2๐ ๐๐๐ ,๐ ๐ก
where is defined as the probability that the response amplitude ๐๐ will exceed the prescribed barrier B over the time interval t i,jโ1 , t i,j , given that no crossings have occurred prior to time t i,jโ1 . Next, invoking the Markovian property of the response amplitude ๐๐ , one gets ๐ต ๐น๐,๐ = ๐๐๐๐ ๐๐ ๐ก๐,๐ โฅ ๐ต โฉ ๐๐ ๐ก๐,๐ โ1 < ๐ต
๐๐๐๐ ๐๐ ๐ก๐,๐ โ1 < ๐ต
(23)
=
๐ต ๐ป๐,๐ โ1,๐
=
(25)
๐ต ๐ป๐,๐ โ1
where โฉ denotes the intersection of sets. B Utilizing Eq.(18) Hi,jโ1 can be determined as ๐ต ๐ต ๐ป๐,๐ โ1 =
๐ ๐๐,๐ โ1 , ๐ก๐,๐ โ1 ๐๐๐,๐ โ1 = 0
= 1 โ ๐๐ฅ๐ โ
๐ก๐,๐ โ1 , ๐ก๐,๐ , ๐ = 1,2, โฆ , ๐, ๐ก๐,0 = 0, ๐ก๐,๐ = ๐, ๐ก๐,๐ โ ๐ก๐,๐ โ1 =
B Fi,j
๐ต2 2๐๐ (๐ก๐,๐ โ1 )
(26)
B whereas Hi,jโ1,j is defined as a double integral of the form โ ๐ต ๐ป๐,๐ โ1,๐ = ๐ต
๐๐๐,๐ ร ๐ต
๐ ๐๐,๐ โ1 , ๐ก๐,๐ โ1 ; ๐๐,๐ , ๐ก๐,๐ ๐๐๐,๐ โ1
(27)
0
Considering the slowly time-varying behavior of the response amplitude ๐๐ , it is assumed that it is constant over the time interval t i,jโ1 , t i,j . In this regard, the survival probability PiB also has a constant value over the same time interval. Obviously, the survival probability is given by
5
Assuming next that ฮฒeq ,i t i,j = ฮฒeq ,i t i,jโ1 and ฯeq ,i t i,j = ฯeq ,i t i,jโ1 over the time interval t i,jโ1 , t i,j , it has been shown in (Spanos and Solomos 1983, Spanos and Kougioumtzoglou 2014) that the joint response amplitude PDF p ฮฑi,jโ1 , t i,jโ1 ; ฮฑi,j , t i,j is given in the form
Reliability Assessment of Nonlinear MDOF Systems Subject to Evolutionary Stochastic Excitation I.P. Mitseas, I.A. Kougioumtzoglou, P. D. Spanos, and M. Beer
๐ฟ๐,๐ ๐ 2 ๐ =1 2๐
๐ ๐ผ๐,๐ โ1 , ๐ก๐,๐ โ1 ; ๐ผ๐,๐ , ๐ก๐,๐ = ๐ผ๐,๐ โ1 ๐ผ๐,๐ ร 2 ๐๐ ๐ก๐,๐ โ1 ๐๐ ๐ก๐,๐ 1 โ ๐๐,๐ 2 2 ๐ผ๐,๐ ๐ ๐ก๐,๐ โ1 + ๐ผ๐,๐ โ1 ๐๐ ๐ก๐,๐ ๐๐ฅ๐ โ 2 2๐๐ ๐ก๐,๐ โ1 ๐๐ ๐ก๐,๐ 1 โ ๐๐,๐
with ร
๐ฟ๐,๐ =
2 1 โ ๐๐,๐
๐๐ ๐ก๐,๐ โ1 ๐๐ ๐ก๐,๐
๐+1
4๐ ๐๐ ๐ก๐,๐ โ1
๐ผ๐,๐ ๐ผ๐,๐ โ1 ๐๐,๐
๐ผ0
(33)
(28)
๐ค๐ 1 + ๐,
๐+2
ร
2 2๐๐ ๐ก๐,๐ โ1 1 โ ๐๐,๐
โ ๐ค๐ 1 + ๐, 0
where 2 ๐๐,๐ =
2 ๐๐ ๐ก๐,๐ 1 โ ๐๐,๐ ๐ต2
๐๐ ๐ก๐,๐ โ1 ๐๐ ๐ก๐,๐
1 โ ๐ฝ๐๐ ,๐ ๐ก๐,๐ โ1 (๐ก๐,๐ โ1 โ ๐ก๐,๐ )
.
(29)
Next, I0 (. ) denotes the modified Bessel function of the first kind of order zero. I0 (. ) can be further expanded in the form ๐ฅ2 ๐ฅ4 ๐ฅ6 ๐ผ0 ๐ฅ = 1 + 2 + 2 2 + 2 2 2 + โฏ 2 2 4 2 4 6
(30)
Considering Eqs.(27), (28) and (30) and manipulating yields N ๐ต ๐ป๐,๐ โ1,๐
= Ai,0 +
Ai,n
(31)
n=1
+
๐ต2 ๐๐ ๐ก๐,๐
โ ๐ค๐ 1 + ๐,
๐ด๐,0 = ๐๐ฅ๐ 1 โ ๐๐ฅ๐
ii. ร ร
2 2๐๐ ๐ก๐,๐ โ1 1 โ ๐๐,๐
iii.
2 1 โ ๐๐,๐
(32) iv.
and ๐ด๐,๐
2๐ ๐๐,๐
= ๐๐ ๐ก๐,๐ โ1 ๐๐ ๐ก๐,๐
๐+1
2๐+1
1โ
v.
ร
2 ๐๐,๐
6
2๐๐ ๐ก๐,๐
โ๐
๐ค๐ 1 + ๐ ๐ต2 1 โ ๐๐2
๐ต2๐ (34)
Concisely, the developed technique comprises of the following steps: i.
2 2๐๐ ๐ก๐,๐ 1 โ ๐๐,๐ 2 โ๐ต
2 1 โ ๐๐,๐
๐ค๐ 1 + ๐
In Eq.(34) ฮi z represents the Gamma โ function defined as ฮi z = 0 t zโ1 eโt dt; and ฮi [ฮณ, z] represents the incomplete Gamma โ function defined as ฮi [ฮณ, z] = z t ฮณโ1 eโt dt.
where โ๐ต2
๐
2 1 โ ๐๐,๐
โ ๐๐ ๐ก๐,๐
ร
Determination of the MDOF system response variances (Eqs.(13-14)). Determination of the time-varying elements ๐ฝ๐๐ ,๐ ๐ก and ๐๐๐ ,๐ ๐ก by solving the 2x2 system of algebraic equations (Eqs.(16-17)). Determination of ๐๐ ๐ก via numerically integrating the ODE Eq.(20). Determination of the equivalent natural period ๐๐๐ ,๐ ๐ก (Eq.(23)) and discretization of the time domain via Eq.(22). Determination of the parameters ๐ต ๐ต ๐ป๐,๐ โ1 and ๐ป๐,๐ โ1,๐ via Eqs.(26) and (27).
Computational Stochastic Mechanics โ Proc. of the 7th International Conference (CSM-7) G. Deodatis and P.D. Spanos (eds.) Santorini, Greece, June 15-18, 2014
vi.
4
Determination of the survival probability ๐๐๐ต ๐ via Eq.(24) and of the corresponding first-passage PDF ๐๐๐ต ๐ via Eq.(21). Numerical Application
In this section, a nonlinear three-degree-offreedom structural system following the BoucWen hysteretic model (Wen 1980, Ikhouane and Rodellar 2007) subject to evolutionary stochastic earthquake excitation is considered to demonstrate the reliability of the technique. The survival probabilities and the firstpassage PDFs obtained via the developed approximate technique are compared with survival probability and first-passage PDF estimates obtained via pertinent Monte Carlo simulations (10,000 realizations). A standard fourth-order Runge-Kutta numerical integration scheme is employed for solving the nonlinear system differential equation of motion (Eq.(1)), whereas the barrier level B is expressed as a fraction ฮป of the maximum value of the non-stationary response displacement standard deviation of the first floor, i.e. B = ฮปmaxโก ( c1 (t)). Further, considering relative displacements, the 3-DOF nonlinear structural system is governed by the equation ๐ด๐ + ๐ช๐ + ๐ฒ๐ + ๐ ๐, ๐ = ๐ญ ๐ก
(35)
where ๐ชT = q1 q2 q3 z1 z2 z3
(36)
and ๐=
๐๐๐ ๐๐๐
๐๐๐ ๐๐๐
(37)
with
๐๐๐
m1 m = 2 m3
0 m2 m3
0 0 m3
and 0 ๐๐๐ = ๐๐๐ = ๐๐๐ = 0 0
0 0 0
0 0 0
(39)
In Eq.(38) m1 = 2.0615 ร 105 , m2 = 5 2.0559 ร 10 and m3 = 2.0261 ร 105 . Further, ๐=
๐ ๐๐ ๐ ๐๐
๐ ๐๐ ๐ ๐๐
(40)
with ๐ ๐๐ =
ฮฑk1 0 0
โฮฑk 2 ฮฑk 2 0
๐ ๐๐ (1 โ ฮฑ)k1 0 = 0
0 โฮฑk 3 , ฮฑk 3
โ(1 โ ฮฑ)k 2 (1 โ ฮฑ)k 2 0
(41)
0 โ(1 โ ฮฑ)k 3 (42) (1 โ ฮฑ)k 3
and 0 ๐ ๐๐ = ๐ ๐๐ = 0 0
0 0 0
0 0 0
(43)
In Eqs.(41) and (42) k1 = 3.9668 ร 108 , k 2 = 3.5007 ร 108 and k 3 = 2.6927 ร 108 whereas ฮฑ stands for the rigidity ratio which can be viewed as a form of post-yield to preyield stiffness ratio. Further, the 6-by-6 damping matrix of the structural system C is assumed to be partly proportional to the stiffness matrix; that is, ๐=
๐๐๐ ๐๐๐
๐๐๐ ๐๐๐
with ๐๐๐ = c๐ ๐๐ and
7
(38)
(44)
Reliability Assessment of Nonlinear MDOF Systems Subject to Evolutionary Stochastic Excitation I.P. Mitseas, I.A. Kougioumtzoglou, P. D. Spanos, and M. Beer
0 ๐๐๐ = ๐๐๐ = 0 0 1 C22 = 0 0
0 1 0
0 0 0
0 0, 0
and (45) ๐ ๐๐ช =
0 0 1
(46)
where c is taken equal to 2 ร 10โ3 . For the specific example ฮณ = ๐๐ , and the loading vector becomes ๐
(t)T = f t f t f t 0 0 0
(47)
Further, ๐ ๐ช, ๐ช
๐
= ( 0 0 0 โg1 q1 , z1 โฆ โg 2 q2 , z2 โ g 3 q3 , z3 )
(48)
In the Bouc-Wen model the additional state zi is associated with the displacement qi via the equation z i = g i q i , zi
(49)
๐ ๐๐ช๐๐ ๐ ๐๐ช๐๐
๐ ๐๐ช๐๐ , ๐ ๐๐ช๐๐
(54)
0 ๐ ๐๐ช๐๐ = ๐ ๐๐ช๐๐ = ๐ ๐๐ช๐๐ = 0 0 k eq 1 0 0
๐ ๐๐ช๐๐ =
0 k eq 2 0
0 0 0
0 0 0
0 0
(55)
(56)
k eq 3
Further, the elements ceq i and k eq i are given by the expressions
ceq i =
2 E(qi zi ) ฮณ + ฮฒ E(zi 2 ) โ A ฯ 2 E(qi )
(57)
2 E(qi zi ) ฮณ E(qi 2 ) + ฮฒ ฯ E(zi 2 )
(58)
and k eq i =
where g i q i , zi = โฮณ q i zi zi โฆ + ฮq i
nโ1
โ ฮฒqi zi
n
โฆ (50)
The parameters ฮณ, ฮฒ, ฮ and n are capable of representing a wide range of hysteresis loops (Ikhouane and Rodellar 2007). In the study herein the following values are considered: a = 0.15, ฮฒ=ฮณ=0,5, n=1 and A=1. The equivalent linear matrices take the form (e.g. Roberts and Spanos 2003) ๐๐๐ช =
๐๐๐ช๐๐ ๐๐๐ช๐๐
๐๐๐ช๐๐ , ๐๐๐ช๐๐
0 ๐๐๐ช๐๐ = ๐๐๐ช๐๐ = ๐๐๐ช๐๐ = 0 0 ๐๐๐ช๐๐
ceq 1 = 0 0
0 ceq 2 0
0 0 0 0 0
0 0 0
respectively. Furthermore, the excitation EPS Sฮฑ ฯ, t takes the form Sฮฑ ฯ, t = g t
2
SCP ฯ
(59)
where SCP ฯ represents the widely used in earthquake engineering Clough-Penzien power spectrum (Clough and Penzien 1993) and g t denotes a time-modulating envelope function given by
(51)
g t = k eโat โ eโbt
(52)
where a=0.1 and b=0.3; and k is a normalization constant so that g t max = 1. The Clough-Penzien spectrum is given by
(53)
ceq 3
8
SCP ฯ =
(60)
Computational Stochastic Mechanics โ Proc. of the 7th International Conference (CSM-7) G. Deodatis and P.D. Spanos (eds.) Santorini, Greece, June 15-18, 2014
S0
(ฯ/ฯf )4 1 โ (ฯ/ฯf )2 2 + 4ฮพf 2 (ฯ/ฯf )2 ฯg 4 + 4(ฮพg )2 ฯg 2 ฯ2
(ฯg 2 โ ฯ2 )2 + 4ฮพg 2 ฯg 2 ฯ2
ร (61)
where S0 is the amplitude of the bedrock excitation spectrum, modeled as a white noise process. The parameters values used are S0 = 20m2 /sec 3 , ฮพg = 0.7, ฯg = 2rad/sec, ฮพf = 0.6 and ฯf = 12.5rad/sec (see Fig.1). The total duration of the excitation is 20 sec.
Figure 1.Nonstationary separable excitation power spectrum Sฮฑ ฯ, t
Figure 3: Time-varying equivalent linear damping ฮฒeq ,i t coefficients.
Figure 4: Time-varying survival probability of the systemโs first degree-of-freedom for various values of the parameter ฮป; comparisons with MCS (10,000 realizations).
Figure 2: Time-varying equivalent linear stiffness ฯeq .i t coefficients. Figure 5: First-passage PDF of the systemโs first degree-of-freedom for various values of the parameter ฮป; comparisons with MCS (10,000 realizations).
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Reliability Assessment of Nonlinear MDOF Systems Subject to Evolutionary Stochastic Excitation I.P. Mitseas, I.A. Kougioumtzoglou, P. D. Spanos, and M. Beer
Figure 6: Time-varying survival probability of the systemโs second degree-of-freedom for various values of the parameter ฮป; comparisons with MCS (10,000 realizations).
Figure 7: First-passage PDF of the systemโs second degree-of-freedom for various values of the parameter ฮป; comparisons with MCS (10,000 realizations).
Figure 8: Time-varying survival probability of the systemโs third degree-of-freedom for various values of the parameter ฮป; comparisons with MCS (10,000 realizations).
10
Figure 9: First-passage PDF of the systemโs third degree-of-freedom for various values of the parameter ฮป; comparisons with MCS (10,000 realizations).
In Figs. (2) and (3) the equivalent timevarying stiffness ฯeq .i t and damping ๐ฝ๐๐ ,i ๐ก elements corresponding to each DOF are plotted. Note that the hysteretic/degrading behavior of the structural system is captured by the decreasing with time trend of the stiffness element, as well as the increasing with time trend of the damping element. Further, in Figs. (4) and (5) the survival probabilities PiB T as well as the corresponding first-passage PDFs pBi T for the first DOF of the hysteretic MDOF structural system are plotted for various barrier levels; comparisons with MCS utilizing a spectral representation approach (Shinozuka and Deodatis 1991) (10,000 realizations) demonstrate a satisfactory degree of agreement. Furthermore, in Figs. (6), (7), (8) and (9) the survival probabilities PiB T as well as the associated first-passage PDFs pBi T corresponding to the second and third DOF are plotted for various barrier levels. Comparisons with MCS demonstrate a satisfactory degree of accuracy in these cases as well. 5
Conclusions
An approximate analytical technique for determining the survival probability and firstpassage PDF of nonlinear/hysteretic MDOF structural systems subject to evolutionary
Computational Stochastic Mechanics โ Proc. of the 7th International Conference (CSM-7) G. Deodatis and P.D. Spanos (eds.) Santorini, Greece, June 15-18, 2014
stochastic excitation has been developed. Specifically, based on an efficient dimension reduction approach and relying on the concepts of stochastic averaging/linearization reliability statistics of the nonlinear MDOF system have been determined at a low computational cost. A significant advantage of the technique relates to the fact that it can readily treat a broad range of nonlinear/hysteretic behaviors as well as stochastic excitations with arbitrary EPS forms. Acknowledgments The first author acknowledges the financial support of the State Scholarships Foundation in Greece (IKY).
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Priestley M.B, 1965. Evolutionary spectra and nonstationary processes. J. Roy Stat Soc; 27: 204โ 237. Schueller G. I., Pradlwarter H. J., Koutsourelakis P. S., 2004. A critical appraisal of reliability estimation procedures for high dimensions, Probabilistic Engineering Mechanics, vol. 19: 463-474. Shinozuka M., Deodatis G., 1991. Simulation of stochastic processes by spectral representation, Applied Mechanics Reviews, vol. 44, no. 4, pp. 191-204. Spanos P.D, 1978. Non-stationary random vibration of a linear structure, International Journal of Solids and Structures, vol 14: 861867. Spanos P. D, Kougioumtzoglou I. A., 2014. Survival probability determination of nonlinear oscillators subject to evolutionary stochastic excitation, Journal of Applied Mechanics, 81(5), 051016. Spanos P. D, Kougioumtzoglou I. A., 2012. Harmonic wavelets based statistical linearization for response evolutionary power spectrum determination, Probabilistic Engineering Mechanics; 27: 57-68. Spanos P. D., Lutes L.D, 1980. Probability of response to evolutionary process, J. Eng Mech. Div. Am. Soc. Civil Eng., 106, 213-224. Spanos P. D., Solomos G. P., 1983. Markov approximation to transient vibration, Journal of Engineering Mechanics, vol. 109: 1134-1150. Wen Y. K., 1980. Equivalent linearization for hysteretic systems under random excitation, Journal of Applied Mechanics, vol. 47: 150154. Books Clough R. W., Penzien J., 1993. Dynamics of structures, McGraw-Hill. Ikhouane F., Rodellar J., 2007. Systems with hysteresis: analysis, identification and control using the Bouc-Wen model, John Wiley and Sons. Roberts J. B., Spanos P. D., 2003. Random Vibration and Statistical Linearization. New York: Dover Publications.
Reliability Assessment of Nonlinear MDOF Systems Subject to Evolutionary Stochastic Excitation I.P. Mitseas, I.A. Kougioumtzoglou, P. D. Spanos, and M. Beer
Edited Volumes Mitseas I.P., Kougioumtzoglou I.A., Beer M., Optimal design of nonlinear structures under evolutionary stochastic earthquake excitations, Proceedings of the Intnl Conf. on Engrg. and
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Appl. Science Optimiz., OPT-i 2014, Kos, Greece, 2014.