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Frontiers of Structural and Civil Engineering

ISSN 2095-2430

ISSN 2095-2449(Online)

CN 10-1023/X

Postal Subscription Code 80-968

2018 Impact Factor: 1.272

Front. Struct. Civ. Eng.    2019, Vol. 13 Issue (3) : 526-541    https://doi.org/10.1007/s11709-018-0495-1
RESEARCH ARTICLE
Reliability and variance-based sensitivity analysis of arch dams during construction and reservoir impoundment
M. Houshmand KHANEGHAHI1, M. ALEMBAGHERI2(), N. SOLTANI2
1. Department of Civil Engineering, Shahid Beheshti University, Tehran, Iran
2. Department of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran
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Abstract

The static performance of arch dams during construction and reservoir impoundment is assessed taking into account the effects of uncertainties presented in the model properties as well as the loading conditions. Dez arch dam is chosen as the case study; it is modeled along with its rock foundation using the finite element method considering the stage construction. Since previous studies concentrated on simplified models and approaches, comprehensive study of the arch dam model along with efficient and state-of-the-art uncertainty methods are incorporated in this investigation. The reliability method is performed to assess the safety level and the sensitivity analyses for identifying critical input factors and their interaction effects on the response of the dam. Global sensitivity analysis based on improved Latin hypercube sampling is employed in this study to indicate the influence of each random variable and their interaction on variance of the responses. Four levels of model advancement are considered for the dam-foundation system: 1) Monolithic dam without any joint founded on the homogeneous rock foundation, 2) monolithic dam founded on the inhomogeneous foundation including soft rock layers, 3) jointed dam including the peripheral and contraction joints founded on the homogeneous foundation, and 4) jointed dam founded on the inhomogeneous foundation. For each model, proper performance indices are defined through limit-state functions. In this manner, the effects of input parameters in each performance level of the dam are investigated. The outcome of this study is defining the importance of input factors in each stage and model based on the variance of the dam response. Moreover, the results of sampling are computed in order to assess the safety level of the dam in miscellaneous loading and modeling conditions.

Keywords concrete arch dams      reliability      randomness      improved Latin hypercube sampling      variance-based sensitivity analysis      exceedance probability      Sobol′ index     
Corresponding Author(s): M. ALEMBAGHERI   
Just Accepted Date: 28 May 2018   Online First Date: 27 June 2018    Issue Date: 05 June 2019
 Cite this article:   
M. Houshmand KHANEGHAHI,M. ALEMBAGHERI,N. SOLTANI. Reliability and variance-based sensitivity analysis of arch dams during construction and reservoir impoundment[J]. Front. Struct. Civ. Eng., 2019, 13(3): 526-541.
 URL:  
https://academic.hep.com.cn/fsce/EN/10.1007/s11709-018-0495-1
https://academic.hep.com.cn/fsce/EN/Y2019/V13/I3/526
Fig.1  (a) Aerial view of Dez arch dam; (b) the finite element mesh of the dam-foundation models; (c) the considered five construction stages; (d) position of the soft rock layer; (e) configuration of the contraction joints
model foundation joints random variables failure scenario
1 homogenous no concrete Young’s modulus: Ec
concrete density: rc
concrete Poisson’s ratio: n
concrete coefficient of thermal expansion: ac
rock Young’s modulus: Ef
rock Poisson’s ratio: nf
post-cooling temperature decrease: DT
(1) tensile overstressing of the dam body
2 inhomogeneous no concrete Young’s modulus: Ec (1) tensile overstressing of the dam body
concrete density: rc
concrete Poisson’s ratio: nc
concrete coefficient of thermal expansion: ac
rock Young’s modulus: Ef
soft rock Young’s modulus: Efr
rock Poisson’s ratio: nf
soft rock Poisson’s ratio: nfr
post-cooling temperature decrease: DT
3 homogenous yes concrete Young’s modulus: Ec
concrete density: rc
concrete Poisson’s ratio: nc
concrete coefficient of thermal expansion: ac
rock Young’s modulus: Ef
rock Poisson’s ratio: nf
peripheral joint friction angel: jp
contraction joint friction angel: jc
post-cooling temperature decrease: DT
(1) tensile overstressing of the dam body
(2) opening and sliding of the joints
4 inhomogeneous yes concrete Young’s modulus: Ec
concrete density: rc
concrete Poisson’s ratio: nc
concrete coefficient of thermal expansion: ac
rock Young’s modulus: Ef
soft rock Young’s modulus: Efr
rock Poisson’s ratio: nf
soft rock Poisson’s ratio: nfr
peripheral joint friction angel: jp
contraction joint friction angel: jc
post-cooling temperature decrease: DT
(1) tensile overstressing of the dam body
(2) opening and sliding of the joints
Tab.1  The models studied in this paper, along with their assumptions, random variables and failure scenarios
Fig.2  The constitutive behavior of the joints in (a) normal, and (b) tangential direction [49]
value type rc (kg/m3) Ec (GPa) nc ac (10?6/°C) Ef (GPa) Efr (GPa) nf nfr jp (° ) jc (° ) DT (°C)
mean 2400 30.00 0.20 6.00 15.00 5.00 0.23 0.28 50.00 50.00 11.00
standard deviation 480 5.28 0.04 0.90 2.64 0.88 0.05 0.06 6.50 6.50 3.46
min value ? 18.00 0.13 4.44 9.00 3.00 0.15 0.18 ? ? 5.00
max value ? 42.00 0.27 7.59 21.00 7.00 0.31 0.38 ? ? 17.00
Tab.2  The properties of the random variables used in the reliability analysis of Dez dam-foundation system
random variables probability distribution
concrete density, rc lognormal
concrete Young’s modulus, Ec truncated lognormal
concrete Poisson’s ratio, nc uniform
concrete coefficient of thermal expansion, ac uniform
rock Young’s modulus, Ef truncated lognormal
soft rock Young’s modulus, Efr truncated lognormal
rock Poisson’s ratio, nf uniform
soft rock Poisson’s ratio, nfr uniform
peripheral joint friction angel, jp normal
contraction joint friction angel, jc normal
post-cooling temperature decrease, DT uniform
Tab.3  The probability distributions of the random variables
Fig.3  The procedure of implementing reliability framework along with deterministic model
model loading sequence exceedance probability (%)
1 construction Stage 1 3.95 × 10
construction Stage 2 6.03 × 10
construction Stage 3 8.01 × 10
construction Stage 4 9.13 × 10
construction Stage 5 9.85 × 10
reservoir impoundment 9.45 × 10
2 construction Stage 1 3.44 × 10
construction Stage 2 4.63 × 10
construction Stage 3 6.21 × 10
construction Stage 4 8.72 × 10
construction Stage 5 9.78 × 10
reservoir impoundment 9.02 × 10
3 construction Stage 1 6.00 × 103
construction Stage 2 5.00 × 103
construction Stage 3 4.00 × 103
construction Stage 4 2.00 × 10?3
construction Stage 5 2.00 × 103
reservoir impoundment 9.00 × 103
4 construction Stage 1 8.00 × 103
construction Stage 2 7.00 × 103
construction Stage 3 5.00 × 103
construction Stage 4 4.00 × 103
construction Stage 5 2.00 × 103
reservoir impoundment 1.10 × 102
Tab.4  The exceedance probability of the tensile overstressing limit-state function for the threshold value of 3 MPa
Fig.4  The contours of the exceedance probability of the tensile overstressing limit-state function on the dam’s upstream and downstream faces depicted for Models 1 and 2
Fig.5  The exceedance probability versus threshold value (concrete tensile stress) for all models. (a) Model 1; (b) Model 2; (c) Model 3; (d) Model 4
Fig.6  The histograms of the maximum tensile stress illustrated for all models. The horizontal and vertical axes are the tensile stress and number of observations, respectively. μ: Mean; σ: Standard deviation; CoV: Coefficient of variation
model exceedance probability (%)
sliding limit-state (threshold= 2 mm) opening limit-state (threshold= 0.5 mm)
3 3.30 6.20
4 7.30 26.90
Tab.5  The exceedance probability of the sliding and opening limit-state functions
Fig.7  The exceedance probability curves for (a) the joints sliding, and (b) the joints opening limit-state functions
Fig.8  First order (Si) and total (STi) sensitivity measures for all the models are presented
Fig.9  Sensitivity indices calculated based on opening and sliding limit-state function (LSF) for: (a) Model 3, and (b) Model 4.
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