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Frontiers of Structural and Civil Engineering

Front. Struct. Civ. Eng.    2019, Vol. 13 Issue (5) : 1007-1019     https://doi.org/10.1007/s11709-019-0521-y
RESEARCH ARTICLE
Risk-based probabilistic thermal-stress analysis of concrete arch dams
Narjes SOLTANI1, Mohammad ALEMBAGHERI1(), Mohammad Houshmand KHANEGHAHI2
1. Department of Civil and Environmental Engineering, Tarbiat Modares University, Tehran 1411713116, Iran
2. Department of Civil Engineering, Shahid Beheshti University, Tehran 1983969411, Iran
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Abstract

The probabilistic risk of arch dam failure under thermal loading is studied. The incorporated uncertainties, which are defined as random variables, are associated with the most affecting structural (material) properties of concrete and thermal loading conditions. Karaj arch dam is selected as case study. The dam is numerically modeled along with its foundation in three-dimensional space; the temperature and thermal stress distribution is investigated during the operating phase. The deterministic thermal finite element analysis of the dam is combined with the structural reliability methods in order to obtain thermal response predictions, and estimate the probability of failure in the risk analysis context. The tensile overstressing failure mode is considered for the reliability analysis. The thermal loading includes ambient air and reservoir temperature variations. The effect of solar radiation is considered by an increase in the ambient temperatures. Three reliability methods are employed: the first-order second-moment method, the first-order reliability method, and the Monte-Carlo simulation with Latin Hypercube sampling. The estimated failure probabilities are discussed and the sensitivity of random variables is investigated. Although most of the studies in this line of research are used only for academic purposes, the results of this investigation can be used for both academic and engineering purposes.

Keywords arch dams      probabilistic analysis      thermal stress      sensitivity      reliability     
Corresponding Authors: Mohammad ALEMBAGHERI   
Online First Date: 04 March 2019    Issue Date: 11 September 2019
 Cite this article:   
Narjes SOLTANI,Mohammad ALEMBAGHERI,Mohammad Houshmand KHANEGHAHI. Risk-based probabilistic thermal-stress analysis of concrete arch dams[J]. Front. Struct. Civ. Eng., 2019, 13(5): 1007-1019.
 URL:  
http://journal.hep.com.cn/fsce/EN/10.1007/s11709-019-0521-y
http://journal.hep.com.cn/fsce/EN/Y2019/V13/I5/1007
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Narjes SOLTANI
Mohammad ALEMBAGHERI
Mohammad Houshmand KHANEGHAHI
Fig.1  (a) Aerial view of Karaj arch dam; (b) finite element mesh of the dam and its foundation
parameter unit probability function mean standard deviation
concrete density (r) kg/m3 lognormal 2400 480
young’s modulus (E) GPa lognormal 30 6
thermal conductivity (k) W/[m2·°C−1] uniform 2.91 0.85
convection coefficient (h) W/[m2·°C−1] lognormal 20.90 6.27
emissivity (e) - uniform 0.775 0.072
coefficient of thermal expansion (α) 1/°C uniform 1.02E-05 1.62E-06
specific heat (Cp) J/[kg·°C−1] uniform 967 65
Tab.1  Random variables related to the structural system properties
Parameter Unit Probability function Mean Standard deviation
Air temperature amplitude (Aair) °C Uniform 11.5 2.02
Mean annual air temperature (T¯air) °C Normal 24 4.8
Tab.2  Independent random variables related to the environmental actions
Fig.2  FOSM Pf - ft plot
Fig.3  FOSM importance measures for random variables of: (a) structural system properties; (b) thermal loading parameters
Fig.4  Contour of exceedance probability from the FOSM for ft = 2 MPa: (a) upstream face; (b) downstream face
Fig.5  FORM Pfft plot. In legend, the numbers in parentheses show the number of random variables
Fig.6  Components of the FORM’s α-vectors for random variables of: (a) structural system properties; (b) thermal loading parameters
Fig.7  Histograms of MC-LHS simulations along with the best distribution fit, its mean and standard deviation (STDEV): (a) 1000 samples; (b) 2000 samples; (c) 4000 samples. The horizontal axis is the concrete tensile strength
Fig.8  MC-LHS Pf - ft plots for 1000, 2000; and 4000 samples
Fig.9  MC-LHS Pf - ft plots for 30, 7, and 5 random variables (R.V.) with 1000 samples
Fig.10  Comparison of Pf - ft curves obtained from the FOSM, FORM, and MC-LHS methods considering 30 random variables
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