Risk-based probabilistic thermal-stress analysis of concrete arch dams

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

References

[1] U.S. Army Corps of Engineering. Engineering and Design: Arch dam design, Engineer manual 1110-2-2201, 1994

[2] Wieland M, Kirchen G F. Long-term dam safety monitoring of Punt dal Gall arch dam in Switzerland. Frontiers of Structural and Civil Engineering, 2012, 6(1): 76–83

[3] Sheibany F, Ghaemian M. Effects of environmental action on thermal stress analysis of Karaj concrete arch dam. Journal of Engineering Mechanics, 2006, 132(5): 532–544

[4] Agullo L, Aguado A. Thermal behavior of concrete dams due to environmental actions. Dam Engineering, 1995, VI(1): 3–21

[5] Daoud M, Galanis N, Ballivy G. Calculation of the periodic temperature field in a concrete dam. Canadian Journal of Civil Engineering, 1997, 24(5): 772–784

[6] Léger P, Venturelli J, Bhattacharjee S S. Seasonal temperature and stress distributions in concrete gravity dams (Parts I and II). Canadian Journal of Civil Engineering, 1993, 20(6): 999–1017

[7] Meyer T, Mouvet L. Behavior analysis of the Vieux-Emosson arch gravity dam under thermal loads. Dam Engineering, 1995, VI(4): 275–292

[8] Zhang Z, Garga V K. State of temperature and thermal stress in mass concrete structures subjected to thermal shock. Dam Engineering, 1996, VIII(4): 336–350

[9] Jin F, Chen Z, Wang J, Yang J. Practical procedure for predicting non-uniform temperature on the exposed face of arch dams. Applied Thermal Engineering, 2010, 30(14–15): 2146–2156

[10] Bernier C, Padgett J E, Proulx J, Paultre P. Seismic fragility of concrete gravity dams with spatial variation of angle of friction: case study. Journal of Structural Engineering, 2016, 142(5): 05015002

[11] Vu-Bac N, Lahmer T, Zhuang X, Nguyen-Thoi T, Rabczuk T. A software framework for probabilistic sensitivity analysis for computationally expensive models. Advances in Engineering Software, 2016, 100: 19–31

[12] Vu-Bac N, Silani M, Lahmer T, Zhuang X, Rabczuk T. A unified framework for stochastic predictions of mechanical properties of polymeric nanocomposites. Computational Materials Science, 2015, 96: 520–535

[13] Hamdia K M, Silani M, Zhuang X, He P, Rabczuk T. Stochastic analysis of the fracture toughness of polymeric nanoparticle composites using polynomial chaos expansions. International Journal of Fracture, 2017, 206(2): 215–227

[14] Vu-Bac N, Lahmer T, Keitel H, Zhao J, Zhuang X, Rabczuk T. Stochastic predictions of bulk properties of amorphous polyethylene based on molecular dynamics simulations. Mechanics of Materials, 2014, 68: 70–84

[15] Vu-Bac N, Lahmer T, Zhang Y, Zhuang X, Rabczuk T. Stochastic predictions of interfacial characteristic of polymeric nanocomposites (PNCs). Composites Part B, Engineering, 2014, 59: 80–95

[16] Altarejos-García L, Escuder-Bueno I, Serrano-Lombillo A, de Membrillera-Ortuño M G. Methodology for estimating the probability of failure by sliding in concrete gravity dams in the context of risk analysis. Structural Safety, 2012, 36–37: 1–13

[17] Vu-Bac N, Rafiee R, Zhuang X, Lahmer T, Rabczuk T. Uncertainty quantification for multiscale modeling of polymer nanocomposites with correlated parameters. Composites Part B, Engineering, 2015, 68: 446–464

[18] Liel A B, Haselton C B, Deierlein G G, Baker J W. Incorporating modeling uncertainties in the assessment of seismic collapse risk of buildings. Structural Safety, 2009, 31(2): 197–211

[19] Luísa M, Farinha B, de Lemos J V, Neves E M. Analysis of foundation sliding of an arch dam considering the hydromechanical behavior. Frontiers of Structural and Civil Engineering, 2012, 6(1): 35–43

[20] Raychowdhury P, Jindal S. Shallow foundation response variability due to soil and model parameter uncertainty. Frontiers of Structural and Civil Engineering, 2014, 8(3): 237–251

[21] Nariman N A, Lahmer T, Karampour P. Uncertainty quantification of stability and damage detection parameters of coupled hydrodynamic-ground motion in concrete gravity dams. Frontiers of Structural and Civil Engineering, doi: 10.1007/s11709-018-0462-x

[22] Haukaas T, Der Kiureghian A. Parameter sensitivity and importance measures in nonlinear finite element reliability analysis. Journal of Engineering Mechanics, 2005, 131(10): 1013–1026

[23] Val D, Bljuger F, Yankelevsky D. Reliability evaluation in nonlinear analysis of reinforced concrete structures. Structural Safety, 1997, 19(2): 203–217

[24] Mckay M D, Beckman R J, Conover W J. Comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 2000, 42(1): 55–61

[25] Reddy J N, Gartling D K. The Finite-element Method in Heat Transfer and Fluid Dynamics. Boca Raton: CRC, 2001, 5–108

[26] Iman R. Latin hypercube sampling. In: Encyclopedia of Statistical Sciences. Wiley: New York. 1980

[27] Melchers R E. Structural Reliability Analysis and Prediction. 2nd ed. JohnWiley & Sons, 1999

[28] Broding W C, Diederich F W, Parker P S. Structural optimization and design based on a reliability design criterion. Journal of Spacecraft, 1964, 1(1): 56–61

[29] Chauhan S S, Bowles D S. Dam safety risk assessment with uncertainty analysis. ANCOLD Bulletin, 2004, 73–88

[30] Haselton C B. Assessing seismic collapse safety of modern reinforced concrete frame buildings. Dissertation for the Doctoral Degree. San Francisco: Stanford University, 2006

[31] EPRI (Electrical Power Research Institute). Uplift pressures, shear strengths, and tensile strengths for stability analysis of concrete gravity dams. Report No. EPRI TR-100345, 1992

[32] Lo K Y, Lukajic B, Wang S, Ogawa T, Tsui K K. Evaluation of strength parameters of concrete-rock interface for dam safety assessment. In: Canadian Dam Safety Conf, Toronto: Canadian Dam Association, 1990, 71–94

[33] Champagne K, Rivard P, Quirion P M. Shear strength parameters of concrete gravity dams in Quebec. In: CDA 2013 Annual Conf. Toronto: Canadian Dam Association, 2012

[34] Bureau of Reclamation. Guidelines for achieving public protection in dam safety decision making. Technical report. 2003

[35] Australian Committee on Large Dams. Guidelines on risk assessment, 2003

[36] Munger D F, Bowles D S, Boyer D D, Davis D W, Margo D A, Moser D A, Regan P J, Snorteland N. Interim tolerable risk guidelines for US Army Corps of Engineering dams, 2009

RIGHTS & PERMISSIONS

2019 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature