Simplified reliability analysis of multi hazard risk in gravity dams via machine learning techniques
Wybrane pełne teksty z tego czasopisma
Deterministic analysis does not provide a comprehensive model for concrete dam response under multi-hazard risk. Thus, the use of probabilistic approach is usually recommended which is problematic due to high computational demand. This paper presents a simplified reliability analysis framework for gravity dams subjected to flooding, earthquakes, and aging. A group of time-variant degradation models are proposed for different random variables. Response of the dam is presented by explicit limit state functions. The probability of failure is directly computed by either classical Monte Carlo simulation or the refined importance sampling technique. Next, three machine learning techniques (i.e., K-nearest neighbor, support vector machine, and naive Bayes classifier) are adopted for binary classification of the structural results. These methods are then demonstrated in terms of accuracy, applicability and computational time for prediction of the failure probability. Results are then generalized for different dam classes (based on the height-to-width ratio), various water levels, earthquake intensity, degradation rate, and cross-correlation between the random variables. Finally, a sigmoid-type function is proposed for analytical calculation of the failure probability for different classes of gravity dams. This function is then specialized for the hydrological hazard and the failure surface is presented as a direct function of the dam's height and width.
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- Department of Civil Environmental and Architectural Engineering, University of Colorado Boulder, 428 UCB, Boulder, CO 80309, USA, firstname.lastname@example.org
- Department of Applied Mathematics, University of Colorado Boulder, 526 UCB, Boulder, CO 80309, USA, email@example.com
-  FERC-PFMA, FERC guidance document: potential failure mode analysis, Tech. Rep., Federal Emergency Regulatory Committee, 2005.
-  M.A. Hariri-Ardebili, Performance based earthquake engineering for concrete dams (Ph.D. thesis), University of Colorado, Boulder, CO, 2015.
-  R. Charlwood, Predicting the long term behaviour and service life of concrete dams, in: Proceedings of the 2nd International Conference in Long Term Behavior of Dams, Graz, Austria, 2009.
-  ASDSO, State and federal oversight of dam safety must be improved, Magazine of Association of State Dam Safety Officials (ASDSO).
-  M.A. Hariri-Ardebili, M.R. Kianoush, Integrative seismic safety evaluation of a high concrete arch dam, Soil Dyn. Earthq. Eng. 67 (2014) 85–101.
-  K. Bury, H. Kreuzer, Assessing the failure probability of gravity dams, Int. Water Power Dam Construct. 37 (11) (1985) 46–50.
-  V. Saouma, Reliability based nonlinear fracture mechanics analysis of a concrete dam; a simplified approach, Dam Eng. 16 (3) (2006) 219–241.
-  C. Carvajal, L. Peyras, C. Bacconnet, J. Bécue, Probability modelling of shear strength parameters of RCC gravity dams for reliability analysis of structural safety, Eur. J. Environ. Civil Eng. 13 (2009) 91–119.
-  L. Peyras, C. Carvajal, H. Felix, C. Bacconnet, P. Royet, J. Becue, D. Boissier, Probability-based assessment of dam safety using combined risk analysis and reliability methods – application to hazards studies, Eur. J. Environ. Civil Eng. 16 (2012) 795–817.
-  L. Altarejos-Garcia, I. Escuder-Bueno, A. Serrano-Lombillo, M. de Membrillera-Ortuno, Methodology for estimating the probability of failure by sliding in concrete gravity dams in the context of risk analysis, Struct. Saf. 36–37 (2012) 1–13.
-  C. Yu, Time-variant finite element reliability for performance degradation assessment of concrete gravity dam, in: 2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC), IEEE, 2015 73–76.
-  A. Krounis, Sliding stability re-assessment of concrete dams with bonded concrete-rock interfaces (Ph.D. thesis), KTH Royal Institute of Technology, 2016.
-  F. Salazar, M. Toledo, E. Oñate, R. Morán, An empirical comparison of machine learning techniques for dam behaviour modelling, Struct. Saf. 56 (2015) 9–17.
-  F. Salazar, R. Morán, M.Á. Toledo, E. Oñate, Data-based models for the prediction of dam behaviour: a review and some methodological considerations, Arch. Comput. Methods Eng. (2015) 1–21.
-  V. Saouma, E. Hansen, B. Rajagopalan, Statistical and 3d nonlinear finite element analysis of Schlegeis dam, in: Proceedings of the Sixth ICOLD Benchmark Workshop on Numerical Analysis of Dams, 2001, 17–19.
-  A. Gaspar, F. Lopez-Caballero, A. Modaressi-Farahmand- Razavi, A. Gomes-Correia, Methodology for a probabilistic analysis of an RCC gravity dam construction. Modelling of temperature, hydration degree and ageing degree fields, Eng. Struct. 65 (2014) 99–110.
-  J. Mata, N.S. Leit ao, A.T. de Castro, J.S. da Costa, Construction of decision rules for early detection of a developing concrete arch dam failure scenario. A discriminant approach, Comput. Struct. 142 (2014) 45–53.
-  R. Melchers, Simulation in time-invariant and time-variant reliability problems, in: Reliability and Optimization of Structural Systems' 91, Springer, 1992 39–82.
-  S. Marelli, R. Schobi, B. Sudret, UQLab user manual - Structural Reliability, Tech. Rep., Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, report UQLab-V0. 92-107, 2016.
-  R.E. Melchers, Structural Reliability Analysis and Prediction, John Wiley & Son Ltd, 1999.
-  J. Jeppsson, Reliability-based assessment procedures for existing concrete structures (Ph.D. thesis), Lund University, 2003.
-  Q. Li, C. Wang, B.R. Ellingwood, Time-dependent reliability of aging structures in the presence of non-stationary loads and degradation, Struct. Saf. 52 (2015) 132–141.
-  A. Harbitz, An efficient sampling method for probability of failure calculation, Struct. Saf. 3 (2) (1986) 109–115.
-  M. McKay, R. Beckman, W. Conover, A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics 21 (2) (1979) 239–245.
-  S.-K. Au, J.L. Beck, Estimation of small failure probabilities in high dimensions by subset simulation, Probab. Eng. Mech. 16 (4) (2001) 263–277.
-  Y. Lee, D. Hwang, A study on the techniques of estimating the probability of failure, J. Chungcheong Math. Soc. 21 (4) (2008) 573–583.
-  G.I. Schuëller, R. Stix, A critical appraisal of methods to determine failure probabilities, Struct. Saf. 4 (4) (1987) 293–309.
-  R. Melchers, Search-based importance sampling, Struct. Saf. 9 (2) (1990) 117–128.
-  S. Au, J.L. Beck, A new adaptive importance sampling scheme for reliability calculations, Struct. Saf. 21 (2) (1999) 135–158.
-  T. Hastie, R.J. Tibshirani, J.H. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, 2011.
-  K. Tanioka, H. Yadohisa, Effect of data standardization on the result of k-means clustering, in: Challenges at the Interface of Data Analysis, Springer, (2012) 59–67.
-  X. Wu, et al., Top 10 algorithms in data mining, Knowl. Inf. Syst. 14 (1) (2008) 1–37.
-  B. Schölkopf, A. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, MIT press, 2002.
-  C.M. Bishop, Pattern Recognition and Machine Learning, Information Science and Statistics, Springer-Verlag New York, Inc., 2006, , ISBN: 0387310738.
-  F. Pourkamali-Anaraki, S. Hughes, Kernel compressive sensing, in: IEEE International Conference on Image Processing, 2013, 494–498.
-  F. Pourkamali-Anaraki, S. Becker, A randomized approach to efficient kernel clustering, in: IEEE Global Conference on Signal and Information Processing, 2016, 207–211.
-  J. Spross, A Critical Review of the Observational Method (Licentiate dissertation), 2014 Stockholm. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-144207.
-  M. Westberg, Reliability-based assessment of concrete dam stability, Licenciate Thesis, Division of Structural Engineering, Lund Institute of Technology, Lund University, 2010 Report TVBK-1033.
-  M. Westberg Wilde, F. Johansson, System reliability of concrete dams with respect to foundation stability: application to a spillway, J. Geotech. Geoenviron. Eng. 139 (2) (2013) 308–319.
-  S. Huaizhi, H. Jiang, Z. Wen, Service life predicting of dam systems with correlated failure modes, ASCE J. Perform. Construct. Facil. 27 (2013) 252–269.
-  A. Morales-Torres, I. Escuder-Bueno, L. Altarejos-Garcia, A. Serrano-Lombillo, Building fragility curves of sliding failure of concrete gravity dams integrating natural and epistemic uncertainties, Eng. Struct. 125 (2016) 227–235.
-  S.-I. Yang, D.M. Frangopol, L.C. Neves, Service life prediction of structural systems using lifetime functions with emphasis on bridges, Reliab. Eng. Syst. Saf. 86 (1) (2004) 39–51.
-  E. Bretas, A. Batista, J. Lemos, P. Léger, Seismic analysis of gravity dams: a comparative study using a progressive methodology, in: Proc. of the EURODYN 2014 – 9th International Conference on Structural Dynamics, Oporto, 2014.
-  USACE, Gravity Dam Design, Tech. Rep. EM 1110-2-2200, Department of the Army, U.S. Army Corps of Engineers, Washington, D.C., USA, 1995.
-  H. Westergaard, Water pressures on dams during earthquakes, Trans. Am. Soc. Civil Eng. 98 (1933) 418–433.
-  MATLAB, version 9.1 (R2016b), The MathWorks Inc., Natick, MA, 2016.
-  J. Han, C. Moraga, The influence of the sigmoid function parameters on the speed of backpropagation learning, in: International Workshop on Artificial Neural Networks, Springer, 1995 195–201.
-  H. Akaike, A new look at the statistical model identification, IEEE Trans. Autom. Control 19 (6) (1974) 716–723.
-  M.A. Hariri-Ardebili, Analytical failure probability model for generic gravity dam classes, Proc. Inst. Mech. Eng. Part O: J. Risk Reliab. 231 (5) (2017) 546–557.
-  J. Ghosh, J. Padgett, Aging considerations in the development of time-dependent seismic fragility curves, J. Struct. Eng. 136 (2010) 1497–1511.
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018)