Numerical modelling of the impact of flood wave cyclicality on the stability of levees
Testo completo
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(41) . Corresponding author: franczyk@geol.agh.edu.pl. © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/)..
(42) E3S Web of Conferences 7, 03022 (2016) FLOODrisk 2016 - 3rd European Conference on Flood Risk Management. DOI: 10.1051/ e3sconf/2016 0703022.
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(62) . . For each considered parameter, four sensitivity ratios are computed and can be separated into two categories: local and global. For the local category, input parameter value is varied within a small interval of a random set. In the global sensitivity ratio, the input value of a given parameter is varied across the whole range of a random set (fig. 1).. 3 Flood wave cyclicality modelling 2
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(70) the performing of the coupled mechanical-fluid and flow-thermal processes used in embankment stability modelling [17]. 3.1 Description of the geological model Numerical calculations were performed using a numerical model of the experimental embankment built for the ISMOP project. The geometry of the assumed numerical model that is consistent with the cross- section through the experimental embankment is depicted in (fig. 2).. Figure 1. Local and range intervals and schematic representation of sensitivity calculated from sensitivity ratio. Modified after [12].. Sensitivity score KSS is a more robust method of evaluating the uncertainty of a given model. It is obtained by normalising and weighting the sensitivity ratio value in an input parameter. It can be written with the following expression:. KSS KSR. max xG min xG
(71) . x. Figure 2. Geometry of the geological model assumed for the numerical calculation.. )'*. Material parameters for the assumed model are presented in the table below (Tab. 1).. The normalisation procedure makes the sensitivity score independent of the input value units of a given parameter. Sensitivity score KSS,i i= 1,2..N is calculated for all N number of basic parameters being considered. The total relative sensitivity for each input variable is computed as a summation of all sensitivity scores (local and range) for each input parameter KSS,I on the respective results such as displacement, forces, pore pressure, or factor of safety. It can be written as (3):. D xi
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(108) E3S Web of Conferences 7, 03022 (2016) FLOODrisk 2016 - 3rd European Conference on Flood Risk Management. DOI: 10.1051/ e3sconf/2016 0703022. 3.3 Numerical calculations.. 3.2 Description of the sensitivity analysis of the flooding process.. 3
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(171) Two intervals of parameters were determined independently. The first set was determined by analysis of historical floods in Poland in the basin of the Vistula River where the experimental embankment was built [18]. The second was assumed according to the ISMOP project documentation that applied certain limited values to the rate of water level changes and the height of water level (experimental knowledge). &
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(192) E3S Web of Conferences 7, 03022 (2016) FLOODrisk 2016 - 3rd European Conference on Flood Risk Management. DOI: 10.1051/ e3sconf/2016 0703022.
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(306) . 6 References 1.. 2.. Figure 10. Differences of the total relative sensitivity values between single and double flood wave cycles.. DOI: 10.1051/ e3sconf/2016 0703022. 3..
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(376) . 7 . 6.. 7.. 8.. 9.. 10.. successive flood waves and should be especially examined as part of the ISMOP project.. 11.. Acknowledgements. This work is financed by the National Centre for Research and Development (NCBiR), Poland, project PBS1/B9/18/2013 - (no 180535). This work was partly supported by AGH - University of Science and Technology, Faculty of Geology, Geophysics and Environmental Protection, as a part of statutory research project.. 12.. 5. Van, m. A., Zwanenburg, C., Koelewijn, A. R. and van Lottum, H. (2009). Evaluation of Full Scale Levee Stability Tests at Booneschans and Corresponding Centrifuge Tests. Proc. 17th Int. Conf. on Soil Mech. and Geot. Eng., Alexandria, Egypt, pp. 2048-2051. Erdbrink, C.D., Krzhizhanovskaya, V.V. and Sloot, P.M.A., (2012), Controlling flow-induced vibrations of flood barrier gates with data-driven and finite-element modelling. Comprehensive Flood Risk Management ± Klijn & Schweckendiek (eds) © Taylor & Francis Group, London, pp. 425434. Krzhizhanovskaya V. V., Shirshov G. S., Melnikova N. B., Belleman R. G., Rusadi F. I., Broekhuijsen B. J,. Gouldby B. P., Lhomme
(377) Pyayt A. L., Mokhov I. I., Ozhigin A. V., Lang B. and Meijer R. J. (2011). Flood early warning system: design, implementation and computational modules, Procedia Computer Science 4, 106115. Radzicki, K., Bonelli, S. (2012). Monitoring of the suffusion process development using thermal analysis performed with IRFTA model, w Proc. of 6th ICSE, 593-600. Moscicki J.W., Bania, G., Cwiklik, M. and Borecka A. (2014). DC resistivity studies of shallow geology in the vicinity of Vistula river flood bank in Czernichów Village (near Kraków in Poland). Studia Geotechnica et Mechanica. 36 (1), 6370 a, M., M., C., Piórkowski, A., and A model of a system for stream data storage and analysis dedicated to sensor networks of embankment monitoring. Computer Information Systems and Industrial Management, Lecture Notes in Computer Science. Berlin: Springer Berlin Heidelberg, 8838. pp. 514-525. Chuchro, M., Lupa, !"# and A concept of time windows length selection in stream databases in the context of sensor networks monitoring. Advances in databases and information systems and associated satellite events. ADBIS 2014 Advances in Intelligent Systems and Computing. Springer International Publishing. pp. 173-174. $" % $ (2015). Random set method application to flood embankment stability modeling. Procedia Computer Science, 51, 26682677. Clemson, B., Tang, Y., Pyne, J. and Unal, R. (1995). Efficient methods for sensitivity analysis, System Dynamics Review 11, 31-49. Lomas, K.J. and Eppel, H. (1992). Sensitivity analysis techniques for building thermal simulation programs, Energy and Buildings 19, 21-44. Iman R. L. and Helton, J. C. (1988). An Investigation of Uncertainty and Sensitivity Analysis Techniques for Computer Models. Risk Analysis 8, 71-90. Peschl G. M. (2004). Reliability analysis in &!%' %# " ' ' (!) # * ) method. PhD thesis. Graz: Graz University of Technology, (unpublished)..
(378) E3S Web of Conferences 7, 03022 (2016) FLOODrisk 2016 - 3rd European Conference on Flood Risk Management. 13. Shen, H., and Abbas, S. (2013). Rock slope reliability analysis based on distinct element method and random set theory. Int. J. of Rock Mech. and Mining Sci, 15-22. 14. Pilecki, Z., Stanisz, $" % $ +!, - and Pilecka, E. (2014). Numerical stability analysis of slope with use of Random Set Theory. Zeszyty Naukowe IGSMiE PAN 86, 5 17. 15. Schweiger, H. F., and Peschl, G. M. (2005). Reliability analysis in geotechnics with the random set finite element method. Computers and Geotechnics, 422 435. 16. ."! $" % $ (2015) Numerical and experimental stability analysis of earthen levees. IAMG 2015 : the 17th annual conference of the International Association for Mathematical Geosciences : Freiberg, Germany, Short Abstract: pp.163-164, Full Paper (DVD): pp. 857-864 17. Itasca Consulting Group, I. (2011). FLAC Fast Lagrangian Analysis of Continua and FLAC/Slope /#0# 18. Kret E. (2015). ISMOP Internal Raport, Task 2.2.. 6. DOI: 10.1051/ e3sconf/2016 0703022.
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