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Numerical analysis of tailing dam with calibration based on genetic algorithm and geotechnical monitoring data

Szczepan Grosel

Szczepan Grosel

Wroclaw University of Science and TechnologyWroclaw, Poland
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Grosel, Szczepan
  
24 nov 2020
Numerical analysis of tailing dam with calibration based on genetic algorithm and geotechnical monitoring data's Cover Image
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Categoría del artículo: Original Study
Publicado en línea: 24 nov 2020
Páginas: 34 - 47
Recibido: 23 jul 2020
Aceptado: 21 sept 2020
DOI: https://doi.org/10.2478/sgem-2020-0008
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© 2021 Szczepan Grosel, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1

Example of noised data.
Example of noised data.

Figure 2

Geometry and mesh of numerical model at computational step 0.
Geometry and mesh of numerical model at computational step 0.

Figure 3

Geometry and mesh of numerical model at computational step 60.
Geometry and mesh of numerical model at computational step 60.

Figure 4

Geometry and mesh of numerical model at computational step 120.
Geometry and mesh of numerical model at computational step 120.

Figure 5

Geometry and mesh of numerical model at computational step 160.
Geometry and mesh of numerical model at computational step 160.

Figure 6

Geometry and mesh of numerical model at computational step 205.
Geometry and mesh of numerical model at computational step 205.

Figure 7

Sensor location in numerical model.
Sensor location in numerical model.

Figure 8

Comparison of best individuals’ error in each generation.
Comparison of best individuals’ error in each generation.

Figure 9

Results of horizontal (left) and vertical (right) displacement in benchmark U1.
Results of horizontal (left) and vertical (right) displacement in benchmark U1.

Figure 10

Results of horizontal (left) and vertical (right) displacement in benchmark U2.
Results of horizontal (left) and vertical (right) displacement in benchmark U2.

Figure 11

Results of horizontal (left) and vertical (right) displacement in benchmark U3.
Results of horizontal (left) and vertical (right) displacement in benchmark U3.

Figure 12

Results of piezometric head in piezometer P1 (left) and P2 (right).
Results of piezometric head in piezometer P1 (left) and P2 (right).

Figure 13

Results of piezometric head in piezometer P3 (left) and P4 (right).
Results of piezometric head in piezometer P3 (left) and P4 (right).

Figure 14

Geometry of model at the intermediate step (230) of prediction.
Geometry of model at the intermediate step (230) of prediction.

Figure 15

Geometry of model at the final step (250) of prediction
Geometry of model at the final step (250) of prediction

Figure 16

Changes of FOS value in time for different sets of parameters.
Changes of FOS value in time for different sets of parameters.

Figure 17

Failure surface at step 170 for dataset IV.
Failure surface at step 170 for dataset IV.

Figure 18

Failure surface at step 210 for dataset IV.
Failure surface at step 210 for dataset IV.

Figure 19

Failure surface at step 230 for dataset IV.
Failure surface at step 230 for dataset IV.

Figure 20

Failure surface at step 250 for dataset IV.
Failure surface at step 250 for dataset IV.

Figure 21

Predictions of horizontal (left) and vertical (right) displacement in benchmark U1.
Predictions of horizontal (left) and vertical (right) displacement in benchmark U1.

Figure 22

Prediction of horizontal (left) and vertical (right) displacement in benchmark U2.
Prediction of horizontal (left) and vertical (right) displacement in benchmark U2.

Figure 23

Prediction of horizontal (left) and vertical (right) displacement in benchmark U3.
Prediction of horizontal (left) and vertical (right) displacement in benchmark U3.

Figure 24

Prediction of piezometric head in piezometer P1 (left) and P2 (right).
Prediction of piezometric head in piezometer P1 (left) and P2 (right).

Figure 25

Prediction of piezometric head in piezometer P3 (left) and P4 (right).
Prediction of piezometric head in piezometer P3 (left) and P4 (right).

Parameters resulting from calibration_

Mat.Par.RealRangeMut.IIIIIIIVV
1Aϕ [°]5.15÷201815161510
c [kPa]181÷20117161622
E [MPa]4820÷8037040792556
v [−]0.250.1÷0.40.030.330.120.40.390.31
kx [m/s]1.0E-115E-12÷5E-102*2.0E-101.0E-102.0E-112.0E-101.3E-10
2Bϕ [°]14.510÷2011415111411
c [kPa]51÷10187175
E [MPa]1810÷6032823331922
v [−]0.250.1÷0.40.030.150.270.20.250.34
kx [m/s]5.0E-105E-11÷5E-92*2.5E-102.0E-101.0E-102.0E-105.0E-10
3Cϕ [°]105÷14179171216
c [kPa]11÷101108426
E [MPa]1810÷6032415152616
v [−]0.250.1÷0.40.030.30.150.220.270.19
kx [m/s]5.0E-105E-11÷5E-92*2.0E-092.0E-101.0E-091.0E-095.0E-09
4Dϕ [°]2810÷3011522281822
c [kPa]5.21÷10157476
E [MPa]4820÷8035523482439
v [−]0.250.1÷0.40.030.220.210.340.120.38
kx [m/s]1.0E-085E-9÷5E-72*1.0E-071.0E-082.5E-075.0E-081.0E-07
5Eϕ [°]3620÷4012935203424
c [kPa]5.11÷10184432
E [MPa]12090÷14039311213513093
v [−]0.20.1÷0.40.030.330.180.240.280.36
kx [m/s]1.0E-055E-6÷5E-42*5.0E-045.0E-065.0E-065.0E-051.0E-04
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Geociencias, Geociencias, otros, Ciencia de los materiales, Compuestos, Materiales porosos, Física, Mecánica y dinámica de fluidos
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