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

Szczepan Grosel

doi:10.2478/sgem-2020-0008

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

Studia Geotechnica et Mechanica

Volume 43 (2021): Issue 1 (April 2021)

By: Szczepan Grosel

Open Access

|Nov 2020

Figures & Tables

Geometry and mesh of numerical model at computational step 0.

Geometry and mesh of numerical model at computational step 60.

Geometry and mesh of numerical model at computational step 120.

Geometry and mesh of numerical model at computational step 160.

Geometry and mesh of numerical model at computational step 205.

Table 1

Parameters resulting from calibration.

Mat.	Par.	Real	Range	Mut.	I	II	III	IV	V
1A	ϕ [°]	5.1	5÷20	1	8	15	16	15	10
	c [kPa]	18	1÷20	1	17	16	16	2	2
	E [MPa]	48	20÷80	3	70	40	79	25	56
	v [−]	0.25	0.1÷0.4	0.03	0.33	0.12	0.4	0.39	0.31
	k_x [m/s]	1.0E-11	5E-12÷5E-10	2^*	2.0E-10	1.0E-10	2.0E-11	2.0E-10	1.3E-10
2B	ϕ [°]	14.5	10÷20	1	14	15	11	14	11
	c [kPa]	5	1÷10	1	8	7	1	7	5
	E [MPa]	18	10÷60	3	28	23	33	19	22
	v [−]	0.25	0.1÷0.4	0.03	0.15	0.27	0.2	0.25	0.34
	k_x [m/s]	5.0E-10	5E-11÷5E-9	2^*	2.5E-10	2.0E-10	1.0E-10	2.0E-10	5.0E-10
3C	ϕ [°]	10	5÷14	1	7	9	17	12	16
	c [kPa]	1	1÷10	1	10	8	4	2	6
	E [MPa]	18	10÷60	3	24	15	15	26	16
	v [−]	0.25	0.1÷0.4	0.03	0.3	0.15	0.22	0.27	0.19
	k_x [m/s]	5.0E-10	5E-11÷5E-9	2^*	2.0E-09	2.0E-10	1.0E-09	1.0E-09	5.0E-09
4D	ϕ [°]	28	10÷30	1	15	22	28	18	22
	c [kPa]	5.2	1÷10	1	5	7	4	7	6
	E [MPa]	48	20÷80	3	55	23	48	24	39
	v [−]	0.25	0.1÷0.4	0.03	0.22	0.21	0.34	0.12	0.38
	k_x [m/s]	1.0E-08	5E-9÷5E-7	2^*	1.0E-07	1.0E-08	2.5E-07	5.0E-08	1.0E-07
5E	ϕ [°]	36	20÷40	1	29	35	20	34	24
	c [kPa]	5.1	1÷10	1	8	4	4	3	2
	E [MPa]	120	90÷140	3	93	112	135	130	93
	v [−]	0.2	0.1÷0.4	0.03	0.33	0.18	0.24	0.28	0.36
	k_x [m/s]	1.0E-05	5E-6÷5E-4	2^*	5.0E-04	5.0E-06	5.0E-06	5.0E-05	1.0E-04

Remark: Mat. – Material, Par. – Parameter, Real – value used to create synthetic monitoring data, Range – range used to create initial population, Mut. – mutation value,

* – mutation realized by multiplication or division.

Comparison of best individuals’ error in each generation.

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

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

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

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

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

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

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

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

Failure surface at step 170 for dataset IV.

Failure surface at step 210 for dataset IV.

Failure surface at step 230 for dataset IV.

Failure surface at step 250 for dataset IV.

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

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

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

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

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

References

Authors

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Articles in this issue

DOI: https://doi.org/10.2478/sgem-2020-0008 | Journal eISSN: 2083-831X (formerly 0137-124X) | Journal ISSN: 0137-6365

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Language: English

Page range: 34 - 47

Submitted on: Jul 23, 2020

Accepted on: Sep 21, 2020

Published on: Nov 24, 2020

Published by: Wroclaw University of Science and Technology

In partnership with: Paradigm Publishing Services

Keywords:

observational method,

Related subjects:

Physics,

Mechanics and fluid dynamics

© 2020 Szczepan Grosel, published by Wroclaw University of Science and Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 43 (2021): Issue 1 (April 2021)

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

Figures & Tables

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Table 1

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