journal article·2025-01-30Open access

The Approach of Using a Horizontally Layered Soil Model for Inhomogeneous Soil, by Taking into Account the Deeper Layers of the Soil, and Determining the Model’s Parameters Using Evolutionary Methods

Marko Jesenik, Mislav Trbušić

Applied Sciences· Volume 15· Issue 3

Abstract

A new approach using a horizontally layered analytical soil model for inhomogeneous soil is presented. The presented approach also considers deeper soil layers, which is not the case when simply dividing the area of interest into smaller subareas. The finite element method model was used to prepare test data because, in such a case, the soil parameters are known. Six lines simulating Wenner’s method were used, and their results were combined appropriately to determine the soil parameters of nine subareas. To determine the soil parameters in the scope of each subarea, different optimization methods were used and compared to each other. The results were analyzed, and Artificial Bee Colony was selected as the most appropriate method among those tested. Additionally, the convergence of the methods was analyzed, and Memory Assistance is presented, with the aim of shortening the calculation time. In this study, three-, four-, five-, and six-layered soil models were tested, and it is concluded that the three-layered model is most appropriate. A finite element method model based on the soil determination results was constructed to verify the results. The results of the Wenner’s method simulation in the cases of the test data and final model were compared to confirm the accuracy of the presented method.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Geotechnical and Geomechanical Engineering · Soil Geostatistics and Mapping · Soil and Unsaturated Flow

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška Cesta 46, 2000 Maribor, Slovenia

Funding

Slovenian Research and Innovation Agency

References (70)

Show source-provided references
1. Zhu, L., Jiang, H., Yang, F., Luo, H., Li, W., and Han, J. (2020). FEM Analysis and Sensor-Based Measurement Scheme of Current Distribution for Grounding Electrode. Appl. Sci., 10.10.3390/app10228151 ↗
2. Finite-Element Model for Simulation of Ferrite-Core Eddy-Current Probe10.1109/TMAG.2009.2034651 ↗
3. Axial-Flux-Machine Modeling with the Combination of FEM-2-D an Analytical Tools10.1109/TIA.2012.2199450 ↗
4. Reduction of Computing Time for Steady-State Solutions of Magnetic Field and Circuit Coupled Problems Using Time-Domain Finite-Element Method10.1109/TMAG.2012.2199285 ↗
5. Extended Finite-Element Method for Electric Field Analysis of Insulating Plate with Crack
6. The FEM-BEM Analysis of Complex Grounding Systems10.1109/TMAG.2003.810201 ↗
7. 3D FEM–BEM-coupling method to solve magnetostatic Maxwell equations10.1016/j.jmmm.2012.01.016 ↗
8. Theoretical–experimental comparison of a modeling procedure for magnetic components using finite element analysis and a circuit simulator10.1016/j.jmmm.2008.04.124 ↗
9. 3-D FEM analysis of thermal degradation in writing and reading characteristics of a perpendicular magnetic head10.1016/j.jmmm.2008.08.001 ↗
10. Finite element models for nonlinear analysis of steel–concrete composite beams with partial interaction in combined bending and shear10.1016/j.finel.2010.09.006 ↗
11. The partition of unity finite element method: Basic theory and applications10.1016/S0045-7825(96)01087-0 ↗
12. A finite element method for the simulation of strong and weak discontinuities in solid mechanics10.1016/j.cma.2003.12.041 ↗
13. Robles, K.P.V., Yee, J.-J., and Kee, S.-H. (2021). Effect of the Geometrical Constraints to the Wenner Fout-Point Electrical Resistivity Test of Reinforced Concrete Stabs. Sensors, 21.10.3390/s21134622 ↗
14. Chik, Z., and Islam, T. (2013, January 12–14). Near Surface Soil Characterizations Through Soil Apparent Resistivity: A Case Study. Proceedings of the 7th International Conference on Intelligent Data Acquisition and Advanced Computing System: Technology and Applications, Berlin, Germany.10.1109/IDAACS.2013.6662639 ↗
15. Using Fall-of-Potential Measurements to Improve Deep Soil Resistivity Estimates10.1109/TIA.2015.2428679 ↗
16. Determination of Three-Layer Earth Model from Wenner Four-Probe Test Data10.1109/20.952690 ↗
17. Karnas, G., Maslowski, G., Ziemba, R., and Wyderka, S. (2012, January 2–7). Influence of Different Multilayer Soil Models on Grounding System Resistance. Proceedings of the International Conference on Lightning Protection ICLP, Vienna, Austria.10.1109/ICLP.2012.6344277 ↗
18. Estimation of Multilayer Soil Parameters Using Genetic Algorithms10.1109/TPWRD.2004.836833 ↗
19. Behaviour of grounding systems in multilayer soils: A parametric analysis10.1109/61.277704 ↗
20. A Comparion among Eight Different Techniques to Achieve an Optimum Estimation of Electrical Grounding Parameters in Two-Layer Earth10.1109/61.248299 ↗
21. A second Order Gradient Technique for an Improved Estimation of Soil Parameters in a Two-Layer Earth10.1109/61.85863 ↗
22. Interpretation of Resistivity Sounding Measurements in N-Layer Soil using Electrostatic Images10.1109/61.517490 ↗
23. Fakhraei, M., Mahmoudian, M., and Rodrigues, E.M.G. (2020). Grounding System Modeling and Evaluation Using Integrated Circuit Based Fast Relaxed Vector Fitting Approach, Considering Soil Ionization. Appl. Sci., 10.10.3390/app10165632 ↗
24. Perng, J.-W., Kuo, Y.-C., and Lu, S.-P. (2018). Grounding System Cost Analysis Using Optimization Algorithms. Energies, 11.10.3390/en11123484 ↗
25. Parameters Estimation of Horizontal Multilayer Soil Using Genetic Algorithm10.1109/TPWRD.2010.2040845 ↗
26. Validation of the Calculation and Measurement Techniques of Earth Resistance Values10.3844/ajassp.2008.1313.1317 ↗
27. Zhiqiang, H., and Bin, Z. (2011, January 1–4). Soil Model’s Inversion Calculation Base on Genetic Algorithm. Proceedings of the 7th Asia-Pacific International Conference on Lightning, Chengdu, China.10.1109/APL.2011.6110113 ↗
28. Horizontal Multilayer Soil Parameter Estimation Through Differential Evolution10.1109/TPWRD.2015.2475637 ↗
29. Parameter Estimation of Horizontal Multilayer Earth by Complex Image Method10.1109/TPWRD.2004.834673 ↗
30. Lagace, P.J., and Vuong, M.H. (2006, January 9–13). Graphical User Interface for Interpreting and Validating Soil Resistivity Measurements. Proceedings of the IEEE ISIE (International Symposium on Industrial Electronics), Montreal, QC, Canada.10.1109/ISIE.2006.295852 ↗
31. Two-Stage Algorithm for Inverting Structure Parameters of the Horizontal Multilayer Soil10.1109/TMAG.2004.825013 ↗
32. A Nonlinear Regression based Approach for Multilayer Soil Parameter Estimation10.14257/ijca.2014.7.2.07 ↗
33. IEEE Power and Energy Society (2012). IEEE Guide for Measuring Earth Resistivity, Ground Impedance, and Earth Surface Potentials of Grounding Systems, The Institute of Electrical and Electronics Engineers, Inc.
34. Validity limits of the expression for measuring soil resistivity by the Wenner method according to IEEE Standard 81-1983
35. On the influence of the number of algorithms, problems, and independent runs in the comparison of evolutionary algorithms10.1016/j.asoc.2017.01.011 ↗
36. A chess rating system for evolutionary algorithms: A new method for the comparison and ranking of evolutionary algorithms10.1016/j.ins.2014.02.154 ↗
37. Differential evolution algorithm with ensemble of parameters and mutation strategies10.1016/j.asoc.2010.04.024 ↗
38. Differential Evolution: A Survey of the State-of-the-Art10.1109/TEVC.2010.2059031 ↗
39. Differential evolution and simulated annealing algorithms for mechanical systems design
40. Mokan, M., Sharma, K., Sharma, H., and Verma, C. (2014, January 15–17). Gbest guided differential evolution. Proceedings of the 9th International Conference on Industrial and Information Systems, Gwalior, India.10.1109/ICIINFS.2014.7036663 ↗
41. Optimization of Control Parameters of Differential Evolution Technique for the Design of FIR Pulse-shaping Filter in QPSK Modulated System10.4304/jcm.6.7.558-570 ↗
42. Differential evolution with adaptive mutation and parameter control using Levy probability distribution10.1007/s11390-012-1283-3 ↗
43. Analytical modelling of a magnetization curve obtained by the measurements of magnetic materials’ properties using evolutionary algorithms10.1016/j.asoc.2016.10.027 ↗
44. Jesenik, M., Ravber, M., and Trbušić, M. (2024). Innovative Approach for the Determination of a DC Motor’s and Drive’s Parameters Using Evolutionary Methods and Different Measured Current and Angular Speed Responses. Mathematics, 12.10.3390/math12010042 ↗
45. Saad, A., Engelbrecht, A.P., and Khan, S.A. (2024). An Analysis of Differential Evolution Population Size. Appl. Sci., 14.10.3390/app14219976 ↗
46. Wang, X., and Yu, X. (2024). Differential Evolution Algorithm with Three Mutation Operators for Global Optimization. Mathematics, 12.10.3390/math12152311 ↗
47. Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems10.1016/j.cad.2010.12.015 ↗
48. Teaching-Learning-Based Optimization: An optimization method for continuous non-linear large scale problems10.1016/j.ins.2011.08.006 ↗
49. An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems
50. A note on Teaching-learning-based optimization algorithm10.1016/j.ins.2012.05.009 ↗
51. Comments on “A note on teachnig-learning-based optimization algorithm”10.1016/j.ins.2012.11.009 ↗
52. Is a comparison of results meaningful from the inexact replications of computational experiments10.1007/s00500-014-1493-4 ↗
53. A hybrid discrete optimization algorithm based on teaching-probabilistic learning mechanism for no-wait flow shop scheduling10.1016/j.knosys.2016.06.011 ↗
54. Teaching-learning based optimization algorithm based fuzzy-PID controller for automatic generation control of multi-area power system10.1016/j.asoc.2014.11.027 ↗
55. On the convergence and original bias of the Teaching-Learning-Based-Optimization algorithm10.1016/j.asoc.2016.04.029 ↗
56. Lu, S., Liu, D., Li, D., and Shao, X. (2023). Enhanced Teaching-Learning-Based Optimization Algorithm for the Mobile Robot Path Planning Problem. Appl. Sci., 13.10.3390/app13042291 ↗
57. Ma, Y., Li, Y., and Yong, L. (2024). Teaching-Learning-Based Optimization Algorithm with Stochastic Crossover Self-Learning and Blended Learning Model and Its Application. Mathematics, 12.10.3390/math12101596 ↗
58. A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm10.1007/s10898-007-9149-x ↗
59. On the Performance of Artificial Bee Colony (ABC) Algorithm10.1016/j.asoc.2007.05.007 ↗
60. A Comparative Study of Artificial Bee Colony Algorithm
61. On clarifying misconceptions when comparing variants of the Artificial Bee Colony Algorithm by offering a new implementation10.1016/j.ins.2014.08.040 ↗
62. A novel clustering approach: Artificial Bee Colony (ABC) algorithm10.1016/j.asoc.2009.12.025 ↗
63. The Analysis of Peculiar Control Parameters of Artificial Bee Colony Algorithm on the Numerical Optimization Problems10.4236/jcc.2014.24017 ↗
64. Artificial bee colony algorithm for scheduling and rescheduling fuzzy flexible job shop problem with new job insertion10.1016/j.knosys.2016.06.014 ↗
65. An improved artificial bee colony and its application10.1016/j.knosys.2016.05.052 ↗
66. Harmony search based remodularization for object-oriented software systems
67. Nagy, Z., Werner-Stark, A., and Dulai, T. (2022). An Artificial Bee Colony Algorithm for Static and Dynamic Capacitated Arc Routing Problems. Mathematics, 10.10.3390/math10132205 ↗
68. Differential evolution algorithm with wavelet basis function and optimal mutation strategy for complex optimization problem10.1016/j.asoc.2020.106724 ↗
69. Replication and comparison of computational experiments in applied evolutionary computing: Common pitfalls and guidelines to avoid them10.1016/j.asoc.2014.02.009 ↗
70. Črepinšek, M., Liu, S.H., Mernik, M., and Ravber, M. (2019). Long Term Memory Assistance for Evolutionary Algorithms. Mathematics, 7.10.3390/math7111129 ↗