MATHEMATICAL-STATISTICAL AND COMPUTER MODELING METHODS USED IN ESTIMATING AND PREDICTING DEFORMATION INDICATORS
Keywords:
Ground deformation, variable voltages, mathematical-statistical analysis, regression models, artificial neuron networks, computer modeling, limited elements method, resilient module, collapse potential.Abstract
In the article Fergana in the province wide widespread loess-like, alluvial-proluvial and irrigated small particulate of the ground variable voltages in the conditions deformation behavior assessment and prophecy in doing applicable mathematical-statistical and computer modeling methods scientific in terms of is based on. In the study laboratory ( cyclic triaxial, consolidation, collapse ) and field ( plate) loading, LWD and others ) tests from the results consists of information base formed and based on it resilient module M_r, permanent deformation ε_p, collapse potential I_cand settlement indicators are determined, their statistical structure, distribution and correlation relationships. Using multifactor regression models, simple and physically understandable equations are developed to estimate M_rand I_c, and their accuracy is analyzed in the cross-section of geotechnical zones. In order to take into account nonlinear relationships in more depth, predictive models based on artificial neural networks (ANN) are built, providing a significant improvement in R² and RMSE indicators compared to regression models. At the same time, settlement and deformations in the foundation-soil system are estimated using computer models based on the finite element method (FEM). territorial and depth according to distribution, moisture and collapse under the influence differential sediments dynamics is studied and laboratory-field results with verification is done. Obtained results based on regression, ANN and FEM approaches integrating regional deformation prediction methodology offer in the conditions of the Fergana region buildings the foundation reliable design, deformation danger zones separation and geotechnical risks reduce for scientific and methodological basis created.
References
1.Shahin MA, Jaksa MB, Maier HR Artificial neural network applications in geotechnical engineering // Australian Geomechanics. - 2001. - Vol. 36, No. 1. – P. 49–62.
2.Shahin MA, Jaksa MB, Maier HR State of the art of artificial neural networks in geotechnical engineering // Electronic Journal of Geotechnical Engineering. - 2008. - Vol. 8. – P. 1–26.
3.Shahin MA, Jaksa MB, Maier HR Recent advances and future challenges for artificial neural networks in geotechnical engineering applications // Advances in Artificial Neural Systems. - 2009. - Vol. 2009. – Article ID 308239.
4.Tamošiūnas T., Skuodis Š. Predictive stress modeling of resilient modulus in sandy subgrade soils // Infrastructures. - 2023. - Vol. 8, No. 2. – Article 29.
5.Onyelowe KC, Ebid AM, Sujatha ER and etc. Extensive overview of soil constitutive relations and applications for geotechnical engineering problems // Heliyon. - 2023. - Vol. 9, No. 3. – e14465.
6.Phoon K.-K., Kulhawy FH Characterization of geotechnical variability // Canadian Geotechnical Journal. - 1999. - Vol. 36, No. 4. – P. 612–624.
7.Phoon K.-K., Santoso AM Probabilistic analysis of geotechnical problems // Geotechnical Safety and Risk. – 2012. – P. 11–44.
8.Brinkgreve RBJ, Kumarswamy S, Swolfs WM and etc. PLAXIS 2D – Reference Manual. – Delft: Plaxis bv, 2015. – 300+ p.
9.Potts DM, Zdravković L. Finite Element Analysis in Geotechnical Engineering: Application. – London: Thomas Telford, 2001. – 438 p.
10.Das SK, Basudhar PK Prediction of residual settlement of shallow foundations on cohesionless soils using artificial neural networks // Computers and Geotechnics. - 2006. - Vol. 33, No. 4–5. - P. 427-442.
11.Goh ATC Back-propagation neural networks for modeling complex systems // Artificial Intelligence in Engineering. - 1995. - Vol. 9, No. 3. – P. 143–151.
12.Erdal HI, Karakurt O. Advances in machine learning applications for geotechnical engineering // Innovative Infrastructure Solutions. - 2018. - Vol. 3, Article 40.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
