Prediction of Design Parameters for Retaining Walls with Gaussian Process Regression
VII. International Applied Statistics Congress, İstanbul, Turkey, 11 - 13 May 2026, vol.1, no.1388, pp.470-485, (Full Text)
- Publication Type: Conference Paper / Full Text
- Volume: 1
- City: İstanbul
- Country: Turkey
- Page Numbers: pp.470-485
- Karadeniz Technical University Affiliated: Yes
Abstract
Possessing
knowledge about the inclination (αcr) of potential failure surface,
the magnitude (Pae) and application point (zae) of active
earth thrust guides engineers for the proper dimensioning of retaining walls
under static and seismic conditions. The study aims to use Gaussian Process
Regression (GPR) as an alternative method, considering 4 covariance functions
and 2 data sampling techniques, that predicts the three design parameters taking
into account wall height, surcharge load, cohesion and internal friction angle
of backfill soil, vertical and horizontal seismic acceleration coefficients,
adhesion, and tension cracks problem for a retaining wall in the case of broken
terrain. For this purpose, the two datasets are employed to build the predictive
mathematical models and then to verify the usability of the GPR models,
respectively. The accuracy and efficiency of the GPR models are evaluated by
comparison with the results of an enlightening article using the six
statistical indicators (e.g. R2, R2adj, MSE, RMSE, MAE,
and MAPE). The statistical indicators show that GPR is a very effective machine
learning algorithm to estimate Pae and αcr. However, GPR
indicates a slightly weaker performance for zae. This study also
involves a statistical benchmarking to achieve the superior advantage of GPR
with Support Vector Regression (SVR).