TY - EJOU
AU - Momeni, Ehsan
AU - He, Biao
AU - Abdi, Yasin
AU - Armaghani, Danial Jahed
TI - Novel Hybrid XGBoost Model to Forecast Soil Shear Strength Based on Some Soil Index Tests
T2 - Computer Modeling in Engineering \& Sciences
PY - 2023
VL - 136
IS - 3
SN - 1526-1506
AB - When building geotechnical constructions like retaining walls and dams is of interest, one of the most important
factors to consider is the soil’s shear strength parameters. This study makes an effort to propose a novel predictive
model of shear strength. The study implements an extreme gradient boosting (XGBoost) technique coupled with
a powerful optimization algorithm, the salp swarm algorithm (SSA), to predict the shear strength of various soils.
To do this, a database consisting of 152 sets of data is prepared where the shear strength (τ) of the soil is considered
as the model output and some soil index tests (e.g., dry unit weight, water content, and plasticity index) are set as
model inputs. The model is designed and tuned using both effective parameters of XGBoost and SSA, and the most
accurate model is introduced in this study. The prediction performance of the SSA-XGBoost model is assessed based
on the coefficient of determination (R2) and variance account for (VAF). Overall, the obtained values of R2 and VAF
(0.977 and 0.849) and (97.714% and 84.936%) for training and testing sets, respectively, confirm the workability of
the developed model in forecasting the soil shear strength. To investigate the model generalization, the prediction
performance of the model is tested for another 30 sets of data (validation data). The validation results (e.g., R2
of 0.805) suggest the workability of the proposed model. Overall, findings suggest that when the shear strength
of the soil cannot be determined directly, the proposed hybrid XGBoost-SSA model can be utilized to assess this
parameter.
KW - Predictive model; salp swarm algorithm; soil index tests; soil shear strength; XGBoost
DO - 10.32604/cmes.2023.026531