title = "Application of {MGGP} in Predicting Bearing Capacity
of a Strip Footing Resting on the Crest of a Marginal
Soil Hillslope",
journal = "KSCE Journal of Civil Engineering",
year = "2024",
volume = "28",
number = "10",
pages = "4244--4257",
keywords = "genetic algorithms, genetic programming, GPTIPS, Strip
footing on slope, Finite element analysis, FE model,
Tree GP, Maximum bearing strength, MGGP, Sensitivity
assessment",
abstract = "A set of finite element investigations are performed
to examine the maximum bearing strength of strip
footings positioned on the crest of a
cohesive-frictional marginal soil hill slope. In this
regard, the influence of contributing geometrical and
geotechnical parameters on the maximum bearing strength
of the footing are illustrated. It is revealed that the
nearness of slope face has negligible influence on the
bearing strength of footing if it is located at a
setback distance beyond six times the footing width.
Further, using multi-gene genetic programming
technique, a predictive relationship between the
maximum bearing strength and the contributory factors
is established and validated through relevant
experimental findings. The hyper-parameters of the MGGP
model are suitably optimised, as indicated by the
coefficient of correlation attaining high magnitudes. A
sensitivity analysis based on local perturbation is
conducted to recognise the importance ranking of the
contributory parameters. It is revealed that the
friction angle of slope material predominantly
influences the evaluation of maximum bearing strength
for strip footing on slopes, followed by other
contributing factors",
notes = "See also https://doi.org/10.1007/s12205-024-7217-0
'This erratum is published to notify a correction of
the incorrect date in the original article. See the
corrected version below: Was altered as: Published
Online 17 August 2024'
Dept. of Civil Engineering, DIT University, 24009,
Dehradun, Uttarakhand, India",