Volume 12, Issue 1 And B (Transaction B: Geotechnical Engineering, January 2014)
IJCE 2014, 12(1 And B): 55-64 |
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Shahnazari H, Shahin M A, Tutunchian M A. Evolutionary-based approaches for settlement prediction of shallow foundations on cohesionless soils. IJCE 2014; 12 (1) :55-64
URL: http://ijce.iust.ac.ir/article-1-931-en.html
URL: http://ijce.iust.ac.ir/article-1-931-en.html
Evolutionary-based approaches for settlement prediction of shallow foundations on cohesionless soils
Abstract: (19136 Views)
Due to the heterogeneous nature of granular soils and the involvement of many effective parameters in the geotechnical
behavior of soil-foundation systems, the accurate prediction of shallow foundation settlements on cohesionless soils is a
complex engineering problem. In this study, three new evolutionary-based techniques, including evolutionary polynomial
regression (EPR), classical genetic programming (GP), and gene expression programming (GEP), are utilized to obtain more
accurate predictive settlement models. The models are developed using a large databank of standard penetration test (SPT)-based case histories. The values obtained from the new models are compared with those of the most precise models that have
been previously proposed by researchers. The results show that the new EPR and GP-based models are able to predict the
foundation settlement on cohesionless soils under the described conditions with R2
values higher than 87%. The artificial
neural networks (ANNs) and genetic programming (GP)-based models obtained from the literature, have R2
values of about
85% and 83%, respectively which are higher than 80% for the GEP-based model. A subsequent comprehensive parametric
study is further carried out to evaluate the sensitivity of the foundation settlement to the effective input parameters. The
comparison results prove that the new EPR and GP-based models are the most accurate models. In this study, the feasibility of
the EPR, GP and GEP approaches in finding solutions for highly nonlinear problems such as settlement of shallow
foundations on granular soils is also clearly illustrated. The developed models are quite simple and straightforward and can
be used reliably for routine design practice.
Keywords: Shallow foundations, Settlement prediction, Evolutionary polynomial regression, Genetic programming, Gene expression programming, Cohesionless soils
Type of Study: Research Paper |
Subject:
Geotechnique
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