Information Sciences—Informatics and Computer Science: An International Journal - Special issue: FEA 2002
Machine-learning paradigms for selecting ecologically significant input variables
Engineering Applications of Artificial Intelligence
Prediction of compressive and tensile strength of limestone via genetic programming
Expert Systems with Applications: An International Journal
Genetic programming approach to predict a model acidolysis system
Engineering Applications of Artificial Intelligence
The effects of constant neutrality on performance and problem hardness in GP
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
Engineering Applications of Artificial Intelligence
A hybrid computational approach to derive new ground-motion prediction equations
Engineering Applications of Artificial Intelligence
Support vector regression based modeling of pier scour using field data
Engineering Applications of Artificial Intelligence
A comparison of linear genetic programming and neural networks inmedical data mining
IEEE Transactions on Evolutionary Computation
Multi-stage genetic programming: A new strategy to nonlinear system modeling
Information Sciences: an International Journal
Hi-index | 0.00 |
Soil deformation modulus is an essential parameter for the analysis of behavior of substructures. Despite its importance, little attention is paid to developing empirical models for predicting the deformation moduli obtained from the field tests. To cope with this issue, this paper presents the development of a new prediction model for the pressuremeter soil deformation modulus utilizing a linear genetic programming (LGP) methodology. The LGP model relates the soil secant modulus obtained from the pressuremeter tests to the soil index properties. The best model was selected after developing and controlling several models with different combinations of the influencing parameters. The experimental database used for developing the models was established upon several pressuremeter tests conducted on different soil types at depths of 3-40m. To verify the applicability of the derived model, it was employed to estimate the soil moduli of portions of test results that were not included in the analysis. Further, the generalization capability of the model was verified via several statistical criteria. The sensitivity of the soil deformation modulus to the influencing variables was examined and discussed. Moisture content and soil dry unit weight were found to be efficient representatives of the initial state and consolidation history of the soil for determining its deformation modulus. The results indicate that the LGP approach accurately characterizes the soil deformation modulus leading to a very good prediction performance. The correlation coefficients between the experimental and predicted soil modulus values are equal to 0.908 and 0.901 for the calibration and testing data sets, respectively.