Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
The Practical Handbook of Genetic Algorithms: Applications, Second Edition
The Practical Handbook of Genetic Algorithms: Applications, Second Edition
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence (Studies in Computational Intelligence)
Permanent deformation analysis of asphalt mixtures using soft computing techniques
Expert Systems with Applications: An International Journal
Genetic-based modeling of uplift capacity of suction caissons
Expert Systems with Applications: An International Journal
A new predictive model for compressive strength of HPC using gene expression programming
Advances in Engineering Software
Predicting axial capacity of driven piles in cohesive soils using intelligent computing
Engineering Applications of Artificial Intelligence
Multi-stage genetic programming: A new strategy to nonlinear system modeling
Information Sciences: an International Journal
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This study presents two Genetic Programming (GP) models for damping ratio and shear modulus of sand-mica mixtures based on experimental results. The experimental database used for GP modelling is based on a laboratory study of dynamic properties of saturated coarse rotund sand and mica mixtures with various mix ratios under different effective stresses. In the tests, shear modulus, and damping ratio of the geomaterials have been measured for a strain range of 0.001% up to 0.1% using a Stokoe resonant column testing apparatus. The input variables in the developed NN models are the mica content, effective stress and strain, and the outputs are damping ratio and shear modulus. The performance of accuracies of proposed NN models are quite satisfactory (R^2=0.95 for damping ratio and R^2=0.98 for shear modulus).