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- Tien Thinh, Le (1)
- Tien-Thinh, Le (1)
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- Carbon nanotubes (1)
- Neural Network (1)
- square concrete-filled... (1)
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This work develops a Neural Network (NN) model for the prediction of the tensile modulus of carbon nanotube (CN)/polymer nanocomposites, based on experimental database. A data set composed of 282 configurations is collected from available resources. Considered input variables of the dataset are such as mechanical properties of separated phases, density of polymer matrix, processing method, geometry of CN, modification method at the CN surface, etc. while the problem output is the tensile modulus of nanocomposite. Parametric studies have been performed in finding optimum architecture of the proposed NN model. |
A Machine Learning (ML) model based on Gaussian regression, using different kernel functions, is introduced in this paper to assess the load-carrying capacity of square concrete-filled steel tubular (CFST) columns. The input data used to develop the prediction model, which consists of 314 datasets including the structural geometrical parameters and the mechanical properties of the materials, was collected from available resources in the literature. The performance of the prediction model has also been validated by comparing with: (i) other ML models such as Artificial neural network, Support vector machine, etc.; and (ii) existing formulations in the literature for predicting load-carrying capacity of square CFST columns (including several codes such as EC4, AISC and ACI). The obtai... |
