Abstract
The evaluation of torsional effects on multistory buildings remains an open issue, despite considerable research efforts and numerous publications. In this study, a large number of multiple test structures are considered with normally distributed topological attributes, in order to quantify the statistically derived relationships between the torsional criteria and response parameters. The linear regression analysis results, depict that the center of twist and the ratio of torsion (ROT) index proved numerically to be the most reliable criteria for the prediction of the modal rotation and displacements, however the residuals distribution and R-squared derived for the ductility demands prediction, was not constant and low respectively. Thus, the assessment of the torsional parameters' contribution to the nonlinear structural response was investigated using artificial neural networks. Utilizing the connection weights approach, the Center of Strength, Torsional Stiffness and the Base Shear Torque curves were found to exhibit the highest impact numerically, while all the other torsional indices' contribution was investigated and quantified.
| Original language | English |
|---|---|
| Pages (from-to) | 55-74 |
| Number of pages | 20 |
| Journal | Coupled Systems Mechanics |
| Volume | 6 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 1 Mar 2017 |
Keywords
- Artificial neural networks
- Omega ratio
- Ratio of torsion
- Regression analysis
- Shear center
- Statistical inferences
- Torsional radius