Abstract
Scientifically evidenced that beam-column joints are a critical point in the reinforced concrete (RC) structure under the fluctuation loads effects. In this novel hybrid data-intelligence model developed to predict the joint shear behavior of exterior beam-column structure frame. The hybrid data-intelligence model is called genetic algorithm integrated with deep learning neural network model (GA-DLNN). The genetic algorithm is used as prior modelling phase for the input approximation whereas the DLNN predictive model is used for the prediction phase. To demonstrate this structural problem, experimental data is collected from the literature that defined the dimensional and specimens' properties. The attained findings evidenced the efficitveness of the hybrid GA-DLNN in modelling beam-column joint shear problem. In addition, the accurate prediction achived with less input variables owing to the feasibility of the evolutionary phase.
| Original language | English |
|---|---|
| Article number | 012025 |
| Journal | IOP Conference Series: Earth and Environmental Science |
| Volume | 143 |
| Issue number | 1 |
| DOIs | |
| State | Published - 12 Apr 2018 |
| Externally published | Yes |
| Event | 2nd International Conference on Sustainable Development in Civil, Urban and Transportation Engineering, CUTE 2018 - Hochiminh City, Viet Nam Duration: 17 Apr 2018 → 19 Apr 2018 |
Bibliographical note
Publisher Copyright:© Published under licence by IOP Publishing Ltd.
ASJC Scopus subject areas
- General Environmental Science
- General Earth and Planetary Sciences