Name
Abstract | ||
|---|---|---|
The squat reinforced concrete (RC) shear wall having low aspect ratio is a crucial structural component for both conventional buildings and nuclear-related structures due to the substantial role in resisting the lateral seismic loading. The prediction model for shear capacity of these walls becomes essential in ensuring the seismic safety of the building. Therefore, a model to predict the shear strength of squat RC walls has been proposed using a hybrid intelligence algorithm including the artificial neural network and particle swarm optimization algorithm (ANN–PSO). A total of 139 test results of squat walls are collected and utilized to train and test the hybrid ANN–PSO model. The performance of the proposed model has been assessed against the other shear strength models. The proposed model demonstrates good prediction capability with high accuracy for predicting shear strength of the RC walls. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s00366-017-0547-5 | Eng. Comput. (Lond.) |
Keywords | Field | DocType |
Artificial neural network, Hybrid intelligence algorithm, Particle swarm optimization, Squat reinforced concrete walls, Shear strength | Reinforced concrete,Particle swarm optimization,Mathematical optimization,Shear wall,Shear (sheet metal),Shear strength,Seismic loading,Artificial neural network,Mathematics,Squat,Structural engineering | Journal |
Volume | Issue | ISSN |
34 | 2 | 0177-0667 |
Citations | PageRank | References |
6 | 0.54 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xi Chen | 1 | 333 | 70.76 |
| Jianping Fu | 2 | 19 | 1.91 |
| J. L. Yao | 3 | 6 | 0.54 |
| J. F. Gan | 4 | 6 | 0.54 |