Buy article PDF
The purchased file will be sent to you
via email after the payment is completed.
US$ 35
|
Structural Engineering and Mechanics Volume 61, Number 2, January25 2017 , pages 283-293 DOI: https://doi.org/10.12989/sem.2017.61.2.283 |
|
|
|
Intelligent fuzzy inference system approach for modeling of debonding strength in FRP retrofitted masonry elements |
||
Mohsen Khatibinia and Mohammad Reza Mohammadizadeh
|
||
| Abstract | ||
| The main contribution of the present paper is to propose an intelligent fuzzy inference system approach for modeling the debonding strength of masonry elements retrofitted with Fiber Reinforced Polymer (FRP). To achieve this, the hybrid of meta-heuristic optimization methods and adaptive-network-based fuzzy inference system (ANFIS) is implemented. In this study, particle swarm optimization with passive congregation (PSOPC) and real coded genetic algorithm (RCGA) are used to determine the best parameters of ANFIS from which better bond strength models in terms of modeling accuracy can be generated. To evaluate the accuracy of the proposed PSOPC-ANFIS and RCGA-ANFIS approaches, the numerical results are compared based on a database from laboratory testing results of 109 sub-assemblages. The statistical evaluation results demonstrate that PSOPC-ANFIS in comparison with ANFIS-RCGA considerably enhances the accuracy of the ANFIS approach. Furthermore, the comparison between the proposed approaches and other soft computing methods indicate that the approaches can effectively predict the debonding strength and that their modeling results outperform those based on the other methods. | ||
| Key Words | ||
| debonding strength; Fiber Reinforced Polymer; adaptive-network-based fuzzy inference system; particle swarm optimization; real coded genetic algorithm | ||
| Address | ||
| Mohsen Khatibinia: Department of Civil Engineering, University of Birjand, Birjand, Iran Mohammad Reza Mohammadizadeh: Department of Civil Engineering, Hormozgan University, Bandar Abbas, Iran | ||