Buy article PDF
The purchased file will be sent to you
via email after the payment is completed.
US$ 35
|
Steel and Composite Structures Volume 34, Number 5, March 2020 , pages 743-767 DOI: https://doi.org/10.12989/scs.2020.34.5.743 |
|
|
|
Computational estimation of the earthquake response for fibre reinforced concrete rectangular columns |
||
Chanjuan Liu, Xinling Wu, Karzan Wakil, Kittisak Jermsittiparsert, Lanh Si Ho,
Hisham Alabduljabbar, Abdulaziz Alaskar, Fahed Alrshoudi, Rayed Alyousef and Abdeliazim Mustafa Mohamed
|
||
| Abstract | ||
| Due to the impressive flexural performance, enhanced compressive strength and more constrained crack propagation, Fibre-reinforced concrete (FRC) have been widely employed in the construction application. Majority of experimental studies have focused on the seismic behavior of FRC columns. Based on the valid experimental data obtained from the previous studies, the current study has evaluated the seismic response and compressive strength of FRC rectangular columns while following hybrid metaheuristic techniques. Due to the non-linearity of seismic data, Adaptive neuro-fuzzy inference system (ANFIS) has been incorporated with metaheuristic algorithms. 317 different datasets from FRC column tests has been applied as one database in order to determine the most influential factor on the ultimate strengths of FRC rectangular columns subjected to the simulated seismic loading. ANFIS has been used with the incorporation of Particle Swarm Optimization (PSO) and Genetic algorithm (GA). For the analysis of the attained results, Extreme learning machine (ELM) as an authentic prediction method has been concurrently used. The variable selection procedure is to choose the most dominant parameters affecting the ultimate strengths of FRC rectangular columns subjected to simulated seismic loading. Accordingly, the results have shown that ANFIS-PSO has successfully predicted the seismic lateral load with R2 = 0.857 and 0.902 for the test and train phase, respectively, nominated as the lateral load prediction estimator. On the other hand, in case of compressive strength prediction, ELM is to predict the compressive strength with R2 = 0.657 and 0.862 for test and train phase, respectively. The results have shown that the seismic lateral force trend is more predictable than the compressive strength of FRC rectangular columns, in which the best results belong to the lateral force prediction. Compressive strength prediction has illustrated a significant deviation above 40 Mpa which could be related to the considerable non-linearity and possible empirical shortcomings. Finally, employing ANFIS-GA and ANFIS-PSO techniques to evaluate the seismic response of FRC are a promising reliable approach to be replaced for high cost and time-consuming experimental tests. | ||
| Key Words | ||
| ANFIS; PSO; GA; ELM; fibre-reinforced concrete; Seismic response; compressive strength | ||
| Address | ||
| Chanjuan Liu: School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China Xinling Wu: South China Business College, Guang Dong University of Foreign Studies, Guangzhou 510545, China Karzan Wakil: Research Center, Sulaimani Polytechnic University, Sulaimani 46001, Kurdistan Region, Iraq; Research Center, Halabja University, Halabja 46018, Kurdistan Region, Iraq Kittisak Jermsittiparsert: Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam; Faculty of Social Sciences and Humanities, Ton Duc Thang University, Ho Chi Minh City, Vietnam Lanh Si Ho: Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam. Hisham Alabduljabbar,Rayed Alyousef and Abdeliazim Mustafa Mohamed: Department of Civil Engineering, College of Engineering, Prince Sattam bin Abdulaziz University, Al-kharj 11942, Saudi Arabia Abdulaziz Alaskar and Fahed Alrshoudi: Department of Civil Engineering, College of Engineering, King Saud University, Riyadh 11362, Saudi Arabia | ||