Evaluation of peak and residual conditions of actively confined concrete using neuro-fuzzy and neural computing techniques-Reference-Cited by-同舟云学术

Evaluation of peak and residual conditions of actively confined concrete using neuro-fuzzy and neural computing techniques

Published:2016-07-25 Issue:3 Volume:29 Page:873-888
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Mansouri Iman,Gholampour Aliakbar,Kisi Ozgur,Ozbakkaloglu Togay

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/article/10.1007/s00521-016-2492-4/fulltext.html

Reference54 articles.

1. Richart FE, Brandtzaeg A, Brown RL (1929) The failure of plain and spirally reinforced concrete in compression. Bulletin no. 190. Engineering experiment station. University of Illinois, Urbana

2. Saatcioglu M, Razvi SR (1992) Strength and ductility of confined concrete. J Struct Eng ASCE 118:1590–1607

3. Ozbakkaloglu T, Saatcioglu M (2006) Seismic behavior of high-strength concrete columns confined by fiber-reinforced polymer tubes. J Compos Constr ASCE 10:538–549

4. Ozbakkaloglu T, Lim JC, Vincent T (2013) FRP-confined concrete in circular sections: review and assessment of stress–strain models. Eng Struct 49:1068–1088

5. Richart FE, Brandtzaeg A, Brown RL (1928) A study of the failure of concrete under combined compressive stresses. Bulletin no. 185. Engineering experimental station. University of Illinois, Champaign

Cited by 96 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Predicting the compressive strength of CFRP-confined concrete using deep learning;Engineering Structures;2024-11

2. Predicting strength and strain of circular concrete cross-sections confined with FRP under axial compression by utilizing artificial neural networks;COMPUT CONCRETE;2024

3. Machine Learning Approaches for Predicting Compressive and Shear Strength of EB FRP-Reinforced Concrete Elements: A Comprehensive Review;Studies in Systems, Decision and Control;2024

4. Forecasting the strength characteristics of concrete incorporating waste foundry sand using advance machine algorithms including deep learning;Case Studies in Construction Materials;2023-12

5. Numerical simulation and intelligent prediction of thermal transport of a water-based copper oxide nanofluid in a lid-driven trapezoidal cavity;Physics of Fluids;2023-09-01