Data-driven analysis on ultimate axial strain of FRP-confined concrete cylinders based on explicit and implicit algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{CHEN:2021:CS,

• author = "Wenguang Chen and Jinjun Xu and Minhao Dong and Yong Yu and Mohamed Elchalakani and Fengliang Zhang",
• title = "Data-driven analysis on ultimate axial strain of {FRP-confined} concrete cylinders based on explicit and implicit algorithms",
• journal = "Composite Structures",
• year = "2021",
• volume = "268",
• pages = "113904",
• keywords = "genetic algorithms, genetic programming, FRP-confined concrete, Ultimate axial strain, Bayesian theory, Machine learning, Back-propagation artificial neural network, Multi-gene genetic programming, Support vector machine",
• ISSN = "0263-8223",
• URL = "https://research-repository.uwa.edu.au/en/publications/data-driven-analysis-on-ultimate-axial-strain-of-frp-confined-con",
• URL = "https://www.sciencedirect.com/science/article/pii/S0263822321003640",
• DOI = "doi:10.1016/j.compstruct.2021.113904",
• abstract = "The existing models for predicting the ultimate axial strain of FRP-confined concrete cylinders are mainly derived from the regression analyses on small datasets. Such models usually targeted more specific use cases and could give inaccurate outcomes when generalized. To this end, this paper presents the data-driven Bayesian probabilistic and machine learning prediction models (i.e., back-propagation artificial neural network, multi-gene genetic programming and support vector machine) with high accuracy. First, a comprehensive database containing 471 test results on the ultimate conditions of FRP-confined concrete cylinders was elaborately compiled from the open literature, and the quality of the database was examined and evaluated in detail. Then, an updating procedure characterized by the Bayesian parameter estimation technique was developed to evaluate the critical parameters in the existing models and refine the selected existing models accordingly. The database was also employed for deriving machine learning models. The computational efficiency, transferability and precision of the proposed models are verified. Results show that the proposed Bayesian posterior models, back-propagation artificial neural network, multi-gene genetic programming and support vector machine models achieved outstanding predictive performance, with the support vector machine yielding the highest prediction accuracy. The superior accuracy of the proposed models should assist various stakeholders in optimal use of FRP-confined concrete columns in diverse construction applications",

}

Genetic Programming entries for Wenguang Chen Jinjun Xu Minhao Dong Yong Yu Mohamed Elchalakani Fengliang Zhang

Citations