4.7 Article

Prediction of the FRP reinforced concrete beam shear capacity by using ELM-CRFOA

Journal

MEASUREMENT
Volume 205, Issue -, Pages -

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2022.112230

Keywords

Neural Networks; Shear capacity prediction; Fiber Reinforcement Polymer; Extreme Learning Machine; Chaos Red Fox Optimization Algorithm

Funding

  1. National Social Science Foundation of China of China [18BTJ029]
  2. Key Projects Of National Statistical ScienceResearch Projects [2020LZ10]

Ask authors/readers for more resources

In this study, the shear strength of concrete beams reinforced with composite rebar was predicted using an Extreme Learning Machine network based on Chaos Red Fox Optimization Algorithm (ELM-CRFOA). A sensitivity analysis was also conducted to assess the impact of input parameters on the shear strength of FRP-reinforced concrete beams. The results showed that the proposed model has accurate outcomes in computing the shear strength of concrete beams.
In reinforced concrete structures, the utilization of composite rebar has been increased by considering their high corrosion resistance, anti-magnetic properties, and significant tensile strength. According to the lower elasticity modulus of composite rebar in comparison with steel rebar, concrete beams reinforced including composite rebar possess a relatively lower shear strength by comparing with steel rebar. In addition, in concrete beam, reinforced shear failure by composite rebar is commonly brittle and requires precise performance prediction of the members. Thus, the reinforced concrete beams' shear strength by composite rebar is predicted utilizing an Extreme Learning Machine network based on Chaos Red Fox Optimization Algorithm (ELM-CRFOA) according to a wide range of data. The most important parameters, which are considered in this investigation, are the web width, beam effective depth, the strength of concrete compressive, the ratio of the shear span to depth, FRP longitudinal bars elasticity modulus, and ratio of the longitudinal reinforcement. This method's precision has been proved by having a comparison among the model predictions and the accumulated data and available shear design equations. According to the study outcomes, the presented model has precise outcomes in computing the concrete beams' shear strength in comparison with other existing relations. For assessing input parameters' impact on the FRP-reinforced concrete beams' shear strength, a sensitivity analysis is executed.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available