Comparison of extreme learning machine and deep learning model in the estimation of the fresh properties of hybrid fiber-reinforced SCC

This paper studied the estimation of fresh properties of hybrid fiber-reinforced self-compacting concrete mixtures with different types and combinations of fibers by using two different prediction method named as the methodologies of extreme learning machine and long short-term memory

Ceren Kina; Kazim Turk; Esma Atalay; Izzeddin Donmez; Harun Tanyildizi

2021

Scholarcy highlights

  • This paper studied the estimation of fresh properties of hybrid fiber-reinforced self-compacting concrete mixtures with different types and combinations of fibers by using two different prediction method named as the methodologies of extreme learning machine and long short-term memory
  • In the devised prediction model, the amounts of cement, fly ash, silica fume, blast furnace slag, limestone powder, aggregate, water, high-range water-reducer admixture and the fiber ratios were selected as inputs, while the slump flow, t50 and the J-ring were selected as outputs
  • It was found that the use of more than 0.20% by volume of 6/0.16 micro-steel fiber positively influenced the fresh properties of SCC mixtures with hybrid fiber
  • This study found that extreme learning machine model estimated the slump flow, t50 and J-ring with 99.71%, 81% and 94.21% accuracy, respectively, while deep learning model found the same experimental results with 99.18%, 77.4% and 84.8% accuracy, respectively
  • The financial support for the experimental part of this study was provided by Scientific Research Projects Committee of Inonu University, Turkey

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