Estimation of Strengths of Hybrid FR-SCC by using Deep Learning and Support Vector Regression Models


Kına C., Türk K. , Tanyıldızı H.

Structural Concrete, vol.23, no.10, pp.1-13, 2022 (Journal Indexed in SCI Expanded)

  • Publication Type: Article / Article
  • Volume: 23 Issue: 10
  • Publication Date: 2022
  • Doi Number: 10.1002/suco.202100622
  • Title of Journal : Structural Concrete
  • Page Numbers: pp.1-13

Abstract

In this work, to estimate the compressive, splitting tensile and flexural

strength of self-compacting concrete (SCC) having single fiber and

binary, ternary and quaternary fiber hybridization, the deep learning and

support vector regression (SVR) models were devised. The fiber content

and coarse aggregate/total aggregate ratio (CA/TA) were the variables

for 24 designed mixtures. Four different fibers, which were a macro steel

fiber, two types of micro steel fibers with different aspect ratio and PVA

fiber, were used in SCC mixtures. The specimens of each mixture were

tested to measure the engineering properties for 7, 28 and 90 days. The

amount of cement, fly ash, fine aggregate, coarse aggregate, high-range

water reducing admixture (HRWA), water, macro fiber, PVA fiber, two

type micro steel fibers and curing time were selected as input layers

while the output layers were strength results. The experimental results

were compared with the estimation results. The engineering properties

were estimated using the SVR model with 95.25%, 87.81% and 93.89%

accuracy, respectively. Furthermore, the deep learning model estimated

compressive strength, tensile strength and flexural strength with

99.27%, 98.59% and 99.15% accuracy, respectively. It was found that

the deep learning estimated the engineering properties of hybrid fiber

reinforced (HFR-SCC) with higher accuracy than SVR.