Fuzzy logic model for investigating the effect of steel fibers on mechanical properties of concrete

Rahman, Fayyaz; Raheel, Muhammad; Khan, Rehanullah

doi:10.1007/s42452-019-1226-5

Cited by 3 publications

(1 citation statement)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Barzani et al [10] used a fuzzy logic model to predict the surface roughness of Al-Si-Cu-Fe die-casting alloy doped with strontium, bismuth and antimony; the predicted machining performance surface had an error factor of 5.4%. Rahman et al [11] studied the influence of steel fibers on mechanical properties such as compressive strength, flexural strength and post-peak deformation of steel-fiber reinforced concrete using a fuzzy model the predicted mechanical properties had an error factor of 7.5%. Chibueze et al [12] successfully predicted the impact strength, flexural strength, hardness of sponge gourd and luffer fiber reinforced epoxy composite using a fuzzy logic model.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network and Fuzzy Logic Based Techniques for Numerical Modeling and Prediction of Aluminum-5%Magnesium Alloy Doped with REM Neodymium

Chukwuma,

Godwills,

Nwaeju

et al. 2024

IJNM

View full text Add to dashboard Cite

In this study, the mechanical properties of aluminum-5%magnesium doped with rare earth metal neodymium were evaluated. Fuzzy logic (FL) and artificial neural network (ANN) were used to model the mechanical properties of aluminum-5%magnesium (0 -0.9 wt%) neodymium. The single input (SI) to the fuzzy logic and artificial neural network models was the percentage weight of neodymium, while the multiple outputs (MO) were average grain size, ultimate tensile strength, yield strength elongation and hardness. The fuzzy logic-based model showed more accurate prediction than the artificial neutral network-based model in terms of the correlation coefficient values (R).

show abstract

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network and Fuzzy Logic Based Techniques for Numerical Modeling and Prediction of Aluminum-5%Magnesium Alloy Doped with REM Neodymium

Chukwuma,

Godwills,

Nwaeju

et al. 2024

IJNM

View full text Add to dashboard Cite

show abstract

A stoichiometric approach to find optimum amount of fly ash needed in cement concrete

2020

View full text Add to dashboard Cite

Bioresin based hybrid green composite preparation using Holoptelea integrifolia fibers reinforced by Ziziphus jujuba seed particles: a fuzzy logic assisted optimization of mechanical behaviour

Kandavalli,

Sundramurthy,

Krishnasamy

et al. 2024

Zeitschrift Für Physikalische Chemie

View full text Add to dashboard Cite

In recent years, hybrid natural fiber–reinforced composites prepared by bio-based epoxy resin (BBER) have gained extensive usage due to their exceptional specific strength. Researchers have looked at natural fibre/BBER composites because of the vast array of technical uses for these materials. Experimenting with various composition combinations of hybrid natural fiber composites to improve their mechanical properties requires a significant amount of resources. The purpose of this research was to develop and evaluate a tannic acid (TA) – based bio-based epoxy resin (BBER). The synthesized BBER was analyzed using FTIR spectroscopy. The bio-resin’s thermal characteristics were assessed using TGA, DSC, and OIT studies. Further, this study investigated the mechanical characteristics of hybrid composites prepared by Holoptelea integrifolia (HIBF) bark fibre reinforced with Ziziphus jujuba Seed Particles (ZJSP) which was manufactured using BBER. In the present study, a full factorial design was used to conduct experiments, which included three variables: the percentage of HIBF fibre by weight, the particle size of ZJSP, and the percentage of ZJSP by weight. The experimental results from the mechanical evaluation of the hybrid HIBF/ZJSP using BBER composites are then used to develop a fuzzy model that predicts mechanical parameters such as tensile strength (TS), flexural strength (FS), and impact strength (IS). An accurate prediction of the mechanical characteristics of hybridized composites made by the fuzzy model owing to the membership functions that were built. To further validate the fuzzy model, a set of tests utilising test cases were conducted. The model’s accuracy in predicting the mechanical characteristics of hybrid composites was determined to be 87 % based on the outcomes of the test cases.

show abstract

Fuzzy logic model for investigating the effect of steel fibers on mechanical properties of concrete

Cited by 3 publications

References 21 publications

Artificial Neural Network and Fuzzy Logic Based Techniques for Numerical Modeling and Prediction of Aluminum-5%Magnesium Alloy Doped with REM Neodymium

Artificial Neural Network and Fuzzy Logic Based Techniques for Numerical Modeling and Prediction of Aluminum-5%Magnesium Alloy Doped with REM Neodymium

A stoichiometric approach to find optimum amount of fly ash needed in cement concrete

Bioresin based hybrid green composite preparation using Holoptelea integrifolia fibers reinforced by Ziziphus jujuba seed particles: a fuzzy logic assisted optimization of mechanical behaviour

Contact Info

Product

Resources

About