Formulation of polynomial equation predicting the splitting tensile strength of concrete

Ahmed, Farabi Bin; Ahsan, Khan Abid; Shariff, Tasnuva; Meem, Shafin Rahman

doi:10.1016/j.matpr.2020.10.017

Cited by 11 publications

(6 citation statements)

References 24 publications

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“…The mechanical properties of concrete indicate significant aspects in the analysis and design of concrete structures as it is an important criterion to assist the structural quality of concrete (Ahmed et al , 2021; Eziefula et al , 2020; Abadou et al , 2020; Tantri et al , 2021). Several researchers carried out tests on the concrete cube, beam and cylinder made from RA concrete to study its effect on mechanical properties of concrete, such as compressive strength (CS), flexural strength (FS) and split tensile strength (STS) (Ismail and Ramli, 2013; Ismail and Ramli, 2014; Kou et al , 2011).…”

Section: Introductionmentioning

confidence: 99%

Prediction of recycled coarse aggregate concrete mechanical properties using multiple linear regression and artificial neural network

Patil

Rao

Nayak

2021

JEDT

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Purpose Recycling construction waste is a promising way towards sustainable development in construction. Recycled aggregate (RA) is obtained from demolished concrete structures, laboratory crushed concrete, concrete waste at a ready mix concrete plant and the concrete made from RA is known as RA concrete. The purpose of this study is to apply multiple linear regressions (MLRs) and artificial neural network (ANN) to predict the mechanical properties, such as compressive strength (CS), flexural strength (FS) and split tensile strength (STS) of concrete at the age of 28 days curing made completely from the recycled coarse aggregate (RCA). Design/methodology/approach MLR and ANN are used to develop a prediction model. The model was developed in the training phase by using data from a previously published research study and a developed model was further tested by obtaining data from laboratory experiments. Findings ANN shows more accuracy than MLR with an R2-value of more than 0.8 in the training phase and 0.9 in a testing phase. The high R2-value indicates strong relation between the actual and predicted values of mechanical properties of RCA concrete. These models will help construction professionals to save their time and cost in predicting the mechanical properties of RCA concrete at 28 days of curing. Originality/value ANN with rectified linear unit transfer function and backpropagation algorithm for training is used to develop a prediction model. The outcome of this study is the prediction model for CS, FS and STS of concrete at 28 days of curing.

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Section: Introductionmentioning

confidence: 99%

Prediction of recycled coarse aggregate concrete mechanical properties using multiple linear regression and artificial neural network

Patil

Rao

Nayak

2021

JEDT

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“…In fact, adding 11.7% of fine aggregate as replacement of RCA into a pervious concrete mix can help restore the tensile strength, (f'ct)Mix7 = 1.58 MPa, to that of pervious concrete made with 100% natural coarse aggregate, (f'ct)Mix1 = 1.56 MPa. Many researchers have sought more accurate relationships between the concrete compressive and tensile strengths than what is available in concrete codes and specifications, see for example the work of Bin Ahmad et al for conventional concrete [18] and Gaedicke et al for pervious concrete [19]. Experience has shown that relationships between compressive and corresponding tensile strengths for conventional concrete are not applicable for pervious concrete because the later is relatively weaker in tension due to lack of adequate paste.…”

Section: Resultsmentioning

confidence: 99%

Strength Properties of Pervious Concrete Made with Locally Produced Recycled Coarse Aggregate

Tabsh,

Tamimi,

El-Emam

2024

World Congress on Civil, Structural, and Environmental Engineering

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Pervious concrete is a special type of concrete that addresses critical environmental issues and supports green, sustainable construction. In such concrete, little or no sand is employed in the mix in order to allow the formation of considerable void content leading to high permeability. In this study, seven pervious concrete mixes made with recycled coarse aggregate and containing different amount of natural fine aggregate are tested in the laboratory for their strength. The source of the coarse aggregate is demolished old concrete buildings in the United Arab Emirates. One of the mixes contains 100% natural coarse aggregate without natural sand, another 100% recycled coarse aggregate without natural sand, and the remaining 5 mixes include both recycled coarse aggregate and fine natural aggregate such that the amount of fine-to-coarse aggregate is 1.5-11.7%. Results of the study revealed that that the use of recycled coarse aggregate in pervious concrete without fine aggregate reduced the compressive strength by 36% and tensile strength by 57%. Replacing 4.7% of the recycled coarse aggregate with natural sand in such concrete helped in restoring the compressive strength to the level of the control mix that contained natural coarse aggregate. Likewise, replacing 11.7% of the recycled coarse aggregate with natural sand in concrete mix aided in restoring the split cylinder tensile strength to the level of the control mix that contained natural coarse aggregate. There is a relationship between compressive and tensile strengths, of which a lower bound can be reasonably predicted by models proposed for pervious concrete in the literature. The study confirms the feasibility of using recycled aggregate in pervious concrete mixes if about 10% natural fine aggregate is utilized in the mix as replacement of the recycled coarse aggregate.

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“…The ACI model overestimates split tensile strength for concrete with compressive strength less than 20 MPa and underestimates for concrete with compressive strength greater than 30 MPa Zaina, M et al, (2002). An empirical relationship between split tensile strength (σSP) and compressive strength (σC) suggested by various researchers Luan, C.,et al (2021), Bin, A. F.,et al (2020) and Jaber, A.,et al (2018) is given by eq. ( 2) where, k, n-constant coefficients σC -compressive strength of concrete…”

Section: Literature Reviewmentioning

confidence: 99%

“…The coefficients k and n from experimental given by various researchers are given in Table 1. [Bin, A. F., et al (2020), Jaber, A., et al (2018) Chhorn, C. et al (2018), and Behnood, A. et al (2015]…”

Section: Literature Reviewmentioning

confidence: 99%

Mathematical Modeling of Split Tensile Strength of Agrostone with Different Bio-fillers

Degu

2023

IJSCET

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There has been a growing imbalance in the demand and supply of construction materials with increasing prices day by day at an alarming rate due to escalating cost of energy. Quest for a low cost, environmental friendly material has resulted in the development of Agrostone made out of industrial and agricultural waste while saving the environment from solid waste disposal issues. This also contributes for saving the energy required for production of conventional building materials up to 20 per cent thereby reducing the emissions. Agrostone made of different bio-fillers such as water hyacinth, bagasse and grass, satisfy the strength requirements as per Ethiopian construction material standard which is equivalent to a hollow block. The split tensile strength has been evolved through the compression test by adopting the standard test methods of applied for concrete. This paper presents the correlation of the experimental results split tensile strength with the values obtained using various empirical relations existing in the literature for unreinforced concrete. It is found that all these relations have underestimated the results while the experimental results of Agrostone with water hyacinth bio-filler have better correlation with results of relation given by Arioglu et al. This paper also presents mathematical models developed using linear and polynomial regression analysis to predict the split tensile strength of Agrostone. The correlation coefficients from the regression analysis are obtained using the Stats.blue software. It is found that the polynomial equations of degree five and the correlation indicators r and r2 show a strong relation between the independent compressive strength and the dependent split tensile strength. The linear regression also predicts the values with acceptable degree of accuracy. The resultsobtained by developed mathematical models are very close to the experimental within acceptable range of error.

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Formulation of polynomial equation predicting the splitting tensile strength of concrete

Cited by 11 publications

References 24 publications

Prediction of recycled coarse aggregate concrete mechanical properties using multiple linear regression and artificial neural network

Prediction of recycled coarse aggregate concrete mechanical properties using multiple linear regression and artificial neural network

Strength Properties of Pervious Concrete Made with Locally Produced Recycled Coarse Aggregate

Mathematical Modeling of Split Tensile Strength of Agrostone with Different Bio-fillers

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