Application of response surface methodology: Predicting and optimizing the properties of concrete containing steel fibre extracted from waste tires with limestone powder as filler

Awolusi, T.F.; Oke, O.L.; Akinkurolere, O.O.; Sojobi, Adebayo Olatunbosun

doi:10.1016/j.cscm.2018.e00212

Cited by 81 publications

(60 citation statements)

References 31 publications

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“…The process variables used to achieve this purpose were 140 for aspect ratio (A), 0.26 for water cement ratio (B) and 40% for cement content (C). The validations of these experimental results are available in our earlier studies [31].…”

Section: Optimization By Rsmmentioning

confidence: 77%

“…The materials used in the study include the grade 42.5 ordinary portland cement, lime stone powder as filler, steel fiber, high range water reducing admixture, river sand, granite and potable water. Details of the mix proportion and procedure have published in our earlier study [31]. The experimental design often plays a major role in determining the total number of experiment required during investigation.…”

Section: Methodsmentioning

confidence: 99%

“…The second order model for water absorption, compressive strength, flexural strength, split tensile strength and slump are given by equations 5 to 9 with a coefficient of determination of 0.86, 0.95, 0.98, 0.93 and 0.98 respectively. The equation 5 to 9 gives the regression coefficients for the main, interaction, quadratic and cubic terms[31]. + 0.14ABC + 0.25 2 B+0.25 2 C ………..(5) ℎ ( 2) = +32.99 − 1.15 − 4.46 + 6.24 − 1.24 − 5.04 + 0.19 2 −5.11 2 +0.67 2 C−1.93 2 ………….…”

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confidence: 99%

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<strong></strong>Comparison of Response Surface Methodology and Hybrid-Training Approach of Artificial Neural Network in Modeling the Properties of Concrete Containing Steel Fiber Extracted from Waste Tires

Awolusi¹,

Oke²,

Akinkurolere³

et al. 2019

Preprint

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The study presents a comparative approach between response surface methodology (RSM) and hybridized, genetic algorithm artificial neural network (GA-ANN) in predicting the water absorption, compressive strength, flexural strength split tensile strength and slump for steel fiber reinforced concrete. The effect of process variables such as aspect ratio, water cement ratio and cement content were investigated using the central composite design of response surface methodology.  This same experimental design was used in training the hybrid-training approach of artificial neural network. The predicting ability of both methodologies were compared using the  root mean sqaured error (RMSE), mean absolute error (MAE), model predictive error (MPE) and absolute average deviation (AAD). The RSM model was found more accurate in prediction compared to hybrid GA-ANN.

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Section: Optimization By Rsmmentioning

confidence: 77%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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<strong></strong>Comparison of Response Surface Methodology and Hybrid-Training Approach of Artificial Neural Network in Modeling the Properties of Concrete Containing Steel Fiber Extracted from Waste Tires

Awolusi¹,

Oke²,

Akinkurolere³

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…When these results are acquired the model predictions will be compared to experimental measurements for per-and polyfluorinated organic destruction tests. These ongoing experiments will enable evaluation of the model's predictive capability using model evaluation approaches described elsewhere in the literature [37,38].…”

Section: Comparison Of Model Results To Experimental Measurementsmentioning

confidence: 99%

Computational simulation of incineration of chemically and biologically contaminated wastes

Lemieux

Boe

Tschursin

et al. 2021

Journal of the Air & Waste Management Association

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This paper describes the modeling approach and example results for a newly introduced computational simulation tool to evaluate waste destruction in thermal incineration systems. The Configured Fireside Simulator (CFS) is a software simulator, originally developed for the Department of Defense to evaluate operations of the chemical demilitarization incinerators processing the chemical warfare agent stockpile of the US. The software was later adapted for use by the U.S. Environmental Protection Agency (EPA) to provide for the ability to run "what if" scenarios of waste streams contaminated with chemical/biological (CB) threat agents in four specific incinerators, including the EPA's pilot-scale Rotary Kiln Incinerator Simulator (RKIS) facility, as well as three commercial incinerators based on design criteria for actual operating facilities. These commercial incinerators include a Medical/Pathological Waste Incinerator, a Hazardous Waste Burning Rotary Kiln, and a Waste-to-Energy Stoker-type combustor. The CFS uses three-dimensional computational fluid dynamics coupled with detailed chemical kinetic data for destruction of chemical warfare agents, coupled with kinetic data for biological agent destruction derived from bench-and pilot-scale experiments to predict the way agentcontaining materials will behave under full-scale combustion conditions in several different incinerator types. The objective of this paper is to describe the CFS software, how it works, and potential applications of this software to real-world situations. This software could be a valuable tool for researchers, regulators, and industry to evaluate potential operating conditions to help guide testing activities and develop operational scenarios for difficult-to-manage waste streams. Although this software has been under development for several years, this paper represents the software's first introduction to the scientific community in the peer-reviewed literature.

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“…These samples were investigated for the identification of isolated bacteria. Table 1 lists the investigated data [25][26][27][28][29].…”

Section: Methodsmentioning

confidence: 99%

Experimental Study on Strength Parameters of Self Repairing Concrete

Reddy¹,

Kavyateja²

2019

ACSM

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The microbial self-healing concrete has been a new technology to repair cracks, the former study showed that the precipitated CaCO3 could fill the cracks and reduce the permeability coefficient of cracks. This paper presents the effect on strength parameters with the addition of bacillus subtilis bacteria and calcium lactate. This study helps to improve the strength of self-repairing concrete with the use of bacillus subtilis bacteria and calcium lactate. The production of calcium carbonate in bacterial concrete is limited to the calcium content in cement. Hence calcium lactate is externally added to be an additional source of calcium in the concrete. The influence of this addition on compressive strength and modulus of rupture are highlighted in this study. The Bacillus subtilis bacteria and calcium lactate are mixed in concrete with different concentrations and tested for compressive and modulus of rupture of concrete. The results show that the performance of concrete improves with the addition of bacillus subtilis bacteria and calcium lactate into concrete.

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Application of response surface methodology: Predicting and optimizing the properties of concrete containing steel fibre extracted from waste tires with limestone powder as filler

Cited by 81 publications

References 31 publications

<strong></strong>Comparison of Response Surface Methodology and Hybrid-Training Approach of Artificial Neural Network in Modeling the Properties of Concrete Containing Steel Fiber Extracted from Waste Tires

<strong></strong>Comparison of Response Surface Methodology and Hybrid-Training Approach of Artificial Neural Network in Modeling the Properties of Concrete Containing Steel Fiber Extracted from Waste Tires

Computational simulation of incineration of chemically and biologically contaminated wastes

Experimental Study on Strength Parameters of Self Repairing Concrete

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