Studies on Pumice Lightweight Aggregate Concrete with Quarry Dust Using Mathematical Modeling Aid of ACO Techniques

The lightweight aggregate is an aggregate that weighs less than the usual rock aggregate and the quarry dust is a rock particle used in the concrete for the experimentation. The significant intention of the proposed technique is to frame a mathematical modeling with the aid of the optimization techn...

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Main Authors: J. Rex, B. Kameshwari
Format: Article
Language:English
Published: Hindawi Limited 2016-01-01
Series:Advances in Materials Science and Engineering
Online Access:http://dx.doi.org/10.1155/2016/9583757
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spelling doaj-273344846f9f4195941a671f1dfe9f312020-11-24T20:51:36ZengHindawi LimitedAdvances in Materials Science and Engineering1687-84341687-84422016-01-01201610.1155/2016/95837579583757Studies on Pumice Lightweight Aggregate Concrete with Quarry Dust Using Mathematical Modeling Aid of ACO TechniquesJ. Rex0B. Kameshwari1Department of Civil Engineering, SSM Institute of Engineering and Technology, Dindigul, Tamil Nadu 624 002, IndiaDepartment of Civil Engineering, RVS College of Engineering and Technology, Dindigul, Tamil Nadu 624 005, IndiaThe lightweight aggregate is an aggregate that weighs less than the usual rock aggregate and the quarry dust is a rock particle used in the concrete for the experimentation. The significant intention of the proposed technique is to frame a mathematical modeling with the aid of the optimization techniques. The mathematical modeling is done by minimizing the cost and time consumed in the case of extension of the real time experiment. The proposed mathematical modeling is utilized to predict four output parameters such as compressive strength (Mpa), split tensile strength (Mpa), flexural strength (Mpa), and deflection (in mm). Here, the modeling is carried out with three different optimization techniques like genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO) with 80% of data from experiment utilized for the training and the remaining 20% for the validation. Finally, while testing, the error value is minimized and the performance obtained in the ACO for the parameters such as compressive strength, split tensile strength, flexural strength, and deflection is 91%, 98%, 87%, and 94% of predicted values, respectively, in the mathematical modeling.http://dx.doi.org/10.1155/2016/9583757
collection DOAJ
language English
format Article
sources DOAJ
author J. Rex
B. Kameshwari
spellingShingle J. Rex
B. Kameshwari
Studies on Pumice Lightweight Aggregate Concrete with Quarry Dust Using Mathematical Modeling Aid of ACO Techniques
Advances in Materials Science and Engineering
author_facet J. Rex
B. Kameshwari
author_sort J. Rex
title Studies on Pumice Lightweight Aggregate Concrete with Quarry Dust Using Mathematical Modeling Aid of ACO Techniques
title_short Studies on Pumice Lightweight Aggregate Concrete with Quarry Dust Using Mathematical Modeling Aid of ACO Techniques
title_full Studies on Pumice Lightweight Aggregate Concrete with Quarry Dust Using Mathematical Modeling Aid of ACO Techniques
title_fullStr Studies on Pumice Lightweight Aggregate Concrete with Quarry Dust Using Mathematical Modeling Aid of ACO Techniques
title_full_unstemmed Studies on Pumice Lightweight Aggregate Concrete with Quarry Dust Using Mathematical Modeling Aid of ACO Techniques
title_sort studies on pumice lightweight aggregate concrete with quarry dust using mathematical modeling aid of aco techniques
publisher Hindawi Limited
series Advances in Materials Science and Engineering
issn 1687-8434
1687-8442
publishDate 2016-01-01
description The lightweight aggregate is an aggregate that weighs less than the usual rock aggregate and the quarry dust is a rock particle used in the concrete for the experimentation. The significant intention of the proposed technique is to frame a mathematical modeling with the aid of the optimization techniques. The mathematical modeling is done by minimizing the cost and time consumed in the case of extension of the real time experiment. The proposed mathematical modeling is utilized to predict four output parameters such as compressive strength (Mpa), split tensile strength (Mpa), flexural strength (Mpa), and deflection (in mm). Here, the modeling is carried out with three different optimization techniques like genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO) with 80% of data from experiment utilized for the training and the remaining 20% for the validation. Finally, while testing, the error value is minimized and the performance obtained in the ACO for the parameters such as compressive strength, split tensile strength, flexural strength, and deflection is 91%, 98%, 87%, and 94% of predicted values, respectively, in the mathematical modeling.
url http://dx.doi.org/10.1155/2016/9583757
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