Prediction of the durability of limestone aggregates using computational techniques

The durability of aggregates is an important factor that is used as an input parameter in desirable engineering properties along with resistance to exposure conditions. However, it is sometimes difficult to determine the durability of aggregates in the laboratory (with a magnesium sulfate test) beca...

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Main Authors: Alavi Nezhad Khalil Abad, Seyed Vahid, Yilmaz, Murat, Jahed Armaghani, Danial, Tugrul, Atiye
Format: Article
Published: Springer-Verlag London Ltd 2016
Subjects:
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author Alavi Nezhad Khalil Abad, Seyed Vahid
Yilmaz, Murat
Jahed Armaghani, Danial
Tugrul, Atiye
author_facet Alavi Nezhad Khalil Abad, Seyed Vahid
Yilmaz, Murat
Jahed Armaghani, Danial
Tugrul, Atiye
author_sort Alavi Nezhad Khalil Abad, Seyed Vahid
collection ePrints
description The durability of aggregates is an important factor that is used as an input parameter in desirable engineering properties along with resistance to exposure conditions. However, it is sometimes difficult to determine the durability of aggregates in the laboratory (with a magnesium sulfate test) because this test is time-consuming and expensive. In this paper, the physical and mechanical properties including water absorption and the Los Angeles coefficient are tailored to the specific evaluation of the durability of limestone aggregates. However, the predictive capabilities of artificial neural networks (ANN) and hybrid particle swarm optimization-based (PSO) ANN techniques have been evaluated and compared using the same input variables. To assess the reliability of the model, some performance indices such as the correlation coefficient (R2), the variance account for, and the root-mean-square error were calculated and compared for the two developed models. The results revealed that even though the two developed models reliably predict the durability value (magnesium sulfate value), the proposed PSO–ANN method displays an obvious potential for the reliable assessment of the value of magnesium sulfate according to the model performance criterion.
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spelling utm.eprints-728192017-11-20T08:14:57Z http://eprints.utm.my/72819/ Prediction of the durability of limestone aggregates using computational techniques Alavi Nezhad Khalil Abad, Seyed Vahid Yilmaz, Murat Jahed Armaghani, Danial Tugrul, Atiye TA Engineering (General). Civil engineering (General) The durability of aggregates is an important factor that is used as an input parameter in desirable engineering properties along with resistance to exposure conditions. However, it is sometimes difficult to determine the durability of aggregates in the laboratory (with a magnesium sulfate test) because this test is time-consuming and expensive. In this paper, the physical and mechanical properties including water absorption and the Los Angeles coefficient are tailored to the specific evaluation of the durability of limestone aggregates. However, the predictive capabilities of artificial neural networks (ANN) and hybrid particle swarm optimization-based (PSO) ANN techniques have been evaluated and compared using the same input variables. To assess the reliability of the model, some performance indices such as the correlation coefficient (R2), the variance account for, and the root-mean-square error were calculated and compared for the two developed models. The results revealed that even though the two developed models reliably predict the durability value (magnesium sulfate value), the proposed PSO–ANN method displays an obvious potential for the reliable assessment of the value of magnesium sulfate according to the model performance criterion. Springer-Verlag London Ltd 2016 Article PeerReviewed Alavi Nezhad Khalil Abad, Seyed Vahid and Yilmaz, Murat and Jahed Armaghani, Danial and Tugrul, Atiye (2016) Prediction of the durability of limestone aggregates using computational techniques. Neural Computing and Applications . pp. 1-11. ISSN 0941-0643 (In Press) https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978115048&doi=10.1007%2fs00521-016-2456-8&partnerID=40&md5=964400950cbcabc8b4a7761f11978acf
spellingShingle TA Engineering (General). Civil engineering (General)
Alavi Nezhad Khalil Abad, Seyed Vahid
Yilmaz, Murat
Jahed Armaghani, Danial
Tugrul, Atiye
Prediction of the durability of limestone aggregates using computational techniques
title Prediction of the durability of limestone aggregates using computational techniques
title_full Prediction of the durability of limestone aggregates using computational techniques
title_fullStr Prediction of the durability of limestone aggregates using computational techniques
title_full_unstemmed Prediction of the durability of limestone aggregates using computational techniques
title_short Prediction of the durability of limestone aggregates using computational techniques
title_sort prediction of the durability of limestone aggregates using computational techniques
topic TA Engineering (General). Civil engineering (General)
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AT yilmazmurat predictionofthedurabilityoflimestoneaggregatesusingcomputationaltechniques
AT jahedarmaghanidanial predictionofthedurabilityoflimestoneaggregatesusingcomputationaltechniques
AT tugrulatiye predictionofthedurabilityoflimestoneaggregatesusingcomputationaltechniques