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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Springer-Verlag London Ltd
2016
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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. |
first_indexed | 2024-03-05T20:04:43Z |
format | Article |
id | utm.eprints-72819 |
institution | Universiti Teknologi Malaysia - ePrints |
last_indexed | 2024-03-05T20:04:43Z |
publishDate | 2016 |
publisher | Springer-Verlag London Ltd |
record_format | dspace |
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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