APPLICATION OF SOFT COMPUTING METHODOLOGIES TO PREDICT THE 28-DAY COMPRESSIVE STRENGTH OF SHOTCRETE: A COMPARATIVE STUDY OF INDIVIDUAL AND HYBRID MODELS

Shotcreting is a popular construction technique with wide-ranging applications in mining and civil engineering. Compressive strength is a primary mechanical property of shotcrete with particular importance for project safety, which highly depends on its mix design. But in practice, there is no relia...

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Autores principales:	Mahtab Torkan, Hamid Kalhori, Mohammad Hossein Jalalian
Formato:	article
Lenguaje:	EN HR
Publicado:	Faculty of Mining, Geology and Petroleum Engineering 2021
Materias:	Shotcrete Compressive Strength Machine learning techniques Hybrid model Mining engineering. Metallurgy TN1-997 Geology QE1-996.5
Acceso en línea:	https://doaj.org/article/f5be8c1eb965412db7e306d40fa05da6
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spelling	oai:doaj.org-article:f5be8c1eb965412db7e306d40fa05da62021-12-05T20:13:12ZAPPLICATION OF SOFT COMPUTING METHODOLOGIES TO PREDICT THE 28-DAY COMPRESSIVE STRENGTH OF SHOTCRETE: A COMPARATIVE STUDY OF INDIVIDUAL AND HYBRID MODELS1849-0409https://doaj.org/article/f5be8c1eb965412db7e306d40fa05da62021-01-01T00:00:00Zhttps://hrcak.srce.hr/file/386657https://doaj.org/toc/1849-0409Shotcreting is a popular construction technique with wide-ranging applications in mining and civil engineering. Compressive strength is a primary mechanical property of shotcrete with particular importance for project safety, which highly depends on its mix design. But in practice, there is no reliable and accurate method to predict this strength. In this study, existing experimental data related to shotcretes with 59 different mix designs are used to develop a series of soft computing methodologies, including individual artificial neural network, support vector regression, and M5P model tree and their hybrids with the fuzzy c-means clustering algorithm so as to predict the 28-day compressive strength of shotcrete. Analysis of the results shows the superiority of the hybrid model over the individual models in predicting the compressive strength of shotcrete. Overall, data clustering prior to use of machine learning techniques leads to certain improvement in their performance and reliability and generalizability of their results. In particular, the M5P model tree exhibits excellent capability in anticipating the compressive strength of shotcrete.Mahtab TorkanHamid KalhoriMohammad Hossein JalalianFaculty of Mining, Geology and Petroleum EngineeringarticleShotcreteCompressive StrengthMachine learning techniquesHybrid modelMining engineering. MetallurgyTN1-997GeologyQE1-996.5ENHRRudarsko-geološko-naftni Zbornik, Vol 36, Iss 5, Pp 33-48 (2021)
institution	DOAJ
collection	DOAJ
language	EN HR
topic	Shotcrete Compressive Strength Machine learning techniques Hybrid model Mining engineering. Metallurgy TN1-997 Geology QE1-996.5
spellingShingle	Shotcrete Compressive Strength Machine learning techniques Hybrid model Mining engineering. Metallurgy TN1-997 Geology QE1-996.5 Mahtab Torkan Hamid Kalhori Mohammad Hossein Jalalian APPLICATION OF SOFT COMPUTING METHODOLOGIES TO PREDICT THE 28-DAY COMPRESSIVE STRENGTH OF SHOTCRETE: A COMPARATIVE STUDY OF INDIVIDUAL AND HYBRID MODELS
description	Shotcreting is a popular construction technique with wide-ranging applications in mining and civil engineering. Compressive strength is a primary mechanical property of shotcrete with particular importance for project safety, which highly depends on its mix design. But in practice, there is no reliable and accurate method to predict this strength. In this study, existing experimental data related to shotcretes with 59 different mix designs are used to develop a series of soft computing methodologies, including individual artificial neural network, support vector regression, and M5P model tree and their hybrids with the fuzzy c-means clustering algorithm so as to predict the 28-day compressive strength of shotcrete. Analysis of the results shows the superiority of the hybrid model over the individual models in predicting the compressive strength of shotcrete. Overall, data clustering prior to use of machine learning techniques leads to certain improvement in their performance and reliability and generalizability of their results. In particular, the M5P model tree exhibits excellent capability in anticipating the compressive strength of shotcrete.
format	article
author	Mahtab Torkan Hamid Kalhori Mohammad Hossein Jalalian
author_facet	Mahtab Torkan Hamid Kalhori Mohammad Hossein Jalalian
author_sort	Mahtab Torkan
title	APPLICATION OF SOFT COMPUTING METHODOLOGIES TO PREDICT THE 28-DAY COMPRESSIVE STRENGTH OF SHOTCRETE: A COMPARATIVE STUDY OF INDIVIDUAL AND HYBRID MODELS
title_short	APPLICATION OF SOFT COMPUTING METHODOLOGIES TO PREDICT THE 28-DAY COMPRESSIVE STRENGTH OF SHOTCRETE: A COMPARATIVE STUDY OF INDIVIDUAL AND HYBRID MODELS
title_full	APPLICATION OF SOFT COMPUTING METHODOLOGIES TO PREDICT THE 28-DAY COMPRESSIVE STRENGTH OF SHOTCRETE: A COMPARATIVE STUDY OF INDIVIDUAL AND HYBRID MODELS
title_fullStr	APPLICATION OF SOFT COMPUTING METHODOLOGIES TO PREDICT THE 28-DAY COMPRESSIVE STRENGTH OF SHOTCRETE: A COMPARATIVE STUDY OF INDIVIDUAL AND HYBRID MODELS
title_full_unstemmed	APPLICATION OF SOFT COMPUTING METHODOLOGIES TO PREDICT THE 28-DAY COMPRESSIVE STRENGTH OF SHOTCRETE: A COMPARATIVE STUDY OF INDIVIDUAL AND HYBRID MODELS
title_sort	application of soft computing methodologies to predict the 28-day compressive strength of shotcrete: a comparative study of individual and hybrid models
publisher	Faculty of Mining, Geology and Petroleum Engineering
publishDate	2021
url	https://doaj.org/article/f5be8c1eb965412db7e306d40fa05da6
work_keys_str_mv	AT mahtabtorkan applicationofsoftcomputingmethodologiestopredictthe28daycompressivestrengthofshotcreteacomparativestudyofindividualandhybridmodels AT hamidkalhori applicationofsoftcomputingmethodologiestopredictthe28daycompressivestrengthofshotcreteacomparativestudyofindividualandhybridmodels AT mohammadhosseinjalalian applicationofsoftcomputingmethodologiestopredictthe28daycompressivestrengthofshotcreteacomparativestudyofindividualandhybridmodels
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APPLICATION OF SOFT COMPUTING METHODOLOGIES TO PREDICT THE 28-DAY COMPRESSIVE STRENGTH OF SHOTCRETE: A COMPARATIVE STUDY OF INDIVIDUAL AND HYBRID MODELS

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