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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Faculty of Mining, Geology and Petroleum Engineering
2021
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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) |
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Shotcrete Compressive Strength Machine learning techniques Hybrid model Mining engineering. Metallurgy TN1-997 Geology QE1-996.5 |
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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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