Artificial neural network approach using Levenberg-Marquardt algorithm in the use of bottom ash waste in concrete hollow block design

Neural Network modeling was applied for the prediction of compressive strength of Coal Bottom Ash (CBA). Levenberg-Marquardt was used for the different neural network architectures to find acceptable models than can accurately predict the compressive strength of CHB's and realistically model th...

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Main Author: Ongpeng, Jason Maximino Co
Format: text
Language:English
Published: Animo Repository 2003
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Online Access:https://animorepository.dlsu.edu.ph/etd_masteral/3046
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Institution: De La Salle University
Language: English
id oai:animorepository.dlsu.edu.ph:etd_masteral-9884
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spelling oai:animorepository.dlsu.edu.ph:etd_masteral-98842020-12-07T07:27:55Z Artificial neural network approach using Levenberg-Marquardt algorithm in the use of bottom ash waste in concrete hollow block design Ongpeng, Jason Maximino Co Neural Network modeling was applied for the prediction of compressive strength of Coal Bottom Ash (CBA). Levenberg-Marquardt was used for the different neural network architectures to find acceptable models than can accurately predict the compressive strength of CHB's and realistically model the behavior of CHB's with CBA as partial substitute to sand. In addition, the maximum percentage of CBA content was derived from the ANN (Artificial Neural Network) model based on PNS (Philippine National Standards) types. CBA is a waste by-product of coal-fired power plant. An experimental study utilizing CBA as a partial substitute to sand in the production of CNB's was conducted. Around 429 pieces of four-inch thick CHB's were tested with such variable mix proportions as: water-cement ratio (w/c), cement-aggregate ratio (c/a), weight of the specimen (wt), slump (sl), and coal bottom ash percent substitution (CBA) taken into consideration. 2003-01-01T08:00:00Z text https://animorepository.dlsu.edu.ph/etd_masteral/3046 Master's Theses English Animo Repository Neural networks (Computer science) Evolutionary computation Algorithms Waste products Concrete blocks
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
language English
topic Neural networks (Computer science)
Evolutionary computation
Algorithms
Waste products
Concrete blocks
spellingShingle Neural networks (Computer science)
Evolutionary computation
Algorithms
Waste products
Concrete blocks
Ongpeng, Jason Maximino Co
Artificial neural network approach using Levenberg-Marquardt algorithm in the use of bottom ash waste in concrete hollow block design
description Neural Network modeling was applied for the prediction of compressive strength of Coal Bottom Ash (CBA). Levenberg-Marquardt was used for the different neural network architectures to find acceptable models than can accurately predict the compressive strength of CHB's and realistically model the behavior of CHB's with CBA as partial substitute to sand. In addition, the maximum percentage of CBA content was derived from the ANN (Artificial Neural Network) model based on PNS (Philippine National Standards) types. CBA is a waste by-product of coal-fired power plant. An experimental study utilizing CBA as a partial substitute to sand in the production of CNB's was conducted. Around 429 pieces of four-inch thick CHB's were tested with such variable mix proportions as: water-cement ratio (w/c), cement-aggregate ratio (c/a), weight of the specimen (wt), slump (sl), and coal bottom ash percent substitution (CBA) taken into consideration.
format text
author Ongpeng, Jason Maximino Co
author_facet Ongpeng, Jason Maximino Co
author_sort Ongpeng, Jason Maximino Co
title Artificial neural network approach using Levenberg-Marquardt algorithm in the use of bottom ash waste in concrete hollow block design
title_short Artificial neural network approach using Levenberg-Marquardt algorithm in the use of bottom ash waste in concrete hollow block design
title_full Artificial neural network approach using Levenberg-Marquardt algorithm in the use of bottom ash waste in concrete hollow block design
title_fullStr Artificial neural network approach using Levenberg-Marquardt algorithm in the use of bottom ash waste in concrete hollow block design
title_full_unstemmed Artificial neural network approach using Levenberg-Marquardt algorithm in the use of bottom ash waste in concrete hollow block design
title_sort artificial neural network approach using levenberg-marquardt algorithm in the use of bottom ash waste in concrete hollow block design
publisher Animo Repository
publishDate 2003
url https://animorepository.dlsu.edu.ph/etd_masteral/3046
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