A compressive concrete strength prediction model using artificial neural networks

A building is at a high risk of destruction if the compressive concrete strength does not meet the required specification. Thus, the prediction of compressive concrete strength has become an important research area. Previous prediction models are based on fix numbers of attributes. Consequently, whe...

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Main Author:	Guoji, Zang
Format:	Thesis
Language:	eng eng
Published:	2017
Subjects:	TA Engineering (General) Civil engineering (General) TH Building construction
Online Access:	https://etd.uum.edu.my/6556/1/s817333_01.pdf https://etd.uum.edu.my/6556/2/s817333_02.pdf
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id	my-uum-etd.6556
record_format	uketd_dc
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Ahmad, Faudziah
topic	TA Engineering (General) Civil engineering (General) TH Building construction
spellingShingle	TA Engineering (General) Civil engineering (General) TH Building construction Guoji, Zang A compressive concrete strength prediction model using artificial neural networks
description	A building is at a high risk of destruction if the compressive concrete strength does not meet the required specification. Thus, the prediction of compressive concrete strength has become an important research area. Previous prediction models are based on fix numbers of attributes. Consequently, when the number of attributes increase or decrease, the models could not be used. Thus, a compressive concrete strength prediction model which can work with different numbers of attribute is needed. The purpose of this study is to develop compressive concrete strength prediction models using different combinations of attributes. This study includes five stages: data collection, normalization, parameters identification, model construction and evaluation. The employed data set consists of nine attributes: water, cement, fine aggregate, coarse aggregate, age, fly ash, super plasticizer, blast furnace slag and compressive concrete strength. This study produced eight prediction models where each model has different combination of attributes. It also identified appropriate weights, learning rate, momentum and number of hidden nodes for each of the proposed model, and design a general artificial neural network (ANN) architecture. Model eight of the study produced a higher correlation coefficient (i.e., 0.973) than the existing study (i.e., 0.953). This study has successfully produced eight concrete strength prediction models with good coefficient correlation. The compressive strength prediction models would benefit civil engineers as they can use the models to identify the suitability of additional materials in concrete mix.
format	Thesis
qualification_name	other
qualification_level	Master's degree
author	Guoji, Zang
author_facet	Guoji, Zang
author_sort	Guoji, Zang
title	A compressive concrete strength prediction model using artificial neural networks
title_short	A compressive concrete strength prediction model using artificial neural networks
title_full	A compressive concrete strength prediction model using artificial neural networks
title_fullStr	A compressive concrete strength prediction model using artificial neural networks
title_full_unstemmed	A compressive concrete strength prediction model using artificial neural networks
title_sort	compressive concrete strength prediction model using artificial neural networks
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2017
url	https://etd.uum.edu.my/6556/1/s817333_01.pdf https://etd.uum.edu.my/6556/2/s817333_02.pdf
_version_	1747828090887733248
spelling	my-uum-etd.65562021-05-09T03:00:34Z A compressive concrete strength prediction model using artificial neural networks 2017 Guoji, Zang Ahmad, Faudziah Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences TA Engineering (General). Civil engineering (General) TH Building construction A building is at a high risk of destruction if the compressive concrete strength does not meet the required specification. Thus, the prediction of compressive concrete strength has become an important research area. Previous prediction models are based on fix numbers of attributes. Consequently, when the number of attributes increase or decrease, the models could not be used. Thus, a compressive concrete strength prediction model which can work with different numbers of attribute is needed. The purpose of this study is to develop compressive concrete strength prediction models using different combinations of attributes. This study includes five stages: data collection, normalization, parameters identification, model construction and evaluation. The employed data set consists of nine attributes: water, cement, fine aggregate, coarse aggregate, age, fly ash, super plasticizer, blast furnace slag and compressive concrete strength. This study produced eight prediction models where each model has different combination of attributes. It also identified appropriate weights, learning rate, momentum and number of hidden nodes for each of the proposed model, and design a general artificial neural network (ANN) architecture. Model eight of the study produced a higher correlation coefficient (i.e., 0.973) than the existing study (i.e., 0.953). This study has successfully produced eight concrete strength prediction models with good coefficient correlation. The compressive strength prediction models would benefit civil engineers as they can use the models to identify the suitability of additional materials in concrete mix. 2017 Thesis https://etd.uum.edu.my/6556/ https://etd.uum.edu.my/6556/1/s817333_01.pdf text eng public https://etd.uum.edu.my/6556/2/s817333_02.pdf text eng public other masters Universiti Utara Malaysia Aggarwal, R., Kumar, M., Sharma, R. K., & Sharma, M. K. (2015). Predicting Compressive Strength of Concrete. International Journal of Applied Science and Engineering, (April), 171–185. Akande, K., Owolabi, T., Twaha, S., & Olatunji, S. (2014). Performance Comparison of SVM and ANN in Predicting Compressive Strength of Concrete. IOSR Journal of Computer Engineering, 16(5), 88–94. http://doi.org/10.9790/0661-16518894 Alilou, V. ., & Teshnehlab, M. (2010). Prediction of 28-day compressive strength of concrete on the third day using artificial neural networks. International Journal of Engineering (IJE), (3), 565–576. 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A compressive concrete strength prediction model using artificial neural networks

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