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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| Format: | Thesis |
| Language: | en en |
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2017
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| Online Access: | https://etd.uum.edu.my/6556/1/s817333_01.pdf https://etd.uum.edu.my/6556/2/s817333_02.pdf https://etd.uum.edu.my/6556/ |
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| _version_ | 1833436687203565568 |
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| author | Guoji, Zang |
| author_facet | Guoji, Zang |
| author_sort | Guoji, Zang |
| building | UUM Library |
| collection | Institutional Repository |
| content_provider | Universiti Utara Malaysia |
| content_source | UUM Electronic Theses |
| continent | Asia |
| country | Malaysia |
| 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 |
| id | my.uum.etd-6556 |
| institution | Universiti Utara Malaysia |
| language | en en |
| publishDate | 2017 |
| record_format | eprints |
| spelling | my.uum.etd-65562021-05-09T03:00:34Z https://etd.uum.edu.my/6556/ A compressive concrete strength prediction model using artificial neural networks Guoji, Zang 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 NonPeerReviewed text en https://etd.uum.edu.my/6556/1/s817333_01.pdf text en https://etd.uum.edu.my/6556/2/s817333_02.pdf Guoji, Zang (2017) A compressive concrete strength prediction model using artificial neural networks. Masters thesis, Universiti Utara Malaysia. |
| spellingShingle | TA Engineering (General). Civil engineering (General) TH Building construction Guoji, Zang A compressive concrete strength prediction model using artificial neural networks |
| title | 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_short | A compressive concrete strength prediction model using artificial neural networks |
| title_sort | compressive concrete strength prediction model using artificial neural networks |
| topic | TA Engineering (General). Civil engineering (General) TH Building construction |
| url | https://etd.uum.edu.my/6556/1/s817333_01.pdf https://etd.uum.edu.my/6556/2/s817333_02.pdf https://etd.uum.edu.my/6556/ |
| url_provider | http://etd.uum.edu.my/ |