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Machine learning and RSM models for prediction of compressive strength of smart bio-concrete

In recent years, bacteria-based self-healing concrete has been widely exploited to improve the compressive strength of concrete using different bacterial species. However, both the identification of the optimal involved reaction parameters and theoretical framework information are still limited. In...

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Main Authors: Algaifi, Hassan Amer, Abu Bakar, Suhaimi, Alyousef, Rayed, Mohd. Sam, Abdul Rahman, Alqarni, Ali S., Wan Ibrahim, M. H., Shahidan, Shahiron, Mohammed Ibrahim, Mohammed Ibrahim, Salami, Babatunde Abiodun
Format: Article
Published: Techno-Press 2021
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utm.my/id/eprint/96740/
http://dx.doi.org/10.12989/sss.2021.28.4.535
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spelling my.utm.967402022-08-20T01:55:49Z http://eprints.utm.my/id/eprint/96740/ Machine learning and RSM models for prediction of compressive strength of smart bio-concrete Algaifi, Hassan Amer Abu Bakar, Suhaimi Alyousef, Rayed Mohd. Sam, Abdul Rahman Alqarni, Ali S. Wan Ibrahim, M. H. Shahidan, Shahiron Mohammed Ibrahim, Mohammed Ibrahim Salami, Babatunde Abiodun TA Engineering (General). Civil engineering (General) In recent years, bacteria-based self-healing concrete has been widely exploited to improve the compressive strength of concrete using different bacterial species. However, both the identification of the optimal involved reaction parameters and theoretical framework information are still limited. In the present study, both experimentally and numerical modelling using machine learning (ANN and ANFIS) and response surface methodology (RSM) were implemented to evaluate and optimse the evolution of bacterial concrete strength. Therefore, a total of 58 compressive strength tests of the concrete incorporating new bacterial species were designed using different concentrations of urea, cells concentration, calcium, nutrient and time. Based on the results, the compressive strength of the bacterial concrete improved by 16% due to the decrement of the pore percentage in the concrete skin; specifically, 5 mm from the concrete surface, compared to that of the control concrete. In the same context, both machine the learning and RSM models indicated that the optimal range of urea, calcium, nutrient and bacterial cells were (18-23 g/L), (150-350 mM), (1-3 g/L) and 2×107 cells/mL, respectively. Based on the statistical analysis, RMSE, R2, MPE, RAE and RRSE were (0.793, 0.785), (0.985, 0.986), (1.508, 1.1), (0.11, 0.09) and (0.121, 0.12) from both the ANN and ANFIS models, respectively, while; the following values (0.839, 0.972, 1.678, 0.131 and 0.165) was obtained from RSM model, respectively. As such, it can be concluded that a high correlation and minimum error were obtained, however, machine learning models provided more accurate results compared to that of the RSM model. Techno-Press 2021-10 Article PeerReviewed Algaifi, Hassan Amer and Abu Bakar, Suhaimi and Alyousef, Rayed and Mohd. Sam, Abdul Rahman and Alqarni, Ali S. and Wan Ibrahim, M. H. and Shahidan, Shahiron and Mohammed Ibrahim, Mohammed Ibrahim and Salami, Babatunde Abiodun (2021) Machine learning and RSM models for prediction of compressive strength of smart bio-concrete. Smart Structures and Systems, 28 (4). pp. 535-551. ISSN 1738-1584 http://dx.doi.org/10.12989/sss.2021.28.4.535 DOI:10.12989/sss.2021.28.4.535
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Algaifi, Hassan Amer
Abu Bakar, Suhaimi
Alyousef, Rayed
Mohd. Sam, Abdul Rahman
Alqarni, Ali S.
Wan Ibrahim, M. H.
Shahidan, Shahiron
Mohammed Ibrahim, Mohammed Ibrahim
Salami, Babatunde Abiodun
Machine learning and RSM models for prediction of compressive strength of smart bio-concrete
description In recent years, bacteria-based self-healing concrete has been widely exploited to improve the compressive strength of concrete using different bacterial species. However, both the identification of the optimal involved reaction parameters and theoretical framework information are still limited. In the present study, both experimentally and numerical modelling using machine learning (ANN and ANFIS) and response surface methodology (RSM) were implemented to evaluate and optimse the evolution of bacterial concrete strength. Therefore, a total of 58 compressive strength tests of the concrete incorporating new bacterial species were designed using different concentrations of urea, cells concentration, calcium, nutrient and time. Based on the results, the compressive strength of the bacterial concrete improved by 16% due to the decrement of the pore percentage in the concrete skin; specifically, 5 mm from the concrete surface, compared to that of the control concrete. In the same context, both machine the learning and RSM models indicated that the optimal range of urea, calcium, nutrient and bacterial cells were (18-23 g/L), (150-350 mM), (1-3 g/L) and 2×107 cells/mL, respectively. Based on the statistical analysis, RMSE, R2, MPE, RAE and RRSE were (0.793, 0.785), (0.985, 0.986), (1.508, 1.1), (0.11, 0.09) and (0.121, 0.12) from both the ANN and ANFIS models, respectively, while; the following values (0.839, 0.972, 1.678, 0.131 and 0.165) was obtained from RSM model, respectively. As such, it can be concluded that a high correlation and minimum error were obtained, however, machine learning models provided more accurate results compared to that of the RSM model.
format Article
author Algaifi, Hassan Amer
Abu Bakar, Suhaimi
Alyousef, Rayed
Mohd. Sam, Abdul Rahman
Alqarni, Ali S.
Wan Ibrahim, M. H.
Shahidan, Shahiron
Mohammed Ibrahim, Mohammed Ibrahim
Salami, Babatunde Abiodun
author_facet Algaifi, Hassan Amer
Abu Bakar, Suhaimi
Alyousef, Rayed
Mohd. Sam, Abdul Rahman
Alqarni, Ali S.
Wan Ibrahim, M. H.
Shahidan, Shahiron
Mohammed Ibrahim, Mohammed Ibrahim
Salami, Babatunde Abiodun
author_sort Algaifi, Hassan Amer
title Machine learning and RSM models for prediction of compressive strength of smart bio-concrete
title_short Machine learning and RSM models for prediction of compressive strength of smart bio-concrete
title_full Machine learning and RSM models for prediction of compressive strength of smart bio-concrete
title_fullStr Machine learning and RSM models for prediction of compressive strength of smart bio-concrete
title_full_unstemmed Machine learning and RSM models for prediction of compressive strength of smart bio-concrete
title_sort machine learning and rsm models for prediction of compressive strength of smart bio-concrete
publisher Techno-Press
publishDate 2021
url http://eprints.utm.my/id/eprint/96740/
http://dx.doi.org/10.12989/sss.2021.28.4.535
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score 13.211869

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