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Concrete compressive strength prediction modeling utilizing deep learning long short-term memory algorithm for a sustainable environment

algorithm; compressive strength; concrete; environmental impact assessment; machine learning; numerical model; prediction; sustainability; algorithm; building material; compressive strength; short term memory; Algorithms; Compressive Strength; Construction Materials; Deep Learning; Memory, Short-Ter...

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Main Author: Latif S.D.
Other Authors: 57216081524
Format: Article
Published: Springer Science and Business Media Deutschland GmbH 2023
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spelling my.uniten.dspace-261822023-05-29T17:07:30Z Concrete compressive strength prediction modeling utilizing deep learning long short-term memory algorithm for a sustainable environment Latif S.D. 57216081524 algorithm; compressive strength; concrete; environmental impact assessment; machine learning; numerical model; prediction; sustainability; algorithm; building material; compressive strength; short term memory; Algorithms; Compressive Strength; Construction Materials; Deep Learning; Memory, Short-Term One of the most critical parameters in concrete design is compressive strength. As the compressive strength of concrete is correctly measured, time and cost can be decreased. Concrete strength is relatively resilient to impacts on the environment. The production of concrete compressive strength is greatly influenced by severe weather conditions and increases in humidity rates. In this research, a model has been developed to predict concrete compressive strength utilizing a detailed dataset obtained from previously published studies based on a deep learning method, namely, long short-term memory (LSTM), and a conventional machine learning (ML) algorithm, namely, support vector machine (SVM). The input variables of the model include cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and age of specimens. To demonstrate the efficiency of the proposed models, three statistical indices, namely, the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE), were used. Findings shows that LSTM outperformed SVM with R2=0.98, R2= 0.78, MAE=1.861, MAE=6.152, and RMSE=2.36, RMSE=7.93, respectively. The results of this study suggest that high-performance concrete (HPC) compressive strength can be reliably measured using the proposed LSTM model. � 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature. Final 2023-05-29T09:07:30Z 2023-05-29T09:07:30Z 2021 Article 10.1007/s11356-021-12877-y 2-s2.0-85100899157 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100899157&doi=10.1007%2fs11356-021-12877-y&partnerID=40&md5=b8f656bd429c427e32896609050dad43 https://irepository.uniten.edu.my/handle/123456789/26182 28 23 30294 30302 Springer Science and Business Media Deutschland GmbH Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description algorithm; compressive strength; concrete; environmental impact assessment; machine learning; numerical model; prediction; sustainability; algorithm; building material; compressive strength; short term memory; Algorithms; Compressive Strength; Construction Materials; Deep Learning; Memory, Short-Term
author2 57216081524
author_facet 57216081524
Latif S.D.
format Article
author Latif S.D.
spellingShingle Latif S.D.
Concrete compressive strength prediction modeling utilizing deep learning long short-term memory algorithm for a sustainable environment
author_sort Latif S.D.
title Concrete compressive strength prediction modeling utilizing deep learning long short-term memory algorithm for a sustainable environment
title_short Concrete compressive strength prediction modeling utilizing deep learning long short-term memory algorithm for a sustainable environment
title_full Concrete compressive strength prediction modeling utilizing deep learning long short-term memory algorithm for a sustainable environment
title_fullStr Concrete compressive strength prediction modeling utilizing deep learning long short-term memory algorithm for a sustainable environment
title_full_unstemmed Concrete compressive strength prediction modeling utilizing deep learning long short-term memory algorithm for a sustainable environment
title_sort concrete compressive strength prediction modeling utilizing deep learning long short-term memory algorithm for a sustainable environment
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2023
_version_ 1806424177393008640
score 13.211869

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