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

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dc.citedby20
dc.contributor.authorLatif S.D.en_US
dc.contributor.authorid57216081524en_US
dc.date.accessioned2023-05-29T09:07:30Z
dc.date.available2023-05-29T09:07:30Z
dc.date.issued2021
dc.descriptionalgorithm; 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-Termen_US
dc.description.abstractOne 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s11356-021-12877-y
dc.identifier.epage30302
dc.identifier.issue23
dc.identifier.scopus2-s2.0-85100899157
dc.identifier.spage30294
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85100899157&doi=10.1007%2fs11356-021-12877-y&partnerID=40&md5=b8f656bd429c427e32896609050dad43
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26182
dc.identifier.volume28
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleEnvironmental Science and Pollution Research
dc.titleConcrete compressive strength prediction modeling utilizing deep learning long short-term memory algorithm for a sustainable environmenten_US
dc.typeArticleen_US
dspace.entity.typePublication
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