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Ton Duc Thang University (TDTU) is a public university with the main campus located in vibrant Ho Chi Minh City, Vietnam’s economic and educational hub. Founded in 1997, TDTU has developed into one of the largest and fastest growing universities in Vietnam with more than 22,000 students, enrolled in undergraduate and graduate programs ranging from science, engineering to business management, law, and humanities. To foster the country’s human resources and best serve the nation in the knowledge based economy of the 21st century, TDTU is combining vocational training with high-level research. The establishment of JAEC is one of TDTU’s efforts in this direction. More

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  • Vol 5, No 1 (2021)
  • Shariati

Assessment of Longstanding Effects of Fly Ash and Silica Fume on the Compressive Strength of Concrete Using Extreme Learning Machine and Artificial Neural Network

Mahdi Shariati, Danial Jahed Armaghani, Manoj Khandelwal, Jian Zhou, Majid Khorami

Abstract


Compressive Strength (CS) is an important mechanical feature of concrete taken as an essential factor in construction. The current study has investigated the effect of fly ash and silica fume replacement content on the strength of concrete through Artificial Neural Networks (ANNs) and Extreme Learning Machine (ELM). In this study, different ratios of fly ash with (out) extra quantity of silica fume have been tested. Water cement (w/c) ratio varies during the test. Eight input parameters including Total Cementitious Material (TCM), Silica Fume (SF) replacement ratio, coarse aggregate (ca), fly ash (FA) replacement ratio, Sewage Sludge Ash (SSA) as a combination of cement and fine aggregate replacement, water-cement ratio, High Ratio Water Reducing Agent (HRWRA) and Age of Samples (AS) and one output parameter as the CS of concrete have been investigated through ANN and ELM. Up to now, numerous experimental studies have been used to analyze the compressive strength of concrete while retrofitted with fly ash or silica fume, however, the novelty of this study is in its use of AI models (ELM, ANN). The models have been developed and their outcomes were compared through six statistical indicators (MAE, RMSE, RRMSE, WI, RMAE and R2). Subsequently, both methods were shown as reliable tools for assessing the influence of cementitious material on compressive strength of concrete, however, ANN remarkably was better than ELM. As a result, FA showed less contribution to the strength of concrete at short times, but much at later ages. As a result, the enhanced influence of low amount of SF on CS was not significant. Adding fly ash has reduced the compressive strength in short term, but increased the compressive strength in long term. Adding silica fume raises the strength in short term, but decreases the strength in long term.

Creative Commons License

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.


Keywords


Compressive strength, concrete, replacement material, extreme learning machine, artificial neural network.

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DOI: http://dx.doi.org/10.25073/jaec.202151.308

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Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.