Estimating Slump Flow and Compressive Strength of Self-Compacting Concrete Using Emotional Neural Networks
| Autor: | Jong Wan Hu, Mosbeh R. Kaloop, Pijush Samui, Mohamed Shafeek |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
slump flow
Silica fume Mean squared error self-compacting concrete 0211 other engineering and technologies 02 engineering and technology lcsh:Technology lcsh:Chemistry symbols.namesake 021105 building & construction General Materials Science Sensitivity (control systems) Composite material Instrumentation lcsh:QH301-705.5 Mathematics Fluid Flow and Transfer Processes Cement Artificial neural network lcsh:T Process Chemistry and Technology General Engineering 021001 nanoscience & nanotechnology compressive strength Pearson product-moment correlation coefficient lcsh:QC1-999 Computer Science Applications Compressive strength lcsh:Biology (General) lcsh:QD1-999 lcsh:TA1-2040 Fly ash symbols 0210 nano-technology lcsh:Engineering (General). Civil engineering (General) lcsh:Physics emotional neural network |
| Zdroj: | Applied Sciences, Vol 10, Iss 8543, p 8543 (2020) Applied Sciences Volume 10 Issue 23 |
| ISSN: | 2076-3417 |
| Popis: | The characteristics of fresh and hardened self-compacting concrete (SCC) are an essential requirement for construction projects. Moreover, the sensitivity of admixture contents of SCC in these properties is highly impacted by that cost. The current study investigates to estimate the slump-flow (S) and compressive strength (CS), as fresh and hardened properties of SCC, respectively. Four developed soft-computing approaches were proposed and compared, including the group method of data handling (GMDH), Minimax Probability Machine Regression (MPMR), emotional neural network (ENN), and hybrid artificial neural network-particle swarm optimization (ANN-PSO), to estimate the S and 28-day CS of SCC, which comprises fly ash (FA), silica fume (SF), and limestone powder (LP) as part of cement by mass in total powder content. In addition, the impact of eight admixture components is investigated and evaluated to assess the sensitivity of admixture contents for the modelling of S and CS of SCC. The results demonstrate that the performance prediction of ENN model is more significant than other models in estimating S and CS characteristics of SCC. The overall of Pearson correlation coefficient, r, and root mean square error (RMSE) of ENN model are 97.80% and 20.16 mm, respectively, for the S. These are 96.07% and 2.59 MPa, respectively, for the CS. Furthermore, the sensitivity of the powder content of fly ash is shown to have a high impact on the estimated S and CS values of SCC. |
| Databáze: | OpenAIRE |
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