Prediction model for hardened state properties of silica fume and fly ash based seawater concrete incorporating silicomanganese slag

Matthew Zhi Yeon Ting, Kwong Soon Wong*, Muhammad Ekhlasur Rahman, Meheron Selowarajoo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)
8 Downloads (Pure)


Growing concrete consumption has gradually depleted conventional resources. This research incorporates silicomanganese (SiMn) slag, marine sand and seawater as alternative concreting materials. The use of SiMn slag to replace limestone as coarse aggregate enhances sustainability, though reducing strength and durability of concrete. This research aims to enhance the SiMn slag concrete by incorporating silica fume (SF) and fly ash (FA). The interaction of SF and FA on strength, durability and workability of concrete is investigated by statistically evaluating the experimental result. In this regard, the polynomial function prediction model is developed using the Response Surface Method (RSM) for the optimization of SF and FA contents. Analysis of variance (ANOVA) using p-value at significance level of 0.05 showed that the models were statistically significant and had marginal residual errors. All models had high fitness with R2 value ranging from 0.853 to 0.999. Adequate precision of models was above 4, indicating that the models had a low prediction error and were fit for optimization. Optimization indicated that a combination of 11.5% SF and 16.3% FA produced concrete that met the optimization criteria. Experimental validation showed that the highest prediction error was 3.4% for compressive strength, 3.2% for tensile strength, 4.9% for sorptivity and 18% for chloride permeability. The optimized concrete exhibited compact microstructure with good bonding between aggregate and cement paste. By using the established linear equation with SiMn slag concrete, the models also predicted the compressive strength of limestone concrete containing SF and FA with an error of between 0.9% and 5.4%.

Original languageEnglish
Article number102356
Number of pages18
JournalJournal of Building Engineering
Early online date3 Mar 2021
Publication statusPublished - 1 Sept 2021


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