The effect of chemical oxygen demand of domestic wastewater on workability, mechanical, and durability of self- compacting concrete
Source abstract
Abstract
Due to worldwide water scarcity, especially in arid regions, and the substantial use of drinking water in concrete production, the consideration of gray water usage is growing. However, wastewater contamination adversely affects concrete's mechanical strength and durability, with Chemical Oxygen Demand(COD) as one of the indicator. In the present work, the effect of COD of different types of domestic wastewater on workability, mechanical, and durability properties of self-compacting concrete (SCC) with 400 kg/m3 and 440 kg/m3 of cement, and water-to-cement ratios of (w/c) 0.36 and 0.5 for 12 different SCC mixture designs were investigated. The results of the experiments indicated that increasing the COD of domestic wastewater negatively impacts the workability of fresh concrete. Additionally, as the COD of the wastewater increases, the compressive strength of SCC decreases at 7 and 28 days when using raw sewage, sewage sludge, and artificial wastewaters. However, by 90 days, the compressive strength showed no significant difference compared to SCC made with tap water. With increasing COD of wastewater, the 28-day tensile strengths of SCC decreased by 6–10 %. The COD of wastewater did not significantly affect the flexural strength. However, the fracture toughness, at a water-cement ratio (w/c) of 0.5, decreased with increasing COD, reaching a reduction of 36 % at a COD of 940 mg/L. As the COD concentration rises, water absorption increases. In SCC samples containing sludge water and raw sewage, capillary water absorption was at its maximum due to the presence of impurities, as well as organic and mineral materials.
Source: openalex_inverted_index · Quality: baseline
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OpenAlex topics: Recycled Aggregate Concrete Performance · Concrete and Cement Materials Research · Innovative concrete reinforcement materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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