Now-a-days energy planners are aiming to increase the use of renewable energy sources and nuclear to meet the electricity generation. But till now coal-based power plants are the major source of electricity generation. Disadvantages of coal-based thermal power plants is disposal problem of fly ash and pond ash. It was earlier considered as a total waste and environmental hazard thus its use was limited, but now its useful properties have been known as raw material for various application in construction field. Fly ash from the thermal plants is available in large quantities in fine and coarse form. Fine fly ash is used in construction industry in some amount and coarse fly ash is subsequently disposed over land in slurry forms. In India around 180 MT fly is produced and only around 45% of that is being utilized in different sectors. Balance fly ash is being disposed over land. It needs one acre of land for ash disposal to produce 1MW electricity from coal. Fly ash and pond ash utilization helps to reduce the consumption of natural resources. The fly ash became available in coal based thermal power station in the year 1930 in USA. For its gainful utilization, scientist started research activities and in the year 1937, R.E. Davis and his associates at university of California published research details on use of fly ash in cement concrete. This research had laid foundation for its specification, testing & usages. This study reports the potential use of pond-ash and fly-ash as cement in concrete mixes. In this present study of concrete produced using fly ash, pond ash and OPC 53 grade will be carried. An attempt will be made to investigate characteristics of OPC concrete with combined fly ash and pond ash mixed concrete for Compressive Strength test, Split Tensile Strength test, Flexural Strength test and Durability tests. This paper deals with the review of literature for fly-ash and pond-ash as partial replacement of cement in concrete.
Coupling effect of pond ash and polypropylene fiber on strength and durability of expansive soil subgrades: An integrated experimental and machine learning approach