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Near-net shape casting having complex geometry is manufactured through the sand casting process. However, day by day, the availability of natural or synthetic silica sand has been decreasing and increasing the production cost of sand casting components. Therefore, there is a need to look into low-cost and readily available alternative materials to substitute the commercial-grade silica sand for the sand mould casting process. The constituent of silica sand is primarily silica (SiO 2 ), Al 2 O 3 , and Fe 2 O 3 . The major constituents of industrial wastes such as fly ash, blast furnace slag, ferrochrome slag, stone dust, and red mud have SiO 2 , Al 2 O 3 , and Fe 2 O 3 . Therefore, industrial wastes may be used individually or combined with silica sand at a different ratio to substitute the commercialgrade silica sand in green mould castings. Researchers and scientists have evaluated the suitability of industrial wastes and local riverbed sand as an alternative material for green sand mould castings. The present review summarizes the advantages and constraints of using industrial wastes and local riverbed sand as an alternative to green sand mould casting process.

Cracks are one of the main causes of structural failure and they develop in the structures due to various reasons such as fatigue, temperature variation, excessive load, cyclic load, environmental effects, impact loading etc. Thus, structural health monitoring is necessary to avoid risks, damages and failures. So, in order to avoid an extensive failure or accident, the early prognosis of crack in structures is necessary. Visual inspection and some non-destructive testing (NDT) methods for detection of crack are difficult as it requires time, expenses and are quite inefficient. So the alternative methods are motivated to be developed. In this study, vibration analysis, finite element analysis (FEA) and an alternative way which is artificial neural network (ANN) is used to predict, detect and identify the damages in structures. It is found that the theoretical, experimental, finite element analysis and artificial neural network have good accuracy in predicting the crack characteristics.

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