Development Practices is the Key to the Next Generation for having a progressively imperative and better work concerning Engineering Perspectives. Various sorts of research have been done previously and are being done in the present on building materials extensively used for Constructions. With the ultimate objective to shield the future and proportion the trademark resources, various examinations have been coordinated over some vague period on reactions and wastes leaving undertakings, fantastically warm power plants, to facilitate the use of wastes thusly reusing them and screen the normal resources which are comprehensively using being developed practices[1-5]. A positive quantity of mortar and cement paste from the authentic concrete stays connected to stone particles in the recycled combination when demolished concrete is crushed [11,15]. The adhered mortar presence at the surface of an overwhelmed concrete mixture usually degrades the great of the recycled mixture and therefore the fresh and hardened residences of concrete crafted from it compared to herbal aggregates. As per the investigation, the compressive strength of cement was anticipated utilizing artificial neural system models Firstly, to prepare the ANN model to anticipate the compressive strength of RAC, The predicted compressive strength was contrasted and the exploratory compressive strength and correlation are carried out[12-14]. Training and testing of the ANN model are done utilizing compressive strength results of RAC collected from literature, the practical values obtained are used to validate the ANN model. Then the percentage error between the experiment and predicted compressive strength is obtained

The issues of sustainability are of prime concern these days as we use a large number of natural resources for producing materials such as construction materials. The recent trend in the construction industry is to use the alternative source of construction materials which can substitute the use of natural materials to reduce environmental impact in terms of energy consumption, pollution, waste disposal, and global warming. Aiming at characterizing the behavior of concrete structures made with eggshell powder and coconut shells are replaced in the proportions of the concrete mixture. In the present work, the experimental program was designed to study the properties like strength and workability of concrete by casting the cube of size 150 mm x 150mm x 150 mm and cylinder of size 30 cm height and 15 cm diameter by using M20 grade. This experimental study consists of testing compressive strength of three cubes and split tensile strength of three cylinders of conventional concrete and the comparative cubes and cylinders are made by using different proportions of coconut shells (i.e., 2%, 4%, and 6%) replacement in coarse aggregate and replacement of eggshell powder (i.e., 5%, 10%, and 15%) in place of cement at optimum strength obtained by proportions of coconut shells replaced in coarse aggregate.

To improve the Engineering perspectives towards the eco-friendly environment, many efforts and researches made in the field of concrete. Adding many supplements to the concrete mix mostly results in durable concrete. Thermal power plants produce a large number of wastes like flyash, pond ash, and bottom ash. Flyash is already being used in cement industries to produce Portland Pozzolana Cement. Some researchers concluded that pond ash could also replace river sand in the concrete mix. Anticipating the compressive strength of pond ash concrete has consistently been trouble since the concrete is sensitive to its blend segments, techniques for blending, compaction, curing condition, and so forth. Scientists have given various strategies for foreseeing the properties of concrete. However, some others were not appropriate enough to predict the compressive strength of pond ash concrete. The point of this investigation is to assess the ability of the Artificial Neural Network Model (ANN) in predicting the compressive strength of pond ash concrete after 28 days of curing. Accordingly, considering specific Concrete characteristics as input factors by considering various rates 0% to 25% in steps of 1% increment of Pond Ash replaced in place of sand in Traditional Concrete of M30 Mix, Artificial Neural Network Model is built, and the properties of concrete predicted. Results demonstrated that ANN could be an alternative approach for anticipating the compressive strength properties of pond ash concrete.