Optimization of Intze Type Water Tank Using Machine Learning

India has an urban population that makes up more than 34% of the country's total population. Water, petroleum, oil products, and other liquids are kept in storage tanks and overhead tanks. These tanks have been employed in several different fields, including agriculture, the food industry, paper mills, firefighting, and water distribution. It is well known in the current situation that nearly every industry is heading toward machine-based automation, starting with the most basic systems up to master-level ones. Among them, Machine Learning (ML) is one of the crucial tools that resembles Artificial Intelligence (AI) the most. It does this by allowing some well-known data or experience to automatically enhance or estimate the behaviour or condition of the provided data using a variety of methods. To avoid leaks, every tank is designed with a framework free of cracks. The objective of the structural design process is to provide a safe design that complies with all design code criteria while keeping design expenses to a minimum. A recent idea, structural design optimization is based on random information interchange, determining the best design using more precise mathematical algorithms, and methodologies as opposed to human judgment. This study uses machine learning (ML) to bring down the price of planning and designing a tank to a specific water tank capacity. In this study, programs are created to promote the Intze type reinforced concrete water tank design with the lowest possible cost.