Application of Genetic Algorithms to Optimization of Medium and Heavy Rotating Equipment Foundations

: Optimal structural design involves dealing with three main factors visibly cross-sectional properties of the members, topology and configuration and meeting the intended functional requirements. Most of the traditional optimization techniques are based on the mathematical programming techniques, which assume that the variables are continuous, but whereas the process of structural design is generally characterized by finite often large numbers of variables of discrete in nature. Genetic Algorithm is the technique which can be used efficiently for the design optimization of the structure with discrete variables. From the study on previous work done on GA’s application in civil engineering, it has been noticed that application of GA’s is not attempted in rotating machine foundations where there is scope for determining suitable optimum shape and member sizes to achieve a well-tuned foundation. Dynamic design of machine foundation involves broad criterion such as foundation natural frequency shall be away from the machine operating frequency and foundation displacement amplitudes shall be well within the specified allowable limits. The above criterion largely depends on design factors such as size of members, shape of the foundations, concrete grade and soil characters. Presently obtaining a best suitable solution meeting the frequency and amplitude criteria by varying above four design factors involves many manual trails. This involves lot of computer and human efforts to try various combinations to arrive at the solution. Considerable resources and time need to be spent on arriving a suitable solution. Yet the solution so arrived may not be an optimum solution. In this work, Genetic algorithms is applied for optimization of solution time and foundation volume for industrial medium and heavy rotating equipment foundations. Optimum solution is obtained with above variables by setting frequency as target criteria. The optimum solution obtained from Genetic Algorithms is further verified for its compliance to its intended functional parameters by means of finite element model study.