Optimal Designing of Concrete Gravity Dam using Particle Swarm Optimization Algorithm (PSO)

Abstract

Background/Objectives: Designing and constructing concrete gravity dams must be in a way that not only realize sustained conditions, but also impose the minimum production costs. The major imposed cost in such dams is expenses of excessive use of concrete. Optimizing this cost requires cross-section optimization. Methods/Statistical Analysis: Dams geometrical configuration show that the cross-section area directly depends on the bottom width and upstream slope. Thus, area optimization only requires optimizing bottom width and upstream slope. Forces imposed on dam, especially seismic forces are nonlinear; on the other hand, sustained conditions including sustainability versus overturning, slip, and cracks caused by fatigue in normal and earthquake conditions are nonlinear, too. In such problems where objective function and constraints are nonlinear, applying conventional optimization methods cannot be responsive. Evolutionary algorithms are efficient in solving these problems. This research tries to study dam weight problem through using Particle Swarm Optimization Algorithm (PSO), which is an evolutionary algorithm based on birds' searching. Results: Comparing the numbers obtained in this method with numbers suggested by conventional methods showed that PSO effectively designs concrete weighting dams and optimizes their dimensions. Conclusion/Application: Moreover, some functions were also proposed for optimum designing of weight-concrete dam.