13-06-2012, 03:15 PM
OPTIMIZATION OF TRANSMISSION LINE TOWER USING GENETIC ALGORITHM
OPTIMIZATION OF TRANSMISSION .pptx (Size: 311.32 KB / Downloads: 79)
OBJECTIVE
The main purpose of this project is to introduce genetic
programming into civil engineering problem solving for
transmission line tower. This project describes a genetic
programming-based approach for weight optimization of
transmission line tower trusses. Using STAAD pro, analysis of
this tower has been carried out as a three dimensional structure.
Then, the tower members are Design as an angle Sections.
‘Microsoft visual FoxPro 9’ is used for optimizing transmission
line tower.
OPTIMIZATION:
Optimization is a critical and challenging activity in structural design. Designers are able to produce better designs while saving time and money through optimization.
Design is one of the primary functions in engineering. In design the engineer creates a method, device, process or more broadly, objective being to satisfy a performance requirement while minimizing those factors which reduce the efficiency of the system.
TRANSMISSION TOWER:
Transmission tower are free-standing towers and are usually square in plan.
These are supported on ground by four legs and act as cantilever trusses under horizontal loads.
Power transmission towers have horizontal arms called cross-arms for carrying the conductors.
A structure, which is generally made of a metal such as galvanized steel.
DESIGN OF TRANSMISSION TOWER
In the design of transmission towers, three items should be considered: cost of material, cost of erection and cost of foundation. The cost of material directly related to the number of splices and bolts to be installed. The cost of foundation directly related to the spread of the tower legs and soil conditions.
REPRODUCTION
Reproduction is the first operator applied on a population.
Reproduction is a process in which individual strings are copied
according to their fitness values, f as some measure of profit,
utility or goodness that we want to maximize. Copying strings
according to their fitness value means that strings with a higher
value have a higher probability of contributing one or more
offspring in the next generation.