This research addresses the problem of structural damage 
detection using linear vibration information contained 
in frequency response functions. The structural damage 
identification method is stated as an unconstrained 
optimization problem, which minimizes the error between 
measured and analytically computed vibration signatures 
using minimal measurement information. Two types of genetic 
algorithms are implemented to solve the structured and 
unstructured optimization problem of damage detection. The 
proposed algorithms are evaluated on case study simulations 
for different types of structures with increasing complexity. 
Noisy measurements are included in the simulations to 
investigate their effect on damage detection accuracy. The 
proposed method is compared to existing damage detection 
algorithms using accuracy measures that are based on Euclidean 
geometry. Case study results show that the proposed damage 
identification method is robust even in noisy measurement 
environments. A methodology for optimizing excitation and 
sensor layouts used for detecting damage in structures is 
presented by applying multi-objective genetic algorithms. In 
sensor layout optimization, the objectives are to reduce the 
number of sensors required while trying to increase the amount 
of information contained in the vibration signatures. Several 
case studies were investigated to determine the effect of using 
the optimum sensor layout designs evolved on the performance of 
the FRF-based damage detection method. The results show that the 
quality of the measurement information increased when the optimal 
sensor locations were used and the ability of the damage detection 
method to uniquely identify damaged elements was enhanced.