Impact damage detection in composite structures has gained a considerable interest in many engineering
areas. The capability to detect damage at the early stages reduces any risk of catastrophic failure. This paper
compares two advanced signal processing methods for impact location in composite aircraft structures. The first
method is based on a modified triangulation procedure and Genetic Algorithms whereas the second technique
applies Artificial Neural Networks. A series of impacts is performed experimentally on a composite aircraft wing�box structure instrumented with low-profile, bonded piezoceramic sensors. The strain data are used for learning in
the Neural Network approach. The triangulation procedure utilises the same data to establish impact velocities for
various angles of strain wave propagation. The study demonstrates that both approaches are capable of good
impact location estimates in this complex structure
