An efficient method is proposed to detect the multiple damages of structural systems. Natural frequency changes of a structure are considered as a criterion for damage presence. Finite element method is also employed to evaluate the required natural frequencies. A multi-stage optimization approach based on the swarm intelligence algorithm is presented to accurately identify the locations and extent of eventual damages. Test example results demonstrate the computational advantages of the proposed method to precisely determine the sites and extent of multiple structural damages.
Bakhtiari-NejadF.RahaiA.EsfandiariA. (2005). “A structural damage detection method using static noisy data”, Engineering Structures, Vol. 27, No. 12, pp. 1784–1793.
2.
GomesH.M.SilvaN.R.S. (2008). “Some comparisons for damage detection on structures using genetic algorithms and modal sensitivity method”, Applied Mathematical Modelling, Vol. 32, No. 11, pp. 2216–2232.
3.
GuoH.Y.LiZ.L. (2009). “A two-stage method to identify structural damage sites and extent by using evidence theory and micro-search genetic algorithm”, Mechanical Systems and Signal Processing, Vol. 23, No. 3, pp. 769–782.
4.
HeR.S.HwangS.F. (2007). “Damage detection by a hybrid real-parameter genetic algorithm under the assistance of grey relation analysis”, Engineering Application of Artificial IntelligenceVol. 20, No. 7, pp. 980–992.
5.
KohB.H.DykeS.J. (2007). “Structural health monitoring for flexible bridge structures using correlation sensitivity modal data”, Computers & Structures, Vol. 85, No. 3–4, pp. 117–130.
6.
MessinaA.WilliamsE.J.ContursiT. (1998). “Structural damage detection by a sensitivity and statistical-based method”, Journal of Sound and Vibration, Vol. 216, No. 5, pp. 791–808.
7.
PapadrakakisM.LagarosN.D. (2003). “Soft computing methodologies for structural optimization”, Applied Soft Computing, Vol. 3, No. 3, pp. 283–300.
8.
SeyedpoorS.M.SalajeghehJ.SalajeghehE.GolizadehS. (2009). “Optimum shape design of arch dams for earthquake loading using a fuzzy inference system and wavelet neural networks”, Engineering Optimization, Vol. 41, No. 5, pp. 473–493.
9.
SeyedpoorS.M.SalajeghehJ. (2009). “Adaptive neuro-fuzzy inference system for high-speed computing in shape optimal design of arch dams subjected to earthquake loading”, Mechanics Based Design of Structures and Machines, Vol. 37, No. 1, pp. 31–59.
10.
SeyedpoorS.M.SalajeghehJ.SalajeghehE.GholizadehS. (2011). “Optimal design of arch dams subjected to earthquake loading by a combination of simultaneous perturbation stochastic approximation and particle swarm algorithms”, Applied Soft Computing, Vol. 11, No. 1, pp. 39–48.
11.
Vakil-BaghmishehM.T.PeimaniM.SadeghiM. HomayounEttefaghM.M. (2008). “Crack detection in beam-like structures using genetic algorithms”, Applied Soft Computing, Vol. 8, No. 2, pp. 1150–1160.
12.
WangX.HuN.FukunagaH.YaoZ.H. (2001). “Structural damage identification using static test data and changes in frequencies”, Engineering Structures, Vol. 23, No. 6, pp. 610–621.
