Document Type

Structural health monitoring and damage identification paper

Publication details

Makki Alamdari, M, Li, J, Samali, B 2014, 'Application of symbolic time series analysis for damage localisation in truss structures', in ST Smith (ed.), 23rd Australasian Conference on the Mechanics of Structures and Materials (ACMSM23), vol. II, Byron Bay, NSW, 9-12 December, Southern Cross University, Lismore, NSW, pp. 1179-1184. ISBN: 9780994152008.

Peer Reviewed



Reliability of truss bridges can be significantly affected by local damages as damage changes the load path in the structure. As damage increases, the load-carrying capacity of the structure considerably reduces which might result in catastrophic failure. Hence, it is important to detect structural damages as early stage as possible to avoid further propagation. In the present work, a time series-based method is proposed to detect and localise damage in truss structures. The method works based on Symbolic Time Series Analysis (STSA) of time responses to localise a gradually evolving deterioration in the structure according to the changes in the statistical behaviour of symbol sequences. First, the symbol sequences are generated by transforming the measured time data to symbol space to reduce the dimension of information and then the probability vectors for each symbol sequence is created. Damage localisation is carried out by comparing the probability vectors of different measured locations. It is expected that the damaged member shows a higher degree of variation in the probability vector which is introduced as damage sensitive feature. Numerical demonstrations on a plane truss are presented to illustrate the accuracy and efficiency of the proposed method. Gradually evolving damage is introduced by the stiffness reduction in truss members. Finite element technique is employed to obtain the time response of the structure subjected to ambient vibration. The simulated responses are polluted with random noise to take into account the influence of practical uncertainties. Simulation results under various damage conditions demonstrate the effectiveness of the proposed algorithm in detection and localisation of gradually evolving damage in single or multiple states in presence of measurement noise up to 5%.