We present a statistical time series based algorithm to characterize a progressively deteriorating crack in a steel reinforced concrete arch bridge section. The method relies only on the measured time responses. The captured time responses are transformed from the state space into “symbol space”, ultimately to reduce the dimension of the system. Symbol space is constructed according to the measured time responses in the nominal state of the structure; it consists of a finite number of partitions which are mutually exclusive and exhaustive and each partition is assigned with a distinct symbol. By mapping time data from state space into the constructed symbol space, each time series is described by a sequence of symbols according to the placement of each data point of time series in the symbol space. Symbol sequence is statically characterized by probability of occurrence of each symbol in that sequence; therefore, a vector containing the probability of occurrence of all the symbols is formed which defines damage sensitive feature vector. Any change in the structural conditions leads to a deviation in the damage sensitive vector from its nominal state. The feasibility of the method is demonstrated through the detection and localization of a gradually evolving deterioration. A test bed was constructed to replicate a concrete jack arch which is a main structural component on the Sydney Harbor Bridge– one of Australia’s iconic structures. The structure is a concrete cantilever beam with an arch section which is located on the eastern side of the bridge underneath the bus lane. It is assumed that the structure is subjected to Gaussian white noise excitation. A crack is introduced in the structure using a cutting saw and its length is progressively increased in four stages while the depth was constant; these four damage cases correspond to less than 0.5% reduction in the first three modes of the structure. The presented method not only can detect the change in the condition of the structure but also can localize the location of deterioration. The damage identification algorithm developed demonstrated the feasibility of applying symbolic time series analysis to dimensionality reduction and damage characterization in structural health monitoring.

doi: 10.12783/SHM2015/284