  As a common appearance defect of concrete bridges, cracks are important
indices for bridge structure health assessment. Although there has been much
research on crack identification, research on the evolution mechanism of bridge
cracks is still far from practical applications. In this paper, the
state-of-the-art research on intelligent theories and methodologies for
intelligent feature extraction, data fusion and crack detection based on
data-driven approaches is comprehensively reviewed. The research is discussed
from three aspects: the feature extraction level of the multimodal parameters
of bridge cracks, the description level and the diagnosis level of the bridge
crack damage states. We focus on previous research concerning the quantitative
characterization problems of multimodal parameters of bridge cracks and their
implementation in crack identification, while highlighting some of their major
drawbacks. In addition, the current challenges and potential future research
directions are discussed.
