Intelligent Feature Extraction, Data Fusion and Detection of Concrete Bridge Cracks: Current Development and Challenges

TL;DR

This paper reviews current intelligent methods for feature extraction, data fusion, and detection of concrete bridge cracks, emphasizing challenges and future directions in understanding crack evolution and damage assessment.

Contribution

It provides a comprehensive overview of data-driven approaches for bridge crack analysis, highlighting limitations and proposing future research directions.

Findings

Review of state-of-the-art intelligent crack detection methods

Identification of key challenges in multimodal data fusion

Discussion of future research directions in crack evolution analysis

Abstract

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.