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

arXiv: https://arxiv.org/abs/2212.13258 · HTML: https://mlsanity.com/paper/2212.13258/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 74 references that are in the map.
- First posted on arXiv 24 Dec 2022.

## Builds on (most cited first, 2 of 2)

- [Wavelet based multivariate signal denoising using Mahalanobis distance and EDF statistics](https://mlsanity.com/paper/2005.11616/) (2020)
- [An Adaptive Structural Learning of Deep Belief Network for Image-based Crack Detection in Concrete Structures Using SDNET2018](https://mlsanity.com/paper/2110.12700/) (2021)

## Abstract (arXiv)

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.