Abstract
The present work reports an efficient way of capturing real-time crack propagation in concrete structures. The modified spectral analysis based algorithm and finite element modeling (FEM) were utilised for crack detection and quantitative analysis of crack propagation. Crack propagation was captured in cement-based composite (CBC) containing saw dust and M20 grade concrete under compressive loading using a simple and inexpensive 8-megapixel mobile phone camera. The randomly selected images showing crack initiation and propagation in CBCs demonstrated the crack capturing capability of developed algorithm. A measure of oriented energy was provided at crack edges to develop a similarity spatial relationship among the pairwise pixels. FE modelling was used for distress anticipation, by analysing stresses during the compressive test in constituents of CBCs. FE modeling jointly with the developed algorithm, can provide real-time inputs from the crack-prone areas and useful in early crack detection of concrete structures for preventive support and management.
Highlights
Cracks and distress in concrete structures are generally due to restrained shrinkage, improper load balancing or material degradation
The present work proposes an initial investigation for a methodology that will provide inputs for unmanned structure maintenance using a modified spectral clustering algorithm, finite element modeling (FEM), and SEM investigation
The developed image processing algorithm can effectively extract the crack features from the concrete crack images taken with an ordinary mobile phone camera
Summary
Cracks and distress in concrete structures are generally due to restrained shrinkage, improper load balancing or material degradation. Their early detection and repair is a priority for proper maintenance as their development might lead to fatal damage and structural collapse. The images captured using a digital camera for concrete crack detection is frequently reported[5,6,7]. The crack detection using the structural feature (morphological and multidirectional shape of the crack) based algorithm on camera images has been developed and tested in a few s tudies[8,9]. A cost-effective, reliable commercial application of the image processing technologies for crack detection in the structures still requires significant research efforts. Kaur et al.[24] reported a comparative study for extracting curves, edges, and other features of the crack and pointed out that no single method is sufficient for every image type
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