Automatic mapping of cracking patterns on concrete surfaces with biological stains using hyper-spectral images processing

Abstract

Summary Despite all technological advances, mapping cracks on concrete structures mostly remains to be evaluated through sketches based on on‐site observation and photographs. Methods based on image processing have been developed with clear advantages. However, most studies rely on perfectly identified areas or on single cracks without any other pathologies, being therefore unsuitable for on‐site application. In addition, the accuracy is not usually quantified due to the absence of ground‐truth. Thus, methods for automatic mapping of cracking patterns, sufficiently robust to deal with the surrounding pathologies, are of great interest. The Super Cluster‐Crack method (SC‐Crack method) is herein presented. It was developed for crack detection in concrete surfaces, with biological stains, by processing hyperspectral images. SC‐Crack performs k‐means clustering, followed by grouping clusters to composing a super cluster that stands for the cracks. The method was calibrated and validated by classifying hyperspectral images of concrete specimens, within bandwidths of 25 nm in a wavelength range between 425 nm and 950 nm. Results are discussed by comparison with the ground‐truth image. Finally, the super cluster composition is also validated. The SC‐Crack method performs successfully both on clean and on surface with biological stains. In the latter case, hyperspectral images help to avoid mixing biological stains with crack pattern. Concerning the main goal of mapping the cracking pattern, the method performs perfectly on concrete clean surfaces, allowing to detect all the crack branches. In the case of surface with biological stains, the SC‐Crack also detects the majority of cracking pattern, except for the thinner branches.

A Review of Emerging Technologies for an Assessment of Safety and Seismic Vulnerability and Damage Detection of Existing Masonry Structures

“…The importance close-range remote sensing and using multispectral cameras for damage assessment and crack detection on buildings was emphasized by numerous researchers [45][46][47]. In the last few years, a growing number of studies have shown the great potential of hyperspectral cameras for detection and mapping cracks on buildings [48,49]. Unmanned aerial vehicles (UAV) can be used for i.e., disaster management [50,51], crack identification using image processing [47,52], seismic vulnerability [53], architectural assessment of heritage sites and structures [54], and structural assessment using image processing techniques [55].…”

Intelligent Detection Method for Internal Cracks in Aircraft Landing Gear Images under Multimedia Processing

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Automatic bridge crack identification from concrete surface using ResNeXt with postprocessing

“…Kim and Cho studied the automated vision‐based detection of cracks on concrete surfaces using a deep learning technique, 19 and Dunga explored the autonomous concrete crack detection using deep fully CNN, but pre‐training on ImageNet, migrating to the crack data set for secondary training is detrimental 20 . Santos et al carried out research on automatic mapping of cracking patterns on concrete surfaces with biological stains using hyperspectral images processing 21 . In recent years, with the increasing number of hyperparameters, especially when multiple layers are present, design architectures have become increasingly difficult.…”