@conference {2006,
	title = {Automatic Detection of Surface Damages on Steel Structures using Near Infrared Hyperspectral Imaging},
	year = {2018},
	abstract = {Surface damages such as corrosion and coating delamination are very common on steel structures and, if not prevented, can accelerate degradation and eventually lead to failure of the whole structure. Hence, field monitoring is of utmost importance to guarantee the structural health and reduce the maintenance cost. Visual inspection is very perilous (especially for offshore structures), time-consuming, and largely depends on the experience of the inspector (Price \& Figueira 2017). Current non-destructive testing (NDT) techniques (e.g. based on ultrasonic or eddy current technology, acoustic emission ([Calabrese et al 2016; Wu et al 2016] or Electrical Resistance [ Li et al 2007; Mathiesen et al 2016]), require a direct contact with the surface, which causes difficulties when the structure or parts of it are not accessible.
Image processing techniques have been considered as a fast and easier alternative to contact-based techniques. These techniques are usually based on measuring the textural and morphological changes in digital images from the structures (Feliciano et al 2015; Li \& Cheng 2016). However, in reality other materials on steel structures such as dirt, paint, bird droppings, oil leakage, and biofouling can significantly complicate the inspection. Moreover, some effects of corrosion not necessarily reveal themselves in the visible wavelength region. Hyperspectral imaging outside the visible range is an advanced imaging technique that can reveal chemical-mineralogical surface changes that are not observable by the naked eye.
This paper studied the feasibility of detecting coating damages on steel components using hyperspectral imaging. Two different samples of industrial components, provided by ENGIE Laborelec, were scanned with a hyperspectral camera: a part of a flame tube of a heavy-duty gas turbine as well as an artificially corroded sample of coated steel. The images were collected with a snapshot hyperspectral camera in the visible and Near Infrared range (manufactured by IMEC) and analyzed using a k-means clustering method. The outputs showed that despite the visual similarities between coating layers, corroded, and base material, hyperspectral imaging was capable to distinguish between them. Spectral plots showed that differences in spectral behavior of corroded and coated materials were more apparent when the wavelength shifts from the visible towards the near-infrared range. While further investigation is needed, the current results demonstrated that hyperspectral image analysis is a promising technology to complement or even replace digital image analysis within the visible range for characterizing steel surfaces in situ.},
	author = {Zohreh Zahiri and Bart Ribbens and Steve Vanlanduit and Paul Scheunders}
}
@inproceedings {2005,
	title = {Classification of hardened cement and lime mortar using short-wave infrared spectrometry data},
	booktitle = {11th international conference on Structural Analysis of Historical Constructions},
	year = {2018},
	abstract = {This paper evaluated the feasibility of using spectrometry data in the short-wave infrared range (1,300-2,200 nm) two distinguish lime mortar and Type S cement mortar. 42 samples of 40x40x40mm were created in the lab (21 lime-based, 21 cement-based). A Partial Least Square Discriminant Analysis model was developed using the mean spectra of 28 specimens as the calibration set. The results were tested on the mean spectra of the remaining 14 specimens as a validation set. The results showed that spectrometry data were able to fully distinguish modern mortars (made with cement) from historic lime mortars with a 100\% classification accuracy, which can be very useful in archaeological and architectural conservation applications. Specifically, being able to distinguish mortar composition in situ can provide critical information about the construction history of a structure as well as to inform an appropriate intervention scheme when historic material needs to be repaired or replaced.},
	author = {Zohreh Zahiri and Debra Laefer and Aoife Gowen}
}
@article {2004,
	title = {The feasibility of short-wave infrared spectrometry in assessing water-to-cement ratio and density of hardened concrete},
	journal = {Construction and Building Materials},
	year = {2018},
	abstract = {This paper describes the feasibility of using short-wave infrared (SWIR) spectrometry to classify concretes by their water-to-cement (w/c) ratios and predict their density. Concrete spectra of three w/c ratios (50\%, 65\%, 80\%) were studied in the 1300{\textendash}2200 nm range. A Partial Least Square Discriminant Analysis model was developed from the spectra of 36 samples, resulting in an 89\% correct classification for the 18 validation samples, thereby demonstrating that SWIR spectrometry can detect differences in initial w/c ratios for hardened concretes. Additionally, differences in density and compressive strength as a function of the w/c ratio could be indirectly estimated through SWIR spectrometry.},
	url = {https://www.sciencedirect.com/science/article/pii/S0950061818317598},
	author = {Zohreh Zahiri and Debra Laefer and Aoife Gowen}
}
@article {2003,
	title = {Using Short-wave Infrared Range Spectrometry Data to Determine Brick Characteristics},
	journal = {INTERNATIONAL JOURNAL OF ARCHITECTURAL HERITAGE},
	year = {2018},
	abstract = {Characterizing material strength in-situ for existing structures poses a major problem for a range of civil engineering applications including structural modelling for tunnelling-vulnerability assessment and pre-earthquake resiliency evaluation, especially for unreinforced masonry buildings. Present methods require expensive testing equipment often requiring access to the structure and possible destruction of historic material. This article introduces spectrometry as a non-destructive means for identifying different brick clays and their firing levels, both of which influence the masonry{\textquoteright}s mechanical behavior. The experiments herein considered bricks of 2 clay groups (red and yellow) fired at 3 kiln temperatures (700{\textordmasculine}C, 950{\textordmasculine}C, 1,060{\textordmasculine}C). Samples were examined via spectrometry within the short-wave infrared range (1,300{\textendash}2,200 nm). A Partial Least Square Discriminant Analysis (PLS-DA) model was calibrated using 96 samples and tested on a set of 48 samples, resulting in a 98\% success rate in the classification of the two clay types and a 100\% success rate for classification among the 3 firing levels. The ability of the PLS-DA model to reliably distinguish well-fired bricks from other samples, irrespective of raw material configuration, shows the potential to use this approach as a new, non-destructive means for in-situ assessment of brick for architectural conservation, as well as for safety and serviceability assessments.},
	doi = {10.1080/15583058.2018.1503362},
	url = {https://doi.org/10.1080/15583058.2018.1503362},
	author = {Debra Laefer and Zohreh Zahiri and Aoife Gowen}
}
@conference {2002,
	title = {Distinguishing Fa{\c c}ade Material Change using Hyperspectral Imaging},
	year = {2017},
	abstract = {To conduct city-scale computational modelling for infrastructure planning, micro-climate analysis, and disaster
mitigation, not only must the geometry of the built environment be detectable automatically but the component
materials must be as well. While extensive work has been undertaken for geometric recognition and feature
detection on buildings (e.g. VO et al., 2015), relatively little has been done for material identification (ZHU \&
WOODCOCK, 2014). Furthermore, most of that work has been for the classification of urban land cover, with
extremely limited analysis applied to building material identification.
Today, remote sensing data in the form of hyperspectral imagery are widely used for identification of materials
in agriculture, environment, geology, astronomy, and more. Despite the widespread application of hyperspectral
imaging in many areas, this method has seldom been used in building material detection. As aerial hyperspectral
imagers do not get adequate information from building fa{\c c}ades, close-range hyperspectral imaging, which is
a newer technique applied from the ground and recently used to study geological outcrops (KURZ et al.,
2013), can be applied to evaluate building fa{\c c}ade material. This paper will investigate how different building materials can be differentiated using close-range remote sensing technology in the form of hyperspectral (near-infrared) data. For this purpose, the fa{\c c}ade of a building containing multiple materials in Bergen, Norway, was scanned by a close-range, hyperspectral instrument. After masking non-building material, several pre-processing techniques were applied on the hyperspectral images including atmospheric and brightness correction, morphology effect removal, and bad pixel correction. Different materials on the building were then classified using supervised classification techniques (Linear Spectral Unmixing and Spectral Angle Mapper). The results showed the ability of hyperspectral data in the range of near-infrared to differentiate distinctive building materials.},
	url = {https://www.vrvis.at/publications}
}
