@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}
}
