2015
DOI: 10.1016/j.conbuildmat.2015.06.062
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Multivariate analysis of multi-sensor data for assessment of timber structures: Principles and applications

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Cited by 27 publications
(14 citation statements)
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“…Finally, the adoption of multivariate analysis techniques is advisable, when dealing with a high number of data/signals collected by means of different sensors [30].…”
Section: Applicationsmentioning
confidence: 99%
“…Finally, the adoption of multivariate analysis techniques is advisable, when dealing with a high number of data/signals collected by means of different sensors [30].…”
Section: Applicationsmentioning
confidence: 99%
“…The data include objectively assessed characteristics, such as colour, gloss, chemical composition, and roughness. PLS can also incorporate new sets of specific characteristics and thus estimate the progress of weathering in previously unknown samples (Sandak et al 2015a;b, Sandak et al 2016). This method can predict the degradation level by placing a sample somewhere in between the brand new and completely degraded.…”
Section: Methods For Estimating Service Life Durationmentioning
confidence: 99%
“…However, multi-sensor monitoring creates new challenges since different types of sensors are based on different structural features. In this situation, Sandak et al [18] developed a multi-sensor method in timber structural evaluation. With the monitoring data in place, multivariate analysis techniques were considered to assess the performance of the timber structure.…”
Section: Introductionmentioning
confidence: 99%
“…With the monitoring data in place, multivariate analysis techniques were considered to assess the performance of the timber structure. Sandak et al [18] summarized these multivariate methods, including cluster analysis, identity tests, multiple linear regression, expert systems, neural networks, fuzzy logic, smart algorithms, and so on. Among these methods, an expert system was developed based on fuzzy logic [18,19], which can handle uncertain data and be easily adopted by both experts and non-experts.…”
Section: Introductionmentioning
confidence: 99%