Use of the cointegration strategies to remove environmental effects from data acquired on historical buildings. (15th March 2019)

• Record Type: Journal Article
• Authors: Coletta, Giorgia; Miraglia, Gaetano; Pecorelli, Marica; Ceravolo, Rosario; Cross, Elizabeth; Surace, Cecilia; Worden, Keith
• Is Part Of: Engineering structures. Volume 183(2019)
• Page Start: 1014
• Page End: 1026
• Publication Date: 2019-03-15
• Subjects: Structural health monitoring -- Nonlinear cointegration -- Support vector machine -- Relevant vector machine -- Dynamic monitoring system -- Sanctuary of Vicoforte -- Novelty detection; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2018.12.044
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: Nonlinear cointegration is proposed for removing EOVs from diagnostic features. The cointegration approach refers to two class of machine learners: SVM and RVM. The performances of two statistical learning machines, SVM and RVM, are compared. Data from the Sanctuary of Vicoforte, the world's largest oval dome, are analyzed. Abstract: The theory of cointegration, usually employed in econometric studies, has proved very powerful in the context of Structural Health Monitoring (SHM), where it can be used to distinguish operational and environmental changes of dynamic features from those related to the evolution of damage. The different nature of the effects imposed by operational and environmental variations on structural response required here an extension of the theory of cointegration from the linear to the nonlinear field. For this purpose, a nonlinear multivariate regression has been developed. This paper proposes a regression obtained through a particular class of machine learners, based on statistical learning theory and its Bayesian variants The algorithms considered, Support Vector Machines (SVMs) and Relevance Vector Machines (RVMs), are applied to data from the Sanctuary of Vicoforte, which was dynamically monitored over a period of four months and modelled with finite elements to simulate structural damage. The SVMs and the RVMs have the advantage of working well with sparse data sets. The algorithms also provide information about the most informative data points (support and relevance vectors) which could prove valuable in an active or query learning context.