Updating of structural multi-scale monitoring model based on multi-objective optimisation. Issue 5 (April 2019)

• Record Type: Journal Article
• Authors: Cui, Yan; Lu, Wei; Teng, Jun
• Is Part Of: Advances in structural engineering. Volume 22:Issue 5(2019)
• Page Start: 1073
• Page End: 1088
• Publication Date: 2019-04
• Subjects: F test -- global and local updating -- Kriging approximate surrogate model -- multi-objective optimisation -- multi-scale monitoring model; Structural engineering -- Periodicals; Construction, Technique de la; Structural engineering; Periodicals; 624.1
• DOI: 10.1177/1369433218805235
• Languages: English
• ISSNs: 1369-4332

Abstract

Structural safety assessments are implemented based on measured data, but the limited number of sensors restricts the comprehensive acquisition of response information in large complex structures. A concurrent multi-scale model utilises global and local simulation characteristics to expand the insufficient measured data. Thus, good global and local simulation capability is necessary for structural health monitoring-oriented multi-scale model, and the updating of this monitoring model needs to consider the multi-type responses that are obtained from different structural scales. However, the existing methods usually integrate multi-type responses into a single-objective function, which cannot ensure the acquisition of the optimal parameters. Moreover, in common parameter screening method, the perturbation and threshold are set artificially, which causes a strong subjectivity, and the common polynomial response surface fits poorly for highly non-linear problem. Therefore, an updating method of the structural multi-scale monitoring model based on multi-objective optimisation is proposed. For the proposed method, a variance analysis based on the orthogonal experimental design is used to screen the unique significant influence parameters. The Kriging spatial interpolation technique is used to establish the approximate surrogate model between the response and its corresponding influence parameters. Simultaneously, the responses obtained from the global and local structural scales are used to define the sub-objectives of the multi-objective function vector in order to avoid the introduction of weight coefficients. Then, the multi-objective optimisation algorithm NSGA-II is used to obtain the optimal parameter values and realise the comprehensive updating of the initial multi-scale monitoring model. Finally, based on the health monitoring system of the large shell structure of the Zhuhai Opera House, the initial multi-scale monitoring model is updated using the proposed method. The structural dynamic characteristics and local stress obtained from the initial model, updated model and the real structure are compared to validate the effectiveness of the proposed method.