Shape Sensing in Plate Structures through Inverse Finite Element Method Enhanced by Multi-Objective Genetic Optimization of Sensor Placement and Strain Pre-Extrapolation

Abstract

The real-time reconstruction of the displacement field of a structure from a network of in situ strain sensors is commonly referred to as “shape sensing”. The inverse finite element method (iFEM) stands out as a highly effective and promising approach to perform this task. In the current investigation, this technique is employed to monitor different plate structures experiencing flexural and torsional deformation fields. In order to reduce the number of installed sensors and obtain more accurate results, the iFEM is applied in synergy with smoothing element analysis (SEA), which allows the pre-extrapolation of the strain field over the entire structure from a limited number of measurement points. For the SEA extrapolation to be effective for a multitude of load cases, it is necessary to position the strain sensors appropriately. In this study, an innovative sensor placement strategy that relies on a multi-objective genetic algorithm (NSGA-II) is proposed. This approach aims to minimize the root mean square error of the pre-extrapolated strain field across a set of mode shapes for the examined plate structures. The optimized strain reconstruction is subsequently utilized as input for the iFEM technique. Comparisons are drawn between the displacement field reconstructions obtained using the proposed methodology and the conventional iFEM. In order to validate such methodology, two different numerical case studies, one involving a rectangular cantilevered plate and the other encompassing a square plate clamped at the edges, are investigated. For the considered case studies, the results obtained by the proposed approach reveal a significant improvement in the monitoring capabilities over the basic iFEM algorithm with the same number of sensors.

Optimizing the Three-Dimensional Multi-Objective of Feeder Bus Routes Considering the Timetable

“…Additionally, under dynamic and time-varying conditions, Zou et al [37] crafted a multi-objective optimization model using NSGA-II to refine hazardous chemical transport routes, balancing risk, cost, and carbon emissions. Furthermore, to achieve the rational placement of strain sensors in a structural-health-monitoring system, Del Priore et al [38] innovated a sensor placement strategy for structural-health-monitoring systems with NSGA-II. Finally, to maximize urban GDP and minimize total water resources, Qu et al [39] has applied the NSGA-II algorithm to solve a non-linear multi-objective model, addressing the inherent information loss problems of the traditional water quota method.…”