A predator-prey optimization for structural health monitoring problems

• Published in: MATEC Web of Conferences Vol. 281; p. 1004
• Main Authors: Geara, Christelle, Faddoul, Rafic, Chateauneuf, Alaa, Raphaël, Wassim
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Les Ulis EDP Sciences 2019
• Subjects: Damage detection; Frame structures; Genetic algorithms; Methodology; Model accuracy; Optimization; Populations; Predator-prey simulation; Sensors; Structural health monitoring; Structural members
• ISSN: 2261-236X; 2274-7214; 2261-236X
• DOI: 10.1051/matecconf/201928101004

Monitoring a structure using permanent sensors has been one of the most interesting topics, especially with the increase of the number of aging structures. Such a technique requires the implementation of sensors on a structure to predict the condition states of the structural elements. However, due to the costs of sensors, one must judiciously install few sensors at some defined locations in order to maximize the probability of detecting potential damages. In this paper, we propose a methodology based on a genetic algorithm of type predator-prey with a Bayesian updating of the structural parameters, to optimize the number and location of the sensors to be placed. This methodology takes into consideration all uncertainties related to the degradation of the elements, the mechanical model and the accuracy of sensors. Starting with two initial populations representing the damages (prey) and the sensors (predator), the genetic algorithm evolves both populations in order to converge towards the optimal configuration of sensors, in terms of number and location. The proposed methodology is illustrated by a two-story concrete frame structure.