Abstract
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.
Highlights
Following the growing expansion of civil engineering infrastructures and the aging of some structures, the Structural Health Monitoring (SHM) is currently given a great and particular importance [1,2,3]
According to the European Investment Bank (EIB), public investment in infrastructure is at its lowest level in 20 years (EIB Investment Report 2017/2018)
Structural health monitoring can be divided into two approaches: (i) The Local SHM based on a direct evaluation of an element or a part of a structure to evaluate its state [4] and (ii) the Global SHM based on a mechanical modeling of the structure where few sensors are used to monitor the whole structure [5]
Summary
Following the growing expansion of civil engineering infrastructures and the aging of some structures, the Structural Health Monitoring (SHM) is currently given a great and particular importance [1,2,3]. A cost-effective plan is needed in order to monitor structures and follow their state of degradation through the years. Structural health monitoring can be divided into two approaches: (i) The Local SHM based on a direct evaluation of an element or a part of a structure to evaluate its state [4] and (ii) the Global SHM based on a mechanical modeling of the structure where few sensors (whose number and locations are to be optimized) are used to monitor the whole structure [5]. Papadimitriou (2004) used the information entropy to measure the performance of the sensors [6], Sun et Büyüköztürk (2015) proposed a discrete version of the artificial bee colony algorithm [7]
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