A multiobjective sensor placement optimization for SHM systems considering Fisher information matrix and mode shape interpolation

Gomes, Guilherme Ferreira; Almeida, Fabrício Alves de; Alexandrino, Patrícia da Silva Lopes; Cunha, Sebastião Simões da; Sousa, Bruno Silva de; Ancelotti, Antônio Carlos

doi:10.1007/s00366-018-0613-7

Cited by 72 publications

(42 citation statements)

References 49 publications

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“…The set of feasible solutions X is obtained by applying equality, inequality and bound constraint on Rn depending upon the nature of the optimization problem. The vector function f (x) consists of j objective functions and set Rn is termed as variable decision space (Gomes et al, 2018; Jaimes et al, 2009).…”

Section: Fundamentalsmentioning

confidence: 99%

Multi-objective sensor placement optimization for structural response estimation under spatially varying dynamic loading of bridges

Saleem

2021

Advances in Structural Engineering

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Although a lot of different types of sensors are available in the market only a limited number of sensors can be installed on a structure. Proper placement of these sensors plays a vital role in effectively achieving the objectives of a monitoring system. Sensor placement becomes especially critical in the case of bridges where the applied loading keeps on changing its location. A sensor layout that provides good quality structural response estimates for a given applied loading may not yield acceptable results for a different loading arrangement. Further, usually different types of sensors are installed on a structure e.g. strain gauges and accelerometers. These sensors measure different physical quantities having different units and orders of magnitude thus cannot be easily incorporated in a sensor placement optimization (SPO) process. So, this research work proposes a multi-objective sensor placement optimization approach that can effectively deal with different types of sensor measurements and spatially varying loading such as in the case of bridges. The proposed method employs an augmented Kalman filter (AKF) for structural response estimation and a multi-objective genetic algorithm for SPO. The AKF can effectively estimate structural response using a few heterogeneous noisy measurements while incorporating the modeling error. The effectiveness of the proposed method is demonstrated using a numerical example of a 3D truss bridge structure. The results show that the proposed multi-objective optimization method yields a sensor arrangement that remains effective against spatially varying dynamic loading.

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Section: Fundamentalsmentioning

confidence: 99%

Multi-objective sensor placement optimization for structural response estimation under spatially varying dynamic loading of bridges

Saleem

2021

Advances in Structural Engineering

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“…In these approaches, sensor locations have been ranked based on their ability to assess model-parameter values. Other researchers have used modal properties to estimate sensor utility for structural identification based on Fisher Information Matrix [34], modal assurance criterion [35], frequency-response functions [36] and a combination of above-mentioned criteria [37]. Nevertheless, these metrics can only be used for dynamic load testing, while entropy-based metrics, such as the approach used in this paper, are also applicable to static load tests.…”

Section: Introductionmentioning

confidence: 99%

A methodology to design measurement systems when multiple model classes are plausible

Bertola

Pai

Smith

2021

J Civil Struct Health Monit

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The management of existing civil infrastructure is challenging due to evolving functional requirements, aging and climate change. Civil infrastructure often has hidden reserve capacity because of conservative approaches used in design and during construction. Information collected through sensor measurements has the potential to improve knowledge of structural behavior, leading to better decisions related to asset management. In this situation, the design of the monitoring system is an important task since it directly affects the quality of the information that is collected. Design of optimal measurement systems depends on the choice of behavior-model parameters to identify using monitoring data and non-parametric uncertainty sources. A model that contains a representation of these parameters as variables is called a model class. Selection of the most appropriate model class is often difficult prior to acquisition of information regarding the structural behavior, and this leads to suboptimal sensor placement. This study presents strategies to efficiently design measurement systems when multiple model classes are plausible. This methodology supports the selection of a sensor configuration that provides significant information gain for each model class using a minimum number of sensors. A full-scale bridge, The Powder Mill Bridge (USA), and an illustrative beam example are used to compare methodologies. A modification of the hierarchical algorithm for sensor placement has led to design of configurations that have fewer sensors than previously proposed strategies without compromising information gain.

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“…Dai and Ji (2015) put forward the effective independence-average modal kinetic/strained energy coefficient methods and introduced a weight coefficient reflecting the contribution proportion of a higher-order model. Gomes et al (2019) adopted multi-objective genetic algorithm to search the position of sensors, and proposed a multi-objective optimization function combining Fisher information matrix (FIM) and mode interpolation information. Lin et al (2019) developed a multi-objective OSP method based on response covariance.…”

Section: Introductionmentioning

confidence: 99%

A distance coefficient-multi objective information fusion algorithm for optimal sensor placement in structural health monitoring

Cao

Chen

et al. 2020

Advances in Structural Engineering

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Optimal sensor placement (OSP) plays a key role in the construction and implementation of an effective structural health monitoring system (SHM). In this study, a novel and effective method named the distance coefficient-multi objective information fusion algorithm (D-MOIF), which is different from the conventional method and easier to be implemented, is developed to select the best sensor location for large-scale structures. An integrated information matrix including mode independence, damage sensitivity and modal strain energy is deduced from the structural motion equation to meet multiple needs of SHM. A European distance derived from the analytic geometry is proposed to overcome the information redundancy between sensors. Based on the principle of information entropy, an optimized objective function is constructed, which could balance the sensitivity and robustness of the algorithm. A computational case of a high arch dam is implemented to demonstrate the effectiveness of the modified algorithm, and three classical evaluation criteria are used to estimate the comparison between the D-MOIF algorithm and four traditional OSP methods. Finally, the optimization of the number of sensors based on different algorithms is discussed in detail. Results indicate that the proposed D-MOIF algorithm could generate more applicable sensor configurations for large-scale structures.

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A multiobjective sensor placement optimization for SHM systems considering Fisher information matrix and mode shape interpolation

Cited by 72 publications

References 49 publications

Multi-objective sensor placement optimization for structural response estimation under spatially varying dynamic loading of bridges

Multi-objective sensor placement optimization for structural response estimation under spatially varying dynamic loading of bridges

A methodology to design measurement systems when multiple model classes are plausible

A distance coefficient-multi objective information fusion algorithm for optimal sensor placement in structural health monitoring

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