Structural Health Monitoring Sensor Placement Optimization Under Uncertainty

Guratzsch, Robert F.; Mahadevan, Sankaran

doi:10.2514/1.28435

Cited by 59 publications

(28 citation statements)

References 33 publications

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“…A large number of formulations of the objective function have been developed in prior literature. These can be grouped as: 1) Fisher information matrix (FIM) [12][13][14] for minimizing the covariance of the parameter estimation error; 2) modal assurance criterion (MAC) 15 for minimizing the maximum off-diagonal value (or the highest degree of linearity between different modal vectors) in the MAC matrix; 3) information entropy 16 for minimizing the uncertainty in model parameter estimates; 4) probability of detection 17 for maximizing probability of damage detection or minimizing false alarm rate; and 5) mean squared error in estimating structural parameter of interest (e.g., mode shape 14 ). An objective function chosen to validate sensor placement will vary greatly with respect to the application.…”

Section: The Algorithm Assumed a Shape Function And Classicalmentioning

confidence: 99%

Optimal sensor placement within a hybrid dense sensor network using an adaptive genetic algorithm with learning gene pool

Downey

Laflamme

2017

Structural Health Monitoring

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This work develops optimal sensor placement within a hybrid dense sensor network used in the construction of accurate strain maps for large-scale structural components. Realization of accurate strain maps is imperative for improved strain-based fault diagnosis and prognosis health management in large-scale structures. Here, an objective function specifically formulated to reduce type I and II errors and an adaptive mutation-based genetic algorithm for the placement of sensors within the hybrid dense sensor network are introduced. The objective function is based on the linear combination method and validates sensor placement while increasing information entropy. Optimal sensor placement is achieved through a genetic algorithm that leverages the concept that not all potential sensor locations contain the same level of information. The level of information in a potential sensor location is taught to subsequent generations through updating the algorithm's gene pool. The objective function and genetic algorithm are experimentally validated for a cantilever plate under three loading cases. Results demonstrate the capability of the learning gene pool to effectively and repeatedly find a Pareto-optimal solution faster than its non-adaptive gene pool counterpart.

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Section: The Algorithm Assumed a Shape Function And Classicalmentioning

confidence: 99%

Optimal sensor placement within a hybrid dense sensor network using an adaptive genetic algorithm with learning gene pool

Downey

Laflamme

2017

Structural Health Monitoring

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“…Gokce et al (2012) presented a movable bridge case study discussing the structural identification of performance prediction taking account of uncertainties. Guratzsch and Mahadevan (2010) discussed the sensor placement optimization problem under conditions of uncertainty in SHM.…”

Section: Research Focus Of Bhm Benchmark Studymentioning

confidence: 99%

Review of Benchmark Studies and Guidelines for Structural Health Monitoring

Zhou

Yan

Wang

et al. 2013

Advances in Structural Engineering

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In the past three decades, Structural Health Monitoring (SHM) has attracted considerable attention. The great achievements in theoretical study and the engineering applications of SHM are very encouraging and promising. Numerous approaches and techniques have been proposed and applied to different structures, making it difficult to compare and contrast the merits of the various methodologies. An SHM benchmark study provides a platform for comparing and evaluating the performance of SHM approaches and techniques on a consistent basis. This paper reviews the SHM benchmark studies. For each case, the physical structure, the numerical study, the scaled laboratorial model experiment as well as the comparisons made between results obtained from the variety of approaches are reviewed. The latest developments in guidelines on the application of SHM techniques is also be reviewed.

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“…Use of piezoelectric cement-based sensor and actuators for structural health monitoring and active vibration control of civil structures has been studied by (Brunner et al, 2009;Chen et al, 1998;Guratzsch and Mahadevan, 2010;Olmi et al, 2011;Sabra and Huston, 2011), and are considered favourable candidates for this application because homogeneous piezoelectric ceramics exhibit high dielectric constants and piezoelectric strain coefficients (Bhattacharya and Tummala, 2000;Kim et al, 2004;Ulrich, 2004). The inherent brittleness of homogeneous piezoelectric ceramics, high acoustic impedance and relatively high density, make them unsuitable for application to cement-based structures because of material incompatibility with the host structure, which is typically cement (Dong and Li et al, 2002Li et al, , 2005.…”

Section: Introductionmentioning

confidence: 99%

Influence of aluminium inclusions on dielectric properties of three-phase PZT–cement–aluminium composites

Banerjee

Cook-Chennault

2014

Advances in Cement Research

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Novel three-phase piezoelectric composites that comprised lead zirconate titanate (PZT), aluminium and Portland cement were fabricated at a low poling voltage of 0 . 6 kV/mm and temperature of 1608C. Aluminium and PZT particles were distributed in a Portland cement matrix, and the dielectric constant, tan ä and strain coefficients were experimentally investigated as a function of inclusion volume fraction. The three-phase piezoelectric composites were found to possess higher piezoelectric strain coefficients, d 33 , than their two-phase counterparts (PZT and Portland cement composites). The highest value of d 33 observed for the three-phase composite was 8 . 1 pC/N for volume fractions of 0 . 7 and 0 . 2 for PZT and aluminium respectively, which was 164% of the value observed for the two-phase composite, at the same PZT volume fraction. An analytical model was used to predict values for the effective dielectric constant of the three-phase composites, and these values compared reasonably well to the empirical data. An investigation of sample degradation as a function of time was performed. Samples showed the highest degree of reduced dielectric performance occurred within two days of data capture, and minimal subsequent reduction in performance after 300 d. Electrical properties of the composites are influenced by the oxidation of aluminium by the alkaline constituents in the cement matrix, distribution of PZT and aluminium within the matrix, particle agglomeration, inclusion size, contact resistance between particles and air voids. The increased dielectric and piezoelectric strain coefficients, demonstrate that these types of materials may be useful in applications such as structural health monitoring and energy harvesting.

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Structural Health Monitoring Sensor Placement Optimization Under Uncertainty

Cited by 59 publications

References 33 publications

Optimal sensor placement within a hybrid dense sensor network using an adaptive genetic algorithm with learning gene pool

Optimal sensor placement within a hybrid dense sensor network using an adaptive genetic algorithm with learning gene pool

Review of Benchmark Studies and Guidelines for Structural Health Monitoring

Influence of aluminium inclusions on dielectric properties of three-phase PZT–cement–aluminium composites

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