Mode shape expansion techniques for prediction - Experimental evaluation

Levine-West, Marie B.; Milman, Mark H.; Kissil, Andy

doi:10.2514/3.13145

Cited by 31 publications

(7 citation statements)

References 5 publications

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“…They are similar in concept to expanded modeshapes. 13 Note that a particular model of the model class M is therefore completely parameterized by θ and φ.…”

Section: Structural Model Classmentioning

confidence: 99%

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Monitoring Structural Health Using a Probabilistic Measure

Beck

Vanik

2001

Computer aided Civil Eng

123

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A Bayesian probabilistic methodology for structural health monitoring is presented. The method uses a sequence of identified modal parameter data sets to continually compute the probability of damage. In this approach, a high likelihood of a reduction in model stiffness at a location is taken as a proxy for damage at the corresponding structural location. The concept extends the idea of using as indicators of damage the changes in model parameters identified using a linear finite-element model and modal parameter data sets from the structure in undamaged and possibly damaged states. This extension is needed because of uncertainties in the updated model parameters that in practice obscure health assessment. These uncertainties arise due to effects such as variation in the identified modal parameters in the absence of damage, as well as unavoidable model error. The method is illustrated by simulating on-line monitoring, wherein specified modal parameters are identified on a regular basis and the probability of damage for each substructure is continually updated. Examples are given for abrupt onset of damage and progressive deterioration.

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“…They are similar in concept to expanded modeshapes. 13 Note that a particular model of the model class M is therefore completely parameterized by θ and φ.…”

Section: Structural Model Classmentioning

confidence: 99%

“…form of the model parameter PDF. Using Equations (5), (10),(13), and(14) in Equation(4)leads to the final form of p(θ, φ|D): p(θ, φ|D) = c exp − 1 2 J (θ, φ)…”

mentioning

confidence: 99%

Monitoring Structural Health Using a Probabilistic Measure

Beck

Vanik

2001

Computer aided Civil Eng

123

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“…Therefore, performance metrics such as the Modal Clarity Index ( MCI ) and Modal Relative Error ( MRE ) that do not rely on the orthogonality of the modal vectors need to be employed. A few heterogeneous sensor placement indices based on these metrics have been proposed in the literature, including the weighted sum of MCI and M RE [27], and the ratio of MCI and MRE [25]. In this work, the ratio of MCI and MRE is employed as the placement quality index to quantify the quality of the sensor placement.…”

Section: Optimization Problem Formulation In Hwsnsmentioning

confidence: 99%

“…The larger the corresponding MRE , the clearer the calculated modal information would be (and vice versa) [25]. To consider both effects of MRE and MCI , the ratio of the two metrics is defined as the placement quality index to measure the modal identification accuracy [9]:

F_{M} = \frac{I_{M R E}}{I_{M C I}},

…”

Section: Optimization Problem Formulation In Hwsnsmentioning

confidence: 99%

“…By employing the ratio of Modal Clarity Index ( MCI ) [23] and Mode Shape Expansion ( MSE ) [24], Jalsan et al [25] took the information quality of the measured data collected from a HWSN into account to represent placement quality of strain and acceleration sensors. However, their work did not consider the allocation of data packets along the end-to-end path from a sensor to the base station, and therefore the optimization within HWSNs could be further improved.…”

Section: Introductionmentioning

confidence: 99%

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Energy-Efficient Heterogeneous Wireless Sensor Deployment with Multiple Objectives for Structural Health Monitoring

Liu

Jiang

Wang

et al. 2016

Sensors

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Heterogeneous wireless sensor networks (HWSNs) are widely adopted in structural health monitoring systems due to their potential for implementing sophisticated algorithms by integrating a diverse set of devices and improving a network’s sensing performance. However, deploying such a HWSN is still in a challenge due to the heterogeneous nature of the data and the energy constraints of the network. To respond to these challenges, an optimal deployment framework in terms of both modal information quality and energy consumption is proposed in this study. This framework generates a multi-objective function aimed at maximizing the quality of the modal information identified from heterogeneous data while minimizing the consumption of energy within the network at the same time. Particle swarm optimization algorithm is then implemented to seek solutions to the function effectively. After laying out the proposed sensor-optimization framework, a methodology is present to determine the clustering of the sensors to further conserve energy. Finally, a numerical verification is performed on a four-span pre-stressed reinforced concrete box-girder bridge. Results show that a set of strategically positioned heterogeneous sensors can maintain a balanced trade-off between the modal information accuracy and energy consumption. It is also observed that an appropriate cluster-tree network topology can further achieve energy saving in HWSNs.

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Verification of multi‐degree‐of‐freedom building modelling for seismic response prediction based on microtremor measurement

Ikeda

Kurata

Jinzhe

2022

Earthq Engng Struct Dyn

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The applicability of the proposed dynamic response model for buildings is investigated using shaking‐table tests with a four‐storey steel specimen. This approach derives the equation of motion for a multi‐degree‐of‐freedom linear building based on microtremor measurements. Under a linear assumption, the equation can estimate the seismic response accelerations, velocities, and displacements at microtremor sensor locations without the need for information about the mass, damping, stiffness matrices or need for structural design documents to estimate peak responses that are linked with seismic damages of structural and non‐structural components. The modelling is unconstrained by structural shape, composition of frames, connections of structural members, or the assumption of a rigid floor. In comparison to the previous methods assuming simple/regular building shape with standard/typical rigid floor, the proposed model is applicable to large‐scale low‐rise buildings with irregular shapes, flat expanses, and open spaces such as large atria and skylights as well. The applicability study considers two practical scenarios: natural frequencies and damping ratios based on microtremors that can be updated by an earthquake and a standard assumption for structural design. The prediction accuracy is best when the participation vector for seismic input is obtained from sensors located on the upper floors; the structure mostly exhibits elastic response; a modal system identification is applied to the seismic measurement; and local damage does not affect the global seismic response of the structure. The reason is that this method assumes that identified mode shapes do not change due to the occurrence of an earthquake.

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Mode shape expansion techniques for prediction - Experimental evaluation

Cited by 31 publications

References 5 publications

Monitoring Structural Health Using a Probabilistic Measure

Monitoring Structural Health Using a Probabilistic Measure

Energy-Efficient Heterogeneous Wireless Sensor Deployment with Multiple Objectives for Structural Health Monitoring

Verification of multi‐degree‐of‐freedom building modelling for seismic response prediction based on microtremor measurement

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