Integrated multi‐type sensor placement and response reconstruction method for high‐rise buildings under unknown seismic loading

Hu, Rongpan; Xu, You‐Lin; Lu, Xiao; Zhang, Chaodong; Zhang, Qilin; Jiemin, Ding

doi:10.1002/tal.1453

Cited by 16 publications

(16 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By neglecting the higher modes of vibration, a set of modes of vibration

normalΦ \in ℜ^{N_{d} \times s}

can be selected, where s (≪ N d ) denotes the number of total selected effective modal shapes. The effective mode set can be selected by calculating the contribution coefficient of each mode to the total structural responses, and the details can be found in the study of Hu et al The selected modal set can be expressed as

boldΦ = {[\begin{matrix} left \\ φ_{1, 1} φ_{1, 2} \dots \dots φ_{1, s} \\ φ_{2, 1} φ_{2, 2} \dots \dots φ_{2, s} \\ ⋮ ⋮ ⋱ ⋮ \\ ⋮ ⋮ ⋱ ⋮ \\ φ_{N_{d}, 1} φ_{N_{d}, 2} \dots \dots φ_{N_{d}, s} \end{matrix}]}_{N_{d} \times s}

Because of the orthogonality of modal shapes and the proportional damping assumption, Φ T M Φ = I ∈ ℜ s × s , Φ T K Φ = Ω 2 ∈ ℜ s × s , and Φ T C Φ = 2 ΞΩ ∈ ℜ s × s . Thus, the second‐order dynamic equation expressed in the modal coordinate can be obtained as

\overset{q}{true} ((), t) + 2 boldΞΩ \overset{q}{true} ((), t) + Ω^{2} normalq ((), t) = - Φ^{T} ML \overset{g}{true} ((), t)

where Ξ ∈ ℜ s × s is the damping ratio matrix and Ω ∈ ℜ s × s is the modal frequency matrix.…”

Section: Osp Frameworkmentioning

confidence: 99%

“…The GPS enables the real‐time monitoring of both static and dynamic displacement responses of high‐rise buildings and has been regarded as a feasible way for deformation monitoring of large‐scale structures . In consideration of the limitation that GPS can only be installed on the roofs of the high‐rise building, inclinometers have also been implemented in high‐rise buildings for deformation monitoring due to its capability of measuring the angular rotations of columns and walls . To accurately capture the torsional responses of the high‐rise building under bidirectional ground motions, the torsional accelerometers are also included in the multi‐type sensor monitoring system.…”

Section: Osp Frameworkmentioning

confidence: 99%

“…With the significant advances in sensing and computer technologies, structural health monitoring systems have been installed on high‐rise buildings to provide real‐time measurements of structural responses and ground motions during earthquakes . However, it is well recognized that only a limited number of sensors can be installed on a high‐rise building owing to the huge size of the building structure and the total cost of the monitoring system.…”

Section: Introductionmentioning

confidence: 99%

“…OSP in most previous studies aimed at maximizing the modal information where the modal assurance criteria or the information entropy is often selected as optimization objective . Other studies proposed OSP schemes with the objectives of achieving the best global response reconstructions, which are then used to assess the story‐by‐story seismic performance of the building . Some scholars also investigated sensor placement strategies for structural damage detection .…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Optimal multi‐type sensor placement for monitoring high‐rise buildings under bidirectional long‐period ground motions

Zhao

2020

Struct Control Health Monit

Self Cite

View full text Add to dashboard Cite

Summary Towards a complete and accurate seismic assessment of a super high‐rise building under bidirectional long‐period ground motions, this study proposes an optimal multi‐type sensor placement framework for the best reconstruction of structural responses and ground motions based on a full‐scale 3D finite element (FE) model of the building. The selected locations and types of sensors are capable of not only capturing the coupled bending‐torsional vibration but also providing an unbiased estimation of the unmeasured responses of seismic‐vulnerable components and bidirectional ground motions. A Kalman filtering algorithm‐based data fusion technique is employed to deal with the measurement data from multi‐type sensors. With the target of achieving the best reconstruction, an optimal sensor placement (OSP) configuration, which minimizes the reconstruction error, is achieved through a sequential sensor placement algorithm. To assess the feasibility of the proposed OSP framework and the accuracy of reconstructed responses and ground motions, a numerical study is also performed on the full‐scale 3D FE model of a super high‐rise building. The torsional effects of the building under bidirectional long‐period ground motions are assessed by comparison with the results without considering the torsional effect. The numerical results show that the proposed framework is feasible and the reconstructed responses and ground motions are accurate and that the torsional effects cannot be neglected in the seismic assessment of the building.

show abstract

“…By neglecting the higher modes of vibration, a set of modes of vibration

normalΦ \in ℜ^{N_{d} \times s}

boldΦ = {[\begin{matrix} left \\ φ_{1, 1} φ_{1, 2} \dots \dots φ_{1, s} \\ φ_{2, 1} φ_{2, 2} \dots \dots φ_{2, s} \\ ⋮ ⋮ ⋱ ⋮ \\ ⋮ ⋮ ⋱ ⋮ \\ φ_{N_{d}, 1} φ_{N_{d}, 2} \dots \dots φ_{N_{d}, s} \end{matrix}]}_{N_{d} \times s}

\overset{q}{true} ((), t) + 2 boldΞΩ \overset{q}{true} ((), t) + Ω^{2} normalq ((), t) = - Φ^{T} ML \overset{g}{true} ((), t)

where Ξ ∈ ℜ s × s is the damping ratio matrix and Ω ∈ ℜ s × s is the modal frequency matrix.…”

Section: Osp Frameworkmentioning

confidence: 99%

Section: Osp Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal multi‐type sensor placement for monitoring high‐rise buildings under bidirectional long‐period ground motions

Zhao

2020

Struct Control Health Monit

Self Cite

View full text Add to dashboard Cite

show abstract

“…Tailoring this necessity to civil and industrial applications, the concurrent usage of accelerometers and gyroscopes provides a set of complementary quantities which can compensate for each other. Experimental validations of this integrated strategy have been conducted for high-rise buildings [11], showing that the joined exploitation of acceleration and tilt sensors yields a more precise understanding of the structural deformation at higher frequencies. Similarly, coupled linear and rotational measurements have shown to have superior performance in monitoring wind induced vibrations in tall infrastructures [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

A Tilt Sensor Node Embedding a Data-Fusion Algorithm for Vibration-Based SHM

et al. 2019

View full text Add to dashboard Cite

This work describes a miniaturized sensor network based on low-power, light-weight and small footprint microelectromechanical (MEMS) sensor nodes capable to simultaneously measure tri-axial accelerations and tri-axial angular velocities. A real-time data fusion algorithm based on complementary filters is applied to extract tilt angles. The resulting device is designed to show competitive performance over the whole frequency range of the inertial units. Besides the capability to provide accurate measurements both in static and dynamic conditions, an optimization process has been designed to efficiently make the fusion procedure running on-sensor. An experimental campaign conducted on a pinned-pinned steel beam equipped with a network comprising several sensor nodes was used to evaluate the reliability of the developed architecture. Performance metrics revealed a satisfactory agreement to the physical model, thus making the network suitable for real-time tilt monitoring scenarios.

show abstract

Multi‐channel response reconstruction using transformer based generative adversarial network

Zheng

et al. 2023

Earthq Engng Struct Dyn

View full text Add to dashboard Cite

Accurate measurement data are a basic prerequisite for effective structural health monitoring (SHM). However, data loss are inevitable in the long‐term monitoring of large‐scale structures. To solve this problem, this research proposes a transformer‐based generative adversarial network (GAN) to reconstruct lost measurements from observed measurements. The generator of GAN is an encoder‐decoder structure using transformer as the backbone combined with discrete wavelet transform. Skip connections are used between the encoder part and decoder part to promote multi‐scale information flow. A novel discriminator is designed to assess the reality of wavelet spectra of reconstructed samples. To deceive the discriminator, the generator must generate samples that are accurate over the full frequency band. The developed model is used to reconstruct linear responses of a footbridge under pedestrian excitations and nonlinear responses of a suspension bridge under typhoon events. Experimental results demonstrate that lost responses can be reconstructed accurately, even when a large proportion of data are lost. The effectiveness of the proposed method is further verified by comparing the reconstruction accuracy of the proposed model with those of other three state‐of‐the‐art models. The results demonstrate that an improved performance of applying the proposed approach for dynamic structural response reconstruction is achieved and validated with in‐field testing data under ambient and extreme excitation conditions.

show abstract

Integrated multi‐type sensor placement and response reconstruction method for high‐rise buildings under unknown seismic loading

Cited by 16 publications

References 23 publications

Optimal multi‐type sensor placement for monitoring high‐rise buildings under bidirectional long‐period ground motions

Optimal multi‐type sensor placement for monitoring high‐rise buildings under bidirectional long‐period ground motions

A Tilt Sensor Node Embedding a Data-Fusion Algorithm for Vibration-Based SHM

Multi‐channel response reconstruction using transformer based generative adversarial network

Contact Info

Product

Resources

About