A statistical average algorithm for the dynamic compound inverse problem

Li, Jie; Chen, Jun

doi:10.1007/s00466-002-0369-0

Cited by 22 publications

(3 citation statements)

References 10 publications

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“…The convergence property of this approach needs to be addressed as a basic requirement. Li and Chen (1999) have proved the convergence of the solution when parameters of both the system and the excitations are unknown. Zhu and Law (2007) proposed a method for simultaneous identification of the interaction force time histories and structural parameters iteratively using a two step identification procedure with responses of the bridge subjected to moving vehicular loads that serve as input.…”

Section: Iterative Procedures For Vehicle-bridgementioning

confidence: 99%

Structural Health Monitoring Based on Vehicle-Bridge Interaction: Accomplishments and Challenges

Zhu

Law

2015

Advances in Structural Engineering

118

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The use of the vehicle-bridge interaction (VBI) data for structural health monitoring has received considerable interest in the last decade. Compared with the traditional bridge health monitoring, the VBI based approach allows the target bridges to be monitored or assessed under operating conditions. The VBI system has time-variant features and the vehicle can serve as a moving exciter and a mobile sensor in the system. Many bridge damage identification techniques based on VBI have been developed, and they could be divided into three categories, namely, technique based on the bridge responses, technique based on the vehicle responses and technique based on both the vehicle and bridge responses. This paper presents a review on the structural health monitoring based on VBI and the challenges for its general implementation in practice.

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Section: Iterative Procedures For Vehicle-bridgementioning

confidence: 99%

Structural Health Monitoring Based on Vehicle-Bridge Interaction: Accomplishments and Challenges

Zhu

Law

2015

Advances in Structural Engineering

118

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“…In terms of earthquake input estimation, earlier efforts were focused on reconstructing unknown earthquake inputs and estimating unknown structural parameters at the same time. For example, Li and Chen 13 proposed a statistical average algorithm to estimate structural parameters and ground motion excitation of a discrete multidegree‐of‐freedom linear system formulated in relative coordinates, that is, motion with respect to the ground. As a result, the response measurements needed for applying the algorithm, including acceleration, velocity, and displacement, must be obtained in relative coordinates.…”

Section: Introductionmentioning

confidence: 99%

Earthquake input and state estimation for buildings using absolute floor accelerations

Taher

Fang

2020

Earthq Engng Struct Dyn

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After earthquakes, structural response such as interstory drift is critical for accurate structural assessment for buildings. Typically, direct integration of absolute floor accelerations does not yield reliable floor displacements due to the long‐period drifts caused by noise, a widely acknowledged challenge. In this case, model‐based estimation strategies can be employed, which often require the ground input for better accuracy. However, in many cases the ground input may not be available for lack of instrumentation or even be unmeasurable due to soil‐structure interaction, hence needs to be estimated. Earthquake input estimation in this case is particularly challenging due to the lack of direct feedthrough term, leading to low observability of system input. As a result, input estimation is sensitive to modeling error, measurement noise, and incomplete measurements. To address this challenge, a hybrid strategy is proposed to estimate earthquake input, states, and acceleration response at unmeasured floors using limited absolute floor acceleration measurements. First, the earthquake input is estimated through a maximum a posteriori (MAP) estimation method, and then the estimated input is combined with Kalman filter to further estimate states and unmeasured responses. A comprehensive assessment was performed through a series of numerical and experimental tests including a comparative study with a popular online model‐based method. While the online method demonstrated certain sensitivity to modeling error and measurement noise due to weak observability, the proposed strategy showed robustness and accuracy under realistic and challenging conditions. Further verification is also performed using a real‐world building structure that experienced earthquake events.

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“…In the past decades, some researchers have proposed algorithms for simultaneous identification of structural parameters and unknown excitation, for example, the iterative leastsquare estimation approach [6,7], the statistical average algorithm [8], the recursive least-square estimation [9], genetic algorithms [10], hybrid identification method [11], the dynamic response sensitivity method [11], the extended Kalman filter with unknown excitation inputs (EKF-UI) [12], the sequential nonlinear least-square estimation (SNLSE) [13], and structural parameters and dynamic loading identification from incomplete measurements [14]. However, these approaches suffered the deficiencies of either all structural response being assumed available or the analytical and numerical identification procedures being rather complex.…”

Section: Introductionmentioning

confidence: 99%

A Probabilistic Damage Identification Approach for Structures under Unknown Excitation and with Measurement Uncertainties

Lei

Shen

2013

Journal of Applied Mathematics

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Recently, an innovative algorithm has been proposed by the authors for the identification of structural damage under unknown external excitations. However, identification accuracy of this proposed deterministic algorithm decreases under high level of measurement noise. A probabilistic approach is therefore proposed in this paper for damage identification considering measurement noise uncertainties. Based on the former deterministic algorithm, the statistical values of the identified structural parameters are estimated using the statistical theory and a damage index is defined. The probability of identified structural damage is further derived based on the reliability theory. The unknown external excitations to the structure are also identified by statistical evaluation. A numerical example of the identification of structural damage of a multistory shear-type building and its unknown excitation shows that the proposed probabilistic approach can accurately identify structural damage and the unknown excitations using only partial measurements of structural acceleration responses contaminated by intensive measurement noises.

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A statistical average algorithm for the dynamic compound inverse problem

Cited by 22 publications

References 10 publications

Structural Health Monitoring Based on Vehicle-Bridge Interaction: Accomplishments and Challenges

Structural Health Monitoring Based on Vehicle-Bridge Interaction: Accomplishments and Challenges

Earthquake input and state estimation for buildings using absolute floor accelerations

A Probabilistic Damage Identification Approach for Structures under Unknown Excitation and with Measurement Uncertainties

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