Bridge influence line identification based on adaptive B‐spline basis dictionary and sparse regularization

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Section: Introductionmentioning

confidence: 99%

“…Structural Health Monitoring (SHM) is a process that uses measurement information of a structure with respect to the specific environmental conditions to investigate the structural integrity status and estimate its remaining service life 1–4 . Many practical problems encountered in SHM, such as damage detection, 5–8 model updating, 9–11 and other problems, 12–14 require deterministic or probabilistic regression of a linear model, which can be expressed as

bold-italicy = boldX bold-italicw + bold-italicɛ

and its least‐squares fitting objective function is defined as

J ((), w) = \min_{bold-italicw \in ℝ^{p}} ({}, {(||||, bold-italicy - boldX bold-italicw)}_{2}^{2})

The least‐squares optimization result is obtained as

{\hat{bold-italicw}}_{LS} = {({boldX}^{T} X)}^{- 1} X^{T} boldy

, where

bold-italicy = {(y_{1}, \dots, y_{N_{q}})}^{T} \in ℝ^{N_{q}}

is the centered response vector;

boldX = {(x_{1}, \dots, x_{N_{q}})}^{T} \in ℝ^{N_{q} \times N_{e}}

is the standardized observation matrix of

N_{e}

predictor variables;

bold-italicɛ \in ℝ^{N_{q}}

is the independent and identically distributed normal error vector with a mean value of 0 and a variance value of

σ^{2}

; and is the regression coefficient vector to be estimated. This study only focuses on the problem of structural damage detection in SHM, where

bold-italicw

represents the unknown damage severity vector.…”

Section: Introductionmentioning

confidence: 99%

Structural damage identification with limited modal measurements and ultra‐sparse Bayesian regression

Guo

Wang

et al. 2021

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“…Zheng et al 20 used the empirical mode decomposition method to extract bridge influence line from dynamic response induced by high speed vehicle. Chen et al 16,21 then introduced the regularization penalty function to the least square method to reduce the unreasonable fluctuation in the identified result. Froseth et al 22 extracted bridge influence lines from the frequency domain and increased the computational efficiency by several orders of magnitude compared with the computational efficiency of a traditional time domain model.…”

Section: Introductionmentioning

confidence: 99%

Bridge influence surface identification method considering the spatial effect of vehicle load

Yang

et al. 2021

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Summary Bridge influence surface is an important tool to study the bridge responses under the moving loads, which contains tremendous structural information. The structural deterioration during the service life would induce the variation of the bridge influence surface, which makes it essential to identify the influence surface exactly from the field measured data under moving vehicle loads. This paper firstly summarizes current development of bridge influence surface identification methods and classifies the methods in which the vehicle is respectively equivalent to line loads and concentrated loads. After that, the actual spatial distribution of the vehicle load is considered, based on which a new identification method is presented to solve the boundary problem of current influence surface identification methods. Then a numerical slab model with two fixed ends is illustrated to prove the validity of the method, and the accuracy and identifiable region of different methods in this condition are compared with the other methods. Influence factors of identification accuracy including noise level, axle number of vehicle and loading point are analyzed. Finally, laboratory verification using the same slab model is prepared to verify the effectiveness of the proposed identification method. Results of this study provide an effective identification method of bridge influence surface with very high identification accuracy, which can be used to full bridge influence surface test.

“…To remove the unreasonable fluctuation in identified influence line, Chen et al present Tikhonov regularization method in their work. Then Chen et al use adaptive B‐spline based method to improve the identification accuracy of the bridge influence line. Frøseth et al convert the influence line identification problem into frequency domain and identify the bridge influence line by fast Fourier transformation.…”

Section: Introductionmentioning

confidence: 99%

Bridge influence line identification from structural dynamic responses induced by a high‐speed vehicle

Yang

et al. 2020

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Summary The bridge influence line can reflect the structural behavior under moving vehicle loads, which is becoming a proposing tool to study the bridge behavior during service life. A high‐speed vehicle will induce dynamic effect on the bridge, which will significantly change the shape of response curve. It is essential to consider the dynamic response for the accurate identification of the bridge influence line. This paper firstly summarizes current research activities for bridge influence line identification and components of the bridge dynamic responses. A dynamic fluctuation part elimination method based on empirical mode decomposition is proposed, based on which the bridge static influence line is identified by static bridge influence line identification method. Finally, a vehicle–bridge coupling model of a simply supported beam excited by passing vehicles with three different speeds is introduced to illustrate the effectiveness of the proposed identification method. The result of this study provides an effective identification method of the bridge static influence line, which can lead to a satisfactory identification results based on the dynamic responses induced by high‐speed vehicle loads.