Reliability Assessment of Deflection Limit State of a Simply Supported Bridge using vibration data and Dynamic Bayesian Network Inference

Liu, Hanbing; He, Xin; Jiao, Yubo; Wang, Xirui

doi:10.3390/s19040837

Cited by 13 publications

(15 citation statements)

References 45 publications

(69 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e optimization method adopted in this paper is the artificial bee colony (ABC) algorithm, which is a new intelligent optimization algorithm produced by bees' nectar gathering behavior in nature. At present, it has been successfully applied in engineering optimization [24][25][26][27]. Its principle refers to the mechanism of bees finding the best nectar source and creating data flows around the beehive by roaming, which is a behavior of bees that constitutes the basics of the swarm intelligence [28].…”

Section: Model Correction Methodmentioning

confidence: 99%

Torsional Stiffness Correction of the Split‐Type Triple‐Box Steel Box Girder Based on Refined Simulation

Tang

Yue

et al. 2021

Advances in Civil Engineering

View full text Add to dashboard Cite

Split-type steel box girders are widely used in long-span bridges because of their good wind-resistance performance. In the design stage, a simple finite element model is usually established based on the beam element for wind-resistance design. However, since the irregular cross-beams and diaphragms in the split steel box girder cannot be virtually established, the stiffness of the girder will be underestimated. To improve the accuracy in simulating stiffness of the split-type triple-box steel box girder (STSBG) with the beam element model (BEM), a correction is made to the initial beam element model (IBEM) based on the result of a more refined finite element model. ANSYS is adopted to make a refined model (RM) of a bridge with STSBG as its girder and to calculate its aerostatic responses and dynamic characteristics in 3 typical construction states and 1 finished state. With the reference value, an objective function of the overall residual sum of squares is constructed for the torsion angle of the girder and the frequency of the bridge. Then, the beam element is used for conventional modelling of the bridge, and artificial bee colony (ABC) algorithm is adopted for the optimization and correction of structure parameters of the BEM of the girder. Finally, static and dynamic characteristics of the IBEM and the corrected beam element model (CBEM) are compared with values of the corresponding RM to evaluate the validity of the correction of the model. The results show that the aerostatic responses and dynamic characteristics of the CBEM are close to calculated values of the RM. In more detail, the relative error between the torsion angle of the girder in the middle span of the BEM and the corresponding reference value in the finished state is decreased from +61.71% to +4.94%, and the relative error of torsional fundamental frequency is decreased from −17.43% to +3.66%. According to the calculated value of the RM, ABC algorithm would satisfactorily improve the accuracy in simulating torsional stiffness of the STSBG with the IBEM. This research is expected to provide reference for beam element modelling, which is conducive to accurately simulating torsional stiffness of the STSBG.

show abstract

Section: Model Correction Methodmentioning

confidence: 99%

Torsional Stiffness Correction of the Split‐Type Triple‐Box Steel Box Girder Based on Refined Simulation

Tang

Yue

et al. 2021

Advances in Civil Engineering

View full text Add to dashboard Cite

show abstract

“…Deng et al (2018) presented an approach for reliability-based assessment of long span suspension bridges by using the data from traffic-induced deflections. However, the accuracy of extreme value-based approaches depends on the ambient thermal and unexpected traffic operational conditions of the bridges (Liu et al, 2019). Other methods like time series analysis can be used to capture the changes in the structural health of the bridges.…”

Section: Introductionmentioning

confidence: 99%

LSTM approach for condition assessment of suspension bridges based on time-series deflection and temperature data

Wang

Ansari

et al. 2022

Advances in Structural Engineering

View full text Add to dashboard Cite

Deflection data provides important information about the mechanical characteristics and structural health condition of bridges. The study presented here pertains to development of a deep learning based approach for structural health monitoring by employing the bridge deflections. The method presented herein uses the long short-term memory (LSTM) framework in detecting the state of damage by tracking the feature changes of time-series deflection and temperature data. Deflection and temperature data of Chongqing Egongyan Rail Transit Suspension Bridge was employed over a period of 15 months to develop the proposed method. The concept of square error index (SE) is introduced as an assessment tool for estimation of the bridge damage level. Results from the present study indicated that the statistical characteristics of SE index are proportional to the level of damage, and are only sensitive to abnormal changes in deflection. Structural health monitoring data over the period of 15 months indicated that the proposed approach has the capability to detect cable damages as low as 0.5%.

show abstract

“…BNs are suitable for causal reasoning tasks, and thus, they present a greater potential for reliability analysis and safety evaluation of engineering structures. [17][18][19][20][21][22][23] Using the discrete BNs, Yazdani et al 20 assigned the random variables, component events, and failure paths with parent nodes. The child nodes represented failure probability to achieve the reliability analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Hackl and Kohler 21 regarded the concrete grade, concrete cover, exposure environment, and curing time as the BN nodes to perform the degradation reliability evaluation. In addition, BNs can predict bridge deflections, 22 and they can be incorporated with the analytic hierarchy process for structural system reliability analysis. 23 According to the nodal variable types (discrete, continuous, or their hybrid), BNs can be categorized as discrete BNs, continuous BNs, and hybrid BNs.…”

Section: Introductionmentioning

confidence: 99%

Safety evaluation of truss bridges using continuous Bayesian networks

Tan

Fang

2021

Structural Contr & Hlth

View full text Add to dashboard Cite

show abstract

Reliability Assessment of Deflection Limit State of a Simply Supported Bridge using vibration data and Dynamic Bayesian Network Inference

Cited by 13 publications

References 45 publications

Torsional Stiffness Correction of the Split‐Type Triple‐Box Steel Box Girder Based on Refined Simulation

Torsional Stiffness Correction of the Split‐Type Triple‐Box Steel Box Girder Based on Refined Simulation

LSTM approach for condition assessment of suspension bridges based on time-series deflection and temperature data

Safety evaluation of truss bridges using continuous Bayesian networks

Contact Info

Product

Resources

About