Improved ABC Algorithm Optimizing the Bridge Sensor Placement

Yang, Jianhui; Peng, Zhenrui

doi:10.3390/s18072240

Cited by 11 publications

(7 citation statements)

References 47 publications

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“…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

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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.

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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

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“…Recently, a modified version of the ABC algorithm is applied in (Yang and Peng 2018 ) aimed at determining the optimal location of sensors on bridges by minimizing the off-diagonal terms of the MAC matrix. The optimal placement of accelerometers and strain sensors is considered in (Wu et al.…”

Section: Computational Intelligence In Shmmentioning

confidence: 99%

Seismic assessment of bridges through structural health monitoring: a state-of-the-art review

Karakostas,

Quaranta,

Chatzi

et al. 2023

Bull Earthquake Eng

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The present work offers a comprehensive overview of methods related to condition assessment of bridges through Structural Health Monitoring (SHM) procedures, with a particular interest on aspects of seismic assessment. Established techniques pertaining to different levels of the SHM hierarchy, reflecting increasing detail and complexity, are first outlined. A significant portion of this review work is then devoted to the overview of computational intelligence schemes across various aspects of bridge condition assessment, including sensor placement and health tracking. The paper concludes with illustrative examples of two long-span suspension bridges, in which several instrumentation aspects and assessments of seismic response issues are discussed.

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“…The problem of optimal sensor olacement (OSP), particularly in applications for large structures, is not a trivial task and is combinatorial in nature. Optimization methods such as artificial neural networks (ANNs) [43], artificial bee colonies (ABCs) [44] or its extension-IABC [45], or genetic algorithms (GAs) [46] could be used. In the case of the tested bridge, a variation and extension of an iterative method, effective independence (EFI), was used.…”

Section: The Tested Bridgementioning

confidence: 99%

Monitoring of the Static and Dynamic Displacements of Railway Bridges with the Use of Inertial Sensors

Olaszek

Wyczałek

Sala

et al. 2020

Sensors

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In the case of the monitoring of bridges, the determination of vertical displacements is one of the most important issues. A new measuring system has been developed and implemented for assessment of railway bridges based on measurements of the structural response to passing trains. The system uses inertial sensors: Inclinometers and accelerometers that do not need any referential points. The system records signals related to the passage of a train over a monitored bridge. The signals from inclinometers before the train’s entry are used to determine the static movement. Integrated signals from inclinometers and accelerometers are used to determine dynamic displacements when the train goes through the bridge. Signals from inclinometers are used to determine the so-called “quasi-static” component of the displacement and signal from the accelerometer to determine the dynamic component. Field tests have been carried out on a viaduct along a high-speed railway line. Periodic comparative measurements are carried out using a Total Station to verify static measurements and using inductive sensors to verify dynamic measurements. Tests of the system carried out so far have proven its usefulness for monitoring bridges in a high-speed railway (up to 200 km/h) with high accuracy while determining dynamic displacements.

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Improved ABC Algorithm Optimizing the Bridge Sensor Placement

Cited by 11 publications

References 47 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

Seismic assessment of bridges through structural health monitoring: a state-of-the-art review

Monitoring of the Static and Dynamic Displacements of Railway Bridges with the Use of Inertial Sensors

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