Model Updating in Complex Bridge Structures using Kriging Model Ensemble with Genetic Algorithm

Qin, Shiqiang; Zhou, Yunlai; Cao, Hongyou; Wahab, Magd Abdel

doi:10.1007/s12205-017-1107-7

Cited by 49 publications

(19 citation statements)

References 26 publications

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“…The kriging model [ 32 ] is a half-parameterized interpolation model including one parametric and one non-parametric part that has been applied in model updating [ 33 ]; for, details one can refer to [ 33 ]. Since the relations from structural response to parameters are implicit, one needs to obtain data samples to describe their implicit relationship via the design of experiments (DOE).…”

Section: Kriging Modelmentioning

confidence: 99%

“…Sampling algorithms for DOE can be divided into two categories: standard designs and computer-generated designs (CGDs) [ 34 ]. Further discussion for certain categories can also refer to [ 33 ]. The standard designs, such as full factorial design (FFD) and central composite design (CCD), highlight the precision and randomness of design points, while CGDs such as Latin hypercube sampling (LHS) and uniform design (UD) mainly focus on space filling and the uniformity of design points.…”

Section: Kriging Modelmentioning

confidence: 99%

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Dynamic Model Updating for Bridge Structures Using the Kriging Model and PSO Algorithm Ensemble with Higher Vibration Modes

Qin

Zhang

Zhou

et al. 2018

Sensors

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This study applied the kriging model and particle swarm optimization (PSO) algorithm for the dynamic model updating of bridge structures using the higher vibration modes under large-amplitude initial conditions. After addressing the higher mode identification theory using time-domain operational modal analysis, the kriging model is then established based on Latin hypercube sampling and regression analysis. The kriging model performs as a surrogate model for a complex finite element model in order to predict analytical responses. An objective function is established to express the relative difference between analytically predicted responses and experimentally measured ones, and the initial finite element (FE) model is hereinafter updated using the PSO algorithm. The Jalón viaduct—a concrete continuous railway bridge—is applied to verify the proposed approach. The results show that the kriging model can accurately predict the responses and reduce computational time as well.

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Section: Kriging Modelmentioning

confidence: 99%

Section: Kriging Modelmentioning

confidence: 99%

Dynamic Model Updating for Bridge Structures Using the Kriging Model and PSO Algorithm Ensemble with Higher Vibration Modes

Qin

Zhang

Zhou

et al. 2018

Sensors

Self Cite

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“…Structural health monitoring and safety assessments for long-span bridges have become a dominant research topic, and have received the special attention of infrastructure authorities in recent years. Around the world, for many large-scale bridges, monitoring systems are installed to evaluate the health condition of the bridge and to guarantee operational safety management [1]. The Finite Element Method (FEM) is the standard tool for modeling structural behavior.…”

Section: Introductionmentioning

confidence: 99%

“…The response surface was selected as an objective function, and GA was employed to find the best solution. Qin et al [1] updated a complex railway bridge by using GA combined with the Kriging model. While the Kriging model acted as a surrogate to reduce the deviation between the structural parameters and responses, GA provided the opportunity for obtaining the global best solution.…”

Section: Introductionmentioning

confidence: 99%

Model Updating for Nam O Bridge Using Particle Swarm Optimization Algorithm and Genetic Algorithm

Tran-Ngoc

Khatir

Roeck

et al. 2018

Sensors

133

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Vibration-based structural health monitoring (SHM) for long-span bridges has become a dominant research topic in recent years. The Nam O Railway Bridge is a large-scale steel truss bridge located on the unique main rail track from the north to the south of Vietnam. An extensive vibration measurement campaign and model updating are extremely necessary to build a reliable model for health condition assessment and operational safety management of the bridge. The experimental measurements are carried out under ambient vibrations using piezoelectric sensors, and a finite element (FE) model is created in MATLAB to represent the physical behavior of the structure. By model updating, the discrepancies between the experimental and the numerical results are minimized. For the success of the model updating, the efficiency of the optimization algorithm is essential. Particle swarm optimization (PSO) algorithm and genetic algorithm (GA) are employed to update the unknown model parameters. The result shows that PSO not only provides a better accuracy between the numerical model and measurements, but also reduces the computational cost compared to GA. This study focuses on the stiffness conditions of typical joints of truss structures. According to the results, the assumption of semi-rigid joints (using rotational springs) can most accurately represent the dynamic characteristics of the truss bridge considered.

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“…At present, commonly-used optimization methods include mathematical planning methods and artificial intelligence optimization methods, wherein the mathematical planning methods generally have the characteristics of unstable solution and slow operation speed [6], and the artificial intelligence optimization methods include Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC) algorithm, etc. [7][8]. Holland first applied GA to the optimization and selection of plans, and then GA has become a typical heuristic random search algorithm [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Optimization Algorithm for Maintenance Scheme Based on Life Cycle Cost

Liu¹,

Li²,

Wang³

et al. 2020

JESA

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In engineering practice, to make sure that a project can achieve safe operation while minimizing the overall cost during the whole life cycle, the supervisor of the project generally needs to make optimal decisions for the Life Cycle Cost (LCC) of the project. To this end, this paper adopted Genetic Algorithms (GA) and LCC theory to propose and implement a kind of optimization algorithms suitable for solving maintenance scheme problems. Combining with the selection of three actual maintenance scenarios of "take no maintenance measure/preventive maintenance measures only", "take major maintenance measures", and "take major maintenance measures and preventive maintenance measures", the proposed algorithm adopted real number coding to give optimization solutions from two perspectives of "control service life and calculate cost" and "control cost and calculate service life"; moreover, the paper conducted a comparative analysis on the maintenance schemes of reinforced concrete bridge decks using Matlab and verified the reliability and efficiency of the proposed algorithm. Keywords:life cycle cost (LCC), intelligent optimization, genetic algorithm (GA), optimal solution, maintenance scheme LCC ANALYSIS OF THE EXAMPLE PROJECT Description of the problemLCC analysis is the basis of engineering projects and scheme optimization, this paper took the reinforced concrete bridge deck as the research object, the initial reliability was set to be β=8.0, the degradation start time was set as T1=15, and Journal Européen des Systèmes Automatisés

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Model Updating in Complex Bridge Structures using Kriging Model Ensemble with Genetic Algorithm

Cited by 49 publications

References 26 publications

Dynamic Model Updating for Bridge Structures Using the Kriging Model and PSO Algorithm Ensemble with Higher Vibration Modes

Dynamic Model Updating for Bridge Structures Using the Kriging Model and PSO Algorithm Ensemble with Higher Vibration Modes

Model Updating for Nam O Bridge Using Particle Swarm Optimization Algorithm and Genetic Algorithm

Intelligent Optimization Algorithm for Maintenance Scheme Based on Life Cycle Cost

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