Multi-criteria reliability optimization for a complex system with a bridge structure in a fuzzy environment: A fuzzy multi-criteria genetic algorithm approach

Mutingi, Michael; Mbohwa, Charles; Kommula, Venkata P.

doi:10.17531/ein.2016.3.18

Cited by 4 publications

(1 citation statement)

References 33 publications

(50 reference statements)

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“…By using the genetic algorithm together with other methods to capture the change in structural properties or responses to load, the damage of bridge structures can be identified, and the optimal design parameters can be obtained [17][18][19]. The reliability analysis based on the genetic algorithm can skillfully identify the critical failure modes of the bridge components or the whole structure and estimate the probability of failure in an effective and accurate way, which offers the decision maker an important reference for making practical solutions [20][21][22][23]. In addition, the improved genetic algorithm can also be used to optimize the arrangement of the measurement points of bridge monitoring sensors, layout of tendons for prestressed concrete, or realize the mathematical optimization course of cable force, so as to improve the working efficiency and reduce the cost [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Optimization of the Traffic Load Model for Suspenders of a Super-Long-Span Suspension Bridge Considering Influence Line Geometry and Extreme Load Effect Scenarios

Wei,

Ruan,

et al. 2024

Applied Sciences

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The reasonable expression of live load and its accuracy are important to the safety and design rationality of highway bridge structures. In this study, the optimization issue of the traffic load model for the suspenders of large-scale suspension bridges is studied. Taking a 2300-m main span suspension bridge as an example, a method for suspender classification based on the geometric feature of the influence lines is proposed, and the extreme traffic load effect scenarios are analyzed and used as an optimization reference. Multi-objective optimization based on a genetic algorithm is used to explore the improvement of the traffic load model of the suspender. The traffic load model of the suspender is optimized with three objectives, i.e., accuracy, convenience, and improvement, and the optimization results regarding the load value and loading length are obtained. The value of the uniformly distributed load of the optimized model ranges from 6.4 kN/m to 8.9 kN/m, and the maximum value of the concentrated force could reach 1433 kN. By comparing the obtained optimized model with the current specification model and the extreme load effect scenario model, the improved applicability of the optimized model in the analysis of the load effect of the suspender can be verified. The optimized method and relevant conclusions can provide useful references for the improved design and operation management of similar bridge structures.

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