Abstract

To study the effects of the fatigue performance due to the major design parameter of the orthotropic steel deck and to obtain a better design parameter, a construction parameter optimization method based on a backpropagation neural network (BPNN) and simulated annealing (SA) algorithm was proposed. First, the finite element (FE) model was established, and the numerical results were validated against available full-scale fatigue experimental data. Then, by calculating the influence surface of each fatigue detail, the most unfavorable loading position of each fatigue detail was obtained. After that, combined with the data from actual engineering applications, the weight coefficient of each fatigue detail was calculated by an analytic hierarchy process (AHP). Finally, to minimize the comprehensive stress amplitude, a BPNN and SA algorithm were used to optimize the construction parameters, and the optimization results for the conventional weight coefficients were compared with the construction parameters. It can be concluded that compared with the FE method through single-parameter optimization, the BPNN and SA method can synthetically optimize multiple parameters. In addition, compared with the common weighting coefficients, the weighting coefficients proposed in this paper can be better optimized for vulnerable parts. The optimized fatigue detail stress amplitude is minimized, and the optimization results are reliable. For these reasons, the parameter optimization method presented in this paper can be used for other similar applications.

Highlights

  • As a pivotal structure form of steel bridge decks, orthotropic steel decks possess features such as a low selfweight, high ultimate bearing capacity, and wide range of applications [1,2,3]

  • finite element (FE) Simulation Test on Fatigue Performance of New Steel Bridge Deck e fatigue test results on the new steel bridge deck have been recorded in the literature [10]. is model measured 7000 mm long, and the lane width was set to 3500 mm, including three diaphragms and four ribs, and two vertical ribs were set at the ends of the model considering the border effect. e wheel load position was 600 mm away from the diaphragm, and it was at the top of the rib-to-deck plate weld in the transverse direction. e 50-T actuator was selected as Fatigue test Verified

  • Optimizing the Parameter of the New Orthotropic Steel Bridge Deck Based on BPSA-analytic hierarchy process (AHP) Method e fatigue performance and fatigue life of the fatigue details are related to the stress amplitude under the wheel load [35, 36]

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Summary

Introduction

As a pivotal structure form of steel bridge decks, orthotropic steel decks possess features such as a low selfweight, high ultimate bearing capacity, and wide range of applications [1,2,3]. To address the problem outlined above, a new semiopened longitudinal rib orthotropic steel deck was designed (referred to as the new steel bridge deck in the following paper, Figure 1), and the 10 million cycles fatigue test on a full-scale partial model was completed. Kainuma et al [11] performed full-scaled fatigue tests on orthotropic steel decks with different structure parameters, and the outcomes showed that raising the penetration rate had positive influences on preventing root cracking. To optimize the fatigue performance of the fatigue details outlined above, the construction form of the steel bridge deck was redesigned, and a new orthotropic steel deck structure was proposed. FE Simulation Test on Fatigue Performance of New Steel Bridge Deck e fatigue test results on the new steel bridge deck have been recorded in the literature [10]. is model measured 7000 mm long, and the lane width was set to 3500 mm, including three diaphragms and four ribs, and two vertical ribs were set at the ends of the model considering the border effect. e wheel load position was 600 mm away from the diaphragm, and it was at the top of the rib-to-deck plate weld in the transverse direction. e 50-T actuator was selected as

Objective practical engineering coefficient function
Design AHP Common h
Findings
Conclusion e main conclusions can be summarized as follows:
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