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Theoretical Analysis of Ultimate Main Span Length for Arch Bridge

Author

Listed:
  • Xianxiong Zhang

    (Poly Changda Engineering Co., Ltd., Guangzhou 510620, China)

  • Zhuozhang Deng

    (State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai 200092, China)

  • Genshen Fang

    (State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai 200092, China)

  • Yaojun Ge

    (State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai 200092, China)

Abstract

The advancement of construction techniques and high-performance sustainable materials enables the increase of span length for arch bridge. It is of great importance to study the theoretical ultimate span length of arch bridge. Based on the parabolic and catenary arch axes, the analytical solutions of ultimate span length of arch bridge are solved using theoretical derivation accounting for the strength, in-plane stability and out-plane stability conditions, respectively. Then, the use of high-performance concrete, reactive powder concrete and high-strength steel is considered to study the relationship between theoretical ultimate span length and rise-span ratio as well as material strength for concrete and steel arch bridges. The results show that the theoretical ultimate span length derived by catenary arch axis is smaller by about 2–6% than that obtained by parabolic arch axis, but the difference is insignificant. When the rise-span ratio is 1/5, the theoretical ultimate span length for concrete arch bridge using R200 reactive powder concrete can reach 2000 m (2161 m for catenary arch axis and 2099 m for parabolic arch axis) while the main span of steel arch bridge using Q690 high-strength steel can be longer than 2500 m (2948 m for catenary arch axis and 2865 m for parabolic arch axis).

Suggested Citation

  • Xianxiong Zhang & Zhuozhang Deng & Genshen Fang & Yaojun Ge, 2022. "Theoretical Analysis of Ultimate Main Span Length for Arch Bridge," Sustainability, MDPI, vol. 14(24), pages 1-14, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:24:p:17043-:d:1008329
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    References listed on IDEAS

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    1. Hu, Xiaonong & Fang, Genshen & Yang, Jiayu & Zhao, Lin & Ge, Yaojun, 2023. "Simplified models for uncertainty quantification of extreme events using Monte Carlo technique," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
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